nils-herrmann/clean_scirepeval huggingface dataset:
Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network

This paper describes a practical system for Multi Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step (â€œresponse modelingâ€ ) fits a user-level model for purchase of a product as a function of the userâ€™s exposure to ads. The second (â€œcredit allocationâ€ ) uses the fitted model to allocate the incremental part of the observed purchase due to advertising, to the ads the user is exposed to over the previous T days. To implement step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data in a semi-parametric way. The specific RNN formulation we implement captures the impact of advertising intensity, timing, competition, and user-heterogeneity, which are known to be relevant to ad-response. To implement step two, we compute Shapley Values, which have the advantage of having axiomatic foundations and satisfying fairness considerations. The specific formulation of the Shapley Value we implement respects incrementality by allocating the overall incremental improvement in conversion to the exposed ads, while handling the sequence-dependence of exposures on the observed outcomes. The system is under production at JD.com, and scales to handle the high dimensionality of the problem on the platform (attribution of the orders of about 300M users, for roughly 160K brands, across 200+ ad-types, served about 80B ad-impressions over a typical 15-day period).
Natural and Adversarial Error Detection using Invariance to Image Transformations

We propose an approach to distinguish between correct and incorrect image classifications. Our approach can detect misclassifications which either occur $\it{unintentionally}$ ("natural errors"), or due to $\it{intentional~adversarial~attacks}$ ("adversarial errors"), both in a single $\it{unified~framework}$. Our approach is based on the observation that correctly classified images tend to exhibit robust and consistent classifications under certain image transformations (e.g., horizontal flip, small image translation, etc.). In contrast, incorrectly classified images (whether due to adversarial errors or natural errors) tend to exhibit large variations in classification results under such transformations. Our approach does not require any modifications or retraining of the classifier, hence can be applied to any pre-trained classifier. We further use state of the art targeted adversarial attacks to demonstrate that even when the adversary has full knowledge of our method, the adversarial distortion needed for bypassing our detector is $\it{no~longer~imperceptible~to~the~human~eye}$. Our approach obtains state-of-the-art results compared to previous adversarial detection methods, surpassing them by a large margin.
Sequential Evaluation and Generation Framework for Combinatorial Recommender System

In the combinatorial recommender systems, multiple items are fed to the user at one time in the result page, where the correlations among the items have impact on the user behavior. In this work, we model the combinatorial recommendation as the problem of generating a sequence(ordered list) of items from a candidate set, with the target of maximizing the expected overall utility(e.g. total clicks) of the sequence. Toward solving this problem, we propose the Evaluation-Generation framework. On the one hand of this framework, an evaluation model is trained to evaluate the expected overall utility, by fully considering the user, item information and the correlations among the co-exposed items. On the other hand, generation policies based on heuristic searching or reinforcement learning are devised to generate potential high-quality sequences, from which the evaluation model select one to expose. We propose effective model architectures and learning metrics under this framework. We also offer series of offline tests to thoroughly investigate the performance of the proposed framework, as supplements to the online experiments. Our results show obvious increase in performance compared with the previous solutions.
Temperature-independent thermal radiation

Conventional wisdom states that the hotter an object is, the brighter it glows. This is the case for thermal light at any wavelength and enables applications such as infrared imaging and noncontact thermometry. We demonstrate a coating that emits the same amount of thermal radiation irrespective of temperature, within a temperature range of about 30 °C. This is accomplished using samarium nickel oxide—a quantum material that changes strongly but gradually as a function of temperature. This is the first time that temperature-independent thermal radiation has been demonstrated, and has substantial implications for infrared camouflage, privacy shielding, and radiative heat transfer. Thermal emission is the process by which all objects at nonzero temperatures emit light and is well described by the Planck, Kirchhoff, and Stefan–Boltzmann laws. For most solids, the thermally emitted power increases monotonically with temperature in a one-to-one relationship that enables applications such as infrared imaging and noncontact thermometry. Here, we demonstrated ultrathin thermal emitters that violate this one-to-one relationship via the use of samarium nickel oxide (SmNiO3), a strongly correlated quantum material that undergoes a fully reversible, temperature-driven solid-state phase transition. The smooth and hysteresis-free nature of this unique insulator-to-metal phase transition enabled us to engineer the temperature dependence of emissivity to precisely cancel out the intrinsic blackbody profile described by the Stefan–Boltzmann law, for both heating and cooling. Our design results in temperature-independent thermally emitted power within the long-wave atmospheric transparency window (wavelengths of 8 to 14 µm), across a broad temperature range of ∼30 °C, centered around ∼120 °C. The ability to decouple temperature and thermal emission opens a gateway for controlling the visibility of objects to infrared cameras and, more broadly, opportunities for quantum materials in controlling heat transfer.
Ramsey numbers of ordered graphs under graph operations

