Miracle-dz/UniFAR-FTUs huggingface dataset:
Productivity and Employment in Apac Economies: A Comparison with the EU Using Firm-Level Information

We provide an overview of productivity development and other related indicators in Asia and Pacific (APAC) countries, with comparisons with the Europe region. We use the seventh vintage firm-level data from the Productivity Research Network in the APAC region and CompNet in Europe for our study. The overall results show that the productivity growth in developed APAC countries (Australia, New Zealand, and the Republic of Korea) is significantly ahead of the growth in developing APAC countries (India and the People’s Republic of China) and on par with the EU’s growth. There is an ongoing process of bottom firms catching up with top firms in the Republic of Korea and the richest EU countries. Regarding employment and labor skills, employment growth has generally been quite stagnant in all regions. Labor skills, for which we use the wage premium as a proxy, are quite similar across most regions, with the richest EU countries showing a higher premium than the rest. Our test of the productivity–employment link indicates that the size of employment tends to have a greater impact on productivity in APAC countries, while labor skills have greater emphasis in the EU.
An Examination of Hedge Funds Performance Persistence

We check for performance persistence of hedge funds in terms of market price average returns in each style category. In addition, we use regression models to test market timing ability. We use a sample of 773 hedge funds both alive and dead to avoid survivorship, self-selection and backfill bias. We include in our sample funds that have stopped to exist or merged with other funds. Positive returns indicate that the manager is skillful in terms of share picking ability. In addition, I am going to test for market timing ability to measure if the fund manager can outperform the market index. The market timing ability is verified using the Treynor – Mazuy (1966) model and denoted by the coefficient gamma. If the coefficient is positive this indicates that the manager has superior timing ability. Based on my regression results, all out of the nine style sectors display a coefficient of market timing ability that is positive and not statistically significant at 5% level. No sectors showed superior timing market ability. Negative values represent inferior or neutral managerial performance. None out of the nine style categories display negative figures. All nine style sectors display positive figures for the share average returns. Positive returns indicate that the manager is skillful in terms of share picking ability.
Semi-Supervised Text Mining for Monitoring the News About the ESG Performance of Companies

We present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.
Gun Dealer Density and its Effect on Homicide

We explore the relationship between gun prevalence and homicides in the United States from 2003-2019. Unlike previous research, which typically uses an indirect, state-level measure of gun prevalence, we use a direct measure of guns in a narrow geographic area: gun dealers. We find an increase in gun dealer density is significantly and positively associated with increased homicides in subsequent years. We compare estimates from our preferred measure, the number of dealers per 100 square miles in a local area, to those found using other gun prevalence measures and find our preferred measure to be more consistent in magnitude across three different estimation methods and two different data sources. We additionally show the effect of gun dealer density is limited mostly to counties that have a high percent of Black residents. We propose that the so-called ''Ferguson Effect''--a sharp increase in violent crime in urban and Black communities after 2014--might be partially explained by an influx of gun dealers in Black communities, rather than just a change in the propensity of Black residents to call the police or changes in police behavior.
Design Rules, Volume 2: Chapter 12—The Planar Process and Moore's Law

Moore’s Law is the prediction that transistor densities of semiconductor chips will double, speeds double, and prices drop by half approximately every eighteen months to two years. The Law lies at the heart of the high rates of technical change observed in the computer, communication and software industries during the last fifty years. <br><br>The purpose of this chapter is to explain the underpinnings of Moore’s Law and to assess whether the industry-wide rate of technical change is likely to continue. I first discuss the Law’s origin as an empirical observation and its grounding in the physics of semiconductors. I describe how the Law became established as a dynamic equilibrium among semiconductor manufacturers, and how it was challenged and reaffirmed several times. I explain how in the 1990s, the Law was institutionalized in series of an industry-wide roadmaps, which became self-fulfilling prophecies. I go on to describe the performance of semiconductor chips and companies in the five decades since the Law was first proposed. I end with a discussion of the physical limits that now constrain the operation of Moore’s Law and a description of other paths to improved performance that still remain to be explored.
Integrated Vehicle Routing and Monte Carlo Scheduling Approach for the Home Service Assignment, Routing, and Scheduling Problem

We formulate and solve the H-SARA Problem, a Vehicle Routing and Appointment Scheduling Problem motivated by home services management. We assume that travel times, service durations, and customer cancellations are stochastic. We use a two-stage process that first generates teams and routes using a VRP Solver with optional extensions and then uses an MC Scheduler that determines expected arrival times by teams at customers. We further introduce two different models of cancellation and their associated impacts on routing and scheduling. Finally, we introduce the Route Fracture Metaheuristic that iteratively improves an H-SARA solution by replacing the worst-performing teams. We present insights into the problem and a series of numerical experiments that illustrate properties of the optimal routing, scheduling, and the impact of the Route Fracture Metaheuristic for both models of cancellation.
A New Stopping Criterion for Krylov Solvers Applied in Interior Point Methods

