Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors and loose Hamilton cycles in sparse pseudo‐random hypergraphs

We investigate the emergence of subgraphs in sparse pseudo‐random k‐uniform hypergraphs, using the following comparatively weak notion of pseudo‐randomness. A k‐uniform hypergraph H on n vertices is called (p,α,ε)‐pseudo‐random if for all (not necessarily disjoint) vertex subsets A1,…,Ak⊆V(H) with |A1|⋯|Ak|≥αnk we have e(A1,…,Ak)=(1±ε)p|A1|⋯|Ak|. For any linear k‐uniform F, we provide a bound on α=α(n) in terms of p=p(n) and F, such that (under natural divisibility assumptions on n) any k‐uniform (p,α,o(1)) ‐pseudo‐random n‐vertex hypergraph H with a mild minimum vertex degree condition contains an F‐factor. The approach also enables us to establish the existence of loose Hamilton cycles in sufficiently pseudo‐random hypergraphs and, along the way, we also derive conditions which guarantee the appearance of any fixed sized subgraph. All results imply corresponding bounds for stronger notions of hypergraph pseudo‐randomness such as jumbledness or large spectral gap. As a consequence, (p,α,o(1)) ‐pseudo‐random k‐graphs as above contain: (i) a perfect matching if α=o(pk) and (ii) a loose Hamilton cycle if α=o(pk−1) . This extends the works of Lenz–Mubayi, and Lenz–Mubayi–Mycroft who studied the analogous problems in the dense setting.
Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery

Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often has a numerical advantage over the classic gradient method for nonlinear optimization problems. In this paper, we propose the so-called Newton-step-based iterative hard thresholding (NSIHT) and the Newton-step-based hard thresholding pursuit (NSHTP) algorithms for sparse signal recovery. Different from the traditional iterative hard thresholding (IHT) and hard thresholding pursuit (HTP), the proposed algorithms adopt the Newton-like search direction instead of the steepest descent direction. A theoretical analysis for the proposed algorithms is carried out, and sufficient conditions for the guaranteed success of sparse signal recovery via these algorithms are established in terms of the restricted isometry property of a sensing matrix which is one of the standard assumptions used in the field of compressed sensing and signal approximation. The empirical results from synthetic signal recovery indicate that the performance of proposed algorithms are comparable to that of several existing algorithms. The numerical behavior of our algorithms with respect to the residual reduction and parameter changes is also investigated through simulations.
Wallet Contracts on Ethereum

On the blockchain, cryptocurrencies play a role similar to cash, while cryptographic tokens are a universal tool for handling rights and assets. Software wallets interact with blockchains in general and with smart contracts (on-chain programs) in particular. Some wallets are realized (partly) as smart contracts with the intent to increase trust and security by being transparent and by offering features like daily limits, approvals, multiple signatures, and recovery mechanisms.Ethereum is the most prominent platform for both, tokens and smart contracts, and thus also for wallet contracts. We discuss several methods for identifying wallet contracts in a semi-automatic manner by looking at the deployed bytecodes and their interaction patterns. Furthermore, we differentiate characteristics of wallets in use, and group them into six types.
AutoMATES: Automated Model Assembly from Text, Equations, and Software

Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real use, they must also be implemented as software, thus making code a third form of representing models. We introduce the AutoMATES project, which aims to build semantically-rich unified representations of models from scientific code and publications to facilitate the integration of computational models from different domains and allow for modeling large, complicated systems that span multiple domains and levels of abstraction.
Galilean boosts and superfluidity of resonantly driven polariton fluids in the presence of an incoherent reservoir

We theoretically investigate how the presence of a reservoir of incoherent excitations affects the superfluidity properties of resonantly driven polariton fluids. While in the absence of reservoir the two cases of a defect moving in a fluid at rest and of a fluid flowing against a static defect are linked by a formal Galilean transformation, here the reservoir defines a privileged reference frame attached to the semiconductor structure and causes markedly different features between the two settings. The consequences on the critical velocity for superfluidity are highlighted and compared to experiments in resonantly driven excitons polaritons.
Polyharmonic Functions And Random Processes in Cones

We investigate polyharmonic functions associated to Brownian motion and random walks in cones. These are functions which cancel some power of the usual Laplacian in the continuous setting and of the discrete Laplacian in the discrete setting. We show that polyharmonic functions naturally appear while considering asymptotic expansions of the heat kernel in the Brownian case and in lattice walk enumeration problems. We provide a method to construct general polyharmonic functions through Laplace transforms and generating functions in the continuous and discrete cases, respectively. This is done by using a functional equation approach.
Distributed Vehicular Computing at the Dawn of 5G: a Survey

