Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Summarization using Abstract Meaning Representation

Summarization of large texts is still an open problem in language processing. In this work we develop a full ﬂedged pipeline to generate summaries of news articles using the Abstract Meaning Rep-resentation(AMR). We ﬁrst generate the AMR graphs of stories then extract summary graphs from the story graphs and ﬁnally generate sentences from the summary graph. For extracting summary AMRs from the story AMRs we use a two step process. First, we ﬁnd important sentences from the text and then extract the summary AMRs from those selected sentences. We outperform the previous methods using AMR for summarization by more that 3 ROGUE-1 points. On the CNN-Dailymail corpus we achieve results competitive with the strong lead-3 baseline till summary graph extraction step.
Mitigation of Human RF Exposure in 5G Downlink

While research on communications at frequencies above 6 gigahertz (GHz) has been primarily confined to performance improvement, their potentially harmful impacts on human health are not studied as significantly. Most of the existing studies that paid attention to the health impacts above 6 GHz focused only on the uplink due to closer contact with a transmitter to a human body. In this letter, we present the human electromagnetic field (EMF) exposure in the downlink of Fifth-Generation Wireless Systems (5G). Moreover, we propose a downlink protocol that guarantees the EMF exposure under a threshold while keeping the data rate above the 5G requirements.
Mixed Membership Word Embeddings for Computational Social Science Supplementary Material

The Gaussian LDA model of Das et al. (2015) improves the performance of topic modeling by leveraging the semantic information encoded in word embeddings. Gaussian LDA modifies the generative process of LDA such that each topic is assumed to generate the vectors via its own Gaussian distribution. Similarly to our MMSG model, in Gaussian LDA each topic is encoded with a vector, in this case the mean of the Gaussian. It takes pre-trained word embeddings as input, rather than learning the embeddings from data within the same model, and does not aim to perform word embedding.
A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets

In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed. Given a video and a set of detections (obtained by any person detector), we first solve within-camera tracking employing the first two layers of our framework and, then, in the third layer, we solve across-camera tracking by merging tracks of the same person in all cameras in a simultaneous fashion. To best serve our purpose, a constrained dominant sets clustering (CDSC) technique, a parametrized version of standard quadratic optimization, is employed to solve both tracking tasks. The tracking problem is caste as finding constrained dominant sets from a graph. In addition to having a unified framework that simultaneously solves within- and across-camera tracking, the third layer helps link broken tracks of the same person occurring during within-camera tracking. In this work, we propose a fast algorithm, based on dynamics from evolutionary game theory, which is efficient and salable to large-scale real-world applications.
AntMonitor: Network Traffic Monitoring and Real-Time Prevention of Privacy Leaks in Mobile Devices

Mobile devices play an essential role in the Internet today, and there is an increasing interest in using them as a vantage point for network measurement from the edge. At the same time, these devices store personal, sensitive information, and there is a growing number of applications that leak it. We propose AntMonitor -- the first system of its kind that supports (i) collection of large-scale, semantic-rich network traffic in a way that respects users' privacy preferences and (ii) detection and prevention of leakage of private information in real time. The first property makes AntMonitor a powerful tool for network researchers who want to collect and analyze large-scale yet fine-grained mobile measurements. The second property can work as an incentive for using AntMonitor and contributing data for analysis. As a proof-of-concept, we have developed a prototype of AntMonitor, deployed it to monitor 9 users for 2 months, and collected and analyzed 20 GB of mobile data from 151 applications. Preliminary results show that fine-grained data collected from AntMonitor could enable application classification with higher accuracy than state-of-the-art approaches. In addition, we demonstrated that AntMonitor could help prevent several apps from leaking private information over unencrypted traffic, including phone numbers, emails, and device identifiers.
Real-time On-Demand Crowd-powered Entity Extraction ( Supplementary Technical Report )

