Miracle-dz/UniFAR-FTUs huggingface dataset:
SEU Mechanisms in Spintronic Devices: Critical Parameters and Basic Effects

This article investigates radiation-induced switching mechanisms, temperature effects, breakdown voltage, sensitive volume, and critical charge definitions for spin-transfer torque magnetic tunnel junctions. The thermal spike model is adopted to estimate the temperature reached during heavy-ion irradiation, and temperature effects are suggested to be responsible for the magnetic properties’ degradation and for upset processes.
Artificial selection for nonreproductive host killing in a native parasitoid on the invasive pest, Drosophila suzukii

Establishment and spread of invasive species can be facilitated by lack of natural enemies in the invaded area. Host‐range evolution of natural enemies augments their ability to reduce the impact of the invader and could enhance their value for biological control. We assessed the potential of the Drosophila parasitoid, Leptopilina heterotoma (Hymenoptera: Figitidae), to exploit the invasive pest Drosophila suzukii by focusing on three performance indices: (i) attack rate; (ii) host killing, consisting of killing rate and lethal attack rate (killing efficiency); and (iii) successful offspring development (reproductive success). We found significant intraspecific variation in attack rate and killing rate and lethal attack rate among seven European populations, but offspring generally failed to successfully develop from the D. suzukii host. We crossed these European lines to create a genetically variable source population and performed a half‐sib analysis to quantify genetic variation. Using a Bayesian animal model, we found that attack rate and killing rate had a heritability of h2=0.2 , lethal attack rate h2=0.4 , and offspring development h2=0 . We then artificially selected wasps with the highest killing rate of D. suzukii for seven generations to test whether host‐killing could be improved. There was a small and inconsistent response to selection in the three selection lines. Realized heritability ( hr2 ) after four generations of selection was 0.17 but near zero after seven generations of selection. The genetic response might have been masked by an increased D. suzukii fitness resulting from adaptation to laboratory conditions. Our study reveals that native, European, L. heterotoma can attack the invasive pest, D. suzukii and significantly reduce fly survival and that different steps of the parasitization process need to be considered in the evolution of host‐range. It highlights how evolutionary principles can be applied to optimize performance of native species for biological control.
The nexus between COVID-19 fear and stock market volatility

Abstract This study described an empirical link between COVID-19 fear and stock market volatility. Studying COVID-19 fear with stock market volatility is crucial for planning adequate portfolio diversification in international financial markets. The study used AR (1) – GARCH (1,1) to measure stock market volatility associated with the COVID-19 pandemic. Our findings suggest that COVID-19 fear is the ultimate cause driving public attention and stock market volatility. The results demonstrate that stock market performance and GDP growth decreased significantly through average increases during the pandemic. Further, with a 1% increase in COVID-19 cases, the stock return and GDP decreased by 0.8%, 0.56%, respectively. However, GDP growth demonstrated a slight movement with stock exchange. Moreover, public attention to the attitude of buying or selling was highly dependent on the COVID-19 pandemic reported cases index, death index, and global fear index. Consequently, investment in the gold market, rather than in the stock market, is recommended. The study also suggests policy implications for key stakeholders.
Modelling of Mg II lines in solar prominences

Context. Observations of the Mg II h and k lines in solar prominences with IRIS reveal a wide range of line shapes from simple non-reversed profiles to typical double-peaked reversed profiles, and with many other possible complex line shapes. The physical conditions responsible for this variety are not well understood. Aims. Our aim is to understand how physical conditions inside a prominence slab influence shapes and properties of emergent Mg II line profiles. Methods. We compute the spectrum of Mg II lines using a one-dimensional non-LTE radiative transfer code for two large grids of model atmospheres (isothermal isobaric, and with a transition region). Results. The influence of the plasma parameters on the emergent spectrum is discussed in detail. Our results agree with previous studies. We present several dependencies between observables and prominence parameters which will help with the interpretation of observations. A comparison with known limits of observed line parameters suggests that most observed prominences emitting in Mg II h and k lines are cold, low-pressure, and optically thick structures. Our results indicate that there are good correlations between the Mg II k line intensities and the intensities of hydrogen lines, and the emission measure. Conclusions. One-dimensional non-LTE radiative transfer codes allow us to understand the main characteristics of the Mg II h and k line profiles in solar prominences, but more advanced codes will be necessary for detailed comparisons.
Ethical leadership, psychological empowerment and caring behavior from the nurses’ perspective