Recent advances in information technology have revolutionized the automotive industry, paving the way for next-generation smart and connected vehicles. Connected vehicles can collaborate to deliver novel services and applications. These services and applications require 1) massive volumes of data that perceive ambient environments, 2) ultra-reliable and low-latency communication networks, 3) real-time data processing which provides decision support under application-specific constraints. Addressing such constraints introduces significant challenges with current communication and computation technologies. Coincidentally, the fifth generation of cellular networks (5G) was developed to respond to communication challenges by providing an infrastructure for low-latency, high-reliability, and high bandwidth communication. At the core of this infrastructure, edge computing allows data offloading and computation at the edge of the network, ensuring low-latency and context-awareness, and pushing the utilization efficiency of 5G to its limit. In this paper, we aim at providing a comprehensive overview of the state of research on vehicular computing in the emerging age of 5G. After reviewing the main vehicular applications requirements and challenges, we follow a bottom-up approach, starting with the promising technologies for vehicular communications, all the way up to Artificial Intelligence (AI) solutions. We explore the various architectures for vehicular computing, including centralized Cloud Computing, Vehicular Cloud Computing, and Vehicular Edge computing, and investigate the potential data analytics technologies and their integration on top of the vehicular computing architectures. We finally discuss several future research directions and applications for vehicular computation systems.
On Optimal Multi-user Beam Alignment in Millimeter Wave Wireless Systems

Directional transmission patterns (a.k.a. narrow beams) are the key to wireless communications in millimeter wave (mmWave) frequency bands which suffer from high path loss and severe shadowing. In addition, the propagation channel in mmWave frequencies incorporates only a few number of spatial clusters requiring a procedure to align the corresponding narrow beams with the angle of departure (AoD) of the channel clusters. The objective of this procedure, called beam alignment (BA) is to increase the beamforming gain for subsequent data communication. Several prior studies consider optimizing BA procedure to achieve various objectives such as reducing the BA overhead, increasing throughput, and reducing power consumption. While these studies mostly provide optimized BA schemes for scenarios with a single active user, there are often multiple active users in practical networks. Consequently, it is more efficient in terms of BA overhead and delay to design multi-user BA schemes which can perform beam management for multiple users collectively. This paper considers a class of multi-user BA schemes where the base station performs a one shot scan of the angular domain to simultaneously localize multiple users. The objective is to minimize the average of expected width of remaining uncertainty regions (UR) on the AoDs after receiving users' feedbacks. Fundamental bounds on the optimal performance are analyzed using information theoretic tools. Furthermore, a BA optimization problem is formulated and a practical BA scheme, which provides significant gains compared to the beam sweeping used in 5G standard, is proposed.
Hunting for open clusters in Gaia DR2: 582 new open clusters in the Galactic disc

Context. Open clusters are key targets for studies of Galaxy structure and evolution, and stellar physics. Since the Gaia data release 2 (DR2), the discovery of undetected clusters has shown that previous surveys were incomplete. Aims. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in Gaia DR2, and to complete the open cluster sample to enable further studies of the Galactic disc. Methods. We use a machine-learning based methodology to systematically search the Galactic disc for overdensities in the astrometric space and identify the open clusters using photometric information. First, we used an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in Gaia DR2 (l, b, ϖ, μα*, μδ), and then we used a deep learning artificial neural network trained on colour–magnitude diagrams to identify isochrone patterns in these overdensities, and to confirm them as open clusters. Results. We find 582 new open clusters distributed along the Galactic disc in the region |b| < 20°. We detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC 274 of ∼3 Gyr located at ∼2 kpc. Conclusions. Adapting the mentioned methodology to a Big Data environment allows us to target the search using the physical properties of open clusters instead of being driven by computational limitations. This blind search for open clusters in the Galactic disc increases the number of known open clusters by 45%.
On the Impacts of Redundancy, Diversity, and Trust in Resilient Distributed State Estimation

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. Recent attempts to solve this problem impose stringent redundancy requirements on the measurement and communication resources of the network. In this article, we take a step toward alleviating such strict requirements by exploring two complementary directions: 1) making a small subset of the nodes immune to attacks, or “trusted;” and 2) incorporating diversity into the network. We define graph-theoretic constructs that formally capture the notions of redundancy, diversity, and trust. Based on these constructs, we develop a resilient estimation algorithm and demonstrate that even relatively sparse networks that either exhibit node-diversity, or contain a small subset of trusted nodes, can be just as resilient to adversarial attacks as more dense networks. Finally, given a finite budget for network design, we focus on characterizing the complexity of (i) selecting a set of trusted nodes, and (ii) allocating diversity, so as to achieve a desired level of robustness. We establish that, unfortunately, each of these problems is NP-complete.
Length-controllable Abstractive Summarization by Guiding with Summary Prototype

