Miracle-dz/UniFAR-FTUs huggingface dataset:
Band-limited mimicry of point processes by point processes supported on a lattice

We say that one point process on the line $\mathbb{R}$ mimics another at a bandwidth $B$ if for each $n \ge 1$ the two point processes have $n$-level correlation functions that agree when integrated against all bandlimited test functions on bandwidth $[-B, B]$. This paper asks the question of for what values $a$ and $B$ can a given point process on the real line be mimicked at bandwidth $B$ by a point process supported on the lattice $a\mathbb{Z}$. For Poisson point processes we give a complete answer for allowed parameter ranges $(a,B)$, and for the sine process we give existence and nonexistence regions for parameter ranges. The results for the sine process have an application to the Alternative Hypothesis regarding the scaled spacing of zeros of the Riemann zeta function, given in a companion paper.
Optimization of the atom interferometer phase produced by the set of cylindrical source masses to measure the Newtonian gravity constant

An analytical expression for the gravitational field of a homogeneous cylinder is derived. The phase of the atom interferometer produced by the gravity field of the set of cylinders has been calculated. The optimal values of the initial positions and velocities of atomic clouds were obtained. It is shown that at equal sizes of the atomic cloud in the vertical and transverse directions, as well as at equal atomic vertical and transverse temperatures, systematic errors due to the finite size and temperature of the cloud disappear. To overcome the influence of the Earth gravitational field on the accuracy of the phase double difference measurement, it is proposed to use the technique of eliminating gravity-gradient terms. After eliminating, one can use extreme values of the atomic positions and velocities. Nonlinear dependences of the phase on the uncertainties of atomic positions and velocities near those extreme values required us to modify the expression for the standard phase deviation. Moreover, such dependences lead to a phase shift, which was also calculated. The relative accuracy of measurements of Newtonian gravitational constant 10^-4 and 2*10^-5 is predicted for sets of 24 and 630 cylinders, respectively.
Type-safe, Polyvariadic Event Correlation

The pivotal role that event correlation technology plays in todays applications has lead to the emergence of different families of event correlation approaches with a multitude of specialized correlation semantics, including computation models that support the composition and extension of different semantics. However, type-safe embeddings of extensible and composable event patterns into statically-typed general-purpose programming languages have not been systematically explored so far. Event correlation technology has often adopted well-known and intuitive notations from database queries, for which approaches to type-safe embedding do exist. However, we argue in the paper that these approaches, which are essentially descendants of the work on monadic comprehensions, are not well-suited for event correlations and, thus, cannot without further ado be reused/re-purposed for embedding event patterns. To close this gap we propose PolyJoin, a novel approach to type-safe embedding for fully polyvariadic event patterns with polymorphic correlation semantics. Our approach is based on a tagless final encoding with uncurried higher-order abstract syntax (HOAS) representation of event patterns with n variables, for arbitrary $n \in \mathbb{N}$. Thus, our embedding is defined in terms of the host language without code generation and exploits the host language type system to model and type check the type system of the pattern language. Hence, by construction it impossible to define ill-typed patterns. We show that it is possible to have a purely library-level embedding of event patterns, in the familiar join query notation, which is not restricted to monads. PolyJoin is practical, type-safe and extensible. An implementation of it in pure multicore OCaml is readily usable.
XGBoostLSS - An extension of XGBoost to probabilistic forecasting

We propose a new framework of XGBoost that predicts the entire conditional distribution of a univariate response variable. In particular, XGBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continuous, discrete and mixed discrete-continuous distribution, modelling and predicting the entire conditional distribution greatly enhances the flexibility of XGBoost, as it allows to gain additional insight into the data generating process, as well as to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. We present both a simulation study and real world examples that demonstrate the virtues of our approach.
Fundamental limits to radiative heat transfer: Theory

