Miracle-dz/UniFAR-FTUs huggingface dataset:
Unfolding with Generative Adversarial Networks

Correcting measured detector-level distributions to particle-level is essential to make data usable outside the experimental collaborations. The term unfolding is used to describe this procedure. A new method of unfolding the data using a modified Generative Adversarial Network (MSGAN) is presented here. Applied to various distributions, it is demonstrated to perform at par with, or better than, currently used methods.
Quantum coarse graining, symmetries, and reducibility of dynamics

The common idea behind complexity reduction in physical systems is separation of information into "physically meaningful" and "safely ignorable". Here we consider a generic notion of such separation -- implemented by coarse-graining the state-space -- and address the question of what information is indeed safely ignorable if we want to reduce the complexity of dynamics. The general condition for reducibility of dynamics under coarse-graining will be presented for stochastic and quantum systems. In the process we develop the quantum notion of state-space coarse-graining that allows to marginalize selected degrees of freedom. One of our main findings is that there is a broader class of symmetries, beyond those that are considered by Noether's Theorem, that can play a role in the reduction of dynamics. Some examples of quantum coarse-grainings and the reduction of dynamics with symmetries will be discussed.
Multi-Task Learning for Transit Service Disruption Detection

With the rapid growth in urban transit networks in recent years, detecting service disruptions in a timely manner is a problem of increased interest to service providers. Transit agencies are seeking to move beyond traditional customer questionnaires and manual service inspections to leveraging open source indicators like social media for deteting emerging transit events. In this paper, we leverage Twitter data for early detection of metro service disruptions. Inspired by the multi-task learning framework, we propose the Metro Disruption Detection Model, which captures the semantic similarity between transit lines in Twitter space. We propose novel constraints on feature semantic similarity exploiting prior knowledge about the spatial connectivity and shared tracks of the metro network. An algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed model. We run extensive experiments and comparisons to other models with real world Twitter data and transit disruption records from the Washington Metropolitan Area Transit Authority (WMATA) to justify the efficacy of our model.
Teaching ‘Standard English’ or local, code-switched New Englishes in schools in ‘Outer Circle’ countries? Insights from selected secondary school teachers of English in Zimbabwe

The study investigated the English as a Second Language (ESL) teachers’ perceptions on the relationship between code-switching and emerging varieties of English called New Englishes, as well as the teachers’ perceptions on the teaching of such local varieties of English. The inquiry adopted a qualitative research paradigm and focused on two purposively sampled secondary schools comprising one rural day, and one urban boarding school. Four ESL teachers were interviewed (two teachers per school). The inquiry revealed that there was unanimous agreement among the four teachers that there is a relationship between code-switching and New Englishes. In addition, two of the teachers expressed the view that there is nothing wrong with teaching the local variety of English in the schools, while the other two said they preferred the teaching of Standard English.The study recommends that language policy planners in Zimbabwe consider adopting a balance of the endo-normative and the exo-normative models of English for the education system. Such a model, while acknowledging the importance of Standard English, would not undermine the importance of the local variety of English. In addition, the inquiry recommends that the Zimbabwe School Examinations Council (ZIMSEC) be sensitive to aspects of the local variety of English rather than set exo-normative models. Finally, the study recommends that further research be done on the perceptions of ESL teachers on the teaching of Standard English and New Englishes in school types which were not included in the sample for the present study.
Decomposition of wake dynamics in fluid–structure interaction via low-dimensional models

