Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Broader Better? A Monetary Approach to Forecasting Economic Activity

This paper investigates whether the use of broader Divisia monetary aggregates improves money’s performance in forecasting economic activity within a time-varying parameter vector autoregressive (TVP-VAR) framework. We evaluate entire predictive densities from several alternative models of US output growth and inflation, each using eight different Divisia monetary aggregates. Using the broadest, M4 aggregate produces out-of-sample forecasts which consistently outperform those based on narrower measures of money, pooling of forecasts from several models, and a large-scale, 143-variable model. Our results show that TVP-VARs with Divisia M4 forecast economic activity more accurately than constant-parameter models with alternative or no measures of money.
STEM Graduates and High School Curriculum: Does Early Exposure to Science Matter?∗

Increasing the number of Science, Technology, Engineering and Math (STEM) graduates at university is considered a key element for long-term productivity, innovation and competitiveness in the global economy. Still, little is known about what actually drives and shapes students’ choices. This paper explores how being exposed to more science in high school affects enrollment and persistency in STEM majors, the quality of the university attended and the probability of dropping out of high school, by exploiting the different timing in the implementation of a reform that induced high schools in the UK to offer more science to high ability 14 year-old children. I find that taking more science in high school increases the probability of enrolling in a STEM major by 25% and the probability of graduating in these majors by the same amount. Moreover the probability of dropping out from high school decreases for low income students. Finally, the effect is not limited to natural sciences only: students choose to combine more science in high school with more difficult high school and post-high school courses. ∗VERY PRELIMINARY AND INCOMPLETE. PLEASE DO NOT CITE WITHOUT THE AUTHOR’S PERMISSION. I thank Steve Pischke and Esteban Aucejo for very precious guidance, supervision and encouragement. I thank Lorenzo Cappellari, Georg Graetz, Monica Langella, Alan Manning, Barbara Masi, Stephan Maurer, Sandra McNally, Guy Micheals, Michele Pellizzari, Paolo Sestito, Olmo Silva, Alessandro Vecchiato and Giulia Zane and participants to the LSE labour and education work in progress seminars, to the 2015 CEP conference, to the 5th fRDB workshop and to the 6th IWAEE workshop for providing me with very useful comments and information. The views expressed in this article are those of the author alone and do not necessarily reflect the official views of the Bank of Italy. †London School of Economics, Centre of Economic Performance, Bank of Italy. E-mail address: m.dephilippis@lse.ac. uk
Reduction of ripening time of full-scale manganese removal filters with manganese oxide-coated media

Effective manganese removal by conventional aeration-filtration with virgin filter media requires a long ripening time. The aim of this study was to assess the potential of manganese oxide-coated media to shorten the ripening time of filters with virgin media, under practical conditions. A full scale filter filled with virgin sand and a full scale filter filled with anthracite/sand were operated at two groundwater treatment plants, in parallel with (full scale) test filters, with an additional layer of Manganese Oxide-Coated Sand (MOCS) or Manganese Oxide-Coated Anthracite (MOCA). Significantly different ripening times were observed to achieve an effective manganese removal: 55 days for a filter with virgin sand and 16 days for a filter with virgin anthracite/sand, respectively. The observed differences could be attributed to different feed water quality, different iron loading, and backwashing intensity and frequency. In batch experiments fresh MOCA and MOCS showed good manganese adsorptive properties. Addition of a shallow layer of fresh MOCA in test filters eliminated the ripening time, while a layer of aged MOCS did not significantly shorten the ripening period. The poor performance of the aged MOCS was likely caused by changed properties of aged and dried MOCS, that had lost its adsorption capacity, the auto-catalytic activity and the biological activity.
COMPARATIVE ANALYSIS OF RHETORICAL AND ARGUMENTATIVE STRUCTURES IN THE STUDY OF POPULAR SCIENCE DISCOURSE