Near-field radiative heat transfer between bodies at the nanoscale can surpass blackbody limits on thermal radiation by orders of magnitude due to contributions from evanescent electromagnetic fields, which carry no energy to the far-field. Thus far, principles guiding explorations of larger heat transfer beyond planar structures have assumed utility in surface nanostructuring, which can enhance the density of states, and further assumed that such design paradigms can approach Landauer limits, in analogy to conduction. We derive fundamental shape-independent limits to radiative heat transfer, applicable in near- through far-field regimes, that incorporate material and geometric constraints such as intrinsic dissipation and finite object sizes, and show that these preclude reaching the Landauer limits in all but a few restrictive scenarios. Additionally, we show that the interplay of material response and electromagnetic scattering among proximate bodies means that bodies which maximize radiative heat transfer actually maximize scattering rather than absorption. Finally, we compare our new bounds to existing Landauer limits, as well as limits involving bodies maximizing far-field absorption, and show that these lead to overly optimistic predictions. Our results have ramifications for the ultimate performance of thermophotovoltaics and nanoscale cooling, as well as related incandescent and luminescent devices.
Earlier Attention? Aspect-Aware LSTM for Aspect Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.
Outdoor characterization of three PV architectures under clear and shaded conditions

Building Integrated and Building Applied Photovoltaic (BIPV, BAPV) systems often suffer from low performance due to partial shading. New system arch itectures have been proposed to optimize the energy yield. The common approach of these new architectures is to tr ack the Maximum Power Point (MPP) of every solar mo dule individually. Three independent PV systems were ins talled in Eindhoven, a reference string inverter sy stem, a power optimizer (PO) system and a micro inverter system. We designed a sophisticated monitoring system that allows for a detailed comparison of the three PV systems. We cal culated system and component efficiencies and foun d a good agreement with data sheets provided by the manufact urers. Additionally, we analyzed the performance ra tio for DC and AC of the systems for different weather types b a ed on the clearness index. A pole shading coverin g 1 to 2% of the total system surface has been used to evaluate sys m performance under a specific type of partial shading. Module Level Power Electronics (MLPE) are capable of increasing the Performance Ratio (PR) up to 35% under certain partial shading conditions. However, the st ring inverter system outperformed MLPE under unshad ed operation conditions.
xRAC: Execution and Access Control for Restricted Application Containers on Managed Hosts

We propose xRAC to permit users to run special applications on managed hosts and to grant them access to protected network resources. We use restricted application containers (RACs) for that purpose. A RAC is a virtualization container with only a selected set of applications. Authentication verifies the RAC user’s identity and the integrity of the RAC image. If the user is permitted to use the RAC on a managed host, launching the RAC is authorized and access to protected network resources may be given, e.g., to internal networks, servers, or the Internet. xRAC simplifies traffic control as the traffic of a RAC has a unique IPv6 address so that it can be easily identified in the network. The architecture of xRAC reuses standard technologies, protocols, and infrastructure. Those are the Docker virtualization platform and 802.1X including EAP-over-UDP and RADIUS. Thus, xRAC improves network security without modifying core parts of applications, hosts, and infrastructure. In this paper, we review the technological background of xRAC, explain its architecture, discuss selected use cases, and investigate on the performance. To demonstrate the feasibility of xRAC, we implement it based on standard components with only a few modifications. Finally, we validate xRAC through experiments. We publish the testbed setup guide and prototypical implementation on GitHub [1].
Predicting Gene Expression Between Species with Neural Networks

We train a neural network to predict human gene expression levels based on experimental data for rat cells. The network is trained with paired human/rat samples from the Open TG-GATES database, where paired samples were treated with the same compound at the same dose. When evaluated on a test set of held out compounds, the network successfully predicts human expression levels. On the majority of the test compounds, the list of differentially expressed genes determined from predicted expression levels agrees well with the list of differentially expressed genes determined from actual human experimental data.
Playing Flappy Bird via Asynchronous Advantage Actor Critic Algorithm

Flappy Bird, which has a very high popularity, has been trained in many algorithms. Some of these studies were trained from raw pixel values of game and some from specific attributes. In this study, the model was trained with raw game images, which had not been seen before. The trained model has learned as reinforcement when to make which decision. As an input to the model, the reward or penalty at the end of each step was returned and the training was completed. Flappy Bird game was trained with the Reinforcement Learning algorithm Deep Q-Network and Asynchronous Advantage Actor Critic (A3C) algorithms.
Exact Crystalline Structure Recovery in X-ray Crystallography from Coded Diffraction Patterns