Background Care is the basis of the nursing profession and nurse’s caring behavior is one of the important factors in patient satisfaction. On the other hand, psychological empowerment can improve the provision of care services, and leaders have a significant impact on the behavior of followers. This study determined the correlation between ethical leadership, psychological empowerment, and caring behavior from nurses’ perspective. Methods This cross-sectional descriptive study was conducted in 2019. A total of 200 nurses were selected by stratified random sampling. Data were collected with ethical leadership, caring behavior and psychological empowerment questionnaires and analyzed with descriptive statistics (mean and standard deviation, frequency distribution) and inferential statistics (Pearson correlation coefficient, regression, and t-test) using SPSS20. Results The findings showed a significant correlation between ethical leadership, psychological empowerment (P < 0.001, r = 0.24) and caring behavior of nurses (P < 0.001, r = 0.25). Also, there was a significant correlation between psychological empowerment and nurses’ caring behavior (P = 0.002, r = 0.21). The regression analysis showed that ethical leadership and psychological empowerment are a predictor of caring behavior of nurses. Conclusion Applying an ethical leadership style by nursing managers can lead to nurses’ improved psychological empowerment and caring behaviors and ultimately, patient care quality. Thus, considering guidelines at the team and organizational levels and programs such as providing workshops and periodic counseling to cultivate ethical nurse leadership can lead to better caring behavior.
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior knowledge into DRL algorithms is one way to improve learning efficiency since it helps to build helpful representations. In this work, we consider incorporating human knowledge to accelerate the asynchronous advantage actor-critic (A3C) algorithm by pre-training a small amount of non-expert human demonstrations. We leverage the supervised autoencoder framework and propose a novel pre-training strategy that jointly trains a weighted supervised classification loss, an unsupervised reconstruction loss, and an expected return loss. The resulting pre-trained model learns more useful features compared to independently training in supervised or unsupervised fashion. Our pre-training method drastically improved the learning performance of the A3C agent in Atari games of Pong and MsPacman, exceeding the performance of the state-of-the-art algorithms at a much smaller number of game interactions. Our method is light-weight and easy to implement in a single machine. For reproducibility, our code is available at this http URL
Multi-Task Learning for Japanese Predicate Argument Structure Analysis

An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.
Evaluating Style Transfer for Text

Research in the area of style transfer for text is currently bottlenecked by a lack of standard evaluation practices. This paper aims to alleviate this issue by experimentally identifying best practices with a Yelp sentiment dataset. We specify three aspects of interest (style transfer intensity, content preservation, and naturalness) and show how to obtain more reliable measures of them from human evaluation than in previous work. We propose a set of metrics for automated evaluation and demonstrate that they are more strongly correlated and in agreement with human judgment: direction-corrected Earth Mover’s Distance, Word Mover’s Distance on style-masked texts, and adversarial classification for the respective aspects. We also show that the three examined models exhibit tradeoffs between aspects of interest, demonstrating the importance of evaluating style transfer models at specific points of their tradeoff plots. We release software with our evaluation metrics to facilitate research.
Dialogue Act Classification with Context-Aware Self-Attention

Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.
Solvable Systems Featuring 2 Dependent Variables Evolving in Discrete-Time via 2 Nonlinearly-Coupled First-Order Recursion Relations with Polynomial Right-Hand Sides

The evolution equations mentioned in the title of this paper read as follows: where ℓ is the “discrete-time” independent variable taking integer values (ℓ = 0, 1, 2, … ), xn ≡ xn(ℓ) are the 2 dependent variables, , and the 2 functions P(n)(x1, x2), n = 1, 2, are 2 polynomials in the 2 dependent variables x1(ℓ) and x2(ℓ). The results reported in this paper have been obtained by an appropriate modification of a recently introduced technique to obtain analogous results in continuous-time t—in which case xn ≡ xn(t) and the above recursion relations are replaced by first-order ODEs. Their potential interest is due to the relevance of this kind of evolution equations in various applicative contexts.
Estimating 3D Motion and Forces of Person-Object Interactions From Monocular Video

In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person and the object, contact positions, and forces and torques actuated by the human limbs. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of their interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization. Third, we validate our approach on a recent MoCap dataset with ground truth contact forces and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.
Optimal policies for free recall.

There is rich structure in the order in which studied material is recalled in a free recall task (Howard & Kahana, 2002a). Extensive effort has been directed at understanding the processes and representations that give rise to this structure; however, it remains unclear why certain types of recall organization might be favored in the first place. We provide a rational analysis of the free recall task, deriving the optimal policy for recalling items under the internal representations and processes described by the context maintenance and retrieval (CMR) model of memory search (Polyn et al., 2009a). Our model, which we call rational-CMR, shows that the optimal policy for free recall is to start from the beginning of the list and then sequentially recall forwards, providing a rational account of the primacy and forward asymmetry effects typically observed in free recall. In addition, when recall is not initiated from the beginning of list, it is optimal during recall transitions to minimize the amount of forward asymmetry. Predictions from the rational model are confirmed in human behavioral data: Top-performing human participants demonstrate a stronger tendency to initiate recall from the beginning of the list and carry forward recalls, and the amount of forward asymmetry in participants depends on whether they start recall from the beginning or end of the list. We discuss the resemblance of optimal behavior in free recall to participants' behavior when applying mnemonic techniques such as the method of loci. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Burrowing Parrots Cyanoliseus patagonus as Long-Distance Seed Dispersers of Keystone Algarrobos, Genus Prosopis, in the Monte Desert

