Miracle-dz/UniFAR-FTUs huggingface dataset:
Low regularity ill-posedness for elastic waves driven by shock formation

abstract:In this paper, we construct counterexamples to the local existence of low-regularity solutions to elastic wave equations in three spatial dimensions (3D). Inspired by the recent works of Christodoulou, we generalize Lindblad's classic results on the scalar wave equation by showing that the Cauchy problem for 3D elastic waves, a physical system with multiple wave-speeds, are ill-posed in $H^3(\Bbb{R}^3)$. We further prove that the ill-posedness is caused by instantaneous shock formation, which is characterized by the vanishing of the inverse foliation density. The main difficulties of the 3D case come from the multiple wave-speeds and its associated non-strict hyperbolicity. We obtain the desired results by designing and combining a geometric approach and an algebraic approach, equipped with detailed studies and calculations of the structures and coefficients of the corresponding non-strictly hyperbolic system. Moreover, the ill-posedness we depict also applies to 2D elastic waves, which corresponds to a strictly hyperbolic case.
A least squares radial basis function finite difference method with improved stability properties

Localized collocation methods based on radial basis functions (RBFs) for elliptic problems appear to be non-robust in the presence of Neumann boundary conditions. In this paper we overcome this issue by formulating the RBF-generated finite difference method in a discrete least-squares setting instead. This allows us to prove high-order convergence under node refinement and to numerically verify that the least-squares formulation is more accurate and robust than the collocation formulation. The implementation effort for the modified algorithm is comparable to that for the collocation method.
Automatic Generation of Adversarial Examples for Interpreting Malware Classifiers

Author(s): Song, Wei; Li, Xuezixiang; Afroz, Sadia; Garg, Deepali; Kuznetsov, Dmitry; Yin, Heng | Abstract: Recent advances in adversarial attacks have shown that machine learning classifiers based on static analysis are vulnerable to adversarial attacks. However, real-world antivirus systems do not rely only on static classifiers, thus many of these static evasions get detected by dynamic analysis whenever the malware runs. The real question is to what extent these adversarial attacks are actually harmful to the real users? In this paper, we propose a systematic framework to create and evaluate realistic adversarial malware to evade real-world systems. We propose new adversarial attacks against real-world antivirus systems based on code randomization and binary manipulation, and use our framework to perform the attacks on 1000 malware samples and test four commercial antivirus software and two open-source classifiers. We demonstrate that the static detectors of real-world antivirus can be evaded 24.3%-41.9% of the cases and often by changing only one byte. We also find that the adversarial attacks are transferable between different antivirus up to 16% of the cases. We also tested the efficacy of the complete (i.e. static + dynamic) classifiers in protecting users. While most of the commercial antivirus use their dynamic engines to protect the users' device when the static classifiers are evaded, we are the first to demonstrate that for one commercial antivirus, static evasions can also evade the offline dynamic detectors and infect users' machines. Our framework can also help explain which features are responsible for evasion and thus can help improve the robustness of malware detectors.
Conformally maximal metrics for Laplace eigenvalues on surfaces

The paper is concerned with the maximization of Laplace eigenvalues on surfaces of given volume with a Riemannian metric in a fixed conformal class. A significant progress on this problem has been recently achieved by Nadirashvili-Sire and Petrides using related, though different methods. In particular, it was shown that for a given k, the maximum of the k-th Laplace eigenvalue in a conformal class on a surface is either attained on a metric which is smooth except possibly at a finite number of conical singularities, or it is attained in the limit while a "bubble tree" is formed on a surface. Geometrically, the bubble tree appearing in this setting can be viewed as a union of touching identical round spheres. We present another proof of this statement, developing the approach proposed by the second author and Y. Sire. As a side result, we provide explicit upper bounds on the topological spectrum of surfaces.
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution of learning-based 3D feature descriptors, little attention has been drawn to the learning of 3D feature detectors, even less for a joint learning of the two tasks. In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. In particular, we propose a keypoint selection strategy that overcomes the inherent density variations of 3D point clouds, and further propose a self-supervised detector loss guided by the on-the-fly feature matching results during training. Finally, our method achieves state-of-the-art results in both indoor and outdoor scenarios, evaluated on 3DMatch and KITTI datasets, and shows its strong generalization ability on the ETH dataset. Towards practical use, we show that by adopting a reliable feature detector, sampling a smaller number of features is sufficient to achieve accurate and fast point cloud alignment.
Are stellar-mass binary black hole mergers isotropically distributed?