Modern dialog systems rely on accurate entity extraction to understand user utterances. However, entity extraction is brittle due to data scarcity, language variability, and out-ofvocabulary entities. To bridge this gap, we propose a realtime crowdsourcing solution based on the ESP game for image labeling. When multiple players agree, entities can be reliably extracted from an utterance. This approach is advantageous because it does not require training data. Further, it is robust to unexpected input and capable of recognizing new entities. Our approach achieves better F1-scores than that of the automated baseline for complex queries with a reasonable response time. The proposed method is also evaluated via Google Hangouts’ text chat and demonstrates the feasibility of real-time crowd-powered entity extraction.
Simple models for multivariate regular variations and the H\"usler-Reiss Pareto distribution

We revisit multivariate extreme value theory modeling by emphasizing multivariate regular variations and the multivariate Breiman Lemma. This allows us to recover in a simple framework the most popular multivariate extreme value distributions, such as the logistic, negative logistic, Dirichlet, extremal-$t$ and H\"usler-Reiss models. In a second part of the paper, we focus on the H\"usler-Reiss Pareto model and its surprising exponential family property. After a thorough study of this exponential family structure, we focus on maximum likelihood estimation. We also consider the generalized H\"usler-Reiss Pareto model with different tail indices and a likelihood ratio test for discriminating constant tail index versus varying tail indices.
MONODROMY AND PERIOD MAP OF THE WINGER PENCIL II: E -PART

A BSTRACT . The sextic plane curves that are invariant under the standard action of the icosahedral group on the projective plane make up a pencil of genus ten curves (spanned by a sum of six lines and a three times a conic). This pencil was ﬁrst considered in a note by R. M. Winger in 1925 and is nowadays named after him. We gave this a modern treatment and proved among other things that it contains essentially every smooth genus ten curve with icosahedral symmetry. We here consider the monodromy group and the period map naturally deﬁned by the icosahedral symmetry. We showed that this monodromy group is a normal subgroup of ﬁnite index in SL 2 ( Z [ √ 5]) and the period map brings the Winger pencil to a curve on the Hilbert modular surface SL 2 ( Z [ √ 5]) / H 2 .
Flavour Theory: 2009

After an overture and a non-technical exposition of the relevant theoretical framework including a brief discussion of some of the most popular extensions of the Standard Model, we will compile a list of 20 goals in flavour physics that could be reached alrea dy in the next decade. In addition to K, D and Bs,d decays and lepton flavour violation also flavour conserving o bservables like electric dipole moments of the neutron and leptons and (g − 2)μ are included in this list. Flavour violation in high energy processes is also one of these goals. Subsequently we will discuss in more detail some of the most urgent issues for the coming years in the context of several extensions of the Standard Model like models with Minimal Flavour Violation, the general MSSM, the Littlest Higgs Model with T parity, Randall-Sundrum models and supersymmetric flavour models. This presentation is not meant to be a comprehensive review of flav our physics but rather a personal view on this fascinating field and an attempt to collect those routes that with the help of upcoming experiments should allow us to reach a much deeper understanding of physics, in particular flavour physics, at very short distance scales.
Online Longitudinal Bunch Profile and Slice Emittance Diagnostics at the European XFEL

The longitudinal current profile and slice emittance are important bunch parameters for the operation of an X-ray free-electron laser. At the European XFEL, dedicated diagnostic sections equipped with transverse deflecting RF structures (TDS) have been installed for the control and optimisation of these parameters. Travelling-wave TDS in combination with fast kicker magnets and off-axis screens allow for the study of individual bunches without affecting the other bunches in the super-conducting linear accelerator which can generate bunch trains of up to 2700 bunches at 4.5 MHz within 600 microsecond RF pulses at a repetition rate of 10 Hz. The measurement of the slice emittance is realised in a static F0D0 lattice equipped with four individual screen stations. Variations of the longitudinal bunch profile or slice emittance along the bunch train may lead to degraded FEL performance for parts of the train which reduces the effective available number of bunches for FEL operation. By gradually adjusting the timing, individual bunches along the bunch train can be measured in order to optimise the overall beam parameters for all bunches in the train. In this paper, we describe in detail the diagnostic concept and present first measurement results of the projected and slice emittance along the bunch train.
Path integral and instantons for the dynamical process and phase transition rate of Reissner-Nordström-AdS black holes