An ordered graph $\mathcal{G}$ is a simple graph together with a total ordering on its vertices. The (2-color) Ramsey number of $\mathcal{G}$ is the smallest integer $N$ such that every 2-coloring of the edges of the complete ordered graph on $N$ vertices has a monochromatic copy of $\mathcal{G}$ that respects the ordering. In this paper we investigate the effect of various graph operations on the Ramsey number of a given ordered graph, and detail a general framework for applying results on extremal functions of 0-1 matrices to ordered Ramsey problems. We apply this method to give upper bounds on the Ramsey number of ordered matchings arising from sum-decomposable permutations, an alternating ordering of the cycle, and an alternating ordering of the tight hyperpath. We also construct ordered matchings on $n$ vertices whose Ramsey number is $n^{q+o(1)}$ for any given exponent $q\in(1,2)$.
Summary Report of Physics Beyond Colliders at CERN

Physics Beyond Colliders is an exploratory study aimed at exploiting the full scientific potential of CERN's accelerator complex and its scientific infrastructure in the next two decades through projects complementary to the LHC, HL-LHC and other possible future colliders. These projects should target fundamental physics questions that are similar in spirit to those addressed by high-energy colliders, but that require different types of beams and experiments. A kick-off workshop held in September 2016 identified a number of areas of interest and working groups have been set-up to study and develop these directions. All projects currently under consideration are presented including physics motivation, a brief outline of the experimental set-up and the status of the corresponding beam and detector technological studies. The proposals are also put in context of the worldwide landscape and their implementation issues are discussed.  This document (66 pages, 19 figures) is the summary document of the Physics Beyond Colliders (PBC) study. It follows the PBC mandate and draws on a whole set of documents produced in the context of PBC (this https URL), in particular the reports of the QCD and BSM working groups also available at arXiv: 1901.04482 and
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at this https URL
Signatures and Induction Principles for Higher Inductive-Inductive Types

Higher inductive-inductive types (HIITs) generalize inductive types of dependent type theories in two ways. On the one hand they allow the simultaneous definition of multiple sorts that can be indexed over each other. On the other hand they support equality constructors, thus generalizing higher inductive types of homotopy type theory. Examples that make use of both features are the Cauchy real numbers and the well-typed syntax of type theory where conversion rules are given as equality constructors. In this paper we propose a general definition of HIITs using a small type theory, named the theory of signatures. A context in this theory encodes a HIIT by listing the constructors. We also compute notions of induction and recursion for HIITs, by using variants of syntactic logical relation translations. Building full categorical semantics and constructing initial algebras is left for future work. The theory of HIIT signatures was formalised in Agda together with the syntactic translations. We also provide a Haskell implementation, which takes signatures as input and outputs translation results as valid Agda code.
Rethinking Visual Relationships for High-level Image Understanding

