Miracle-dz/UniFAR-FTUs huggingface dataset:
Subjective Expectations and Uncertainty

The notion of uncertainty is intimately related to people's expectations about the future. Therefore, in the recent paper we set out a novel theory of subjective expectations – which attempts to reflect well - known behavioral features of the process of expectation formation – and apply it to assess macroeconomic uncertainty. Using the proposed theoretical framework, we construct a novel uncertainty index based on forecast revisions of professional forecasters. In the empirical part of the study, we calculate this index for the US economy and compare it with the existing alternatives in terms of their performance and their impact on the economy.
Thermodynamics and kinetics of room-temperature microstructural evolution in copper films

We studied the energetics and kinetics of microstructural evolution in copper films by estimating the magnitude of various possible driving forces for microstructural change that can be present in the as-deposited film. A driving force of at least 100 J/cm3 is required to account for the speed at which the grain boundaries move at room temperature. This value cannot be accounted for by only considering the minimization of grain-boundary energy and possible effects related to surface and strain energy as the main driving mechanisms. It is suggested that the additional driving force which is needed to explain the speed at which the grains recrystallize, is originating from a high density of defects within the as-deposited grains.
Dexterity Measures for the Design and Control of Kinematically Redundant Manipulators

In this paper, we have proposed a number of measures for the quantification of dexterity of manipulators. The use of such measures is especially important for kinematically redundant manipulators since they can satisfy secondary cri teria in addition to satisfying a specification of end-effector motion. We will compare several measures for the problems offinding an optimal configuration for a given end-effector position, finding an optimal workpoint, and designing the op timal link lengths of an arm.
Personality and Individual

A robust literature demonstrates that psychopathology and personality pathology are well-represented within quantitatively-derived, hierarchical dimensional models. Nevertheless, the location of core traits comprising psychopathic personality (psychopathy) as defined by the triarchic model has not been clearly explicated. We extended hierarchical structural models of personality pathology to include triarchic psychopathy trait dimensions ( boldness, meanness, disinhibition ) to interface the hierarchical framework of pathological personality dimensions with basic psychopathy trait dimensions. Using data from a racially diverse undergraduate sample ( N = 749), “bass-ackwards” analyses revealed a coherently organized hierarchical structure of personality pathology. Psychopathy dimensions were clearly situated within levels of the hierarchy extending beyond the highest, undifferentiated general-factor level. A broad externalizing factor emerged at level 2, which bifurcated into callous-disinhibition and fearless dominance subfactors at level 3 – encompassing psychopathic traits of meanness and disinhibition (thought to represent the trait commonality between antisocial personality disorder and psychopathy) and boldness (thought to differentiate psychopathy from antisocial personality disorder), respectively, at the final two levels of the hierarchy. These results position triarchic psychopathy traits within an extended externalizing spectrum that accommodates boldness-related content.
Tomographic processing on wireless ground sensor networks

New opportunities for battlefield surveillance and modeling are unfolding with the advent of smart sensors linked via digital wireless networks. One exciting prospect is the use of tomographic techniques in order to create real-time three-dimensional modeling and analysis of the environment that is immediately accessible to battlefield forces. We have developed a small-scale ground sensor network for this application. We discuss initial deployment of this network as a tracking system.
Regularized image up-sampling

This thesis addresses the problem of performing image magnification to achieve higher perceived resolution for grey-scale and color images. A new perspective on the problem is introduced through the new concept of a theoretical camera that can acquire an ideal high resolution image. A new formulation of the problem is then introduced using two ingredients: a newly designed observation model and the total-variation regularizer. An observation model, that establishes a generalized relation between the desired magnified image and the measured lower resolution image, has been newly designed based on careful study of the physical acquisition processes that have generated the images. The result is a major contribution of this thesis: a closed-form solution for obtaining the observation model. This closed form has been implemented and observation models were obtained for different typical scenarios, and their performance was shown to outperform observation models used in the literature. Two new theorems for designing the theoretical camera, adapted to the display device used, on arbitrary lattices have been developed. The thesis presents new analysis with a signal processing perspective that justifies the use of the total-variation regularizer as a priori knowledge for the magnified image; this analysis is defined on both the low and the high resolution lattices simultaneously. The resulting objective function has been minimized numerically using the level-set method with two new motions that interact simultaneously, leading to a solution scheme that is not trapped in constant-image solutions and converges to a unique solution regardless of the initial estimate. For color images, the human visual system characteristics were involved in the choice of the color space used in the implementation. It was found that a proper color space such as YCbCr that focuses on magnifying a better luminance channel provided the same result as a vectorial total-variation formulation, but at a reduced computational cost. The quality of the magnified images obtained by the new approaches of this thesis surpassed the quality of state-of-the-art methods from the literature.
s Changing Epidemiology, Diagnostic and Therapeutic Challenges, and Areas for Future Research

