Miracle-dz/UniFAR-FTUs huggingface dataset:
Preatmospheric Detection of a Meter-sized Earth Impactor

On 2020 September 18 U.S. Government (USG) sensors detected a bolide with peak bolometric magnitude of −19 over the Western Pacific. The impact was also detected by the Geostationary Lightning Mapper instrument on the GOES-17 satellite and infrasound sensors in Hawaii. The USG measurements reported a steep entry angle of 67° from horizontal from a radiant 13° east of north and an impact speed of 11.7 km s−1. Interpretation of all energy yields produces a preferred energy estimate of 0.4 kt TNT, corresponding to a 23,000 kg, 3 m diameter meteoroid. A postimpact search of telescopic images found that the Asteroid Terrestrial-impact Last Alert System survey captured the object just 10 minutes prior to impact at an Earth-centered distance of nearly 11,900 km with apparent magnitude m = 12.5. The object appears as a 0.44° streak originating on the eastern edge of the image, extending one-third of the USG state-vector-based prediction of 1.26° over the 30 s exposure. The streak shows brightness variability consistent with small asteroid rotation. The position of Earth’s shadow, the object’s size, and its consistency with the reported USG state vector confirm the object is likely natural. This is the eighth preatmospheric detection of a near-Earth asteroid (NEA) impactor and the closest initial telescopic detection prior to impact. The high altitude of peak fireball brightness suggests it was a weak object comparable in many respects with 2008 TC3 (the Almahata Sitta meteorite), with an absolute magnitude H = 32.5 and likely low albedo. Therefore, we suggest the NEA was a C-complex asteroid.
Learning Consumer Preferences from Bundle Sales Data

Product bundling is a common selling mechanism used in online retailing. To set proﬁtable bundle prices, the seller needs to learn consumer preferences from the transaction data. When customers purchase bundles or multiple products, classical methods such as discrete choice models cannot be used to estimate customers’ valuations. In this paper, we propose an approach to learn the distribution of consumers’ valuations toward the products using bundle sales data. The approach reduces it to an estimation problem where the samples are censored by polyhedral regions. Using the EM algorithm and Monte Carlo simulation, our approach can recover the distribution of consumers’ valuations. The framework allows for unobserved no-purchases and clustered market segments. We provide theoretical results on the identiﬁability of the probability model and the convergence of the EM algorithm. The performance of the approach is also demonstrated numerically.
Unified State Representation Learning under Data Augmentation

The capacity for rapid domain adaptation is important to increasing the applicability of reinforcement learning (RL) to real world problems. Generalization of RL agents is critical to success in the real world, yet zero-shot policy transfer is a challenging problem since even minor visual changes could make the trained agent completely fail in the new task. We propose USRA: Unified State Representation Learning under Data Augmentation, a representation learning framework that learns a latent unified state representation by performing data augmentations on its observations to improve its ability to generalize to unseen target domains. We showcase the success of our approach on the DeepMind Control Generalization Benchmark for the Walker environment and find that USRA achieves higher sample efficiency and 14.3% better domain adaptation performance compared to the best baseline results.
A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

Irony is a ubiquitous figurative language in daily communication. Previously, many researchers have approached irony from linguistic, cognitive science, and computational aspects. Recently, some progress have been witnessed in automatic irony processing due to the rapid development in deep neural models in natural language processing (NLP). In this paper, we will provide a comprehensive overview of computational irony, insights from linguisic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.
On finding short reconfiguration sequences between independent sets

Assume we are given a graph $G$, two independent sets $S$ and $T$ in $G$ of size $k \geq 1$, and a positive integer $\ell \geq 1$. The goal is to decide whether there exists a sequence $\langle I_0, I_1, . , I_\ell \rangle$ of independent sets such that for all $j \in \{0,\ldots,\ell-1\}$ the set $I_j$ is an independent set of size $k$, $I_0 = S$, $I_\ell = T$, and $I_{j+1}$ is obtained from $I_j$ by a predetermined reconfiguration rule. We consider two reconfiguration rules. Intuitively, we view each independent set as a collection of tokens placed on the vertices of the graph. Then, the Token Sliding Optimization (TSO) problem asks whether there exists a sequence of at most $\ell$ steps that transforms $S$ into $T$, where at each step we are allowed to slide one token from a vertex to an unoccupied neighboring vertex. In the Token Jumping Optimization (TJO) problem, at each step, we are allowed to jump one token from a vertex to any other unoccupied vertex of the graph. Both TSO and TJO are known to be fixed-parameter tractable when parameterized by $\ell$ on nowhere dense classes of graphs. In this work, we show that both problems are fixed-parameter tractable for parameter $k + \ell + d$ on $d$-degenerate graphs as well as for parameter $|M| + \ell + \Delta$ on graphs having a modulator $M$ whose deletion leaves a graph of maximum degree $\Delta$. We complement these result by showing that for parameter $\ell$ alone both problems become W[1]-hard already on $2$-degenerate graphs. Our positive result makes use of the notion of independence covering families introduced by Lokshtanov et al. Finally, we show that using such families one can obtain a simpler and unified algorithm for the standard Token Jumping Reachability problem parameterized by $k$ on both degenerate and nowhere dense classes of graphs.
Convergent FEM for a membrane model of liquid crystal polymer networks