We present a dynamic decomposition analysis of the wake flow in fluid–structure interaction (FSI) systems under both laminar and turbulent flow conditions. Of particular interest is to provide the significance of low-dimensional wake flow features and their interaction dynamics to sustain the free vibration of a square cylinder at a relatively low mass ratio. To obtain the high-dimensional data, we employ a body-conforming variational FSI solver based on the recently developed partitioned iterative scheme and the dynamic subgrid-scale turbulence model for a moderate Reynolds number ( $Re$ ). The snapshot data from high-dimensional FSI simulations are projected to a low-dimensional subspace using the proper orthogonal decomposition (POD). We utilize each corresponding POD mode to detect features of the organized motions, namely, the vortex street, the shear layer and the near-wake bubble. We find that the vortex shedding modes contribute solely to the lift force, while the near-wake and shear layer modes play a dominant role in the drag force. We further examine the fundamental mechanism of this dynamical behaviour and propose a force decomposition technique via low-dimensional approximation. To elucidate the frequency lock-in, we systematically analyse the decomposed modes and their dynamical contributions to the force fluctuations for a range of reduced velocity at low Reynolds number laminar flow. These quantitative mode energy contributions demonstrate that the shear layer feeds the vorticity flux to the wake vortices and the near-wake bubble during the wake–body synchronization. Based on the decomposition of wake dynamics, we suggest an interaction cycle for the frequency lock-in during the wake–body interaction, which provides the interrelationship between the high-amplitude motion and the dominating wake features. Through our investigation of wake–body synchronization below critical $Re$ range, we discover that the bluff body can undergo a synchronized high-amplitude vibration due to flexibility-induced unsteadiness. Owing to the wake turbulence at a moderate Reynolds number of $Re=22\,000$ , a distorted set of POD modes and the broadband energy distribution are observed, while the interaction cycle for the wake synchronization is found to be valid for the turbulent wake flow.
Quantum Information Measures of the One‐Dimensional Robin Quantum Well

Shannon quantum information entropies Sx,k , Fisher informations Ix,k , Onicescu energies Ox,k , and statistical complexities eSx,kOx,k are calculated both in the position (subscript x) and momentum (k) representations for the Robin quantum well characterized by the extrapolation lengths Λ− and Λ+ at the two confining surfaces. The analysis concentrates on finding and explaining the most characteristic features of these quantum information measures in the whole range of variation of the Robin distance Λ for the symmetric, Λ−=Λ+=Λ , and antisymmetric, Λ−=−Λ+=Λ , geometries. Analytic results obtained in the limiting cases of the extremely large and very small magnitudes of the extrapolation parameter are corroborated by the exact numerical computations that are extended to the arbitrary length Λ. It is confirmed, in particular, that the entropic uncertainty relation Sxn+Skn≥1+lnπ and general inequality eSO≥1 , which is valid both in the position and momentum spaces, hold true at any Robin distance and for every quantum state n. For either configuration, there is a range of the extrapolation lengths where the rule Sxn+1(Λ)+Skn+1(Λ)≥Sxn(Λ)+Skn(Λ) that is correct for the Neumann ( Λ=∞ ) or Dirichlet ( Λ=0 ) boundary conditions, is violated. Other analytic and numerical results for all measures are discussed too and their physical meaning is highlighted.
Using Twitter Data to Determine Hurricane Category: An Experiment

Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
The operational methane retrieval algorithm for TROPOMI

Abstract. This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer aboard S5P. The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup. We show that the required accuracy and precision of
Mycological and Bacteriological Quality and Safety of Bottled Water in Ethiopia

Background: Safe water supply is vital and can result in significant benefits to health. However, contaminated bottled water poses a great health risk due to the poor microbiological quality of water. Methods and Materials: A retrospective study was conducted on 222 Bottled water samples collected from various regions of Ethiopia from January 2008 to December 2015, tested and recorded in Ethiopian Public Health Institute to determine heterotrophic plate count and Staphylococcus aureus by pour plate method; for coliforms using multiple tubes fermentation techniques; for mould and yeast count using spread method, and for Salmonellae and Shigella spp. using ES ISO 6579 and ES ISO 21567. The data was analyzed using SPSS 20 statistical package. Results: Among the total samples examined from 44 brands, detections of heterotrophic plate count, mould, yeast, total and thermotolerant coliforms, Escherichia coli and Staphylococcus aureus were observed in 114 (51.4%), 33 (14.9%), 5 (2.3%), 2 (0.9%), 1 (0.5%), 1 (0.5%) and 1 (0.5%) samples respectively, but there were no detections of Salmonellae nor Shigellae species. Conclusion: About 40% of bottled water samples were mycologically and bacteriologically unsafe for human consumption. To prevent public health hazards, regular monitoring of bottled water using quality indicators should be a priority agenda.
Gene Delivery System: Non-Viral Mediated Chemical Approaches

