Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian State-Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy *

We extend the econometric literature on the role of production networks in the propagation of monetary policy shocks along two dimensions. First, we allow for time-varying industry-speciﬁc responses, reﬂecting non-linearities and heterogeneity in direct transmission channels. Second, we allow for time-varying network structures and dependence. This captures both variation in the structure of the production network and differences in cross-industry demand elasticities. Spillover effects among industries appear to be important in periods of elevated economic and ﬁnancial uncertainty, often coinciding with tight credit market conditions and ﬁnancial stress. Cross-sectional differentials can be explained by how close industries are to end-consumers. 500 1 . and 0 . 3 percent. These are in line with . Kuttner and also mirror those of and the context of an identical replication for our period. Allowing for time variation in the coefﬁcient measuring the sensitivity of S&P 500 returns to monetary policy shocks (Model A2) and aggregating the response over time ex post yields marginally larger point estimates with a slightly inﬂated posterior credible interval. We discuss time-varying dynamics below, but note that effect sizes differ strongly over time, a ﬁnding in line with Chen (2007).
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced by Comparable Corpora

Automatically extracting parallel sentence pairs from the multilingual articles available on the Internet can address the data sparsity problem in building multilingual natural language processing applications, especially in machine translation. In this project, we have used an end-to-end siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we found that using the enriched dataset showed improved BLEU scores on both NMT and phrase-based SMT systems when compared to training exclusively on carefully curated bilingual corpora.
Determination of the Accuracy of the Observations, by C. F. Gauss, Translation with preface

Determination of the Accuracy of the Observations by Carl Friedrich Gauss. Translation by Joakim Ekstrom, with preface. Abstract. Bestimmung der Genauigkeit der Beobachtungen is the second of the three major pieces that Gauss wrote on statistical hypothesis generation. It continues the methodological tradition of Theoria Motus, producing estimates by maximizing probability density, however absence of the change-of- variables theorem causes technical difficulties that compromise its elegance. In Theoria Combinationis, Gauss abandoned the aforementioned method, hence placing Bestimmung der Genauigkeit at a cross- roads in the evolution of Gauss’s statistical hypothesis generation methodology. The present translation is paired with a preface discussing the piece and its historical context.
Strongly Correlated Electrons at High Pressure: An Approach by Inelastic X-ray Scattering

This work is a review of experimental research about the electronic properties of strongly correlated electrons systems under high pressure observed by resonant inelastic x-ray scattering (RIXS). General aspects of pressure induced phenomena and basics of RIXS are presented in the introductory material. In the main part, we debate about pressure induced magnetic collapse and metal-insulator transitions in 3d compounds, and valence fluctuations phenomena in 4f and 5f compounds.
GIPA++: A General Information Propagation Algorithm for Graph Learning

Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based on attention, such as dot product by the dense vectors of two nodes. This may cause conflicting noise in nodes to be propagated when doing information propagation. To solve this problem, we propose a General Information Propagation Algorithm (GIPA in short), which exploits more fine-grained information fusion including bit-wise and feature-wise correlations based on edge features in their propagation. Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection. We evaluate the performance of GIPA on the Open Graph Benchmark proteins (OGBN-proteins for short) dataset and the Alipay dataset of Alibaba. Experimental results reveal that GIPA outperforms the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average ROC-AUC of $0.8901\pm 0.0011$, which is better than that of all the existing methods listed in the OGBN-proteins leaderboard.
EVOLUTIONARY ALGORITHMS FOR MULTIOBJECTIVE OPTIMIZATION

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
Parallel Evolutionary Multi-Criterion Optimization for Mobile Telecommunication

In this paper, we propose a new type of parallel genetic algorithm model for multi objective optimization problems. That is called a ”Master-Slave model with Local Cultivation model (MSLC)”. To clarify the characteristics and effectiveness of this model, the proposed model and the various EAs are applied to solve an antenna arrangement problem of mobile telecommunication. Thorough this problem, advantages and disadvantages of these models are made clarified. Keyword: Parallel Distributed Algorithms, Evolutionary Multi-Criterion Optimization, Mobile Telecommunication Netoworks Optimization
Prevalence of type 2 diabetes mellitus in women of childbearing age in Africa during 2000–2016: a systematic review and meta-analysis