The Advanced LIGO and Advanced Virgo gravitational wave detectors have detected a population of binary black hole mergers in their first two observing runs. For each of these events, we have been able to associate a potential sky location region represented as a probability distribution on the sky. Thus, at this point we may begin to ask the question of whether this distribution agrees with the isotropic model of the Universe, or if there is any evidence of anisotropy. We perform Bayesian model selection between an isotropic and a simple anisotropic model, taking into account the anisotropic selection function caused by the underlying antenna patterns and sensitivity of the interferometers over the sidereal day. We find an inconclusive Bayes factor of 1.3: 1, suggesting that the data from the first two observing runs are insufficient to pick a preferred model. However, the first detections were mostly poorly localized in the sky (before the Advanced Virgo joined the network), spanning large portions of the sky and hampering detection of potential anisotropy. It will be appropriate to repeat this analysis with events from the recent third LIGO observational run and a more sophisticated cosmological model.
The binary evolution of SAX J1808.4−3658: implications of an evolved donor star

Observations of the accretion powered millisecond pulsar SAX J1808.4−3658 have revealed an interesting binary evolution, with the orbit of the system expanding at an accelerated rate. We use the recent finding that the accreted fuel in SAX J1808.4−3658 is hydrogen depleted to greatly refine models of the progenitor and prior evolution of the binary system. We constrain the initial mass of the companion star to 1.0–1.2 M⊙, more massive than previous evolutionary studies of this system have assumed. We also infer the system must have undergone strongly non-conservative mass transfer in order to explain the observed orbital period changes. We include mass loss due to the pulsar radiation pressure on the donor star, inducing an evaporative wind which is ejected at the inner Lagrangian point of the binary system. The resulting additional loss of angular momentum resolves the discrepancy between conservative mass transfer models and the observed orbital period derivative of this system. We also include a treatment of donor irradiation due to the accretion luminosity, and find this has a non-negligible effect on the evolution of the system.
Metric-Scale Truncation-Robust Heatmaps for 3D Human Pose Estimation

Heatmap representations have formed the basis of 2D human pose estimation systems for many years, but their generalizations for 3D pose have only recently been considered. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and the Z axis to metric depth around the subject. To obtain metric-scale predictions, these methods must include a separate, explicit post-processing step to resolve scale ambiguity. Further, they cannot encode body joint positions outside of the image boundaries, leading to incomplete pose estimates in case of image truncation. We address these limitations by proposing metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are defined in metric 3D space near the subject, instead of being aligned with image space. We train a fully-convolutional network to estimate such heatmaps from monocular RGB in an end-to-end manner. This reinterpretation of the heatmap dimensions allows us to estimate complete metric-scale poses without test-time knowledge of the focal length or person distance and without relying on anthropometric heuristics in post-processing. Furthermore, as the image space is decoupled from the heatmap space, the network can learn to reason about joints beyond the image boundary. Using ResNet-50 without any additional learned layers, we obtain state-of-the-art results on the Human3.6M and MPI-INF-3DHP benchmarks. As our method is simple and fast, it can become a useful component for real-time top-down multi-person pose estimation systems. We make our code publicly available to facilitate further research.11https://vision.rwth-aachen.de/metro-pose3d
Structural Indexing for Conjunctive Path Queries

Structural indexing is an approach to accelerating query evaluation, whereby data objects are partitioned and indexed reflecting the precise expressive power of a given query language. Each partition block of the index holds exactly those objects that are indistinguishable with respect to queries expressible in the language. Structural indexes have proven successful for XML, RDF, and relational data management. In this paper we study structural indexing for conjunctive path queries (CPQ). CPQ forms the core of contemporary graph query languages such as SPARQL, Cypher, PGQL, and G-CORE. CPQ plays the same fundamental role with respect to contemporary graph query languages as the classic conjunctive queries play for SQL. We develop the first practical structural indexes for this important query language. In particular, we propose a structural index based on k-path-bisimulation, tightly coupled to the expressive power of CPQ, and develop algorithms for efficient query processing with our index. Furthermore, we study workload-aware structural indexes to reduce both the construction and space costs according to a given workload. We demonstrate through extensive experiments using real and synthetic graphs that our methods accelerate query processing by up to multiple orders of magnitude over the state-of-the-art methods, without increasing index size.
A Framework for the Computational Linguistic Analysis of Dehumanization