The multicenter Centers for Disease Control and Prevention (CDC) Etiology of Pneumonia in the Community (EPIC) Study was a prospective, population-based surveillance study of greater than 2300 pediatric CAP hospitalizations in the United States conducted from 2010 to 2012. 4 This study used serology and nasopharyngeal PCR to identify 8 different viruses, culture-based methods and whole-blood PCR (pneumo-coccal lyt-A ) to identify typical bacteria, and nasopharyngeal PCR to identify atypical bacterial pathogens.
Model-Driven Approach to Blockchain-Enabled MLOps

—In recent years, machine learning has reached quite sophisticated level of usability within applications across various domains – ranging from booking reservations and media content delivery to business and healthcare. However, the deployment of machine learning models, together with parameter tuning and periodic training, which are necessary to maintain satisfiable performance, represent time consuming processes, requiring various types of skills - both DevOps and data analysis-related. In this paper, we leverage model-driven approach in synergy with code generation with aim to automatize the so-called MLOps activities, relying on ZenML framework for pipeline automation and Kubernetes for containerized task orchestration. On top of that, we leverage Blockchain for infrastructure provisioning. Our goal is to reduce the cognitive load of infrastructure and services management within systems relying on machine learning. The framework is evaluated in scenarios using PyTorch-based deep learning predictive models. According to the results, the proposed approach reduces both the time and skill required for successful MLOps activities.
Free vibrations of solid and hollow wedge beams with rectangular or circular cross‐sections and carrying any number of point masses

Since the literature relating to the natural frequencies and mode shapes of the double‐tapered wedge beams carrying multiple point masses is rare, the object of this paper is to present some information in this aspect. First of all, the closed‐form solutions in terms of the Bessel functions for the natural frequencies and normal mode shapes of the ‘bare’ wedge beams (without carrying any point masses) were determined. Next, the partial differential equation of motion for the ‘loading’ wedge beams (carrying any number of point masses) were transformed into the matrix equation by using the expansion theorem and the foregoing natural frequencies and normal mode shapes of the ‘bare’ wedge beam. Finally, the eigenvalue equation associated with the last matrix equation was solved to give the natural frequencies and the mode shapes of the ‘loading’ wedge beams. The formulation of this paper is available for the solid and hollow wedge beams with square, rectangular or circular cross sections. In other words, the taper ratio for the width and that for the depth may be equal or unequal. All the numerical results were compared with the existing literature or the conventional finite element method results and good agreement was achieved. Copyright © 2004 John Wiley & Sons, Ltd.
Forgetful Forests: high performance learning data structures for streaming data under concept drift

Database research can help machine learning performance in many ways. One way is to design better data structures. This paper combines the use of incremental computation and sequential and probabilistic filtering to enable"forgetful"tree-based learning algorithms to cope with concept drift data (i.e., data whose function from input to classification changes over time). The forgetful algorithms described in this paper achieve high time performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with at most a 2% loss of accuracy, or at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications.
Identifying AGN host galaxies with convolutional neural networks

Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN requires time-intensive observations. We train a convolutional neural network (CNN) to distinguish AGN host galaxies from non-active galaxies using a sample of 210,000 Sloan Digital Sky Survey galaxies. We evaluate the CNN on 33,000 galaxies that are spectrally classiﬁed as composites, and ﬁnd correlations between galaxy appearances and their CNN classiﬁcations, which hint at evolutionary processes that effect both galaxy morphology and AGN activity. With the advent of the Vera C. Rubin Observatory, Nancy Grace Roman Space Telescope , and other wide-ﬁeld imaging telescopes, deep learning methods will be instrumental for quickly and reliably shortlisting AGN samples for future analyses.
Improving fast-slow Encoder based Transducer with Streaming Deliberation