When an iterative method is applied to solve the linear equation system in interior point methods (IPMs), the attention is usually placed on accelerating their convergence by designing appropriate preconditioners, but the linear solver is applied as a black box with a standard termination criterion which asks for a sufficient reduction of the residual in the linear system. Such an approach often leads to an unnecessary 'oversolving' of linear equations. In this paper, an IPM that relies on an inner termination criterion not based on the residual of the linear system is introduced and analyzed. Moreover, new indicators for the early termination of the inner iterations are derived from a deep understanding of IPM needs. The new technique has been adapted to the Conjugate Gradient (CG) and to the Minimum Residual method (MINRES) applied in the IPM context. The new criterion has been tested on a set of quadratic optimization problems including compressed sensing, image processing and instances with partial differential equation constraints, and it has been compared to standard residual tests with variable tolerance. Evidence gathered from these computational experiments shows that the new technique delivers significant improvements in terms of inner (linear) iterations and those translate into significant savings of the IPM solution time.
SAT Based Analogy Evaluation Framework For Persian Word Embeddings

In recent years there has been a special interest in word embeddings as an approach to convert words to vectors. It has been a focal point to understand how much of the semantics of the words has been transferred into embedding vectors. Intrinsic evaluation of word embeddings is cheaper than evaluating them extrinsically and it is usually costly to evaluate the downstream application end-to-end in order to determine the quality of the used embedding model. Generally the word embeddings are evaluated through a number of tests, including analogy test. In this paper we propose a test framework for Persian embedding models. Persian is a low resource language and there is no rich semantic benchmark to evaluate word embedding models for this language. In this paper we introduce an evaluation framework including a hand crafted Persian SAT based analogy dataset, a colliquial test set (specific to Persian) and a benchmark to study the impact of various parameters on the semantic evaluation task.
Temperature-dependent equilibration of spin orthogonal quantum Hall edge modes

Tanmay Maiti, ∗ Pooja Agarwal, Suvankar Purkait, G J Sreejith, Sourin Das, Giorgio Biasiol, Lucia Sorba, and Biswajit Karmakar Saha Institute of Nuclear Physics, HBNI, 1/AF Bidhannagar, Kolkata 700064, India Indian Institute of Science Education and Research, Pune 411008, India Department of Physical Sciences, IISER Kolkata, Mohanpur, West Bengal 741246, India Istituto Officina dei Materiali CNR, Laboratorio TASC, 34149 Trieste, Italy NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, I-56127 Pisa, Italy
Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges – the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget

In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios and demonstrate a clear advantage in providing a pareto optimal solution in the epidemic intervention problem. 1
How to Train Your MAML to Excel in Few-Shot Classification

Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind many recent algorithms dedicated to the problem. In this paper, we point out several key facets of how to train MAML to excel in few-shot classification. First, we find that MAML needs a large number of gradient steps in its inner loop update, which contradicts its common usage in few-shot classification. Second, we find that MAML is sensitive to the class label assignments during meta-testing. Concretely, MAML meta-trains the initialization of an $N$-way classifier. These $N$ ways, during meta-testing, then have"$N! $"different permutations to be paired with a few-shot task of $N$ novel classes. We find that these permutations lead to a huge variance of accuracy, making MAML unstable in few-shot classification. Third, we investigate several approaches to make MAML permutation-invariant, among which meta-training a single vector to initialize all the $N$ weight vectors in the classification head performs the best. On benchmark datasets like MiniImageNet and TieredImageNet, our approach, which we name UNICORN-MAML, performs on a par with or even outperforms many recent few-shot classification algorithms, without sacrificing MAML's simplicity.
Leveraging Team Dynamics to Predict Open-source Software Projects' Susceptibility to Social Engineering Attacks

Open-source software (OSS) is a critical part of the software supply chain. Recent social engineering attacks against OSS development teams have enabled attackers to become code contributors and later inject malicious code or vulnerabilities into the project with the goal of compromising dependent software. The attackers have exploited interactions among development team members and the social dynamics of team behavior to enable their attacks. We introduce a security approach that leverages signatures and patterns of team dynamics to predict the susceptibility of a software development team to social engineering attacks that enable access to the OSS project code. The proposed approach is programming language-, platform-, and vulnerability-agnostic because it assesses the artifacts of OSS team interactions, rather than OSS code.
Model-independent test of T violation in neutrino oscillations

We propose a method to establish time reversal symmetry violation at future neutrino oscillation experiments in a largely model-independent way. We introduce a general parametrization of flavour transition probabilities which holds under weak assumptions and covers a large class of new-physics scenarios. This can be used to search for the presence of T-odd components in the transition probabilities by comparing data at different baselines but at the same neutrino energies. We show that this test can be performed already with experiments at three different baselines and might be feasible with experiments under preparation/consideration.
Dual Reweighting Domain Generalization for Face Presentation Attack Detection