We design a finite element method (FEM) for a membrane model of liquid crystal polymer networks (LCNs). This model consists of a minimization problem of a non-convex stretching energy. We discuss properties of this energy functional such as lack of weak lower semicontinuity. We devise a discretization with regularization, propose a novel iterative scheme to solve the non-convex discrete minimization problem, and prove stability of the scheme and convergence of discrete minimizers. We present numerical simulations to illustrate convergence properties of our algorithm and features of the model.
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models

For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem. Ad clicks constitute a significant class of user engagements and are often used as the primary signal for the usefulness of ads to users. Additionally, in cost-per-click advertising systems where advertisers are charged per click, click rate expectations feed directly into value estimation. Accordingly, CTR model development is a significant investment for most Internet advertising companies. Engineering for such problems requires many machine learning (ML) techniques suited to online learning that go well beyond traditional accuracy improvements, especially concerning efficiency, reproducibility, calibration, credit attribution. We present a case study of practical techniques deployed in Google's search ads CTR model. This paper provides an industry case study highlighting important areas of current ML research and illustrating how impactful new ML methods are evaluated and made useful in a large-scale industrial setting.
Harnessing Abstractive Summarization for Fact-Checked Claim Detection

Social media platforms have become new battlegrounds for anti-social elements, with misinformation being the weapon of choice. Fact-checking organizations try to debunk as many claims as possible while staying true to their journalistic processes but cannot cope with its rapid dissemination. We believe that the solution lies in partial automation of the fact-checking life cycle, saving human time for tasks which require high cognition. We propose a new workflow for efficiently detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. These queries can then be executed on a general-purpose retrieval system associated with a collection of previously fact-checked claims. We curate an abstractive text summarization dataset comprising noisy claims from Twitter and their gold summaries. It is shown that retrieval performance improves 2x by using popular out-of-the-box summarization models and 3x by fine-tuning them on the accompanying dataset compared to verbatim querying. Our approach achieves Recall@5 and MRR of 35% and 0.3, compared to baseline values of 10% and 0.1, respectively. Our dataset, code, and models are available publicly: https://github.com/varadhbhatnagar/FC-Claim-Det/.
Equidistribution of high-rank polynomials with variables restricted to subsets of $\mathbb{F}_p$

Let p be a prime and let S be a non-empty subset of F p . Generalizing a result of Green and Tao on the equidistribution of high-rank polynomials over ﬁnite ﬁelds, we show that if P : F np → F p is a polynomial and its restriction to S n does not take each value with approximately the same frequency, then there exists a polynomial P 0 : F np → F p that vanishes on S n , such that the polynomial P − P 0 has bounded rank. Our argument uses two black boxes: that a tensor with high partition rank has high analytic rank and that a tensor with high essential partition rank has high disjoint partition rank.
Analysis of an Induction Machine Fed by a Space Vector Modulated VSI

This paper describes the analysis and simulation performed on a three phase squirrel cage induction motor, fed by a Voltage Source Inverter (VSI), modulated using the space vector modulation scheme. The study mainly focused on analyzing the motor performance when fed by a space vector modulated voltage source inverter. The effect of switching frequency on the motor current ripple as well as the frequency spectra of motor current, voltage and torque were studied. The transient response of the inverter-fed motor for a sudden load change was studied and compared to that of a motor fed with an ideal AC source. Results from these studies performed as computer simulations are presented and analyzed.
Truth, or Mere Beauty?

High energy physics features many ingenious tools for extracting finite results from formally divergent expressions. This brief note argues from a new perspective that all such formal infinities are meaningful markers of new physics. As such, they deserve to be explored in detail -- and even the simplest quantum field theories have more of them than is commonly thought.
L O ] 3 D ec 2 00 2 Infinite time Turing machines