The proposed work is performed as a part of an on-going research project aimed at creation of discourse annotated corpus of popular science texts written in Russian. Annotation is carried out within the framework of a multi-level model of discourse, which considers the text from the perspective of genre, rhetorical and argumentative organization. We conduct a comparative study of the rhetorical and argument annotations, discuss their similarities and differences on the segment and structural levels and show them on the examples of standard schemes of reasoning described in D. Walton’s theory of structured argumentation: “Argument from Expert Opinion”, “Argument from Example”, and “Argument from Cause to Effect”. Special attention is paid to discourse markers registered during annotation as key indicators of discourse structure. We report the results of the experiment with argument indicator patterns, based on the list of rhetorical markers, and aimed at the extraction of “from Expert Opinion” arguments.
Multi-scale Momentum Contrast for Self-supervised Image Classification

With the maturity of supervised learning technology, people gradually shift the research focus to the field of self-supervised learning. ”Momentum Contrast” (MoCo) [21] proposes a new self-supervised learning method and raises the correct rate of self-supervised learning to a new level. Inspired by another article ”Representation Learning by Learning to Count” [46], if a picture is divided into four parts and passed through a neural network, it is possible to further improve the accuracy of MoCo. Different from the original MoCo, this MoCo variant (Multi-scale MoCo) does not directly pass the image through the encoder after the augmented images. Multi-scale MoCo crops and resizes the augmented images, and the obtained four parts are respectively passed through the encoder and then summed (upsampled version do not do resize to input but resize the contrastive samples). This method of images crop is not only used for queue q but also used for comparison queue k, otherwise the weights of queue k might be damaged during the moment update. This will further discussed in the experiments chapter between downsampled Multi-scale version and downsampled both Multi-scale version. Human beings also have the same principle of object recognition: when human beings see something they are familiar with, even if the object is not fully displayed, people can still guess the object itself with a high probability. Because of this, Multi-scale MoCo applies this concept to the pretext part of MoCo, hoping to obtain better feature extraction. In this thesis, there are three versions of Multi-scale MoCo, downsampled input samples version, downsampled input samples and contrast samples version and upsampled input samples version. The differences between these versions will be described in more detail later. The neural network architecture comparison includes ResNet50 , and the tested data set is STL-10. The weights obtained in pretext will be transferred to self-supervised learning, and in the process of self-supervised learning, the weights of other layers except the final linear layer are frozen without changing (these weights come from pretext).
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation

Since early 2020, COVID-19 has spread rapidly across the world. At the date of this writing, the disease has been globally reported in 220 countries and regions, infected more than 68 million people, and caused more than 1.5 million deaths (see https://covid19.who.int/) for up-to-date statistics. Avoiding person-to-person transmission is an effective approach to control and prevent the pandemic. However, many daily activities, such as transporting goods in our daily life, inevitably involve person-to-person contact.
Generalized Bayes Quantification Learning under Dataset Shift

Abstract Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data using Kullback–Leibler loss-functions based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and allows 0’s and 1’s in the compositional outputs thereby including categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, which to our knowledge are the first theoretical results for a quantification approach in the presence of local labeled data. We also establish finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift. Supplementary materials for this article are available online.
Quantum complexity of minimum cut

The minimum cut problem in an undirected and weighted graph G is to find the minimum total weight of a set of edges whose removal disconnects G. We completely characterize the quantum query and time complexity of the minimum cut problem in the adjacency matrix model. If G has n vertices and edge weights at least 1 and at most τ, we give a quantum algorithm to solve the minimum cut problem using [EQUATION] queries and time. Moreover, for every integer 1 ≤ τ ≤ n we give an example of a graph G with edge weights 1 and τ such that solving the minimum cut problem on G requires [EQUATION] queries to the adjacency matrix of G. These results contrast with the classical randomized case where Ω(n2) queries to the adjacency matrix are needed in the worst case even to decide if an unweighted graph is connected or not. In the adjacency array model, when G has m edges the classical randomized complexity of the minimum cut problem is [EQUATION]. We show that the quantum query and time complexity are [EQUATION] and [EQUATION], respectively, where again the edge weights are between 1 and τ. For dense graphs we give lower bounds on the quantum query complexity of Ω(n3/2) for τ ≥ 1 and Ω(τn) for any 1 ≤ τ ≤ n. Our query algorithm uses a quantum algorithm for graph sparsification by Apers and de Wolf (FOCS 2020) and results on the structure of near-minimum cuts by Kawarabayashi and Thorup (STOC 2015) and Rubinstein, Schramm and Weinberg (ITCS 2018). Our time efficient implementation builds on Karger's tree packing technique (STOC 1996).
Performance of biological powder activated carbon-membrane bioreactor (BPAC-MBR) for old-landfill leachate treatment.

