Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Social Motives on Bilateral Negotiations: Can Power Change Perceptions of Fairness?

Power, a fundamental characteristic of social interactions, characterizes one’s ability to influence others. Fairness, inherently a type of social preference, impacts distributive decision-making. How does power shape the perceptions of fairness in economic interactions? While previous research finds that power holders tend to take more, it remains unclear whether they are driven by selfish motives to exploit weaker counterparts or act upon the belief that powerful individuals deserve more. With an innovative modified ultimatum game, we analytically and experimentally study how bargaining power interplays with fairness consideration to affect bilateral negotiations. To separate strategic concerns from social motives, we concentrate on behaviors by the responder, whose fairness preferences are elicited in response to shifts in power. We find strong evidence that changes in power can modify what is perceived as a fair outcome. However, such an effect does not arise intrinsically. Instead, it is driven by adaptive behaviors, and more specifically, the experience of interacting with a player who has incentives to respond strategically to power changes. Furthermore, we identify that common knowledge about the relative power of players involved in a relationship is necessary for power to influence distributive behaviors.
Energy Storage and Allocation During Reproduction of Pacific Winged Pearl Oyster Pteria sterna at Bahía de la Paz, Baja California Sur, Mexico

Abstract Seasonal variations in storage, partitioning, and allocation of energy reserves (proteins, carbohydrates, lipids, and triglycerides) between germinal and somatic tissues (gonad, digestive gland, mantle tissue, and adductor muscle), were investigated related to reproduction of Pacific winged pearl oyster Pteria sterna. Tissue samples were collected every three months and analyzed with histological and biochemical techniques. Energy coefficients were also calculated with data from chemical composition of tissues. Gonad samples in almost all developmental stages occurred throughout the year, suggesting that P. sterna is a multispawning species. The evidence indicates that the main reproductive season runs from January through April (21°C to 22°C) and was identified by higher frequency of ripe gonads, more and larger postvitellogenic oocytes and higher protein, lipid, and triglyceride levels in gonad tissue. Within this study period, there were two spawning peaks, July 2003 and January 2004. Gametogenesis was sustained from energy mainly obtained from the digestive gland and secondly from the adductor muscle. Only proteins from these two tissues were mobilized to the gonad for maturation of sex organs, because carbohydrates were stored despite the progress of gametogenesis. The role of mantle tissue was negligible. P. sterna appears to use a combination of stored reserves (conservative strategy) and food supply (opportunistic strategy) as an overall strategy to regulate reproduction.
Important Queries for the Airway Analysis in CBCT Scans: Threshold Tool and Voxel Size Protocol

Introduction: The purpose of this study was to analyze the threshold tool presented in post-processing software for airway volume estimation and the influence of voxel size in these measurements. 316-selected CBCT scans (0.2, 0, 25 and 0.4 voxel sizes) were retrospectively analyzed. A trained and calibrated examiner performed the volume measurements in specific sites in upper airway at 25 and chosen threshold tool using the Dolphin Software. ANOVA was used to compare the thresholds values for each voxel group and the differences of means between the preset and the chosen thresholds, while paired samples t-Test to compare differences between the chosen thresholds for voxel size groups. Results & Discussions: The threshold values range from 26 to 43. The mean of the threshold tool value selected for voxel 0.4 was significantly lower than the mean thresholds of voxel 0.2mm to 0.25mm. As expected, small volumes were obtained with the preset threshold tool for all voxel sizes groups when compared with the chosen threshold. The mean of differences in volumes between preset and chosen threshold decreased with the increase of voxel size. The voxel size protocol influenced the threshold value choice for volume measurements in upper airway analysis. Using Dolphin Software, the thresholds near to 30 seems better filling the airway space. Thus, more studies must be performed to define scan protocols and thresholds parameters for airway evaluation.
Efficacy of Chemical and Biological Agents to Suppress Fusarium and Pythium Damping-Off of Container-Grown Douglas-fir Seedlings

Douglas-fir seedlings are susceptible to Fusarium or Pythium damping-off that currently is controlled by pre-plant soil fumigation in bareroot nurseries and steam pasteurization or chemical drenches of soilless media in container nurseries. However, because few chemical or biological agents are registered for use on conifer seedlings, we tested several on greenhouse-grown seedlings and found that over-seed applications of Cleary’s 3336, Strike, Compass, Compass + Strike, Cygnus, Endura, Medallion, Medallion + Strike, Thiram, and Enzone effectively suppressed pre-emergence damping-off by Fusarium oxysporum, but only Cleary’s 3336WP and Medallion + Strike also reduced post-emergence damping-off. Compass, Medallion, and Thiram reduced post-emergence dampingoff, but not to a statistically significant level. Pre-emergence damping-off by Pythium irregulare was reduced only by Ranman, but post-emergence dampingoff was reduced by Thiram, Hurricane, Ranman, and Subdue MAXX. Over-seed drenches of biological control agents Companion, Kodiak, Subtilex, Taegro, Primastop, SoilGard, Actinovate, Mycostop, RootShield, and Green-Releaf were ineffective in suppressing either Fusarium or Pythium diseases, and combining several agents with chemicals did not improve efficacy. These results provide data in support of registration of some of the effective chemicals, but no biological control agents, for the control of conifer seedling damping-off.
Short Communication Functional diversity in a marine fish community