We propose a new approach to study the dynamical phase transition of RNAdS black holes on the underlying free energy landscape. By formulating a path integral framework, we can quantify the kinetic paths representing the history from the initial state to the end state, which provides us a visualized yet quantified picture about how the phase transition proceeds. Based on these paths, we derive the analytical formulas for the time evolution of the transition probability and provide a physical interpretation of the contribution to the probability from one"pseudomolecule"("anti-pseudomolecule"), which is actually the phase transition rate from the small(large) to the large(small) black hole state. These numerical results show a good consistency with the underlying free energy landscape topography.
Automatic Transcription of a Cappella recordings from Multiple Singers

This work presents a spectrogram factorisation method applied to automatic music transcription of a cappella performances with multiple singers. A variable-Q transform representation of the audio spectrogram is factorised with the help of a 6-dimensional sparse dictionary which contains spectral templates of vowel vocalizations. A post-processing step is proposed to remove false positive pitch detections through a binary classiﬁer, where overtone-based features are used as input into this step. Preliminary experiments have shown promising multi-pitch detection results when applied to audio recordings of Bach Chorales and Barbershop music. Comparisons made with alternative methods have shown that our approach increases the number of true positive pitch detections while the post-processing step keeps the number of false positives lower than those measured in comparative approaches.
Apps, Trackers, Privacy, and Regulators: A Global Study of the Mobile Tracking Ecosystem.

Third-party services form an integral part of the mobile ecosystem: they ease application development and enable features such as analytics, social network integration, and app monetization through ads. However, aided by the general opacity of mobile systems, such services are also largely invisible to users. This has negative consequences for user privacy as third-party services can potentially track users without their consent, even across multiple applications. Using real-world mobile traffic data gathered by the Lumen Privacy Monitor (Lumen), a privacy enhancing app with the ability to analyze network traffic on mobile devices in user space, we present insights into the mobile advertising and tracking ecosystem and its stakeholders. In this study, we develop automated methods to detect third-party advertising and tracking services at the traffic level. Using this technique we identify 2,121 such services, of which 233 were previously unknown to other popular advertising and tracking blacklists. We then uncover the business relationships between the providers of these services and characterize them by their prevalence in the mobile and Web ecosystem. Our analysis of the privacy policies of the largest advertising and tracking service providers shows that sharing harvested data with subsidiaries and third-party affiliates is the norm. Finally, we seek to identify the services likely to be most impacted by privacy regulations such as the European General Data Protection Regulation (GDPR) and ePrivacy directives.
What does not get observed can be used to make age curves stronger : estimating player age curves using regression and imputation

The impact of player age on performance has received attention across sport. Most research has focused on the performance of players at each age, ignoring the reality that age likewise influences which players receive opportunities to perform. Our manuscript makes two contributions. First, we highlight how selection bias is linked to both (i) which players receive opportunity to perform in sport, and (ii) at which ages we observe these players perform. This approach is used to generate underlying distributions of how players move in and out of sport organizations. Second, motivated by methods for missing data, we propose novel estimation methods of age curves by using both observed and unobserved (imputed) data. We use simulations to compare several comparative approaches for estimating aging curves. Imputation-based methods, as well as models that account for individual player skill, tend to generate lower RMSE and age curve shapes that better match the truth. We implement our approach using data from the National Hockey League.
The Mediating Effect of Self-Regulation on the Association Between Growth Mindset About Work and Living a Calling Among Primary and Secondary School Teachers