Relationships, as the bond of isolated entities in images, reflect the interaction between objects and lead to a semantic understanding of scenes. Suffering from visually-irrelevant relationships in current scene graph datasets, the utilization of relationships for semantic tasks is difficult. The datasets widely used in scene graph generation tasks are splitted from Visual Genome by label frequency, which even can be well solved by statistical counting. To encourage further development in relationships, we propose a novel method to mine more valuable relationships by automatically filtering out visually-irrelevant relationships. Then, we construct a new scene graph dataset named Visually-Relevant Relationships Dataset (VrR-VG) from Visual Genome. We evaluate several existing methods in scene graph generation in our dataset. The results show the performances degrade significantly compared to the previous dataset and the frequency analysis do not work on our dataset anymore. Moreover, we propose a method to learn feature representations of instances, attributes, and visual relationships jointly from images, then we apply the learned features to image captioning and visual question answering respectively. The improvements on the both tasks demonstrate the efficiency of the features with relation information and the richer semantic information provided in our dataset.
SensitiveNets: Learning Agnostic Representations with Application to Face Recognition

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task.
A surface-depth theory of the emergence of complex networks

The broadly general characteristics of complex networks found across disciplines-- such as high clustering coefficients and heavy-tailed degree distributions-- has long invited the question of whether there are general generating mechanisms behind them. Here, we propose a theory of such mechanisms and undertake several experiments which validate it. This theory proposes that there are two key principles at work in the emergence of a network, constituting a `surface' factor and a `depth' factor making up the existence probability of network edges. The surface factor describes nodes as having tendencies for attachment which follow a log-normal distribution. The deep factor suggests that beneath these potentials for attachment there exist any number of important latent variables describing the nodes. These variables are formulated as a high-dimensional manifold and the `distances' between pairs of nodes on this manifold constitute a similarity weighting informing on the probability that any two nodes are connected. Using standard network measures, the topology of 110 networks across a variety of disciplines shows agreement with a simple two-parameter model based on this theory. Importantly, we show that the log-normal surface factor can explain the power-law-like degree distributions of sparse networks and, more strongly, the variety of degree distributions found across networks of different densities. We also demonstrate how inverting an estimated surface factor of a complete weighted world city network provides more plausible clusters of nodes than the original network based on geometric and cultural considerations. This theory proposes a new fundamental formulation of complex networks with wide reaching consequences throughout the multidisciplinary domain of complex systems.
From noncommutative diagrams to anti-elementary classes

Anti-elementarity is a strong way of ensuring that a class of structures , in a given first-order language, is not closed under elementary equivalence with respect to any infinitary language of the form L ∞λ. We prove that many naturally defined classes are anti-elementary, including the following: • the class of all lattices of finitely generated convex l-subgroups of members of any class of l-groups containing all Archimedean l-groups; • the class of all semilattices of finitely generated l-ideals of members of any nontrivial quasivariety of l-groups; • the class of all Stone duals of spectra of MV-algebras-this yields a negative solution for the MV-spectrum Problem; • the class of all semilattices of finitely generated two-sided ideals of rings; • the class of all semilattices of finitely generated submodules of modules; • the class of all monoids encoding the nonstable K_0-theory of von Neumann regular rings, respectively C*-algebras of real rank zero; • (assuming arbitrarily large Erd˝os cardinals) the class of all coordinatizable sectionally complemented modular lattices with a large 4-frame. The main underlying principle is that under quite general conditions, for a functor Φ : A → B, if there exists a non-commutative diagram D of A, indexed by a common sort of poset called an almost join-semilattice, such that • Φ D^I is a commutative diagram for every set I, • Φ D is not isomorphic to Φ X for any commutative diagram X in A, then the range of Φ is anti-elementary.
A LOFAR search for steep-spectrum pulsars in supernova remnants and pulsar wind nebulae

Pinpointing a pulsar in its parent supernova remnant (SNR) or resulting pulsar wind nebula (PWN) is key to understanding its formation history and the pulsar wind mechanism, yet only about half the SNRs and PWNe appear associated with a pulsar. Our aim was to find the pulsars in a sample of eight known and new SNRs and PWNe. Using the LOFAR radio telescope at 150 MHz, each source was observed for 3 h. We covered the entire remnants where needed, by employing many tied-array beams to tile out even the largest objects. For objects with a confirmed point source or PWN we constrained our search to those lines of sight. We identified a promising radio pulsar candidate towards PWN G141.2+5.0. The candidate, PSR J0337+61, has a period of 94 ms and a DM of 226 pc cm−3. We re-observed the source twice with increased sensitivities of 30% and 50%, but did not re-detect it. It thus remains unconfirmed. For our other sources we obtain very stringent upper limits of 0.8 − 3.1 mJy at 150 MHz. Generally, we can rule out that the pulsars travelled out of the remnant. From these strict limits we conclude our non-detections towards point sources and PWNe are the result of beaming and propagation effects. Some of the remaining SNRs should host a black hole rather than a neutron star.
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Adversarial Defensiveness