Understanding of ecosystem structure and functioning requires detailed knowledge about plant–animal interactions, especially when keystone species are involved. The recent consideration of parrots as legitimate seed dispersers has widened the range of mechanisms influencing the life cycle of many plant species. We examined the interactions between the burrowing parrot Cyanoliseus patagonus and two dominant algarrobo trees (Prosopis alba and Prosopis nigra) in the Monte Desert, Argentina. We recorded the abundance and foraging behaviour of parrots; quantified the handling, consumption, wasting, and dispersal of ripe and unripe pods; and tested the viability of soft and hard ripe seeds wasted and transported by parrots. We found a high abundance of burrowing parrots. They predated on soft seeds from unripe pods while exclusively feeding upon pulp wrapping hard seeds from ripe pods. Frequent pod wasting beneath the plant or transport at a distance invariably implied the dispersal of multiple seeds in each event. Moreover, soft seeds retained viability after desiccation outside the mother plant, suggesting effective seed dispersal after partial pod predation due to a predator satiation effect. In about half of the foraging flocks, at least one parrot departed in flight with pods in its beak, with 10–34% of the flock components moving pods at distances averaging 238 m (P. alba) and 418 m (P. nigra). A snapshot sampling of faeces from livestock and wild mammals suggested a low frequency of seed dispersal by endozoochory and secondary dispersal by ants and dung beetles. The nomadic movements and long flights of burrowing parrots between breeding and foraging sites can lead to the dispersal of huge amounts of seeds across large areas that are sequentially exploited. Further research should evaluate the role of the burrowing parrot as a functionally unique species in the structure of the Monte Desert woods and the genetic structure of algarrobo species.
On the Relationship between ANOVA Main Effects and Average Treatment Effects

Abstract We adopt a causal inference perspective to shed light into which ANOVA type of sums of squares (SS) should be used for testing main effects and whether main effects should be considered at all in the presence of interactions. We consider balanced, proportional and nonorthogonal designs, and models with and without interactions. When the design is balanced, we show that the average treatment effect is estimated by the main effects obtained by type I, II, and III sums of squares. In proportional designs, we show that the average treatment effect is estimated by the the type I and type II main effects, whereas type III SS yield biased estimates of the average treatment effect if there are interactions. When the design is nonorthogonal, ANOVA type I is always highly biased and ANOVA type II and III main effects are biased if there are interactions. We include a simulation study to illustrate the magnitude of the bias in estimating the average treatment effect across a variety of conditions, and provide recommendations for applied researchers.
Meteorologically Stratified Gust Factors for Forecasting Peak Wind Gusts Across the United States

Wind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin-Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed- and wind direction-stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.
UvA-DARE (Digital Academic Repository) Learning outside the box: Discourse-level features improve metaphor identification

Most current approaches to metaphor identi-ﬁcation use restricted linguistic contexts, e.g. by considering only a verb’s arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identiﬁcation. We train simple gradient boosting classiﬁers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor iden-tiﬁcation task without the complex metaphor-speciﬁc features or deep neural architectures employed by other systems. A qualitative analysis further conﬁrms the need for broader context in metaphor processing.
Investigation of the Pressure Gain Characteristics and Cycle Performance in Gas Turbines Based on Interstage Bleeding Rotating Detonation Combustion

To further improve the cycle performance of gas turbines, a gas turbine cycle model based on interstage bleeding rotating detonation combustion was established using methane as fuel. Combined with a series of two-dimensional numerical simulations of a rotating detonation combustor (RDC) and calculations of cycle parameters, the pressure gain characteristics and cycle performance were investigated at different compressor pressure ratios in the study. The results showed that pressure gain characteristic of interstage bleeding RDC contributed to an obvious performance improvement in the rotating detonation gas turbine cycle compared with the conventional gas turbine cycle. The decrease of compressor pressure ratio had a positive influence on the performance improvement in the rotating detonation gas turbine cycle. With the decrease of compressor pressure ratio, the pressurization ratio of the RDC increased and finally made the power generation and cycle efficiency enhancement rates display uptrends. Under the calculated conditions, the pressurization ratios of RDC were all higher than 1.77, the decreases of turbine inlet total temperature were all more than 19 K, the power generation enhancements were all beyond 400 kW and the cycle efficiency enhancement rates were all greater than 6.72%.
Transverse instability and disintegration of a domain wall of a relative phase in coherently coupled two-component Bose-Einstein condensates

We study transverse instability and disintegration dynamics of a domain wall of a relative phase in two-component Bose-Einstein condensates with a coherent Rabi coupling. We obtain analytically the stability phase diagram of the stationary solution of the domain wall for the one-dimensional coupled Gross-Pitaevskii equations in the plane of the Rabi frequency and the intercomponent coupling constant. Outside the stable region, the domain wall is dynamically unstable for the transverse modulation along the direction perpendicular to the phase kink. The nonlinear evolution associated with the instability is demonstrated through numerical simulations for both the domain wall without edges and that with edges formed by the quantized vortices.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings

We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms “terrorist” and “crazy”, that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them.