X-ray crystallography (XC) is an experimental technique used to determine three-dimensional crystalline structures. The acquired data in XC, called diffraction patterns, is the Fourier magnitudes of the unknown crystalline structure. To estimate the crystalline structure from its diffraction patterns, we propose to modify the traditional system by including an optical element called coded aperture which modulates the diffracted field to acquire coded diffraction patterns (CDP). For the proposed coded system, in contrast with the traditional, we derive exact reconstruction guarantees for the crystalline structure from CDP (up to a global shift phase). Additionally, exploiting the fact that the crystalline structure can be sparsely represented in the Fourier domain, we develop an algorithm to estimate the crystal structure from CDP. We show that this method requires 50% fewer measurements to estimate the crystal structure in comparison with its competitive alternatives. Specifically, the proposed method is able to reduce the exposition time of the crystal, implying that under the proposed setup, its structural integrity is less affected in comparison with the traditional. We discuss further implementation of imaging devices that exploits this theoretical coded system.
Estimating Commuting Patterns from High Resolution Phone GPS Data

The rise of location positioning technologies has generated enormous volumes of digital footprints. Translating this big data into understandable trip patterns plays a crucial role in estimating infrastructure demands. Previous studies were unable to correctly represent commuting patterns on smaller urban scales due to insufficient spatial accuracy. In this study, we investigated if, and to what extent, estimated commuting patterns identified from GPS data can replicate the results from transportation surveys and to what degree these estimates improve the estimates of trips distribution pattern on census tract level using higher resolution data. We inferred average daily home-to-work trips by analyzing phone GPS data and compared these patterns with U.S. Census summary tables. We found that trips detected by GPS data highly correlate with census trips. Furthermore, GPS data is a better proxy for Census tract-pairs with larger numbers of trips.
ANETAC: Arabic Named Entity Transliteration and Classification Dataset

In this paper, we make freely accessible ANETAC, our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79, 924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes. This dataset was developed and used as part of a previous research study done by Hadj Ameur et al. [1].
Probing gravity by holding atoms for 20 seconds

Trapped atoms to probe gravity Exploiting the wave nature of atoms in an interferometer setup can be used to provide a precise measure of gravity. The precision is limited to the time scale of the interferometric measurement, which in turn is limited to the distance that the atoms drop, typically just over a couple of seconds for a 10-meter drop tower. Xu et al. describe a trapped atom interferometer in which the interrogation time of the interferometric measurements can be extended to 20 seconds. The new interferometer design and subsequent improved precision can be used to make fundamental tests of general relativity as well as precision measurements of other potentials. Science, this issue p. 745 Holding trapped atoms in an interferometer for up to 20 seconds provides a sensitive measure of gravity. Atom interferometers are powerful tools for both measurements in fundamental physics and inertial sensing applications. Their performance, however, has been limited by the available interrogation time of freely falling atoms in a gravitational field. By suspending the spatially separated atomic wave packets in a lattice formed by the mode of an optical cavity, we realize an interrogation time of 20 seconds. Our approach allows gravitational potentials to be measured by holding, rather than dropping, atoms. After seconds of hold time, gravitational potential energy differences from as little as micrometers of vertical separation generate megaradians of interferometer phase. This trapped geometry suppresses the phase variance due to vibrations by three to four orders of magnitude, overcoming the dominant noise source in atom-interferometric gravimeters.
Manifolds Homotopy Equivalent to Certain Torus Bundles over Lens Spaces

We compute the topological simple structure set of closed manifolds that occur as total spaces of flat bundles over lens spaces Sl/(ℤ/p) with fiber Tn for an odd prime p and l ≥ 3 provided that the induced ℤ/p‐action on π1(Tn) = ℤn is free outside the origin. To the best of our knowledge this is the first computation of the structure set of a topological manifold whose fundamental group is not obtained from torsionfree and finite groups using amalgamated and HNN‐extensions. We give a collection of classical surgery invariants such as splitting obstructions and ρ‐invariants that decide whether a simple homotopy equivalence from a closed topological manifold to M is homotopic to a homeomorphism. © 2020 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC
Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction

Our entry into the HAHA 2019 Challenge placed $3^{rd}$ in the classification task and $2^{nd}$ in the regression task. We describe our system and innovations, as well as comparing our results to a Naive Bayes baseline. A large Twitter based corpus allowed us to train a language model from scratch focused on Spanish and transfer that knowledge to our competition model. To overcome the inherent errors in some labels we reduce our class confidence with label smoothing in the loss function. All the code for our project is included in a GitHub repository for easy reference and to enable replication by others.
Constraints on the growth rate using the observed galaxy power spectrum

The large-scale structure growth index γ provides a consistency test of the standard cosmology and is a potential indicator of modified gravity. We investigate the constraints on γ from next-generation spectroscopic surveys, using the power spectrum that is observed in redshift space, i.e., the angular power spectrum. The angular power spectrum avoids the need for an Alcock-Packzynski correction. It also naturally incorporates cosmic evolution and wide-angle effects, without any approximation. We include the cross-correlations between redshift bins, using a hybrid approximation when the total number of bins is computationally unfeasible. We show that the signal-to-noise on γ increases as the redshift bin-width is decreased. Shot noise per bin also increases—but this is compensated by the increased number of auto- and cross-spectra. In our forecasts, we marginalise over the amplitude of primordial fluctuations and other standard cosmological parameters, including the dark energy equation of state parameter, as well as the clustering bias. Neglecting cross-bin correlations increases the errors by ∼40–150%. Using only linear scales, we find that a DESI-like BGS survey and an HI intensity mapping survey with the SKA1 precursor MeerKAT deliver similar errors of ∼4–6%, while a Euclid-like survey and an SKA1 intensity mapping survey give ∼3% errors.
Uncovering Download Fraud Activities in Mobile App Markets

Download fraud is a prevalent threat in mobile App markets, where fraudsters manipulate the number of downloads of Apps via various cheating approaches. Purchased fake downloads can mislead recommendation and search algorithms and further lead to bad user experience in App markets. In this paper, we investigate download fraud problem based on a company's App Market, which is one of the most popular Android App markets. We release a honeypot App on the App Market and purchase fake downloads from fraudster agents to track fraud activities in the wild. Based on our interaction with the fraudsters, we categorize download fraud activities into three types according to their intentions: boosting front end downloads, optimizing App search ranking, and enhancing user acquisition&retention rate. For the download fraud aimed at optimizing App search ranking, we select, evaluate, and validate several features in identifying fake downloads based on billions of download data. To get a comprehensive understanding of download fraud, we further gather stances of App marketers, fraudster agencies, and market operators on download fraud. The followed analysis and suggestions shed light on the ways to mitigate download fraud in App markets and other social platforms. To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
Backward Reachability for Polynomial Systems on a Finite Horizon

A method is presented to obtain an inner-approximation of the backward reachable set of a given target tube, along with an admissible controller that maintains trajectories inside this tube. The proposed optimization algorithms are formulated as nonlinear optimization problems, which are decoupled into tractable subproblems and solved by an iterative algorithm using the polynomial S-procedure and sum-of-squares techniques. This framework is also extended to uncertain nonlinear systems with <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_2$</tex-math></inline-formula> disturbances and <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{\infty }$</tex-math></inline-formula> parametric uncertainties. The effectiveness of the method is demonstrated on several nonlinear robotics and aircraft systems with control saturation.
Knowledge-aware Pronoun Coreference Resolution

Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge to better resolve pronoun coreference with a neural model. To ensure the generalization ability of our model, we directly incorporate knowledge in the format of triplets, which is the most common format of modern knowledge graphs, instead of encoding it with features or rules as that in conventional approaches. Moreover, since not all knowledge is helpful in certain contexts, to selectively use them, we propose a knowledge attention module, which learns to select and use informative knowledge based on contexts, to enhance our model. Experimental results on two datasets from different domains prove the validity and effectiveness of our model, where it outperforms state-of-the-art baselines by a large margin. Moreover, since our model learns to use external knowledge rather than only fitting the training data, it also demonstrates superior performance to baselines in the cross-domain setting.