Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Recognition under Consideration of the Emotion Component Process Model

Emotion classification in text is typically performed with neural network models which learn to associate linguistic units with emotions. While this often leads to good predictive performance, it does only help to a limited degree to understand how emotions are communicated in various domains. The emotion component process model (CPM) by Scherer (2005) is an interesting approach to explain emotion communication. It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency. We hypothesize that these components are associated with linguistic realizations: an emotion can be expressed by describing a physiological bodily reaction ("he was trembling"), or the expression ("she smiled"), etc. We annotate existing literature and Twitter emotion corpora with emotion component classes and find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader. We further include the CPM in a multitask learning model and find that this supports the emotion categorization. The annotated corpora are available at https://www.ims. uni-stuttgart.de/data/emotion.
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent

Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estimation process, almost all of them require random samples with the same dimension as the global variable and/or require evaluation of the global cost function, which may induce high estimation variance for large-scale networks. In this paper, we propose a novel distributed zeroth-order algorithm by leveraging the network structure inherent in the optimization objective, which allows each agent to estimate its local gradient by local cost evaluation independently, without use of any consensus protocol. The proposed algorithm exhibits an asynchronous update scheme, and is designed for stochastic non-convex optimization with a possibly non-convex feasible domain based on the block coordinate descent method. The algorithm is later employed as a distributed model-free RL algorithm for distributed linear quadratic regulator design, where a learning graph is designed to describe the required interaction relationship among agents in distributed learning. We provide an empirical validation of the proposed algorithm to benchmark its performance on convergence rate and variance against a centralized ZOO algorithm.
Measuring cosmological parameters with a luminosity–time correlation of gamma-ray bursts

Gamma-ray bursts (GRBs), as a possible probe to extend the Hubble diagram to high redshifts, have attracted much attention recently. In this paper, we select two samples of GRBs that have a plateau phase in X-ray afterglow. One is short GRBs with plateau phases dominated by magnetic dipole (MD) radiations. The other is long GRBs with gravitational-wave (GW) dominated plateau phases. These GRBs can be well standardized using the correlation between the plateau luminosity L0 and the end time of plateau tb. The so-called circularity problem is mitigated by using the observational Hubble parameter data and Gaussian process method. The calibrated L0 – tb correlations are also used to constrain ΛCDM and w(z) = w0 models. Combining the MD-LGRBs sample from Wang et al. (2021) and the MD-SGRBs sample, we find Ωm = 0.33 +0.06 −0.09 and ΩΛ = 1.06 +0.15 −0.34 excluding systematic uncertainties in the nonflat ΛCDM model. Adding type Ia supernovae from Pantheon sample, the best-fitting results are w0 = −1.11 −0.15 and Ωm = 0.34 +0.05 −0.04 in the w = w0 model. These results are in agreement with the ΛCDM model. Our result supports that selection of GRBs from the same physical mechanism is crucial for cosmological purposes.
Benchmarking regulator-sourced 2PI and average 1PI flow equations in zero dimensions

We elucidate the regulator-sourced 2PI and average 1PI approaches for deriving exact flow equations in the case of a zero dimensional quantum field theory, wherein the scale dependence of the usual renormalisation group evolution is replaced by a simple parametric dependence. We show that both approaches are self-consistent, while highlighting key differences in their behaviour and the structure of the would-be loop expansion.
Understanding degeneracy of two-point correlation functions via Debye random media.

It is well known that the degeneracy of two-phase microstructures with the same volume fraction and two-point correlation function S_{2}(r) is generally infinite. To elucidate the degeneracy problem explicitly, we examine Debye random media, which are entirely defined by a purely exponentially decaying two-point correlation function S_{2}(r). In this work, we consider three different classes of Debye random media. First, we generate the "most probable" class using the Yeong-Torquato construction algorithm [Yeong and Torquato, Phys. Rev. E 57, 495 (1998)1063-651X10.1103/PhysRevE.57. 495]. A second class of Debye random media is obtained by demonstrating that the corresponding two-point correlation functions are effectively realized in the first three space dimensions by certain models of overlapping, polydisperse spheres. A third class is obtained by using the Yeong-Torquato algorithm to construct Debye random media that are constrained to have an unusual prescribed pore-size probability density function. We structurally discriminate these three classes of Debye random media from one another by ascertaining their other statistical descriptors, including the pore-size, surface correlation, chord-length probability density, and lineal-path functions. We also compare and contrast the percolation thresholds as well as the diffusion and fluid transport properties of these degenerate Debye random media. We find that these three classes of Debye random media are generally distinguished by the aforementioned descriptors, and their microstructures are also visually distinct from one another. Our work further confirms the well-known fact that scattering information is insufficient to determine the effective physical properties of two-phase media. Additionally, our findings demonstrate the importance of the other two-point descriptors considered here in the design of materials with a spectrum of physical properties.
An FPGA cached sparse matrix vector product (SpMV) for unstructured computational fluid dynamics simulations