Face anti-spoofing approaches based on domain generalization (DG) have drawn growing attention due to their robustness for unseen scenarios. Previous methods treat each sample from multiple domains indiscriminately during the training process, and endeavor to extract a common feature space to improve the generalization. However, due to complex and biased data distribution, directly treating them equally will corrupt the generalization ability. To settle the issue, we propose a novel Dual Reweighting Domain Generalization (DRDG) framework which iteratively reweights the relative importance between samples to further improve the generalization. Concretely, Sample Reweighting Module is first proposed to identify samples with relatively large domain bias, and reduce their impact on the overall optimization. Afterwards, Feature Reweighting Module is introduced to focus on these samples and extract more domain-irrelevant features via a self-distilling mechanism. Combined with the domain discriminator, the iteration of the two modules promotes the extraction of generalized features. Extensive experiments and visualizations are presented to demonstrate the effectiveness and interpretability of our method against the state-of-the-art competitors.
Monte Carlo Variational Auto-Encoders

Variational auto-encoders (VAE) are popular deep latent variable models which are trained by maximizing an Evidence Lower Bound (ELBO). To obtain tighter ELBO and hence better variational approximations, it has been proposed to use importance sampling to get a lower variance estimate of the evidence. However, importance sampling is known to perform poorly in high dimensions. While it has been suggested many times in the literature to use more sophisticated algorithms such as Annealed Importance Sampling (AIS) and its Sequential Importance Sampling (SIS) extensions, the potential benefits brought by these advanced techniques have never been realized for VAE: the AIS estimate cannot be easily differentiated, while SIS requires the specification of carefully chosen backward Markov kernels. In this paper, we address both issues and demonstrate the performance of the resulting Monte Carlo VAEs on a variety of applications.
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach

Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r. t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open datasets neglect the cam-era pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters. The perturbation of objects is very popular in most autonomous driving cases for industrial products. To this end, we propose a novel method to capture camera pose to formulate the detector free from extrinsic perturbation. Specifically, the proposed framework predicts camera extrinsic parameters by detecting vanishing point and horizon change. A converter is designed to rectify perturbative features in the latent space. By doing so, our 3D detector works independent of the extrinsic parameter variations and produces accurate results in realistic cases, e.g., potholed and uneven roads, where almost all existing monocular detectors fail to handle. Experiments demonstrate our method yields the best performance compared with the other state-of-the-arts by a large margin on both KITTI 3D and nuScenes datasets.
Zames-Falb Multipliers: don't panic

Zames-Falb multipliers are mathematical constructs which can be used to prove stability of so-called Lur'e systems: systems that consist of a feedback interconnection of a linear element and a static nonlinear element. The main advantage of Zames-Falb multipliers is that they enable"passivity"-like results to be obtained but with a level of conservatism much lower than \emph{pure} passivity results. However, some of the papers describing the development of the Zames-Falb multiplier machinery are somewhat abstruse and not entirely clear. This article attempts to provide a relatively simple construction of Zames and Falb's main results which will hopefully be understandable to most graduate-level control engineers.
Periodic Event-Triggered Networked Control Systems Subject to Large Transmission Delays

This article studies periodic event-triggered networked control for nonlinear systems, where the plants and controllers are connected by multiple independent communication channels. Several network-induced imperfections are considered simultaneously, including time-varying intersampling times, sensor node scheduling, and especially, large time-varying transmission delays, where the transmitted signal may arrive at the destination node after the next transmission occurs. A new hybrid system approach is provided to model the closed-loop system that contains all communication related behavior. Then, by constructing new storage functions on the system state and updating errors, the relationship between the maximum allowable sampling period and maximum allowable delay number in sampling is analyzed, where the latter denotes how many inter-sampling periods can be included in one transmission delay. Moreover, to efficiently reduce unnecessary transmissions, a new dynamic event-triggered control scheme is proposed, where the event-triggering conditions are detected only at aperiodic and asynchronous sampling instants. From emulation-based method, where the controllers are initially designed by ignoring all the network-induced imperfections, sufficient conditions on the dynamic event-triggered control are given to ensure closed-loop input-to-state stability with respect to external disturbances. Finally, two nonlinear examples are simulated to illustrate the feasibility and efficiency of the theoretical results.
Latent Space Model for Higher-Order Networks and Generalized Tensor Decomposition

Abstract We introduce a unified framework, formulated as general latent space models, to study complex higher-order network interactions among multiple entities. Our framework covers several popular models in recent network analysis literature, including mixture multi-layer latent space model and hypergraph latent space model. We formulate the relationship between the latent positions and the observed data via a generalized multilinear kernel as the link function. While our model enjoys decent generality, its maximum likelihood parameter estimation is also convenient via a generalized tensor decomposition procedure. We propose a novel algorithm using projected gradient descent on Grassmannians. We also develop original theoretical guarantees for our algorithm. First, we show its linear convergence under mild conditions. Second, we establish finite-sample statistical error rates of latent position estimation, determined by the signal strength, degrees of freedom and the smoothness of link function, for both general and specific latent space models. We demonstrate the effectiveness of our method on synthetic data. We also showcase the merit of our method on two real-world datasets that are conventionally described by different specific models in producing meaningful and interpretable parameter estimations and accurate link prediction. Supplementary materials for this article are available online.