Miracle-dz/UniFAR-FTUs huggingface dataset:
HARRY BUNT AND REINHARD MUSKENS COMPUTING THE SEMANTIC INFORMATION IN AN UTTERANCE

To compute the meaning of any given natural language expression is extremely hard. This is partly due to the structural complexity, variability, and flexibility of natural language, but also, and more importantly, to its pervasive ambiiguity . It has been estimated that, due to the fact that individual words as well as their combination in a sentence can usually express a range of semantic concepts and relations, an ordinary sentence of average length can have several million possible meanings. So what do we mean by ‘the meaning of a given natural language expression’? Language users are hardly ever aware of having to resolve an ambiguity, so in practice the understanding of a natural language expression does not mean choosing ‘the right interpretation’ among millions of possibilities. The crucial point is, of course, that as a language user we are never confronted with the task of computing the meaning of a sentence in splendid isolation. That happens only in the linguistic literature. In reality, natural language always occurs in a certain context. In a given context we are not talking about just anything, but we have a certain domain of discourse. This means that those word senses can be ruled out that do not belong to this domain, as well as those interpretations of structural ambiguities which express something that would be impossible or highly implausible in the domain. While the fact that speakers and listeners do not struggle with ambiguity resolution may suggest that context information is sufficient to determine the intended meanings of natural language expressions, it is hard to believe that context information is really sufficient to exclude millions of potential sentence meanings and retain exactly one of them as the meaning. It seems more likely that readers and listeners disambiguate meanings to the extent that is required by the circumstances, and that speakers and writers, expecting this of their listeners and readers, formulate their utterances accordingly. In other words, a con-
Running head: CHILDREN’S EMOTION REGULATION AND PROSOCIAL BEHAVIOR 1 Helping Yourself Help Others: Linking Emotion Regulation to Prosocial Behavior through Sympathy and Trust

While emotionally well-regulated children are more likely to behave prosocially, the psychological processes that connect their emotion regulation abilities and prosocial behavior are less clear. We tested if other-oriented sympathy and trust mediated the links between emotion regulation capacities (i.e., resting respiratory sinus arrhythmia [RSA], negative emotional intensity, and sadness regulation) and prosocial behavior in an ethnically-diverse sample of 4and 8-year-olds (N = 131; 49% girls). Resting RSA was calculated from children’s electrocardiogram and respiration data in response to a nondescript video. Sympathy was child and caregiver reported, whereas negative emotional intensity, sadness regulation, trust, and prosocial behavior were caregiver reported. Regardless of age, higher resting RSA was linked to higher sympathy, which was associated with higher prosocial behavior. The positive link between sadness regulation and prosocial behavior was mediated by higher sympathy and trust. Children’s other-oriented psychological processes may play important roles in translating certain emotion regulation capacities into prosocial behavior.
How visual saliency affects referent accessibility

As referents are more accessible in discourse, they can be referred to with more attenuated expressions, such as pronouns. Accessibility is known to be affected by the referent’s saliency in the linguistic context, but much less is known about the effect of saliency in the visual context. In this paper, we investigate whether a referent’s visual saliency affects the choice of referring expression in a discourse context. The results of a story completion experiment show that visually salient referents induce more attenuated expressions, but only when they are linguistically non-salient. Linguistically salient referents receive more reduced references when they are visually non-salient. We argue that visual saliency affects accessibility when the impact of linguistic factors is moderated. In addition, when the story does not match people’s expectations, processing difficulties might result in the use of less costly expressions.
Oligometastatic Gastroesophageal Adenocarcinoma: Molecular Pathophysiology and Current Therapeutic Approach

Gastric and esophageal cancers are dreaded malignancies, with a majority of patients presenting in either a locally advanced or metastatic state. Global incidences are rising and the overall prognosis remains poor. The concept of oligometastasis has been established for other tumor entities and is also proposed for upper gastrointestinal tract cancers. This review article explores metastasis mechanisms on the molecular level, specific to esophageal and gastric adenocarcinoma. Existing data and recent studies that deal with upper gastrointestinal tumors in the oligometastatic state are reviewed. Furthermore, current therapeutic targets in gastroesophageal cancers are presented and discussed. Finally, a perspective about future diagnostic and therapeutic strategies is given.
Educational data mining: A tutorial for the rattle package in R

Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle ’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.
Soft Risk Disclosure with Feedback Effect

This paper studies firms’ optimal qualitative disclosure about hard-to-quantify risk exposure to affect investors’ information acquisition under the feedback effect channel. Based on a model with unknown payoff distribution, disclosure softness, and ambiguity aversion, I find that firms with lower risk exposure disclose more precisely. Particularly, low (medium) exposure firms provide perfect (partially informative) risk disclosures, whereas high exposure firms always disclose vaguely. In addition, the softness of risk disclosure enables firms to induce different risk perceptions among informed and uninformed investors with one disclosure, which gives firms the flexibility to separately influence the beliefs of the two groups of investors. Finally, I find that lower cost of information acquisition may improve economic efficiency at the expense of risk disclosure quality. JEL classification: D82, D83, G14.
Reminders Work, But for Whom? Evidence from New York City Parking-Ticket Recipients

We investigate heterogeneity in responsiveness to reminder letters among New York City parking ticket recipients. Using variation in the timing of letters, we find a strong aggregate response. But we find large differences across individuals: those with a low baseline propensity to respond to tickets—a natural nudge target—react least to letters. These low-response types, who incur significant late penalties, disproportionately come from already disadvantaged groups. They do react strongly to traditional, incentive-based interventions. We discuss how accounting for response heterogeneity might change one’s approach to policy and how one might use our analysis to target interventions at low-response types. (JEL D04, D12, D91, H71)
Frictionless contact problem for hyperelastic materials with interior point optimizer

This paper presents a method to solve the mechanical problems undergoing finite deformations and the unilateral contact problems without friction for hyperelastic materials. We apply it to an industrial application: contact between a mechanical gasket and an obstacle. The main idea is to formulate the contact problem into an optimization’s one, in order to use the Interior Point OPTimizer (IPOPT) to solve it. Finally, the FreeFEM software is used to compute and solve the contact problem. Our method is validated against several benchmarks and used on an industrial application example.
Formalizing the Informal: Adopting a Formal Culture-fit Measurement System in the Employee Selection Process

Many organizations rely on formal management control systems that align employee values with organizational values (i.e., culture-fit) to shape organizational culture. Yet, this role of formal management control systems is understudied in prior research. This paper contributes by examining the employee performance consequences of adopting a formal culture-fit measurement system in the employee selection process using proprietary data from one company. I exploit the staggered feature of the adoption of the system, and find that, on average, employees selected with the system perform significantly better than those without the system. However, the performance consequences of adopting the culture-fit measurement system exhibit significant variation depending on (1) the interaction with the existing subculture, (2) the extent of shared responsibility among employees in the work environment, and (3) the noise in the measurement of culture-fit due to the gaming behavior of applicants. Taken together, this study has important implications for the design of formal management control systems that can facilitate the diffusion of organizational culture. Human subjects research: This study was reviewed and approved by the Harvard University Area Internal Review Board (HUA IRB). Detailed documentation is available upon request. Please Do Not Distribute Without Permission  Doctoral candidate at Harvard Business School (wcai@hbs.edu). I especially thank the research site for providing the data for this project. I am sincerely grateful for the invaluable guidance and support from my dissertation committee: Dennis Campbell (Chair), Susanna Gallani, and Tatiana Sandino. I appreciate helpful comments from Inna Abramova, Wilbur Chen, Srikant Datar, Carolyn Deller, Aiyesha Dey, Liran Eliner, Patrick Ferguson, Grace Gu, Jeffrey Hales, Paul Healy, Jonas Heese, Rebecca Henderson, Chris Ittner, Bob Kaplan, Serena Loftus (discussant), Paul Madsen, Aditya Mohan, Trung Nguyen, Suzie Noh, Jihwon Park, Gerardo Pérez Cavazos, Ethan Rouen, Alexa Scherf, George Serafeim, Matt Shaffer, Ruidi Shang, Jee-Eun Shin, Siko Sikochi, Bob Simons, Eugene Soltes, Eric Van den Steen, Wim Van der Stede (discussant), Charles Wang, Hashim Zaman, Yuan Zou, and participants at Harvard Business School, the AAA Annual Meeting, and the AAA Management Accounting Section Meeting. I am also thankful for financial support from the Harvard Business School Doctoral Fellowship. All errors are my own. 1 Formalizing the Informal: Adopting a Formal Culture-fit Measurement System in the Employee Selection Process “The only thing of real importance that leaders do is to create and manage culture. If you do not manage culture, it manages you, and you may not even be aware of the extent to which this is happening. ” In Organizational Culture and Leadership (1985) by Edgar Schein
A multidimensional analysis of the effects of innovation on the competitive advantage of small and medium-sized enterprises