Field Programmable Gate Arrays generate algorithmic specific architectures that improve the code's FLOP per watt ratio. Such devices are re-gaining interest due to the rise of new tools that facilitate their programming, such as OmpSs. The computational fluid dynamics community is always investigating new architectures that can improve its algorithm's performance. Commonly, those algorithms have a low arithmetic intensity and only reach a small percentage of the peak performance. The sparse matrix-vector multiplication is one of the most time-consuming operations on unstructured simulations. The matrix's sparsity pattern determines the indirect memory accesses of the multiplying vector. This data path is hard to predict, making traditional implementations fail. In this work, we present an FPGA architecture that maximizes the vector's re-usability by introducing a cache-like architecture. The cache is implemented as a circular list that maintains the BRAM vector components while needed. Following this strategy, up to 16 times of acceleration is obtained compared to a naive implementation of the algorithm.
Estimating high-dimensional Markov-switching VARs

Maximum likelihood estimation of large Markov-switching vector autoregressions (MS-VARs) can be challenging or infeasible due to parameter proliferation. To accommodate situations where dimensionality may be of comparable order to or exceeds the sample size, we adopt a sparse framework and propose two penalized maximum likelihood estimators with either the Lasso or the smoothly clipped absolute deviation (SCAD) penalty. We show that both estimators are estimation consistent, while the SCAD estimator also selects relevant parameters with probability approaching one. A modified EM-algorithm is developed for the case of Gaussian errors and simulations show that the algorithm exhibits desirable finite sample performance. In an application to short-horizon return predictability in the US, we estimate a 15 variable 2-state MS-VAR(1) and obtain the often reported counter-cyclicality in predictability. The variable selection property of our estimators helps to identify predictors that contribute strongly to predictability during economic contractions but are otherwise irrelevant in expansions. Furthermore, out-of-sample analyses indicate that large MS-VARs can significantly outperform ”hard-to-beat” predictors like the historical average. JEL Classifications : C13, C32, C55, G12
AGN−Host Interaction in IC 5063. I. Large-scale X-Ray Morphology and Spectral Analysis

We report the analysis of the deep (∼270 ks) X-ray Chandra data of one of the most radio-loud, Seyfert 2 galaxies in the nearby universe (z = 0.01135), IC 5063. The alignment of the radio structure with the galactic disk and ionized bicone, enables us to study the effects of both radio jet and nuclear irradiation on the interstellar medium (ISM). The nuclear and bicone spectra suggest a low photoionization phase mixed with a more ionized or thermal gas component, while the cross-cone spectrum is dominated by shocked and collisionally ionized gas emission. The clumpy morphology of the soft (<3 keV) X-ray emission along the jet trails, and the large (≃2.4 kpc) filamentary structure perpendicular to the radio jets at softer energies (<1.5 keV), suggest a large contribution of the jet−ISM interaction to the circumnuclear gas emission. The hard X-ray continuum (>3 keV) and the Fe Kα 6.4 keV emission are both extended to kpc size along the bicone direction, suggesting an interaction of nuclear photons with dense clouds in the galaxy disk, as observed in other Compton Thick (CT) active nuclei. The northwest cone spectrum also exhibits an Fe xxv emission line, which appears spatially extended and spatially correlated with the most intense radio hot-spot, suggesting jet−ISM interaction.
Energy-based Unknown Intent Detection with Data Manipulation

Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set. In this paper, we propose using energy scores for this task as the energy score is theoretically aligned with the density of the input and can be derived from any classifier. However, high-quality OOD utterances are required during the training stage in order to shape the energy gap between OOD and in-distribution (IND), and these utterances are difficult to collect in practice. To tackle this problem, we propose a data manipulation framework to Generate high-quality OOD utterances with importance weighTs (GOT). Experimental results show that the energy-based detector fine-tuned by GOT can achieve state-of-the-art results on two benchmark datasets.
Inferring Vortex and Dust Devil Statistics from InSight

The InSight mission has operated on the surface of Mars for nearly two Earth years, returning detections of the first marsquakes. The lander also deployed a meteorological instrument package and cameras to monitor local surface activity. These instruments have detected boundary layer phenomena, including small-scale vortices. These vortices register as short-lived, negative pressure excursions and closely resemble those that could generate dust devils. Although our analysis shows that InSight encountered more than 900 vortices and collected more than 1000 images of the Martian surface, no active dust devils were imaged. In spite of the lack of dust devil detections, we can leverage the vortex detections and InSight’s daily wind speed measurements to learn about the boundary layer processes that create dust devils. We discuss our analysis of InSight’s meteorological data to assess the statistics of vortex and dust devil activity. We also infer encounter distances for the vortices and, therefrom, the maximum vortex wind speeds. Surveying the available imagery, we place upper limits on what fraction of vortices carry dust (i.e., how many are bona fide dust devils) and estimate threshold wind speeds for dust lifting. Comparing our results to detections of dust devil tracks seen in space-based observations of the InSight landing site, we can also infer thresholds and frequency of track formation by vortices. Comparing vortex encounters and parameters with advective wind speeds, we find evidence that high wind speeds at InSight may have suppressed the formation of dust devils, explaining the lack of imaged dust devils.
Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.
Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference

In many observational studies in social science and medicine, individuals are connected in ways that affect the adoption and efficacy of interventions. One individual’s treatment and attributes may affect another individual’s treatment and outcome. In particular, this violates the commonly made stable unit treatment value assumption (SUTVA). Interference is often heterogeneous, and an individual’s outcome is not only affected by how many, but also which, neighbors or connections are treated. In this paper, we propose a flexible framework for heterogeneous partial interference that partitions units into subsets based on observables. We allow interactions to be heterogeneous across subsets, but homogeneous for individuals within a subset. In this framework, we propose a class of estimators for heterogeneous direct and spillover effects from observational data that are shown to be doubly robust, asymptotically normal, and semiparametric efficient. In addition, we discuss a bias-variance tradeoff between robustness to heterogeneous interference and estimation efficiency. We further propose consistent matchingbased variance estimators and hypothesis tests to determine the appropriate specification of interference structure. We apply our methods to the Add Health data and find that regular alcohol consumption exhibits negative effects on academic performance, but the magnitude of effect varies by gender and friends’ alcohol consumption.
Subset selection for linear mixed models

Linear mixed models (LMMs) are instrumental for regression analysis with structured dependence, such as grouped, clustered, or multilevel data. However, selection among the covariates—while accounting for this structured dependence—remains a challenge. We introduce a Bayesian decision analysis for subset selection with LMMs. Using a Mahalanobis loss function that incorporates the structured dependence, we derive optimal linear coefficients for (i) any given subset of variables and (ii) all subsets of variables that satisfy a cardinality constraint. Crucially, these estimates inherit shrinkage or regularization and uncertainty quantification from the underlying Bayesian model, and apply for any well‐specified Bayesian LMM. More broadly, our decision analysis strategy deemphasizes the role of a single “best” subset, which is often unstable and limited in its information content, and instead favors a collection of near‐optimal subsets. This collection is summarized by key member subsets and variable‐specific importance metrics. Customized subset search and out‐of‐sample approximation algorithms are provided for more scalable computing. These tools are applied to simulated data and a longitudinal physical activity dataset, and demonstrate excellent prediction, estimation, and selection ability.
Laguerre reconstruction of the BAO feature in halo-based mock galaxy catalogues