Infinite time Turing machines extend the operation of ordinary Turing machines into transfinite ordinal time. By doing so, they provide a natural model of infinitary computability, a theoretical setting for the analysis of the power and limitations of supertask algorithms. Imagine that we have scaled the vertical asymptote of technological progress, as computers increase in speed each day, and that we have in our possession an infinitely fast computer. What would we do with it? What could we do with it? In order to make sense of these questions, we need a formal model of infinite computability, a mathematical description of the computing machines and of the algorithms that they will be able to carry out. In this article, I introduce such a model of infinitary computability by extending the classical Turing machine model into transfinite ordinal time; the resulting machines, infinite time Turing machines, are able to carry out and complete algorithms involving infinitely many computational steps. These computational steps proceed in time like the ordinal numbers, so that if the computation does not halt at any of the finite stages 0, 1, 2, 3, . , then it arrives at the first infinite stage ω, continuing with stages ω+1, ω+2, ω+3, . and so on, eventually arriving at the second limit stage ω + ω, continuing with stages ω + ω + 1, ω + ω + 2, . and so on through the ordinal numbers. The configuration of the machine at each stage is determined by the earlier configurations and the operation of the fixed finite program that the machine is running. These machines provide a robust model of infinitary computability, one that has shed light on the power and limitations of supertask computation. The essence of an infinitely fast computer, of course, is that it is able to complete infinitely many steps of computation in a finite amount of time. An infinitely fast computation, therefore, when measured by the number of
A Generalized Poor-Verdu Error Bound for Multihypothesis Testing and the Channel Reliability Function

A lower bound on the minimum error probability for multihypothesis testing is established. The bound, which is expressed in terms of the cumulative distribution function of the tilted posterior hypothesis distribution given the observation with tilting parameter theta larger than or equal to 1, generalizes an earlier bound due the Poor and Verdu (1995). A sufficient condition is established under which the new bound (minus a multiplicative factor) provides the exact error probability in the limit of theta going to infinity. Examples illustrating the new bound are also provided. The application of this generalized Poor-Verdu bound to the channel reliability function is next carried out, resulting in two information-spectrum upper bounds. It is observed that, for a class of channels including the finite-input memoryless Gaussian channel, one of the bounds is tight and gives a multi-letter asymptotic expression for the reliability function, albeit its determination or calculation in single-letter form remains an open challenging problem. Numerical examples regarding the other bound are finally presented.
Control Chaos in Hindmarsh-Rose Neuron by Using Intermittent Feedback with One Variable

著名阶段压缩的机制被讨论，并且在 Hindmarsh 玫瑰(H-R ) 模型控制混乱被用来。 阶段压缩计划能作为一个种断断续续的反馈计划被理解，这数字地被证实，它要求适当阀值和反馈系数，并且断断续续的反馈能与海维赛德功能被认识到。 在控制混乱的情况中，输出变量(通常电压或神经原的膜潜力) 被取样并且电的电极与外部标准相比发信号。 当取样的变量超过选择阀值时，在取样的变量和电极的外部标准信号之间的错误仅仅是进系统的输入。 数字模拟结果证实混乱 H-R 系统能被控制到达任意的 n 期刊(n = 1， 2， 3， 4， 5， 6， .) 轨道或稳定的状态甚至当就一个变量是时，喂间歇地退出了进系统。 混乱 Chua 电路也被调查检查它的模型独立和计划的有效性，二个计划的等价再被证实。 [从作者抽象]
The expected measurement precision of the branching ratio of the Higgs decaying to the di-photon at the CEPC

This paper presents the prospects of measuring $\sigma(e^{+}e^{-} \to \mathrm{ZH})\times Br(H\to \gamma\gamma)$ in 3 \ensuremath{Z} decay channels $Z \to q\bar{q}/ \mu^{+}\mu^{-}/ \nu\bar{\nu}$ using the baseline conceptual detector with $\sqrt{s} = 240$ GeV at the Circular Electron Positron Collider (CEPC). The simulated Monte Carlo events are generated and scaled to an integrated luminosity of 5.6 $ab^{-1}$ to mimic the data. Extrapolated results to 20 $ab^{-1}$ are also shown. The expected statistical precision of this measurement after combining 3 channels of Z boson decay is 7.7\%. With some preliminary estimation on the systematical uncertainties, the total precision is 7.9\%. The performance of CEPC electro-magnetic calorimeter (ECAL) is studied by smearing the photon energy resolution in simulated events in $\e^+ e^- \to \mathrm{ZH} \to q\bar{q}\gamma\gamma$ channel. In present ECAL design, the stochastic term in resolution plays the dominant role in the precision of Higgs measurements in $H \to \gamma\gamma$ channel. The impact of the resolution on the measured precision of $\sigma(\mathrm{ZH})\times Br(\mathrm{ZH} \to q\bar{q}\gamma\gamma)$ as well as the optimization of ECAL constant term and stochastic term are studied for the further detector design. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Visual feature graphs and image recognition