This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of the fast-slow encoder based transducer while keeping its latency low by integrating a streaming deliberation model. Specifically, the deliberation model leverages partial hypotheses from the streaming fast encoder and implicitly learns to correct recognition errors. We modify the parallel beam search algorithm for fast-slow encoder based transducer to be efficient and compatible with the deliberation model. In addition, the deliberation model is designed to process streaming data. To further improve the deliberation performance, a simple text augmentation approach is explored. We also compare LSTM and Conformer models for encoding partial hypotheses. Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer. The deliberation model also reduces rare WERs by 3-7% on large-scale in-house data. Compared with vanilla neural transducers, the proposed deliberation model together with fast-slow encoder based transducer obtains relative 10-11% WERRs on Librispeech and around relative 6% WERR on in-house data with smaller emission delays.
Topological Data Analysis Detects Percolation Thresholds in Arctic Melt-Pond Evolution

During the summer melt period, ponds form on the surface of Arctic sea ice as it melts, with important consequences for ice evolution and marine ecology. Due to the ice-albedo feedback, these melt ponds experience uneven heat absorption, and exhibit complex patterns, which has motivated the development of modelling and data analysis to understand their particular dynamics. We provide a multiscale shape analysis using tools from computational algebraic topology, simultaneously capturing convexity, proximity, integrity, and feature size complementing existing single-scale quantification. Of particular interest in modelling the ponds is a percolation threshold at which local pond structure begins merging into macroscopic features. This percolation threshold has previously been observed using fractal dimension techniques. The signed Euclidean distance transform (SEDT) is a topological encoding of heterogeneous shape in binary images, and has been previously applied to porous media for percolation as well as other material behaviours. Here we adapt the SEDT for Arctic melt pond data to give a rich characterization and computation of shape, quantifying overall melt pond development in several complementary ways, and from which classical percolation and dimension results can be extracted. This orientation-invariant topological approach distinguishes different dynamical network models of melt pond evolution of varying complexity.
Let's consider more general nonlinear approaches to study teleconnections of climate variables

The recent work by (Rieger et al 2021) is concerned with the problem of extracting features from spatio-temporal geophysical signals. The authors introduce the complex rotated MCA (xMCA) to deal with lagged effects and non-orthogonality of the feature representation. This method essentially (1) transforms the signals to a complex plane with the Hilbert transform; (2) applies an oblique (Varimax and Promax) rotation to remove the orthogonality constraint; and (3) performs the eigendecomposition in this complex space (Horel et al, 1984). We argue that this method is essentially a particular case of the method called rotated complex kernel principal component analysis (ROCK-PCA) introduced in (Bueso et al., 2019, 2020), where we proposed the same approach: first transform the data to the complex plane with the Hilbert transform and then apply the varimax rotation, with the only difference that the eigendecomposition is performed in the dual (kernel) Hilbert space. The latter allows us to generalize the xMCA solution by extracting nonlinear (curvilinear) features when nonlinear kernel functions are used. Hence, the solution of xMCA boils down to ROCK-PCA when the inner product is computed in the input data space instead of in the high-dimensional (possibly infinite) kernel Hilbert space to which data has been mapped. In this short correspondence we show theoretical proof that xMCA is a special case of ROCK-PCA and provide quantitative evidence that more expressive and informative features can be extracted when working with kernels; results of the decomposition of global sea surface temperature (SST) fields are shown to illustrate the capabilities of ROCK-PCA to cope with nonlinear processes, unlike xMCA.
Exponential l2 − l∞ filtering for discrete‐time switched systems under a new framework

In this paper, a robust exponential l2 − l∞ filtering problem is addressed for discrete‐time switched systems with polytopic uncertainties. The purpose of robust exponential l2 − l∞ filtering is to design a filter such that the resulting filtering error system is robustly exponentially stable with a decay rate and a prescribed exponential l2 − l∞ performance index. The robust exponential l2 − l∞ filtering problem is solved via an average dwell time approach. Sufficient conditions in terms of strict LMI are derived for checking the robust exponential stability of a filter. An explicit expression for the desired robust exponential filter is also given. Finally, a numerical example is provided to demonstrate the potential and effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.
Using Two Losses and Two Datasets Simultaneously to Improve TempoWiC Accuracy