Dehumanization is a pernicious psychological process that often leads to extreme intergroup bias, hate speech, and violence aimed at targeted social groups. Despite these serious consequences and the wealth of available data, dehumanization has not yet been computationally studied on a large scale. Drawing upon social psychology research, we create a computational linguistic framework for analyzing dehumanizing language by identifying linguistic correlates of salient components of dehumanization. We then apply this framework to analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. Overall, we find increasingly humanizing descriptions of LGBTQ people over time. However, we find that the label homosexual has emerged to be much more strongly associated with dehumanizing attitudes than other labels, such as gay. Our proposed techniques highlight processes of linguistic variation and change in discourses surrounding marginalized groups. Furthermore, the ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
Comment on "Inconsistencies in Verlinde's emergent gravity"

In 2016, Erik Verlinde proposed a new theory of gravity called "emergent gravity" by using mathematical formulas used in the theory of elasticity. In 2017, De-Chang Dai and Dejan Stojkovic claimed to point out inconsistencies in Verlinde's emergent gravity. We point out that their claim was based on misunderstanding of the dictionary between emergent gravity and theory of elasticity. In addition, we propose a slightly different formula for Verlinde's emergent gravity.
Superconducting nonlinear thermoelectric heat engine

In a previous work, we predicted that a thermally biased tunnel junction between two different superconductors can display a thermoelectric effect of nonlinear nature in the temperature gradient, under proper conditions. In this work we give a more extended discussion, and we focus on the two main features of the nonlinear contributions: i) the $linear-in-bias$ thermoelectricity, that can be associated to a spontaneous breaking of electron-hole symmetry, ii) the strong contribution at the matching peak singularity, which is typically associated to the maximum output power and efficiency. We discuss the nonlinear origin of the thermoelectricity and its relationship with the non-linear cooling mechanism in superconducting junctions previously discussed in the literature. Finally, we design and characterize the performance of the system as a heat engine, for a realistic design and experimental parameter values. We discuss possible non-idealities demonstrating that the system is amenable to current experimental realization.
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data

Recent improvements in Generative Adversarial Neural Networks (GANs) have shown their ability to generate higher quality samples as well as to learn good representations for transfer learning. Most of the representation learning methods based on GANs learn representations ignoring their post-use scenario, which can lead to increased generalisation ability. However, the model can become redundant if it is intended for a specific task. For example, assume we have a vast unlabelled audio dataset, and we want to learn a representation from this dataset so that it can be used to improve the emotion recognition performance of a small labelled audio dataset. During the representation learning training, if the model does not know the post emotion recognition task, it can completely ignore emotion-related characteristics in the learnt representation. This is a fundamental challenge for any unsupervised representation learning model. In this paper, we aim to address this challenge by proposing a novel GAN framework: Guided Generative Neural Network (GGAN), which guides a GAN to focus on learning desired representations and generating superior quality samples for audio data leveraging fewer labelled samples. Experimental results show that using a very small amount of labelled data as guidance, a GGAN learns significantly better representations.
Price's equation made clear

Price's equation provides a very simple—and very general—encapsulation of evolutionary change. It forms the mathematical foundations of several topics in evolutionary biology, and has also been applied outwith evolutionary biology to a wide range of other scientific disciplines. However, the equation's combination of simplicity and generality has led to a number of misapprehensions as to what it is saying and how it is supposed to be used. Here, I give a simple account of what Price's equation is, how it is derived, what it is saying and why this is useful. In particular, I suggest that Price's equation is useful not primarily as a predictor of evolutionary change but because it provides a general theory of selection. As an illustration, I discuss some of the insights Price's equation has brought to the study of social evolution. This article is part of the theme issue ‘Fifty years of the Price equation’.
TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. However, the hierarchical nature of the meta-learning setting presents several challenges that can render conventional batch normalization ineffective, giving rise to the need to rethink normalization in this setting. We evaluate a range of approaches to batch normalization for meta-learning scenarios, and develop a novel approach that we call TaskNorm. Experiments on fourteen datasets demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient based and gradient-free meta-learning approaches. Importantly, TaskNorm is found to consistently improve performance. Finally, we provide a set of best practices for normalization that will allow fair comparison of meta-learning algorithms.
Statement to an Insufficient Systematic Review on Viscum album L. Therapy

