Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperconnectivity and altered dynamic interactions of a nucleus accumbens network in post-stroke depression

Background Post-stroke depression (PSD) is a common complication after stroke. To date, no consistent locus of injury is associated with this complication. Here, we probed network dynamics in four functional circuits tightly linked to major depressive disorder and investigated structural alterations within these networks in PSD. Methods Forty-four participants with recent stroke and 16 healthy volunteers were imaged with 3T structural, diffusion and resting-state functional MRI and completed the Geriatric Depression Scale (GDS). Associations between GDS and functional connectivity were investigated within networks seeded from nucleus accumbens (NAc), amygdala and dorsolateral prefrontal cortex. In addition, the default mode network (DMN) was identified by connectivity with medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). Circuits that exhibited altered activity associated with GDS were further investigated by extracting within-network volumetric and microstructural measures from structural images. Results Functional connectivity within the NAc-seeded network and DMN correlated positively with depressive symptoms. Normal anticorrelations between these two networks were absent in patients with PSD. PCC grey matter volume as well as microstructural measures in mPFC and the medial forebrain bundle, a major projection pathway interconnecting the NAc-seeded network and links to mPFC, were associated with GDS scores. Conclusions Depression after stroke is marked by reduced mutual inhibition between functional circuits involving NAc and DMN as well as volumetric and microstructural changes within these networks. Aberrant network dynamics present in patients with PSD are therefore likely to be influenced by secondary, pervasive alterations in grey and white matter, remote from the site of injury.
The Mathematical Model of Crane Hoisting Mechanism

Established the mathematical model of hoisting mechanism based on the theory of vibration, through experimental and theoretical method obtained system stiffness coefficient, damping coefficient, finally calculated reel resistance torque. According to the mathematical model, the hoisting mechanism is simulated and analyzed by using virtual prototype technology, it provide a theoretical basis for design of non-contact type weighing sensor.
Operation of Battery Energy Storage System for Governor Free and its Effect

As the development of Battery Energy Storage System(BESS) and the increasing of intermittent energy sources like wind power and photovoltaic, the application of BESS in load frequency control is considered as an effective method. To evaluate the effectiveness of BESS application in frequency control, we defined a governor free model of BESS to conduct dynamic simulation. Using the BESS dynamic model, we implemented the power system dynamic model including steam, gas and hydro turbine generators. In this paper we study the control performance of BESS in primary frequency control. The effect of BESS speed regulation rate and response time on governor free operation is investigated. In addition, we compared BESS from steam turbine generator in view point of frequency regulation.
Human-computer interaction: lessons from theory and practice

This thesis explores the gap between theory and practice within the context of humancomputer interaction (HCI), specifically relating to effective implementation of HCI methods and frameworks within practice. The thesis is structured as follows: three connected but stand-alone papers are presented preceded by an introduction, and followed by a conclusion. The introduction defines HCI, discusses its history and evolution, and how it has been influenced by different disciplines. The first paper covers the usability of personalisation of Web sites and consists of three quantitative studies. The main measurements are effciency, effectiveness, and satisfaction as indicators of usability. Two of the studies show a significant relationship between the amount of content on a page and time taken to find information. The third study shows that when users are only allowed 3-5 seconds to glance through the page (skim), the significance, found in the other two studies, disappeared. There is, however, no indication that subjective satisfaction is affected, regardless of the amount of time users take to find information in any of the studies. The second paper is a case study: a practical evaluation of how usability is implemented in commercial website design projects. It compares the difference between targeting usability issues early in the design and later in the development. The third paper conceptualises involving users early on in design projects, how this affects design projects in the context of Garrett's (2002) framework and how it compares with current and optimal approaches. It shows that involving users early in the design process does not negatively affect time or effort and concludes by defining areas where research should concentrate to provide further evidence towards involving stakeholders in Web design through researchable propositions. Finally, the conclusion chapter summarises each of the paper's limitations and conclusions. It links the three papers through a discussion on how they are related in addition to how this research could benefit the practitioner
Authenticity and its influence on psychological well‐being and contingent self‐esteem of leaders in Singapore construction sector