Article history: Received: October 24, 2019 Received in revised format: November 28 2019 Accepted: December 22, 2019 Available online: December 22, 2019 This paper investigates the effects of different dimensions of innovation on the competitive advantage of small and medium-sized enterprises (SMEs). The main dimensions of the innovation retained in this study are related to different types of innovation, innovation activities, sources of internal and external information, and public support for innovation. In this study, we rely on the principal component analysis of innovation applied to a sample of 231 Tunisian SMEs. The results show that product innovation, organizational innovation and innovation activities were the key variables for enhancing the competitive advantage of SMEs. In contrast, external sources of information have no effects on the SMEs competitive advantage. The originality of this research is to provide a multidimensional framework of the effects of innovation on the competitive position of SMEs. © 2020 by the authors; licensee Growing Science, Canada
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking

Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank documents. Given the same amount of time, a faster re-ranking model can incorporate more documents than a less efficient one, leading to a higher effectiveness. To utilize this property, we propose TK (Transformer-Kernel): a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of Transformer layers (up to three) to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. TK offers an optimal ratio between effectiveness and efficiency: under realistic time constraints (max. 200 ms per query) TK achieves the highest effectiveness in comparison to BERT and other re-ranking models. We demonstrate this on three large-scale ranking collections: MSMARCO-Passage, MSMARCO-Document, and TREC CAR. In addition, to gain insight into TK, we perform a clustered query analysis of TK's results, highlighting its strengths and weaknesses on queries with different types of information need and we show how to interpret the cause of ranking differences of two documents by comparing their internal scores.
If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills-the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.
New empirical constraints on the cosmological evolution of gas and stars in galaxies

We combine the latest observationally motivated constraints on stellar properties in dark matter haloes, along with data-driven predictions for the atomic (HI) and molecular (H$_2$) gas evolution in galaxies, to derive empirical relationships between the build-up of galactic components and their evolution over cosmic time. At high redshift ($z \gtrsim 4$), the frameworks imply that galaxies acquire their cold gas (both atomic and molecular) mostly by accretion, with the fraction of cold gas reaching about 20% of the cosmic baryon fraction. We infer a strong dependence of the star formation rate on the H$_2$ mass, suggesting a near-universal depletion timescale of 0.1-1 Gyr in Milky Way sized haloes (of masses $10^{12} \ M_{\odot}$ at $z = 0$). There is also evidence for a near-universality of the Kennicutt-Schmidt relation across redshifts, with very little dependence on stellar mass, if a constant conversion factor ($\alpha_{\rm CO}$) of CO luminosity to molecular gas mass is assumed. Combining the atomic and molecular gas observations with the stellar build-up illustrates that galactic mass assembly in Milky-Way sized haloes proceeds from smooth accretion at high redshifts, towards becoming merger-dominated at late times ($z \lesssim 0.6$). Our results can be used to constrain numerical simulations of the dominant growth and accretion processes of galaxies over cosmic history.
Automatic Location Type Classification From Social-Media Posts

We introduce the problem of Automatic Location Type Classification from social media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with their corresponding location type, e.g., school, church, restaurant or museum. We provide a dataset of locations associated with tweets posted in close geographical proximity. We explore two approaches to the problem: (a) a pipeline approach where each message is first classified, and then the location associated with the message set is inferred from the individual message labels; and (b) a joint approach where the individual messages are simultaneously processed to yield the desired location type. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, simpler linear classifiers outperform deep neural network alternatives that have shown superior in previous text classification tasks.
Limited‐memory polynomial methods for large‐scale matrix functions