Background Up to 88% of oncological patients apply complementary therapies and up to 77% apply complementary mistletoe therapy in the context of integrative oncological approaches. An evidence-based consultation of oncological health professionals regarding complementary therapies used in Germany is missing. Therefore, a new S3-Guideline for Complementary Medicine in the Treatment of Oncological Patients is under development and is anticipated to be finalized in November 2020. It will be based on evidence-based publications and systematic reviews on complementary therapies in oncology. A recently published two-part systematic review on mistletoe treatment in oncology has been reevaluated. Methods The latest published systematic two-part review on mistletoe has been systematically proofread and checked in compliance with the Cochrane Handbook for Systematic Reviews of Intervention and the AMSTAR 2 (A MeaSurement Tool to Assess Systematic Reviews) tool. Results The here discussed two-part review is incomplete, lacks sound accuracy including insufficient assessment of the risk of bias, and contains imprecise statements. In addition, it does not sufficiently comply with the Cochrane Handbook for Systematic Reviews of Intervention and the AMSTAR 2 tool. Conclusion In view of the approaching release of a new guideline in the field of complementary therapies in oncology, the present statement draws attention to a lack of profound methodology of conductance of a recently released systematic review on mistletoe. In consequence, a comprehensive overview of published mistletoe studies, i.e., a meta-analysis with a sound methodology of conductance, is necessary.
The Price equation and the unity of social evolution theory

The Price equation has been entangled with social evolution theory from the start. It has been used to derive the most general versions of kin selection theory, and Price himself produced a multilevel equation that provides an alternative formulation of social evolution theory, dividing selection into components between and within groups. In this sense, the Price equation forms a basis for both kin and group selection, so often pitted against each other in the literature. Contextual analysis and the neighbour approach are prominent alternatives for analysing group selection. I discuss these four approaches to social evolution theory and their connections to the Price equation, focusing on their similarities and common mathematical structure. Despite different notations and modelling traditions, all four approaches are ultimately linked by a common set of mathematical components, revealing their underlying unity in a transparent way. The Price equation can similarly be used in the derivation of streamlined, weak selection social evolution modelling methods. These weak selection models are practical and powerful methods for constructing models in evolutionary and behavioural ecology; they can clarify the causal structure of models, and can be easily converted between the four social evolution approaches just like their regression counterparts. This article is part of the theme issue ‘Fifty years of the Price equation’.
Designing for Employee Voice

Employee voice and workplace democracy have a positive impact on employee wellbeing and the performance of organizations. In this paper, we conducted interviews with employees to identify facilitators and inhibitors for voice within the workplace and a corresponding set of appropriate qualities: Civility, Validity, Safety and Egalitarianism. We then operationalised these qualities as a set of design goals - Assured Anonymity, Constructive Moderation, Adequate Slowness and Controlled Access - in the design and development of a secure anonymous employee voice system. Our novel take on the Enterprise Social Network aims to foster good citizenship whilst also promoting frank yet constructive discussion. We reflect on a two-week deployment of our system, the diverse range of candid discussions that emerged around important workplace issues and the potential for change within the host organization. We conclude by reflecting on the ways in which our approach shaped discourse and supported the creation of a trusted environment for employee voice.
The Price equation and reproductive value

The Price equation is widely recognized as capturing conceptually important properties of natural selection, and is often used to derive versions of Fisher’s fundamental theorem of natural selection, the secondary theorems of natural selection and other significant results. However, class structure is not usually incorporated into these arguments. From the starting point of Fisher’s original connection between fitness and reproductive value, a principled way of incorporating reproductive value and structured populations into the Price equation is explained, with its implications for precise meanings of (two distinct kinds of) reproductive value and of fitness. Once the Price equation applies to structured populations, then the other equations follow. The fundamental theorem itself has a special place among these equations, not only because it always incorporated class structure (and its method is followed for general class structures), but also because that is the result that justifies the important idea that these equations identify the effect of natural selection. The precise definitions of reproductive value and fitness have striking and unexpected features. However, a theoretical challenge emerges from the articulation of Fisher’s structure: is it possible to retain the ecological properties of fitness as well as its evolutionary out-of-equilibrium properties? This article is part of the theme issue ‘Fifty years of the Price equation’.
EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems

Understanding emotions and responding accordingly is one of the biggest challenges of dialog systems. This paper presents EmpTransfo, a multi-head Transformer architecture for creating an empathetic dialog system. EmpTransfo utilizes state-of-the-art pre-trained models (e.g., OpenAI-GPT) for language generation, though models with different sizes can be used. We show that utilizing the history of emotions and other metadata can improve the quality of generated conversations by the dialog system. Our experimental results using a challenging language corpus show that the proposed approach outperforms other models in terms of Hit@1 and PPL (Perplexity).