We are concerned in this thesis by the problem of automated 2D image classification and general object detection. Advances in this field of research contribute to the elaboration of intelligent systems such as, but not limited to, autonomous robots and the semantic web. In this context, designing adequate image representations and classifiers for these representations constitute challenging issues. Our work provides innovative solutions to both these problems: image representation and classification. In order to generate our image representation, we extract visual features from the image and build a graphical structure based on properties of spatial proximity between the feature points. We show that certain spectral properties of this graph constitute good invariants to rigid geometric transforms. Our representation is based on these invariant properties. Experiments show that this representation constitutes an improvement over other similar representations that do not integrate the spatial layout of visual features. However, a drawback of this method is that it requires a lossy quantisation of the visual feature space in order to be combined with a state-of-the-art support vector machine (SVM) classifier. We address this issue by designing a new classifier. This generic classifier relies on a nearest-neighbour distance to classify objects that can be assimilated to feature sets, i.e: point clouds. The linearity of this classifier allows us to perform object detection, in addition to image classification. Another interesting property is its ability to combine different types of visual features in an optimal manner. We take advantage of this property to produce a new formulation for the classification of visual feature graphs. Experiments are conducted on a wide variety of publicly available datasets to justify the benefits of our approach.
Optimal tension regulation of a strip winding process

Abstract : An optimal tension regulator system is designed for a steel strip winding process. A simplified dynamic model of the winding process contains a parametric nonlinearity due to variations of the coil radius. A parameter imbedding technique is applied to derive the optimum feedback control for a range of parameter values. Simulation results are given for an implementation of the regulator system designed using the numerical values from a real plant. (Author)
Algebraic Invariants of Links

Abelian Covers: Links Homology and Duality in Covers Determinantal Invariants The Maximal Abelian Cover Sublinks and Other Abelian Covers Twisted Polynomial Invariants Applications: Special Cases and Symmetries: Knot Modules Links with Two Components Symmetries Singularities of Plane Curves Free Covers, Nilpotent Quotients and Completion: Free Covers Nilpotent Quotients Algebraic Closure Disc Links.
REVIEW ARTICLE: Spin-flip Raman scattering in p-type semiconductors

Surveys the experimental studies and related analyses of such processes in p-type ZnTe, GaP, SiC and InSb. The observed scattering mechanisms include free heavy-hole spin flip, spin flip of holes bound to shallow acceptors spin flip of photoexcited electrons, and several exciton processes. A variety of photoexcited transitions which yield spin temperatures up to 300K in systems with lattice temperatures below 2K. Multiple spontaneous spin-flip scattering and spin-flip plus phonon scattering mechanisms are discussed.
MHD Simulations of Accretion onto a Magnetized Neutron Star in the

We use axisymmetric magnetohydrodynamic simulations to investigate the spinning-down of magnetars rotating in the propeller regime and moving supersonically through the interstellar medium. The simulations indicate that magnetars spin down rapidly due to this interaction, and faster than for the case of a non-moving star. We discuss this model with respect to soft gamma repeaters (SGRs) and the isolated neutron star candidates.
Duality in optimization and variational inequalities

1. Mathematical Preliminaries 2. Duality in Network Optimization 3. Duality in Linear Systems 4. Duality in Convex Nonlinear Systems 5. Duality in Nonconvex Systems 6. Duality in Variational Inequalities 7. Elements of Multicriteria Optimization 8. Duality in Multicriteria Optimization 9. Duality in Vector Variational Inequalities
An Examination of the Uncertainty in Interpolated Winds and Its Effect on the Validation and Intercomparison of Forecast Models

Abstract Meteorological models need to be compared to long-term, routinely collected meteorological data. Whenever numerical forecast models are validated and compared, verification winds are normally interpolated to individual model grid points. To be statistically significant, differences between model and verification data must exceed the uncertainty of verification winds due to instrument error, sampling, and interpolation. This paper will describe an approach to examine the uncertainty of interpolated boundary layer winds and illustrate its practical effects on model validation and intercomparison efforts. This effort is part of a joint model validation project undertaken by the Environmental Verification and Analysis Center at the University of Oklahoma (http://www.evac.ou.edu) and the Battlefield Environment Directorate of the Army Research Laboratory. The main result of this study is to illustrate that it is crucial to recognize the errors inherent in gridding verification winds when conducting mo.
The probability of escaping interference

Consider two independent sequences of travelers arriving at opposite ends of a one-lane shared pathway. Each traveler attempts to traverse the entire pathway to the opposite end. An attempt fails if the traveler collides with an opposing traveler. In a collision, both oppos- ing travelers are annihilated. We study the probability that a traveler manages to traverse the entire length of the one-lane shared pathway unobstructed. The dynamics of the travelers include the possibility of acting as bodyguards and “running interference” for a more recent ar- rival traveling in the same direction. This model was developed to address some questions in the theory of crystal growth. It may have possible applications in Particle Physics as well as to traﬃc at a one-lane bridge. This paper develops some properties of the model while focusing on the probability that a traveler crosses the entire pathway without interference.