Recent work in deep learning has shown that the artificial neural networks are vulnerable to adversarial attacks, where a very small perturbation of the inputs can drastically alter the classification result. In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,.e.g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions. The traditional back propagation method cannot train the artificial neural networks with aforementioned brain-like learning mechanisms. Numerical results show that various artificial neural networks trained by the new method can significantly improve the defensiveness against the adversarial attacks. Code is available: \url{this https URL} .
On the Low Mach Number Limit for Quantum Navier-Stokes Equations

In this paper we investigate the low Mach number limit for the quantum Navier-Stokes system considered in the three-dimensional space. For general ill-prepared initial data of finite energy, we prove strong convergence of finite energy weak solutions towards weak solutions of incompressible Navier Stokes equations. Our approach relies on a careful dispersive analysis for the acoustic part, governed by the Bogoliubov dispersion relation. The a priori bounds given by the energy and the BD entropy then yield the strong convergence towards the incompressible dynamics.
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation

Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For mixture distributions, established distance measures such as the Wasserstein distance do not take into account imbalanced mixture proportions. Thus, even if two mixture distributions have identical mixture components but different mixture proportions, the Wasserstein distance between them will be large. This often leads to undesired results in distance-based learning methods for mixture distributions. In this paper, we resolve this issue by introducing the Normalized Wasserstein measure. The key idea is to introduce mixture proportions as optimization variables, effectively normalizing mixture proportions in the Wasserstein formulation. Using the proposed normalized Wasserstein measure leads to significant performance gains for mixture distributions with imbalanced mixture proportions compared to the vanilla Wasserstein distance. We demonstrate the effectiveness of the proposed measure in GANs, domain adaptation and adversarial clustering in several benchmark datasets.
Do We Train on Test Data? Purging CIFAR of Near-Duplicates

The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets in computer vision and are often used to evaluate novel methods and model architectures in the field of deep learning. However, we find that 3.3% and 10% of the images from the test sets of these datasets have duplicates in the training set. These duplicates are easily recognizable by memorization and may, hence, bias the comparison of image recognition techniques regarding their generalization capability. To eliminate this bias, we provide the “fair CIFAR” (ciFAIR) dataset, where we replaced all duplicates in the test sets with new images sampled from the same domain. The training set remains unchanged, in order not to invalidate pre-trained models. We then re-evaluate the classification performance of various popular state-of-the-art CNN architectures on these new test sets to investigate whether recent research has overfitted to memorizing data instead of learning abstract concepts. We find a significant drop in classification accuracy of between 9% and 14% relative to the original performance on the duplicate-free test set. We make both the ciFAIR dataset and pre-trained models publicly available and furthermore maintain a leaderboard for tracking the state of the art.
Does Better Governance Guarantee Less Corruption? Evidence of Loss in Effectiveness of the Rule of Law

Corruption is an endemic societal problem with profound implications in the development of nations. In combating this issue, cross-national evidence supporting the effectiveness of the rule of law seems at odds with poorly realized outcomes from reforms inspired in such literature. This paper provides an explanation for such contradiction. By taking a computational approach, we develop two methodological novelties into the empirical study of corruption: (1) generating large within-country variation by means of simulation (instead of cross-national data pooling), and (2) accounting for interactions between covariates through a spillover network. The latter (the network), seems responsible for a significant reduction in the effectiveness of the rule of law; especially among the least developed countries. We also find that effectiveness can be boosted by improving complementary policy issues that may lie beyond the governance agenda. Moreover, our simulations suggest that improvements to the rule of law are a necessary yet not sufficient condition to curve corruption.