We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one summary per source text. However, controllable summarization, especially of the length, is an important aspect for practical applications. Previous studies on length-controllable abstractive summarization incorporate length embeddings in the decoder module for controlling the summary length. Although the length embeddings can control where to stop decoding, they do not decide which information should be included in the summary within the length constraint. Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings. Our model generates a summary in two steps. First, our word-level extractor extracts a sequence of important words (we call it the "prototype text") from the source text according to the word-level importance scores and the length constraint. Second, the prototype text is used as additional input to the encoder-decoder model, which generates a summary by jointly encoding and copying words from both the prototype text and source text. Since the prototype text is a guide to both the content and length of the summary, our model can generate an informative and length-controlled summary. Experiments with the CNN/Daily Mail dataset and the NEWSROOM dataset show that our model outperformed previous models in length-controlled settings.
Towards Fault Localization via Probabilistic Software Modeling

Software testing helps developers to identify bugs. However, awareness of bugs is only the first step. Finding and correcting the faulty program components is equally hard and essential for high-quality software. Fault localization automatically pinpoints the location of an existing bug in a program. It is a hard problem, and existing methods are not yet precise enough for widespread industrial adoption. We propose fault localization via Probabilistic Software Modeling (PSM). PSM analyzes the structure and behavior of a program and synthesizes a network of Probabilistic Models (PMs). Each PM models a method with its inputs and outputs and is capable of evaluating the likelihood of runtime data. We use this likelihood evaluation to find fault locations and their impact on dependent code elements. Results indicate that PSM is a robust framework for accurate fault localization.
Confidence intervals for multiple isotonic regression and other monotone models

We consider the problem of constructing pointwise confidence intervals in the multiple isotonic regression model. Recently, [HZ19] obtained a pointwise limit distribution theory for the block max-min and min-max estimators [FLN17] in this model, but inference remains a difficult problem due to the nuisance parameter in the limit distribution that involves multiple unknown partial derivatives of the true regression function. In this paper, we show that this difficult nuisance parameter can be effectively eliminated by taking advantage of information beyond point estimates in the block max-min and min-max estimators. Formally, let $\hat{u}(x_0)$ (resp. $\hat{v}(x_0)$) be the maximizing lower-left (resp. minimizing upper-right) vertex in the block max-min (resp. min-max) estimator, and $\hat{f}_n$ be the average of the block max-min and min-max estimators. If all (first-order) partial derivatives of $f_0$ are non-vanishing at $x_0$, then the following pivotal limit distribution theory holds: $$ \sqrt{n |[\hat{u}(x_0),\hat{v}(x_0)]| }\big(\hat{f}_n(x_0)-f_0(x_0)\big)\rightsquigarrow \sigma\cdot \mathbb{L}_{1_d}. $$ Here $\sigma$ is the standard deviation of the errors, and $\mathbb{L}_{1_d}$ is a universal limit distribution free of nuisance parameters. This immediately yields confidence intervals for $f_0(x_0)$ with asymptotically exact confidence level and optimal length. Notably, the construction of the confidence intervals, even new in the univariate setting, requires no more efforts than performing an isotonic regression for once using the block max-min and min-max estimators, and can be easily adapted to other common monotone models. Extensive simulation results demonstrate the accuracy of the coverage probability of the proposed confidence intervals, giving strong support to our theory.
Detection of neutral atomic species in the ultra-hot Jupiter WASP-121b

The class of ultra-hot Jupiters comprises giant exoplanets undergoing intense irradiation from their host stars. They have proved to be a particularly interesting population for their orbital and atmospheric properties. One such planet, WASP-121b, is in a highly misaligned orbit close to its Roche limit, and its atmosphere exhibits a thermal inversion. These properties make WASP-121b an interesting target for additional atmospheric characterization. In this paper, we present analyses of archival high-resolution optical spectra obtained during transits of WASP-121b. We model the Rossiter-McLaughlin effect and the Centre-to-Limb Variation and find that they do not significantly affect the transmission spectrum in this case. However, we discuss scenarios where these effects warrant more careful treatment by modelling the WASP-121 system and varying its properties. We report a new detection of atmospheric absorption from H α in the planet with a transit depth of $1.87\pm 0.11{{\ \rm per\ cent}}$. We further confirm a previous detection of the Na i doublet, and report a new detection of Fe i via cross-correlation with a model template. We attribute the H α absorption to an extended Hydrogen atmosphere, potentially undergoing escape, and the Fe i to equilibrium chemistry at the planetary photosphere. These detections help to constrain the composition and chemical processes in the atmosphere of WASP-121b.
Quantitative Aspects of Programming Languages and Systems over the past $2^4$ years and beyond