This research aims to investigate the performance of the combined BPAC-MBR system for old-landfill leachate treatment. The system was operated at two different intermittent aeration modes (aeration or non-aeration) of 120-120 min and 150-150 min. It was found that the BPAC-MBR system operating at 150-150 min could give higher removal efficiencies for all parameters; COD at 83%, colour at 85%, TKN at 97%, and TP at 68%. The reason might be that the degradation mechanism of microorganisms inside the BPAC-MBR system needed sufficient time for sequential aerobic and anaerobic periods to improve biodegradation for slowly biodegradable organic compounds.
Structure-Related Electrochemistry of Sulfur-Poly(acrylonitrile) Composite Cathode Materials for Rechargeable Lithium Batteries

The structure of sulfur-poly(acrylonitrile)-based Li-sulfur batteries is elucidated and correlated with the electrochemical performance of such devices. Apart from the poly(acrylonitrile)-derived backbone, thioamide as well as poly(sulfide) structures are proposed. Furthermore, the intermediary formation of S8 during cycling and the role of the electrolyte in its reintegration during charging into are addressed. In summary, a comprehensive picture of the chemistry and electrochemistry of Li-sulfur batteries is presented.
Purification carbon dioxide.

A method for removing at least a portion of one or more contaminants selected from the group consisting of SO2 and NOx from gaseous carbon dioxide (1, 2, 3, 4, 5, 11, 12, 13), said method comprising : maintaining said gaseous carbon dioxide (5, 13) under pressure (s) elevated (s) in the presence of molecular oxygen ( "O 2") and water and, when it will remove SO 2, NO x, during sufficient to convert SO2 to sulfuric acid (6) and / or NOx to nitric acid (14) time; and separating said sulfuric acid (6) and / or nitric acid (14) of said gaseous carbon dioxide (5, 13) to produce dioxide gas poor carbon NO x, free SO 2 (20), wherein said (s) pressure (s) higher (s) it is at least 0.3 MPa (about 3 bar).
Superhydrophobic and photocatalytic self-cleaning cotton

A superhydrophobic cotton fabric exhibiting photocatalytic self-cleaning properties under visible-light was prepared by step-wise deposition of anatase TiO2, meso-tetra(4-carboxyphenyl)porphyrin (TCPP) and trimethoxy(octadecyl)silane (OTMS). The modified cotton fabrics not only exhibited excellent superhydrophobicity with a water contact angle of 156°, but also showed significant photocatalytic degradation of methylene blue under visible-light irradiation. The fabrics were characterized by FESEM, XRD, NMR and UV-Vis spectroscopy. Textiles with dual functionality of superhydrophobicity and visible-light photocatalysis are promising for a wide range of self-cleaning applications.
Vapor phase polymerization of 3,4‐ethylenedioxythiophene on flexible substrate and its application on heat generation

An oxidative chemical vapor phase polymerization process is applied to coat flexible polyethylene terephthalate (PET) fabrics with a uniform poly(3,4-ethylenedioxythiophene) (PEDOT) film. This vapor phase polymerization method is a solventfree technique, which could yield a low resistance in the PEDOT-coated PET fabric. The resistance as low as 52 Ω square−1 is achieved. The polymerization conditions, such as the concentration of oxidant, reaction time, and temperature, have been systematically investigated. The coated fabric samples exhibit an increase in temperature when subjected to a fixed DC voltage. This result indicates a potential application of these PEDOT-coated fabrics as electric heating materials. Copyright © 2009 John Wiley & Sons, Ltd.
Networked Supervisory Control Synthesis of Timed Discrete-Event Systems