‘Authentic leadership’ has emerged as an important subject in the management science literature. Although several scholars have presented their viewpoints about authenticity and authentic leadership, there is scarcity of empirical evidence on what leaders can really gain from being authentic. The results of the questionnaire-based survey with 32 leaders suggest that authenticity is significantly correlated with psychological well-being and negatively correlated with contingent self‐esteem. Regression analysis also shows that authenticity successfully predicts psychological well‐being. These findings indicate that authenticity results in healthy psychological functioning of leaders and hence several positive work-related outcomes.
BaYaka Adolescent Boys Nominate Accessible Adult Men as Preferred Spear Hunting Models

Humans are selective social learners. In a cultural landscape with many potential models, learners must balance the cost associated with learning from successful models with learning from accessible ones. Using structured interviews, we investigate the model selection biases of Congolese BaYaka adolescent boys learning to hunt with spears (n = 24; mean age [mage] = 15.79 years; range, 12–20 years). Results from social relations models suggest that adolescents nominated accessible adult men (closely related kin and neighbors) as preferred spear hunting models. Direct cues for success were not strong predictors for adolescent nomination in the statistical models, despite learners justifying model selection according to teaching and spear hunting skill. Indirect cues including body mass index, age, and cross-domain prestige were weak predictors for adolescent nomination. We interpret these findings as suggesting that BaYaka spear hunting knowledge is widely shared in the community, with all adult men participating in spear hunting and therefore having the requisite experience to transmit this skill. This supports previous findings that in egalitarian societies with low rates of role specialization, prestige has limited importance for cross-domain learning.
New multipodomerous appendages of stem-group euarthropods from the Cambrian (Stage 4) Guanshan Konservat-Lagerstätte

Stem-group euarthropods are important for understanding the early evolutionary and ecological history of the most species-rich animal phylum on Earth. Of particular interest are fossil taxa that occupy a phylogenetic position immediately crownwards of radiodonts, for this part of the euarthropod tree is associated with the appearance of several morphological features that characterize extant members of the group. Here, we report two new euarthropods from the Cambrian Stage 4 Guanshan Biota of South China. The fuxianhuiid Alacaris? sp. is represented by isolated appendages composed of a gnathobasic protopodite and an endite-bearing endopod of at least 20 podomeres. This material represents the youngest occurrence of the family Chengjiangocarididae, and its first record outside the Chengjiang and Xiaoshiba biotas. We also describe Lihuacaris ferox gen. et sp. nov. based on well-preserved and robust isolated appendages. Lihuacaris ferox exhibits an atypical combination of characters including an enlarged rectangular base, 11 endite-bearing podomeres and a hypertrophied distal element bearing 8–10 curved spines. Alacaris? sp. appendages display adaptations for macrophagy. Lihuacaris ferox appendages resemble the frontal appendages of radiodonts, as well as the post-oral endopods of chengjiangocaridid fuxianhuids and other deuteropods with well-documented raptorial/predatory habits. Lihuacaris ferox contributes towards the record of endemic biodiversity in the Guanshan Biota.
Risk perception and optimism during the early stages of the COVID-19 pandemic

Slowing the spread of COVID-19 requires people to actively change their lives by following protective practices, such as physical distancing and disinfecting their hands. Perceptions about the personal risk of COVID-19 may affect compliance with these practices. In this study, we assessed risk perception and optimism about COVID-19 in a multinational (UK, USA and Germany), longitudinal design during the early stages of the pandemic (16 March 2020; 1 April 2020; 20 May 2020). Our main findings are that (i) people showed a comparative optimism bias about getting infected and infecting others, but not for getting severe symptoms, (ii) this optimism bias did not change over time, (iii) optimism bias seemed to relate to perceived level of control over the action, (iv) risk perception was linked to publicly available information about the disorder, (v) people reported adhering closely to protective measures but these measures did not seem to be related to risk perception, and (vi) risk perception was related to questions about stress and anxiety. In additional cross-sectional samples, we replicated our most important findings. Our open and partly preregistered results provide detailed descriptions of risk perceptions and optimistic beliefs during the early stages of the COVID-19 pandemic in three Western countries.
From compositional semantics to Bayesian pragmatics via logical inference