Purpose: Given that extensive studies have documented the wide-ranging benefits of living a calling, determining whether and how living a calling can be improved is of great importance. Although recent research implies that a growth mindset about work may link to self-regulatory processes and living a calling, little research to date has directly examined the relationships between these variables. Building on Burnette’s SOMA model, this study was designed to examine the premise that a growth mindset about work was a predictor of living a calling and to explore the mediating role of self-regulation in the growth mindset about work—living a calling link. Methods: This cross-sectional study was conducted among educational professionals in China. Data were collected from 608 primary and secondary school teachers on growth mindsets about work, self-regulation and living a calling. A structural equation model was used to test the hypothesis that self-regulation would mediate the relationship between a growth mindset about work and living a calling. Results: Consistent with our premise, a growth mindset about work was positively associated with living a calling. Also, a growth mindset about work was significantly associated with self-regulation strategies in terms of goal setting, perseverance, decision making, and learning from mistakes. As hypothesized, a growth mindset about work was related to higher levels of living a calling via goal setting, perseverance, and learning from mistakes. Conclusion: The findings contribute to the increasing breadth of the growth mindset literature across various contexts and its consequences for one’s living a calling, which expand the nomological web of correlates of growth mindset about work. Furthermore, these findings suggest that fostering a growth mindset about work may facilitate adaptive behaviors and, thereby, serve as a calling intervention. importance of living a calling to employee behaviors and workplace outcomes, factors that predict it and methods to promote it are yet to be completely understood, especially from a personal perspective. Our findings indicate that a growth mindset about work may be a predictor of living a calling, and highlight the need to integrate growth between a growth mindset and living a calling. The current evidence suggests that a growth mindset about work can meaningfully contribute to understanding why some people are committed to, and passionate about their career.
Connector Annotated Repository Data Analyzer BaseAnalyzer Checker Third Party Tool Result Analyzr Application Controllers HTML Templates Web Server

Copyright c ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. DOI: 10.1109/QRS.2017. 51 Abstract—In any sufficiently complex software system there are experts, having a deeper understanding of parts of the system than others. However, it is not always clear who these experts are and which particular parts of the system they can provide help with. We propose a framework to elicit the expertise of developers and recommend experts by analyzing complexity measures over time. Furthermore, teams can detect those parts of the software for which currently no, or only few experts exist and take preventive actions to keep the collective code knowledge and ownership high. We employed the developed approach at a medium-sized company. The results were evaluated with a survey, comparing the perceived and the computed expertise of developers. We show that aggregated code metrics can be used to identify experts for different software components. The identified experts were rated as acceptable candidates by developers in over 90% of all cases.
Making Invisible Visible: Data-Driven Seismic Inversion with Physics-Informed Data Augmentation

—Deep learning and data-driven approaches have shown great potential in scientiﬁc domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientiﬁc applications have emerged as a new direction for obtaining scientiﬁc data recently. However, existing data augmentation techniques originating from computer vision, yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this paper, we develop new physics-informed data augmentation techniques based on convolutional neural networks. Speciﬁcally, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be signiﬁcantly enhanced by using our physics-informed data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.
Mutual Information, Relative Entropy, and Estimation in the Poisson Channel

Let be a nonnegative random variable and let the conditional distribution of a random variable , given , be Poisson , for a parameter . We identify a natural loss function such that: (1) the derivative of the mutual information between and with respect to is equal to the minimum mean loss in estimating based on , regardless of the distribution of ; (2) when is estimated based on by a mismatched estimator that would have minimized the expected loss had , the integral over all values of of the excess mean loss is equal to the relative entropy between and . For a continuous time setting where is a nonnegative stochastic process and the conditional law of , given , is that of a non-homogeneous Poisson process with intensity function , under the same loss function: (1) the minimum mean loss in causal filtering when is equal to the expected value of the minimum mean loss in noncausal filtering (smoothing) achieved with a channel whose parameter is uniformly distributed between 0 and . Bridging the two quantities is the mutual information between and ; (2) this relationship between the mean losses in causal and noncausal filtering holds also in the case where the filters employed are mismatched, i.e., optimized assuming a law on which is not the true one. Bridging the two quantities in this case is the sum of the mutual information and the relative entropy between the true and the mismatched distribution of . Thus, relative entropy quantifies the excess estimation loss due to mismatch in this setting. These results are parallel to those recently found for the Gaussian channel: the I-MMSE relationship of Guo , the relative entropy and mismatched estimation relationship of Verdú, and the relationship between causal and noncasual mismatched estimation of Weissman.