Quantitative aspects of computation are related to the use of both physical and mathematical quantities, including time, performance metrics, probability, and measures for reliability and security. They are essential in characterizing the behaviour of many critical systems and in estimating their properties. Hence, they need to be integrated both at the level of system modeling and within the verification methodologies and tools. Along the last two decades a variety of theoretical achievements and automated techniques have contributed to make quantitative modeling and verification mainstream in the research community. In the same period, they represented the central theme of the series of workshops entitled Quantitative Aspects of Programming Languages and Systems (QAPL) and born in 2001. The aim of this survey is to revisit such achievements and results from the standpoint of QAPL and its community.
Dietary cancer risks of low-income African-American and white women

The diets of 210 low-income African-American and white women were assessed for cancer-related components, and comparisons were made between the two groups on various food components. Energy intakes were inadequate for both groups, with African Americans having a significantly higher intake. Fat intakes for both groups were above the recommended 30% of kilocalories, but fiber intakes were less than half of the recommended amount. Meats and possible meat sources of nitrites and mutagens were not excessive. A majority of both groups consumed inadequate amounts of vitamins A, C, and E; folate; calcium; milk; vegetables; and fruits. Dietary imbalances appeared to pose a significant cancer risk for these women.
Non-stationary Polarization: Proof and Application.

The polarization theory of non-stationary channels and the code construction of secure polar codes for non-stationary cases are considered in this paper. The channel transformation of Ar{\i}kan channel polarization theory is modified to non-stationary channels. As proven, the sequence of non-stationary channels can be polarized the into full noise channels and noiseless channels by the modified non-stationary channel transformation. Also it is proven that the rate of non-stationary polarization can achieve the average capacity of the initial non-stationary channels. Then for non-stationary wiretap channel model, a secure polar coding scheme is constructed by applying the non-stationary polarization. It is proven that the secure polar coding scheme achieves the average secrecy capacity of non-stationary wiretap channel model with both reliability and strong security. For wiretap channel model with non-stationary blocks, virtual bit blocks are constructed, on which a time-bit secure polar coding scheme is constructed by applying the non-stationary polarization. It is proven that the time-bit secure polar coding scheme achieves the average secrecy capacity with both reliability and strong security.
Counter-example Guided Learning of Bounds on Environment Behavior

There is a growing interest in building autonomous systems that interact with complex environments. The difficulty associated with obtaining an accurate model for such environments poses a challenge to the task of assessing and guaranteeing the system's performance. We present a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment. Our approach involves learning a conservative reactive bound of the environment's behavior using data and specification of the system's desired behavior. First, the approach begins by learning a conservative reactive bound on the environment's actions that captures its possible behaviors with high probability. This bound is then used to assist verification, and if the verification fails under this bound, the algorithm returns counter-examples to show how failure occurs and then uses these to refine the bound. We demonstrate the applicability of the approach through two case-studies: i) verifying controllers for a toy multi-robot system, and ii) verifying an instance of human-robot interaction during a lane-change maneuver given real-world human driving data.
Type Ic supernova of a 22 M⊙ progenitor

Type Ic supernovae (SNe Ic) are a sub-class of core-collapse SNe that exhibit no helium or hydrogen lines in their spectra. Their progenitors are thought to be bare carbon–oxygen cores formed during the evolution of massive stars that are stripped of their hydrogen and helium envelopes sometime before collapse. SNe Ic present a range of luminosities and spectral properties, from luminous GRB-SNe with broad-lined spectra to less luminous events with narrow-line spectra. Modelling SNe Ic reveals a wide range of both kinetic energies, ejecta masses, and 56Ni masses. To explore this diversity and how it comes about, light curves and spectra are computed from the ejecta following the explosion of an initially 22 M⊙ progenitor that was artificially stripped of its hydrogen and helium shells, producing a bare CO core of ∼5 M⊙, resulting in an ejected mass of ∼4 M⊙, which is an average value for SNe Ic. Four different explosion energies are used that cover a range of observed SNe. Finally, 56Ni and other elements are artificially mixed in the ejecta using two approximations to determine how element distribution affects light curves and spectra. The combination of different explosion energy and degree of mixing produces spectra that roughly replicate the distribution of near-peak spectroscopic features of SNe Ic. High explosion energies combined with extensive mixing can produce red, broad-lined spectra, while minimal mixing and a lower explosion energy produce bluer, narrow-lined spectra.