Calculations of fluxes in an end-to-end foodweb linear steady-state budget model are used to estimate constraints on the Georges Bank fish community that operate through decade-scale changes in nutrient input and fishing effort. The effects of these constraints are described in terms of fish functional groups and species diversity, and compensatory responses in fish community structure are considered. It is concluded that the overall fish community, rather than individual species, is resilient to external stresses imposed by combined fishing pressure and food limitation.
Stability and sensitivity analysis of the epidemiological model Be-CoDiS predicting the spread of human diseases between countries

The Ebola virus disease is a lethal human and primate disease that requires a particular attention from the international health authorities due to important recent outbreaks in some Western African countries and isolated cases in Europe and North-America. Regarding the emergency of this situation, various decision tools, such as mathematical models, were developed to assist the authorities to focus their efforts in important factors to eradicate Ebola. In a previous work, we proposed an original deterministic spatial-temporal model, called Be-CoDiS (Between-Countries Disease Spread), to study the evolution of human diseases within and between countries by taking into consideration the movement of people between geographical areas. This model was validated by considering numerical experiments regarding the 2014-16 West African Ebola Virus Disease epidemic. In this article, we perform a stability analysis of Be-CoDiS. Our first objective is to study the equilibrium states of simplified versions of this model, limited to the cases of one or two countries, and determine their basic reproduction ratios. Then, we perform a sensitivity analysis of those basic reproduction ratios regarding the model parameters. Finally, we validate the results by considering numerical experiments based on data from the 2014-16 West African Ebola Virus Disease epidemic. For more information see https://ejde.math.txstate.edu/Volumes/2020/62/abstr.html
Impact of Personality Traits on Conflict Management and Leadership Styles

The workforce of the organization plays an important role in achieving the overall objective of the organization. It becomes vital for the organizations to make their employees competent and proficient by providing them suitable opportunities such as healthy work environment, positive motivational drives, effective leadership, proper grievance handling procedure and conflict resolution measures. An effort has been made to study the personality of the employees that can affect the organizational variables such as leadership styles and conflict handling styles. Thus, the purpose of this study is to examine the impact of personality traits on conflict management and leadership styles in the context of insurance sector in Punjab. The data were collected from 500 respondents on the basis of purposive non random sampling technique. Out of the questionnaires distributed, 477 sets of questionnaires were found fit for analysis where the other were not included in the analysis because of lack of proper information. The data were analyzed by using simple percentages, Confirmatory Factor Analysis (CFA), Karl Pearson’s correlation and regression analysis by applying MSExcel (2007), SPSS (21.0) and the AMOS (21.0). The result findings concluded that there exists a positive and significant correlation between leadership styles and conflict management styles with all dimensions of personality, which signified that Transformational leaders and Transactional Leaders tended to be more emotionally stable, attached, welcome the new ideas and suggestions of the employees. Moreover, the impact of all the five personality traits on conflict management styles is statistically significant. Whereas, leadership style was determined in a positive manner by four dimensions of personality, such as extraversion, agreeableness, conscientiousness and openness to experience; the impact of neuroticism comes out to be insignificant.
Identification and characterization of Potato Spindle Tuber Viroid infecting tomato in Italy

Potato spindle tuber viroid (PSTVd), a quarantine plant pathogen in Europe, was detected in tomato plants in northern Italy. Infected plants, showing shortened internodes, deformed and necrotic leaves, and abnormal fruit maturation, were growing close to symptomless Solanum jasminoides plants also infected by PSTVd. Molecular characterization of field isolates, biological assays and sequence analyses support the likelihood of PSTVd transmission to tomato plants from the neighbouring infected S. jasminoides plants. These data provide the first evidence of the risk of PSTVd spread from symptomless ornamental Solanaceae to susceptible horticultural crops when prophylactic measures are not enforced, suggesting that occasional PSTVd outbreaks in tomato crops reported from several European countries in the last two decades could have a similar origin. This report appears also to be the first of PSTVd infections of tomato in Italy.
Finnish shipwreck textiles from the 13th–18th centuries AD