Matrix functions are a central topic of linear algebra, and problems requiring their numerical approximation appear increasingly often in scientific computing. We review various limited‐memory methods for the approximation of the action of a large‐scale matrix function on a vector. Emphasis is put on polynomial methods, whose memory requirements are known or prescribed a priori. Methods based on explicit polynomial approximation or interpolation, as well as restarted Arnoldi methods, are treated in detail. An overview of existing software is also given, as well as a discussion of challenging open problems.
EULER CLASSES: SIX-FUNCTORS FORMALISM, DUALITIES, INTEGRALITY AND LINEAR SUBSPACES OF COMPLETE INTERSECTIONS

Abstract We equate various Euler classes of algebraic vector bundles, including those of [12] and one suggested by M. J. Hopkins, A. Raksit, and J.-P. Serre. We establish integrality results for this Euler class and give formulas for local indices at isolated zeros, both in terms of the six-functors formalism of coherent sheaves and as an explicit recipe in the commutative algebra of Scheja and Storch. As an application, we compute the Euler classes enriched in bilinear forms associated to arithmetic counts of d-planes on complete intersections in $\mathbb P^n$ in terms of topological Euler numbers over $\mathbb {R}$ and $\mathbb {C}$ .
Constraining the Infalling Envelope Models of Embedded Protostars: BHR 71 and Its Hot Corino

The collapse of a protostellar envelope results in the growth of a protostar and the development of a protoplanetary disk, playing a critical role during the early stages of star formation. Characterizing the gas infall in the envelope constrains the dynamical models of star formation. We present unambiguous signatures of infall, probed by optically thick molecular lines, toward an isolated embedded protostar, BHR 71 IRS1. The three-dimensional radiative transfer calculations indicate that a slowly rotating infalling envelope model following the “inside-out” collapse reproduces the observations of both HCO + J = 4 → 3 and CS J = 7 → 6 lines, as well as the low-velocity emission of the HCN J = 4 → 3 line. The envelope has a model-derived age of 12,000 ± 3000 yr after the initial collapse. The envelope model underestimates the high-velocity emission at the HCN J = 4 → 3 and H13CN J = 4 → 3 lines, where outflows or a Keplerian disk may contribute. The ALMA observations serendipitously discover the emission of complex organic molecules (COMs) concentrated within a radius of 100 au, indicating that BHR 71 IRS1 harbors a hot corino. Eight species of COMs are identified, including CH3OH and CH3OCHO, along with H2CS, SO2 and HCN v2 = 1. The emission of methyl formate and 13C-methanol shows a clear velocity gradient within a radius of 50 au, hinting at an unresolved Keplerian rotating disk.
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, they may suffer from catastrophic forgetting. To address this, we propose K-Adapter, which remains the original parameters of the pre-trained model fixed and supports continual knowledge infusion. Taking RoBERTa as the pre-trained model, K-Adapter has a neural adapter for each kind of infused knowledge, like a plug-in connected to RoBERTa. There is no information flow between different adapters, thus different adapters are efficiently trained in a distributed way. We inject two kinds of knowledge, including factual knowledge obtained from automatically aligned text-triplets on Wikipedia and Wikidata, and linguistic knowledge obtained from dependency parsing. Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing and question answering demonstrate that each adapter improves the performance, and the combination of both adapters brings further improvements. Probing experiments further indicate that K-Adapter captures richer factual and commonsense knowledge than RoBERTa.
Self-leadership, critical thinking disposition, satisfaction of clinical practice and clinical practice competency of nursing students

The purpose of this study was to examine the relationship among self-leadership, critical thinking disposition, satisfaction of clinical practice and clinical practice competency of nursing students. Participants were 199 baccalaureate nursing students (3rd and 4th grades) in 2 cities. The data was collected by questionnaires and were analyzed with the SPSS/Win 21.0 program, using descriptive statistics, Pearson`s correlation coefficient and multiple regression. Significant positive correlations were among self-leadership, critical thinking disposition, satisfaction of clinical practice and clinical practice competency. The regression model explained 30.4% of satisfaction of clinical practice. The significant predictors of satisfaction of clinical practice were clinical experience, satisfaction of major, self-leadership and critical thinking disposition. The regression model explained 23.7% of clinical practice competency. Health status, self-leadership and critical thinking disposition were factors influencing clinical practice competency. It should strengthen self-leadership and encourage critical thinking disposition to improve nursing students` satisfaction of clinical practice and clinical practice competency.