WSD (Word Sense Disambiguation) is the task of identifying which sense of a word is meant in a sentence or other segment of text. Researchers have worked on this task (e.g. Pustejovsky, 2002) for years but it’s still a challenging one even for SOTA (state-of-the-art) LMs (language models). The new dataset, TempoWiC introduced by Loureiro et al. (2022b) focuses on the fact that words change over time. Their best baseline achieves 70.33% macro-F1. In this work, we use two different losses simultaneously. We also improve our model by using another similar dataset to generalize better. Our best configuration beats their best baseline by 4.23%.
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management

In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand. In our setting, the constraint on the shared resources (such as the inventory capacity) couples the otherwise independent control for each SKU. We formulate the problem with this structure as Shared-Resource Stochastic Game (SRSG)and propose an efficient algorithm called Context-aware Decentralized PPO (CD-PPO). Through extensive experiments, we demonstrate that CD-PPO can accelerate the learning procedure compared with standard MARL algorithms.
Convergent Data-driven Regularizations for CT Reconstruction

The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography (CT). As the (naïve) solution does not depend on the measured data continuously, regularization is needed to reestablish a continuous dependence. In this work, we investigate simple, but yet still provably convergent approaches to learning linear regularization methods from data. More specifically, we analyze two approaches: one generic linear regularization that learns how to manipulate the singular values of the linear operator in an extension of our previous work, and one tailored approach in the Fourier domain that is specific to CT-reconstruction. We prove that such approaches become convergent regularization methods as well as the fact that the reconstructions they provide are typically much smoother than the training data they were trained on. Finally, we compare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically, discuss their advantages and disadvantages and investigate the effect of discretization errors at different resolutions.
Radiative lifetimes of singly ionized cerium

Radiative lifetimes accurate to ±5% have been measured for 74 levels in Ce II using time-resolved laser-induced fluorescence on a slow beam of cerium ions. The 17 odd-parity and 57 even-parity levels studied here lie in the energy range 24 000–36 000 cm−1. This new set of lifetimes in Ce II is substantially more extensive than previously published sets, to which a detailed comparison is made. The present lifetime results will provide the absolute calibration for a very large set of measured transition probabilities for Ce II. These are needed for research in astrophysics and lighting.
Exact fixed-radius nearest neighbor search with an application to clustering

Fixed-radius nearest-neighbor search is a common database operation that retrieves all data points within a user-speciﬁed distance to a query point. There are efﬁcient approximate nearest neighbor search algorithms that provide fast query responses but they often have a very compute-intensive indexing phase and require parameter tuning. Therefore, exact brute force and tree-based search methods are still widely used. Here we propose a new ﬁxed-radius nearest neighbor search method that signiﬁcantly improves over brute force and tree-based methods in terms of index and query time, returns exact results, and requires no parameter tuning. The method exploits a sorting of the data points by their ﬁrst principal component, thereby facilitating a reduction in query search space. Further speedup is gained from an efﬁcient implementation using high-level Basic Linear Algebra Subprograms (BLAS). We provide theoretical analysis of our method and demonstrate its practical performance when used stand-alone and when applied within a clustering algorithm.
ɛ-inclusion: privacy preserving re-publication of dynamic datasets

This paper presents a novel privacy principle, ɛ-inclusion, for re-publishing sensitive dynamic datasets. ɛ-inclusion releases all the quasi-identifier values directly and uses permutation-based method and substitution to anonymize the microdata. Combined with generalization-based methods, ɛ-inclusion protects privacy and captures a large amount of correlation in the microdata. We develop an effective algorithm for computing anonymized tables that obey the ɛ-inclusion privacy requirement. Extensive experiments confirm that our solution allows significantly more effective data analysis than generalization-based methods.
Rollout Algorithms and Approximate Dynamic Programming for Bayesian Optimization and Sequential Estimation

We provide a unifying approximate dynamic programming framework that applies to a broad variety of problems involving sequential estimation. We consider first the construction of surrogate cost functions for the purposes of optimization, and we focus on the special case of Bayesian optimization, using the rollout algorithm and some of its variations. We then discuss the more general case of sequential estimation of a random vector using optimal measurement selection, and its application to problems of stochastic and adaptive control. We distinguish between adaptive control of deterministic and stochastic systems: the former are better suited for the use of rollout, while the latter are well suited for the use of rollout with certainty equivalence approximations. As an example of the deterministic case, we discuss sequential decoding problems, and a rollout algorithm for the approximate solution of the Wordle and Mastermind puzzles, recently developed in the paper [BBB22].