Formal semantics in the Montagovian tradition provides precise meaning characterisations, but usually without a formal theory of the pragmatics of contextual parameters and their sensitivity to background knowledge. Meanwhile, formal pragmatic theories make explicit predictions about meaning in context, but generally without a well-defined compositional semantics. We propose a combined framework for the semantic and pragmatic interpretation of sentences in the face of probabilistic knowledge. We do so by (1) extending a Montagovian interpretation scheme to generate a distribution over possible meanings, and (2) generating a posterior for this distribution using a variant of the Rational Speech Act (RSA) models, but generalised to arbitrary propositions. These aspects of our framework are tied together by evaluating entailment under probabilistic uncertainty. We apply our model to anaphora resolution and show that it provides expected biases under suitable assumptions about the distributions of lexical and world-knowledge. Further, we observe that the model’s output is robust to variations in its parameters within reasonable ranges.
How Vision Affects Language: Comparing Masked Self-Attention in Uni-Modal and Multi-Modal Transformer

The problem of interpretation of knowledge learned by multi-head self-attention in transformers has been one of the central questions in NLP. However, a lot of work mainly focused on models trained for uni-modal tasks, e.g. machine translation. In this paper, we examine masked self-attention in a multi-modal transformer trained for the task of image captioning. In particular, we test whether the multi-modality of the task objective affects the learned attention patterns. Our visualisations of masked self-attention demonstrate that (i) it can learn general linguistic knowledge of the textual input, and (ii) its attention patterns incorporate artefacts from visual modality even though it has never accessed it directly. We compare our transformer’s attention patterns with masked attention in distilgpt-2 tested for uni-modal text generation of image captions. Based on the maps of extracted attention weights, we argue that masked self-attention in image captioning transformer seems to be enhanced with semantic knowledge from images, exemplifying joint language-and-vision information in its attention patterns.
LinPP: a Python-friendly algorithm for Linear Pregroup Parsing

We define a linear pregroup parser, by applying some key modifications to the minimal parser defined in (Preller, 2007). These include handling words as separate blocks, and thus respecting their syntactic role in the sentence. We prove correctness of our algorithm with respect to parsing sentences in a subclass of pregroup grammars. The algorithm was specifically designed for a seamless implementation in Python. This facilitates its integration within the DisCopy module for QNLP and vastly increases the applicability of pregroup grammars to parsing real-world text data.
Tuning Deep Active Learning for Semantic Role Labeling

Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which makes it challenging to aggregate the numerous decisions into a single score for determining new instances to annotate. In this paper, we apply two ways of aggregating scores across multiple predicates in order to choose query sentences with two methods of estimating model certainty: using the neural network’s outputs and using dropout-based Bayesian Active Learning by Disagreement. We compare these methods with three passive baselines — random sentence selection, random whole-document selection, and selecting sentences with the most predicates — and analyse the effect these strategies have on the learning curve with respect to reducing the number of annotated sentences and predicates to achieve high performance.
Opportunity and Place: Latino Children and America’s Future

We examine the spatial distribution of Hispanic children and analyze its relationship to the geography of opportunity. We describe the spatial distribution of Hispanic children across all U.S. counties, document their exposure to salutary and deleterious conditions, and compare exposure to these conditions among children living in metropolitan and nonmetropolitan counties that represent traditional and new destinations for immigrants. We find clear evidence of racial and geographic differences in opportunity, at least as defined by spatially uneven patterns of intergenerational mobility. We show that the typical Hispanic child is highly isolated, living in a county with a majority-minority population, high rates of poverty, low levels of education, and poor public health. Opportunities are limited in metropolitan core counties, where the large majority of Hispanic children live, and the movement of immigrant families from traditional gateways to new destinations provides little to children in terms of exposure to more opportunity.
Theory of Mind Improves Human’s Trust in an Iterative Human-Robot Game

Trust is a critical issue in human–robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.
Trust, but Verify: Autonomous Robot Trust Modeling in Human-Robot Collaboration

In this work, we propose a computational robot trust model based on the predictability of the human partner as the main factor that impacts robot trust. Using this model and based on the robot’s knowledge about the task, the robot switches its behavior between conservative and normal, and adjusts the implemented safety mechanism as a function of the current robot trust level. We illustrate the proposed model impact on the outcome of the collaboration using a simple scenario. The results show a significant improvement in safety conditions of the human in collaboration with a robot at the cost of justifiable team performance reduction.
Perceptions of Anthropomorphism in a Chatbot Dialogue: The Role of Animacy and Intelligence