This article discusses textile fragments that have been found in shipwrecks off the coast of Finland. The fi nds have been dated to the 13th–18th centuries AD. All samples were researched with visual analysis, but XRF, FTIR, SEM and HPLC was applied to part of the fi nds. The fragments of a woollen 2/1 twill from the Lapuri wreck were here interpreted as the remains of a red ochre treated sail cloth. The fi nds from the Egelskär wreck were interpreted as remains of a sheepskin. Other discussed fi nds are a woollen tabby weave from the Mulan wreck, fragments of a red woollen broadcloth from the Vrouw Maria, and a woollen sock and a silken rococo petticoat with woollen batting from the Sankt Michel. The aim is to shed light on textiles that form an understudied group of Finnish archaeological textile fi nds.
Scalable and Consistent Estimation in Continuous-time Networks of Relational Events

In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) generative model for such networks. We show that applying spectral clustering to adjacency matrices constructed from relational events generated by the CHIP model provides consistent community detection for a growing number of nodes. We also develop consistent and computationally efficient estimators for the model parameters. We demonstrate that our proposed CHIP model and estimation procedure scales to large networks with tens of thousands of nodes and provides superior fits than existing continuous-time network models on several real networks.
Teaching labs during a pandemic: Lessons from Spring 2020 and an outlook for the future

We report results from a survey of lab instructors on how they adapted their courses in the transition to emergency remote teaching due to the COVID-19 pandemic. The purpose of this report is to share the experiences of instructors in order to prepare for future remote teaching of labs. We include summaries of responses to help illustrate the types of lab activities that were done, learning goals for the remote labs, motivations for instructors' choices, challenges instructors faced, and ways in which instructors and students communicated. This is a first step in a larger project as part of an NSF RAPID grant to understand what happened during the switch to remote labs and how it impacted teaching methods and student learning.
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning

Bayesian reinforcement learning (BRL) offers a decision-theoretic solution for reinforcement learning. While "model-based" BRL algorithms have focused either on maintaining a posterior distribution on models or value functions and combining this with approximate dynamic programming or tree search, previous Bayesian "model-free" value function distribution approaches implicitly make strong assumptions or approximations. We describe a novel Bayesian framework, Inferential Induction, for correctly inferring value function distributions from data, which leads to the development of a new class of BRL algorithms. We design an algorithm, Bayesian Backwards Induction, with this framework. We experimentally demonstrate that the proposed algorithm is competitive with respect to the state of the art.
An updated hybrid deep learning algorithm for identifying and locating primary vertices

We present an improved hybrid algorithm for vertexing, that combines deep learning with conventional methods. Even though the algorithm is a generic approach to vertex finding, we focus here on it's application as an alternative Primary Vertex (PV) finding tool for the LHCb experiment. In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible PVs per event, and it will adopt a purely software trigger. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations using proxy distributions to encode the truth in training data. Last year we reported that training networks on our kernels using several Convolutional Neural Network layers yielded better than 90 % efficiency with no more than 0.2 False Positives (FPs) per event. Modifying several elements of the algorithm, we now achieve better than 94 % efficiency with a significantly lower FP rate. Where our studies to date have been made using toy Monte Carlo (MC), we began to study KDEs produced from complete LHCb Run 3 MC data, including full tracking in the vertex locator rather than proto-tracking.
Floodgate: inference for model-free variable importance

Many modern applications seek to understand the relationship between an outcome variable $Y$ and a covariate $X$ in the presence of a (possibly high-dimensional) confounding variable $Z$. Although much attention has been paid to testing whether $Y$ depends on $X$ given $Z$, in this paper we seek to go beyond testing by inferring the strength of that dependence. We first define our estimand, the minimum mean squared error (mMSE) gap, which quantifies the conditional relationship between $Y$ and $X$ in a way that is deterministic, model-free, interpretable, and sensitive to nonlinearities and interactions. We then propose a new inferential approach called floodgate that can leverage any working regression function chosen by the user (allowing, e.g., it to be fitted by a state-of-the-art machine learning algorithm or be derived from qualitative domain knowledge) to construct asymptotic confidence bounds, and we apply it to the mMSE gap. In addition to proving floodgate's asymptotic validity, we rigorously quantify its accuracy (distance from confidence bound to estimand) and robustness. We demonstrate floodgate's performance in a series of simulations and apply it to data from the UK Biobank to infer the strengths of dependence of platelet count on various groups of genetic mutations.
Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees

Abstract In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors due to the computational and under-sampling issues. In this article, we study convergence and statistical properties of two-dimensional canonical correlation analysis under an assumption that data come from a probabilistic model. We show that carefully initialized the power method converges to the optimum and provide a finite sample bound. Then we extend this framework to tensor-valued data and propose the higher-order power method, which is commonly used in tensor decomposition, to extract the canonical directions. Our method can be used effectively in a large-scale data setting by solving the inner least squares problem with a stochastic gradient descent, and we justify convergence via the theory of Lojasiewicz’s inequalities without any assumption on data generating process and initialization. For practical applications, we further develop (a) an inexact updating scheme which allows us to use the state-of-the-art stochastic gradient descent algorithm, (b) an effective initialization scheme which alleviates the problem of local optimum in nonconvex optimization, and (c) a deflation procedure for extracting several canonical components. Empirical analyses on challenging data including gene expression and air pollution indexes in Taiwan, show the effectiveness and efficiency of the proposed methodology. Our results fill a missing, but crucial, part in the literature on tensor data. Supplementary materials for this article are available online.
A new species of the sun-spider genus Mummucia (Arachnida: Solifugae: Mummucidae) from Piauí, northeastern Brazil

The Brazilian solifuge Mummucia ibirapemussu new species (Solifugae, Mummuciidae) from Serra das Confusoes National Park, in the semi-arid Caatinga domain, is herein described, with illustrations and SEM pictures of the main taxonomic characters. Geometrical morphometric analyses were also performed and indicated that this new species can be accurately distinguished from Metacleobis fulvipes regarding the shape of propeltidium.
Thermal destabilization of self-bound ultradilute quantum droplets

We theoretically investigate the temperature effect in a Bose–Bose mixture with attractive inter-species interactions, in the regime where a self-bound ultradilute quantum droplet forms due to the subtle balance between the attractive mean-field force and the repulsive force provided by Lee–Huang–Yang quantum fluctuations. We find that in contrast to quantum fluctuations, thermal fluctuations destabilize the droplet state and completely destroy it above a threshold temperature. We show that the threshold temperature is determined by the intra-species interaction energy. For a three-dimensional Bose–Bose mixture, the threshold temperature is less than one-tenth of the Bose–Einstein condensation temperature under the typical experimental conditions. With increasing temperature, the droplet’s equilibrium density gradually decreases and can be reduced by several tens of percent upon reaching the threshold temperature. We also consider a one-dimensional quantum droplet and find a similar destabilization effect due to thermal fluctuations. The threshold temperature in one dimension is roughly set by the binding energy of the inter-species dimer. The pronounced thermal instability of a self-bound quantum droplet predicted in our work could be examined in future experiments, by measuring the temperature dependence of its central density and observing its sudden disappearance at the threshold temperature.
Current distribution in metallic multilayers from resistance measurements

The in-plane current profile within multilayers of the generic structure Ta/Pt/(CoNi)/Pt/Ta is investigated. A large set of samples where the thickness of each layer was systematically varied was grown, followed by the measurement of the sheet resistance of each sample. The data are analyzed by a series of increasingly elaborate models, from a macroscopic engineering approach to mesoscopic transport theory. Non-negligible variations of the estimated repartition of current between the layers are found. The importance of having additional structural data is highlighted.
Scattering of rod-like swimmers in low Reynolds number environments

In their search for metabolic resources microbes swim through viscous environments that present physical anisotropies, including steric obstacles across a wide range of sizes. Hydrodynamic forces are known to significantly alter swimmer trajectories near flat and low-curvature surfaces. In this work, we imaged hundreds-of-thousands of high-curvature scattering interactions between swimming bacteria and micro-fabricated pillars with radii from ~1 to ~10 cell lengths. As a function of impact parameter, cell-pillar interactions produced distinct chiral distributions for scattering angle -- including unexpected 'counter-rotator' trajectories -- well-described by a sterics-only model. Our data and model suggest that alteration of swimmer trajectories is subject to distinct mechanisms when interacting with objects of different size; primarily steric for objects below ~10 cell lengths and requiring incorporation of hydrodynamics at larger scales. These alterations in trajectory impact swim dynamics and may affect microbial populations in ways that depend on the shape and placement of obstacles within an environment.
Deep Single Image Manipulation

Image manipulation has attracted much research over the years due to the popularity and commercial importance of the task. In recent years, deep neural network methods have been proposed for many image manipulation tasks. A major issue with deep methods is the need to train on large amounts of data from the same distribution as the target image, whereas collecting datasets encompassing the entire long-tail of images is impossible. In this paper, we demonstrate that simply training a conditional adversarial generator on the single target image is sufficient for performing complex image manipulations. We find that the key for enabling single image training is extensive augmentation of the input image and provide a novel augmentation method. Our network learns to map between a primitive representation of the image (e.g. edges) to the image itself. At manipulation time, our generator allows for making general image changes by modifying the primitive input representation and mapping it through the network. We extensively evaluate our method and find that it provides remarkable performance.