Objectives The aim of this research was to estimate the prevalence of type 2 diabetes mellitus (T2DM), impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in African women of childbearing age. Study design Systematic review and meta-analysis of relevant African studies published from January 2000 to December 2016. Data sources We searched several databases, including EMBASE, MEDLINE, CINAHL, grey literature and references of included studies. Setting Studies carried out in African communities or any population-based studies were included. Participants We included studies, carried out in Africa, with non-pregnant women of childbearing age. Studies must have been published between the years 2000 and 2016. Outcomes The primary outcome was prevalent T2DM. The secondary outcomes were IFG and IGT. Data extraction and synthesis Two reviewers independently extracted data and, using the adapted Hoy risk of bias tool, independently assessed for risk of bias. We used random-effects meta-analysis models to pool prevalence estimates across studies. We used Cochran’s Q statistic and the I2 statistic to assess heterogeneity. Results A total of 39 studies from 27 countries were included, totaling 52 075 participants, of which 3813 had T2DM. The pooled prevalence of T2DM was 7.2% (95% CI 5.6% to 8.9%) overall and increased with age. The pooled prevalence was 6.0% (95% CI 4.2% to 8.2%) for impaired fasting glycemia while the prevalence of IGT ranged from 0.9% to 37.0% in women aged 15–24 and 45–54 years, respectively. Substantial heterogeneity across studies was not explained by major studies characteristics such as period of publication, rural/urban setting or whether a study was nationally representative or not. Conclusion This review highlights the need for interventions to prevent and control diabetes in African women of childbearing age, in view of the significant prevalence of T2DM and prediabetes. PROSPERO registration number CRD42015027635
Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks

This section complements our paper by presenting additional information, experimental results and visualizations. First, we provide further comparison results with other state-of-the-arts in Section A.1. In Section A.2, we conduct experiments to find the best way to incorporate positional information. We then describe the architecture details of the baseline and HANet in Section A.3. In Section A.4, we compare height-wise and width-wise class distributions. Finally, we conduct quantitative and qualitative comparisons between ours and the baseline model in Section A.5.
LoRe: A Programming Model for Verifiably Safe Local-First Software

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The unpredictability of such interactions paired with their decentralized nature make reasoning about the correctness of local-first software a challenging endeavor. Yet, existing solutions to develop local-first software do not provide support for automated safety guarantees and instead expect developers to reason about concurrent interactions in an environment with unreliable network conditions. We propose LoRe, a programming model and compiler that automatically verifies developer-supplied safety properties for local-first applications. LoRe combines the declarative data flow of reactive programming with static analysis and verification techniques to precisely determine concurrent interactions that violate safety invariants and to selectively employ strong consistency through coordination where required. We propose a formalized proof principle and demonstrate how to automate the process in a prototype implementation that outputs verified executable code. Our evaluation shows that LoRe simplifies the development of safe local-first software when compared to state-of-the-art approaches and that verification times are acceptable.
A generalized exact tracking control methodology for disturbed nonlinear systems via homogeneous domination approach

A generalized homogeneous exact set‐point tracking control methodology for a class of disturbed higher‐order nonlinear systems is investigated in this paper. By virtue of utilizing the disturbance estimation and active elimination approach, a homogeneous domination feedback technique is then integrated such that a generalized composite active anti‐disturbance control framework is proposed with a delicate handling procedure of the non‐vanishing nonlinearities. The proposed methodology has several new features. First, a genuine nonlinear composite controller employing the system nonlinearities will bring an improvement of the control performances. Second, the flexility of the design methodology renders its suitability for random output tracking with mismatched disturbances in every channel. Moreover, the control function is constructed in a neat nested form and hence easier to be implemented. Numerical simulation results affirm the output tracking control performances. Copyright © 2016 John Wiley & Sons, Ltd.
Dynamic Graph Convolutional Recurrent Network for Trafic Prediction: Benchmark and Solution