Employing cationic lipids and polymers, inorganic nanoparticles, etc. as DNA carriers for gene delivery systems are gaining interest with the passage of time such chemical vectors form condensed complexes or aggregates with negatively charged DNA to not only protect the DNA from nucleases but also facilitate its intracellular uptake and site specific delivery.
Experimental quantification of spatial correlations in quantum dynamics

Correlations between different partitions of quantum systems play a central role in a variety of many-body quantum systems, and they have been studied exhaustively in experimental and theoretical research. Here, we investigate dynamical correlations in the time evolution of multiple parts of a composite quantum system. A rigorous measure to quantify correlations in quantum dynamics based on a full tomographic reconstruction of the quantum process has been introduced recently [Á. Rivas et al., New Journal of Physics, 17(6) 062001 (2015). ]. In this work, we derive a lower bound for this correlation measure, which does not require full knowledge of the quantum dynamics. Furthermore we also extend the correlation measure to multipartite systems. We directly apply the developed methods to a trapped ion quantum information processor to experimentally characterize the correlations in quantum dynamics for two- and four-qubit systems. The method proposed and demonstrated in this work is scalable, platform-independent and applicable to other composite quantum systems and quantum information processing architectures. We apply the method to estimate spatial correlations in environmental noise processes, which are crucial for the performance of quantum error correction procedures.
Consistent Comic Colorization with Pixel-wise Background Classification

Comic colorization is a time-consuming task, acting as a bottleneck in comic drawing. We propose an automatic coloring model based on the observation that the background colors of comics are often consistent but random. From this observation, we introduce a novel background detector that learns to segment backgrounds out even without direct human annotation. This allows the generation of background-consistent colorizations, a feat that previous work fails to achieve.
Subsampling methods for genomic inference

Large-scale statistical analysis of data sets associated with genome sequences plays an important role in modern biology. A key component of such statistical analyses is the computation of p-values and confidence bounds for statistics defined on the genome. Currently such computation is commonly achieved through ad hoc simulation measures. The method of randomization, which is at the heart of these simulation procedures, can significantly affect the resulting statistical conclusions. Most simulation schemes introduce a variety of hidden assumptions regarding the nature of the randomness in the data, resulting in a failure to capture biologically meaningful relationships. To address the need for a method of assessing the significance of observations within large scale genomic studies, where there often exists a complex dependency structure between observations, we propose a unified solution built upon a data subsampling approach. We propose a piecewise stationary model for genome sequences and show that the subsampling approach gives correct answers under this model. We illustrate the method on three simulation studies and two real data examples.
The Regional Evolution of Prices and Wages During the Great Recession

In this paper, we examine the evolution of prices, wages, employment and output across U.S. states during the Great Recession. To do so, we use over 76 billion price observations from the Nielsen Retail Scanner Database between 2006 and 2011 to construct state speci c price indices. We document that local retail prices, controlling for good and store xed e¤ects, varies with local economic conditions. In particular, we nd that a one percentage point increase in the unemployment rate between 2007 and 2010 was associated with a 0.4 percentage point lower price growth during the same period. Nominal wages also responded di¤erentially across U.S. states during the 2007-2010 period. A 1 percentage point increase in the unemployment rate was associated with a 1.2 percentage point lower nominal wage growth during the same period. However, we conclude that failure to account for the di¤erential local ination rates leads one to overstate the variation in real wage growth across U.S. states during the recession. We conclude by constructing a multi-region New Keynesian model with both price and wage rigidity. Using the regional variations in prices and wages, we estimate the relative importance of price and wage stickiness as a nominal rigidity in explaining the regional di¤erences in employment.
Manipulating quantum materials with quantum light

We show that the macroscopic magnetic and electronic properties of strongly correlated electron systems can be manipulated by coupling them to a cavity mode. As a paradigmatic example we consider the Fermi-Hubbard model and find that the electron-cavity coupling enhances the magnetic interaction between the electron spins in the ground-state manifold. At half filling this effect can be observed by a change in the magnetic susceptibility. At less than half filling, the cavity introduces a next-nearest-neighbor hopping and mediates a long-range electron-electron interaction between distant sites. We study the ground-state properties with tensor network methods and find that the cavity coupling can induce a phase characterized by a momentum-space pairing effect for electrons.
Secondary Markets and Firm Profits : Evidence from College Textbooks *