When interacting with embodied agents, users often rely on a variety of cues from the agent’s embodiment to form perceptions of animacy and intelligence, including its appearance and behaviour. Due to chatbots’ disembodiment, users’ perceptions of a chatbot’s animacy and intelligence are mostly dependent on the textual properties of the dialogue. The current study aims to investigate the mediating role of perceptions of chatbot’s intelligence and animacy on users’ perceptions of the chatbot’s anthropomorphism. An online experiment was conducted with a chatbot and a web platform. Both systems asked users three basic questions for providing a restaurant recommendation. By communicating the same content via different modalities of communication (i.e., flowing dialogue and traditional web interface); this study compares the differences in these perceptions between chatbots to traditional web platforms. The results of a mediation analysis entail that the chatbot was perceived as more animate than the web platform, and accordingly, it was perceived as more anthropomorphic than the web platform as users’ perceptions of animacy fully mediated this effect. Also, there is no evidence for differences in users’ perceptions of intelligence between the chatbot and the web platform.
Put that Away and Talk to Me - the Effects of Smartphone induced Ostracism while Interacting with an Intelligent Virtual Agent

This paper presents the implementation of a prototype including a virtual environment in which humans are exposed to a seemingly autonomous Intelligent Virtual Agent (IVA) that ostracises them by using its smartphone during a conversation (a phenomenon called phubbing with adverse outcomes in interhuman communication). Based on the temporal need threat model of ostracism, the effects of phubbing were examined in an online experiment with three conditions (no vs. proactive vs. reactive phubbing) using an IVA. The results showed a significant effect on the warmth attributed to the IVA. Consequently, our study provides partial support that the negative effects of phubbing can be transfered to human-agent interaction. It is thereby not only in line with the underlying theory, but also provides a prototype suitable for future research with phubbing in the context of IVAs.
Cyberbullying Mitigation by a Proxy Persuasion of a Chat Member Hijacked by a Chatbot

In this study, to mitigate cyberbullying in communication tools, we propose a method in which a chatbot pretending to be a member of a chat group defends the victim by proxy without permission (i.e., hijacking and proxy persuasion). An experiment was conducted to verify the effectiveness of the proposed method, the results of which showed that it increases the defending behavior of a user who was temporarily hijacked by the chatbot. However, compared with the existing method, the proposed method was found to have a problem with the continued use of the system.
Key Qualities of Conversational Recommender Systems: From Users’ Perspective

An increasing number of recommender systems enable conversational interaction to enhance the system’s overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of a CRS. This paper presents a model to capture the key qualities of conversational recommender systems and their related user experience aspects. Our model incorporates the characteristics of conversations (such as adaptability, understanding, response quality, rapport, humanness, etc.) in four major user experience dimensions of the recommender system: User Perceived Qualities, User Belief, User Attitudes, and Behavioral Intentions. Following the psychometric modeling method, we validate the combined metrics using the data collected from an online user study of a conversational music recommender system. The user study results 1) support the consistency, validity, and reliability of the model that identifies seven key qualities of a CRS; and 2) reveal how conversation constructs interact with recommendation constructs to influence the overall user experience of a CRS. We believe that the key qualities identified in the model help practitioners design and evaluate conversational recommender systems.
Measuring attitudes as a complex system Structured thinking and support for the Canadian carbon tax

Abstract. We test a method for applying a network-based approach to the study of political attitudes. We use cognitive-affective mapping, an approach that visually represents attitudes as networks of concepts that an individual associates with a given issue. Using a software tool called Valence, we asked a sample of Canadians (n = 111) to draw a cognitive-affective map (CAM) of their views on the carbon tax. We treat these networks as a series of undirected graphs and examine the extent to which support for the tax can be predicted based on each graph's emotional and structural properties. We find evidence that the emotional but not the structural properties significantly predict individuals' attitudes toward the carbon tax. We also find associations between CAMs' structural properties (density and centrality) and several measures of political interest. Our results provide preliminary evidence for the efficacy of CAMs as a tool for studying political attitudes. The study data are available at https://osf.io/qwpvd/? view_only=6834a1c442224e72bf45e7641880a17f