Fitting half-integer generalized Laguerre functions to the evolved, real-space dark matter and halo correlation functions provides a simple way to reconstruct their initial shapes. We show that this methodology also works well in a wide variety of realistic, assembly biased, velocity biased and redshift-space distorted mock galaxy catalogs. We use the linear point feature in the monopole of the redshift-space distorted correlation function to quantify the accuracy of our approach. We find that the linear point estimated from the mock galaxy catalogs is insensitive to the details of the biasing scheme at the subpercent level. However, the linear point scale in the nonlinear, biased, and redshiftspace distorted field is systematically offset from its scale in the unbiased linear density fluctuation field by more than 1%. In the Laguerre reconstructed correlation function, this is reduced to subpercent values, so it provides comparable accuracy and precision to methods that reconstruct the full density field before estimating the distance scale. The linear point in the reconstructed density fields provided by these other methods is likewise precise, accurate, and insensitive to galaxy bias. All reconstructions depend on some input parameters, and marginalizing over uncertainties in the input parameters required for reconstruction can degrade both accuracy and precision. The linear point simplifies the marginalization process, enabling more realistic estimates of the precision of the distance scale estimate for negligible additional computational cost. We show this explicitly for Laguerre reconstruction.
Cross-lingual Transferring of Pre-trained Contextualized Language Models

Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant. In this work, building upon the recent works connecting cross-lingual model transferring and neural machine translation, we thus propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate ``TRILayer"structure that learns from these differences and creates a better transfer in our primary translation direction, as well as a new cross-lingual language modeling objective for transfer training. Additionally, we showcase an embedding aligning that adversarially adapts a PrLM's non-contextualized embedding space and the TRILayer structure to learn a text transformation network across languages, which addresses the vocabulary difference between languages. Experiments on both language understanding and structure parsing tasks show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency. Moreover, despite an insignificant performance loss compared to pre-training from scratch in resource-rich scenarios, our cross-lingual model transferring framework is significantly more economical.
Statistical Guarantees for Fairness Aware Plug-In Algorithms

A plug-in algorithm to estimate Bayes Optimal Classifiers for fairness-aware binary classification has been proposed in (Menon&Williamson, 2018). However, the statistical efficacy of their approach has not been established. We prove that the plug-in algorithm is statistically consistent. We also derive finite sample guarantees associated with learning the Bayes Optimal Classifiers via the plug-in algorithm. Finally, we propose a protocol that modifies the plug-in approach, so as to simultaneously guarantee fairness and differential privacy with respect to a binary feature deemed sensitive.
Transfer Learning in Electronic Health Records through Clinical Concept Embedding

Deep learning models have shown tremendous potential in learning representations, which are able to capture some key properties of the data. This makes them great candidates for transfer learning: Exploiting commonalities between different learning tasks to transfer knowledge from one task to another. Electronic health records (EHR) research is one of the domains that has witnessed a growing number of deep learning techniques employed for learning clinically-meaningful representations of medical concepts (such as diseases and medications). Despite this growth, the approaches to benchmark and assess such learned representations (or, embeddings) is under-investigated; this can be a big issue when such embeddings are shared to facilitate transfer learning. In this study, we aim to (1) train some of the most prominent disease embedding techniques on a comprehensive EHR data from 3.1 million patients, (2) employ qualitative and quantitative evaluation techniques to assess these embeddings, and (3) provide pre-trained disease embeddings for transfer learning. This study can be the first comprehensive approach for clinical concept embedding evaluation and can be applied to any embedding techniques and for any EHR concept.
RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly

Autonomous assembly of objects is an essential task in robotics and 3D computer vision. It has been studied extensively in robotics as a problem of motion planning, actuator control and obstacle avoidance. However, the task of developing a generalized framework for assembly robust to structural variants remains relatively unexplored. In this work, we tackle this problem using a recurrent graph learning framework considering inter-part relations and the progressive update of the part pose. Our network can learn more plausible predictions of shape structure by accounting for priorly assembled parts. Compared to the current state-of-the-art, our network yields up to 10% improvement in part accuracy and up to 15% improvement in connectivity accuracy on the PartNet [23] dataset. Moreover, our resulting latent space facilitates exciting applications such as shape recovery from the point-cloud components. We conduct extensive experiments to justify our design choices and demonstrate the effectiveness of the proposed framework.