Secondary markets create competition between new and used goods, but also raise the value of new goods by facilitating resale. Depending on the relative strength of these forces, firms may gain or lose from closing the secondary market. We investigate these ideas empirically in the context of college textbooks. Using the estimates of a structural model of student and publisher behavior, we find that closing the secondary market would substantially increase publisher profits. We then explore how the impact of the secondary market depends on consumer preferences and other market features like the mechanism determining used prices. We thank Andrew Boysen, Meghan Busse, Gautam Gowrisankaran, Igal Hendel, Ian Larkin, Marvin Lieberman, Peter Rossi, and seminar participants at Bates White, the Kellogg School of Management, and London Business School for thoughtful comments. We also thank Forrest Spence for providing us with additional textbook data. Corresponding author. Email: matthew.schmitt@anderson.ucla. edu Email: tongtong.shi@analysisgroup.com
Information-reality complementarity in photonic weak measurements

The emergence of realistic properties is a key problem in understanding the quantum-to-classical transition. In this respect, measurements represent a way to interface quantum systems with the macroscopic world: these can be driven in the weak regime, where a reduced back-action can be imparted by choosing meter states able to extract different amounts of information. Here we explore the implications of such weak measurement for the variation of realistic properties of two-level quantum systems pre- and post-measurement, and extend our investigations to the case of open systems implementing the measurements.
Fog at the Edge: Experiences Building an Edge Computing Platform

Technology advancement has pushed computation to the network edge, paving the way for a class of IoT applications that leverage CPU, storage and communications in edge devices. Building these new IoT applications is not an easy task however. Two key challenges are supporting the dynamic nature of the edge network and the context-dependent characteristics of application logic. In this paper we report our experience in building an edge computing platform called Distributed Node-RED (DNR) that uses a distributed data flow programming model based on the popular open source Node-RED tool. We describe some of the challenges we faced as well as some novel solutions that were implemented in our platform. A new approach in applying the concept of exogenous coordination is also presented and shown to be necessary in building large-scale IoT applications across the edge, fog and cloud.
The Effect of Glancing Angle Deposition Conditions on the Morphology of a Silver Nanohelix Array

Silver nanohelices were grown on smooth substrates using glancing angle deposition and substrate cooling. Various nanohelix arrays were deposited under different deposition conditions—different deposition rates, substrate spin rates, deposition angles, and substrate temperatures. The effect of deposition conditions on the morphology of each nanohelix array in terms of pitch angle, pitch length, wire diameter, and radius of curvature was investigated. The dependence of circular dichroism on the size of the nanohelix arrays was also measured and demonstrated.
Embeddings for the Identification of Aircraft Faults (MERIT)

Vector representation concept proves its success in solving many real-world problems from a variety of applications. In this paper, we built a novel vector representation model for avionics system for two types of fault messages called MERIT. This new model aims to identify the relationship between the flight deck effects (FDEs) and the maintenance messages (MMSGs) through calculating the embedding co-occurrence matrix between them within a predefined flight leg window. The a vector space embeddings representation of MERIT is able to differentiate between the strong and weak relationship between messages. Moreover, we benefit from the negative sampling method to incorporate the weak relationship between the FDEs and MMSGs from different subsystems (chapters) in assessing this relationship precisely. We called the developed MERIT with specialized negative sampling approach subsystem-wise MERIT. Both developed models can be used as descriptive and predictive tasks based on the flight leg window used (one and three, respectively). The main advantage of the proposed latent aircraft system model (MERIT) is that it needs to be trained only once and can be easily queried using any similarity measurements between the embedding vectors, which means it is more feasible and computationally efficient than traditional machine learning algorithm, where it necessitates building a different model each time for every target FDE. We tested both models on a real Boeing dataset and the experimental results demonstrate the effectiveness of the proposed model in exhibiting the embedded relationships between fault messages and extracting the most relevant predictors.1. 1Mohamed Elshrif et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.