Conventional supervisory control theory assumes full synchronization between the supervisor and the plant. This assumption is violated in a networked-based communication setting due to the presence of delays, and this may result in incorrect behavior of a supervisor obtained from conventional supervisory control theory. This paper presents a technique to synthesize a networked supervisor handling communication delays. For this purpose, first, a networked supervisory control framework is provided, where the supervisor interacts with the plant through control and observation channels, both of which introduce delays. The control channel is FIFO, but the observation channel is assumed to be non-FIFO so that the observation of events may not necessarily be received by the supervisor in the same order as they occurred in the plant. It is assumed that a global clock exists in the networked control system, and so the communication delays are represented in terms of time. Based on the proposed framework, a networked plant automaton is achieved, which models the behavior of the plant under the effects of communication delays and disordered observations. Based on the networked plant, the networked supervisor is synthesized, which is guaranteed to be (timed networked) controllable, nonblocking, time-lock free, (timed networked) maximally permissive, and satisfies control requirements for the plant.
From an Interior Point to a Corner Point: Smart Crossover

Identifying optimal basic feasible solutions to linear programming problems is critical for mixed integer programming and other applications. The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process. This method, compared with the starting method, frequently represents the primary computational bottleneck in practical applications. We propose some approaches to overcome this bottleneck by smartly exploiting problem characteristics and implementing customized strategies. For problems arising from network applications and exhibiting network structures, we take advantage of the graph structure of the problem and the tree structure of the optimal solutions. Based on these structures, we propose a tree-based crossover method, aimed at recovering basic solutions by identifying nearby spanning tree structures. For general linear programs, we propose to recover an optimal basic solution by identifying the optimal face and employing controlled perturbations based on the suboptimal solution provided by interior-point methods. We prove that an optimal solution for the perturbed problem is an extreme point, and its objective value is at least as good as that of the initial interior point solution. Computational experiments show significant speed-ups achieved by our methods compared to state-of-the-art commercial solvers on classical linear programming problem benchmarks, network flow problem benchmarks, and optimal transport problems.
Off-policy Confidence Sequences

We develop confidence bounds that hold uniformly over time for off-policy evaluation in the contextual bandit setting. These confidence sequences are based on recent ideas from martingale analysis and are non-asymptotic, non-parametric, and valid at arbitrary stopping times. We provide algorithms for computing these confidence sequences that strike a good balance between computational and statistical efficiency. We empirically demonstrate the tightness of our approach in terms of failure probability and width and apply it to the"gated deployment"problem of safely upgrading a production contextual bandit system.
Inhomogeneous Knight shift in vortex cores of superconducting FeSe

We report Se NMR data in the normal and superconducting states of a single crystal of FeSe for several different field orientations. The Knight shift is suppressed in the superconducting state for in-plane fields, but does not vanish at zero temperature. For fields oriented out of the plane, little or no reduction is observed below Tc. These results reflect spin-singlet pairing emerging from a nematic state with large orbital susceptibility and spin-orbit coupling. The spectra and spin-relaxation rate data reveal electronic inhomogeneity that is enhanced in the superconducting state, possibly arising from enhanced density of states in the vortex cores. Despite the spin polarization of these states, there is no evidence for antiferromagnetic fluctuations.
Production and polarization of direct 
J/ψ
 to 
O(αs3)
 in the improved color evaporation model in collinear factorization

We calculate the production and polarization of direct J/ψ in the improved color evaporation model at O(α s) in the collinear factorization approach. We present the first calculation of polarization parameters λθ, λφ, and λθφ in the helicity and the Collins-Soper frames, as well as the frame-invariant polarization parameter λ̃ as a function of transverse momentum. We find agreement with both J/ψ cross sections and the invariant polarization parameters at small and moderate pT .
Understanding and Creating Art with AI: Review and Outlook

Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This article provides an integrated review of two facets of AI and art: (1) AI is used for art analysis and employed on digitized artwork collections, or (2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, and computational aesthetics, among others. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

A magnetic resonance imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. While current generative approaches allow only the synthesis of specific sets of MR contrasts, we developed a method to generate synthetic MR images with adjustable image contrast. Therefore, we trained a generative adversarial network (GAN) with a separate auxiliary classifier (AC) network to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, and image orientation). The AC determined the repetition time with a mean absolute error (MAE) of 239.6 ms, the echo time with an MAE of 1.6 ms, and the image orientation with an accuracy of 100%. Therefore, it can properly condition the generator network during training. Moreover, in a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.