Traic prediction is the cornerstone of intelligent transportation system. Accurate traic forecasting is essential for the applications of smart cities, i.e., intelligent traic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network (RNN) based works are not eicient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among diferent methods on the same datasets. To address the above challenges, in this paper, we propose a novel traic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic ilters are generated at each time step. We ilter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-deined static graph. As far as we know, we are irst
Semiparametric Bayesian analysis of high-dimensional censored outcome data

ABSTRACT The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets. This paper develops a cost-effective Markov chain Monte Carlo strategy for censored outcomes to fit a semiparametric bayesian analysis of SEER data of New Mexico. We use an accelerated failure time model, with Dirichlet process random effects for inter-subject variation, and intrinsic conditionally autoregressive random effects for spatial correlations. The results offer insights into differences in breast cancer mortality rates between ethnic groups, tumor grade and spatial effect of counties.
Modeling the Influence of Data Structure on Learning in Neural Networks: The Hidden Manifold Model

Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The types of data where these networks are most successful, such as images or sequences of speech, are characterised by intricate correlations. Yet, most theoretical work on neural networks does not explicitly model training data, or assumes that elements of each data sample are drawn independently from some factorised probability distribution. These approaches are thus by construction blind to the correlation structure of real-world data sets and their impact on learning in neural networks. Here, we introduce a generative model for structured data sets that we call the hidden manifold model (HMM). The idea is to construct high-dimensional inputs that lie on a lower-dimensional manifold, with labels that depend only on their position within this manifold, akin to a single layer decoder or generator in a generative adversarial network. We demonstrate that learning of the hidden manifold model is amenable to an analytical treatment by proving a "Gaussian Equivalence Property" (GEP), and we use the GEP to show how the dynamics of two-layer neural networks trained using one-pass stochastic gradient descent is captured by a set of integro-differential equations that track the performance of the network at all times. This permits us to analyse in detail how a neural network learns functions of increasing complexity during training, how its performance depends on its size and how it is impacted by parameters such as the learning rate or the dimension of the hidden manifold.
Distributed Stabilization of Signed Networks via Self-Loop Compensation

The positive semidefiniteness of Laplacian matrices is a critical guarantee of the consensus of unsigned multi-agent networks, which is not valid for signed Laplacian matrices. In this paper, we first analyze the stability of signed networks by introducing a novel graph-theoretic concept called negative cut set, which indicates that the existence of negative edges in signed networks can easily render the network unstable. This fact hinders the applicability of consensus protocol on signed networks. To this end, inspired by the interplay between diagonal dominance and matrix stability, a local state damping mechanism is introduced using self-loop compensation, which is activated only for those agents who are incident to negative edges and can stabilize signed networks in a distributed manner. Quantitative connections between self-loop compensation and the stability of the compensated signed network are established for a tradeoff between compensation efforts and the stability/consensus of signed networks. The optimality of self-loop compensation is discussed. Furthermore, we extend our results to directed signed networks where the symmetry of signed Laplacian is not free. The correlation between the stability of the compensated dynamics obtained by self-loop compensation and eventually positivity is further discussed. Simulation examples are given to demonstrate the theoretical results.
Mobility Inference on Long-Tailed Sparse Trajectory

Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel states from the raw trajectory data? How can we infer these mobility states from a single user’s trajectory information? How can we further generalize the mobility inference to the real-world trajectory data that span multiple users and are sparsely sampled over time? In this article, based on formal and rigid definitions of the stay/travel mobility, we propose a single trajectory inference algorithm that utilizes a generic long-tailed sparsity pattern in the large-scale trajectory data. The algorithm guarantees a 100% precision in the stay/travel inference with a provable lower bound in the recall metric. Furthermore, we design a transformer-like deep learning architecture on the problem of mobility inference from multiple sparse trajectories. Several adaptations from the standard transformer network structure are introduced, including the singleton design to avoid the negative effect of sparse labels in the decoder side, the customized space-time embedding on features of location records, and the mask apparatus at the output side for loss function correction. Evaluations on three trajectory datasets of 40 million urban users validate the performance guarantees of the proposed inference algorithm and demonstrate the superiority of our deep learning model, in comparison to sequence learning methods in the literature. On extremely sparse trajectories, the deep learning model improves from the single trajectory inference algorithm with more than two times of overall and F1 accuracy. The model also generalizes to large-scale trajectory data from different sources with good scalability.
LEAP System for SRE 2019 CTS Challenge - Improvements and Error Analysis

The NIST Speaker Recognition Evaluation - Conversational Telephone Speech (CTS) challenge 2019 was an open evaluation for the task of speaker verification in challenging conditions. In this paper, we provide a detailed account of the LEAP SRE system submitted to the CTS challenge focusing on the novel components in the back-end system modeling. All the systems used the time-delay neural network (TDNN) based x-vector embeddings. The x-vector system in our SRE19 submission used a large pool of training speakers (about 14k speakers). Following the x-vector extraction, we explored a neural network approach to backend score computation that was optimized for a speaker verification cost. The system combination of generative and neural PLDA models resulted in significant improvements for the SRE evaluation dataset. We also found additional gains for the SRE systems based on score normalization and calibration. Subsequent to the evaluations, we have performed a detailed analysis of the submitted systems. The analysis revealed the incremental gains obtained for different training dataset combinations as well as the modeling methods.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate

This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS research project that is investigating the feasibility of wearable devices and smart phones to monitor individuals with multiple sclerosis (MS), depression or epilepsy. Aspart of the project protocol, heart-rate data was collected from participants using a Fitbit wristband. The presence of COVID-19 in the cohort in this work was either confirmed through a positive swab test, or inferred through the self-reporting of a combination of symptoms including fever, respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal symptoms. Experimental results indicate that our proposed contrastive convolutional auto-encoder (contrastive CAE), i. e. , a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted average recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100% and a specificity of 90.6% on a testset of 19 participants with MS who reported symptoms of COVID-19. Each of these participants was paired with a participant with MS with no COVID-19 symptoms.
Jovian deep magnetotail composition and structure

We analyze plasma ion observations from the Solar Wind Around Pluto instrument on New Horizons as it traveled back through the dusk flank of the Jovian magnetotail from ~600 to more than 2500 Jovian radii behind the planet. We find that at all distances, light ions (mostly protons) dominate the heavy ions (S++ and O+) that are far more abundant in the near Jupiter plasma disk and that were expected to be the primary ions filling the Jovian magnetotail. This key new observation might indicate that heavy ions are confined closer to the equator than the spacecraft trajectory or a substantial addition of light ions via reconnection and/or mixing along the magnetopause boundary. However, because we find no evidence for acceleration of the tail plasma with distance, a more likely explanation seems to be that the heavy ions are preferentially released down the dawn flank of the magnetotail. Perhaps, this occurs as a part of the process where flux tubes, after expanding as they rotate across the near‐tail region, need to pull back inward in order to fit within the dawnside of the magnetopause. A second major finding of this study is that there are two dominant periods of the plasma structures in the Jovian magnetotail: 3.53 (0.18 full width at half maximum (FWHM)) and 5.35 (0.38 FWHM) days. Remarkably, the first of these is identical within the errors to Europa's orbital period (3.55 days). Both of these results should provide important new fodder for Jovian magnetospheric theories and lead to a better understanding of Jupiter's magnetosphere.
Convergence of The Barzilai-Borwein Method for Solving Slightly Unsymmetric Linear Systems

Due to its simplicity and numerical efficiency, the Barzilai and Borwein (BB) gradient method has received numerous attentions in different scientific fields. In this paper, the sufficient condition for convergence of the BB method when the coefficient matrix of linear algebraic equations is slightly unsymmetric with positive definite symmetric part is presented