Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does the Internet Affect the Financial Market ? An Equilibrium Model of Internet-Facilitated Feedback Trading

The ease of Internet stock trading has lured relatively inexperienced investors into the financial markets. We study the consequences of the influx of these uninformed traders with a dynamic equilibrium framework. Our results show that these strategic uninformed online traders who adopt feedback strategies cannot outperform those who do not follow feedback strategies and that feedback trading cannot affect market equilibrium. We also show that an informed trader’s equilibrium strategy and expected profit remain unchanged with or without feedback trading. The presence of feedback trading in the market does not affect the speed at which information gets incorporated into prices. If uninformed traders aggregately adopt a more aggressive feedback trading strategy, they bear a higher risk. It is therefore important to manage and contain these uninformed traders’ risks. We also discuss implications for regulating and designing such Internet trading systems.
The Fusion Nexus: Exploring the Confluence of Virtual and Real Worlds through Biocognitive Audio-Verbal Interface in Immersive XR Environments

In this demo, we present FusionNexus, an asymmetric collaboration experience where a virtual-world player needs to guide a real-world player to make certain actions. A machine learning algorithm was used to do the pose detection of the real-world player. We collect the virtual-world player’s heartbeat to control drum beats in background music played to the real-world player to convey emotional status of the virtual-world player. In this demonstration, two players will collaborate to accomplish a time task.
Computer Self-Efficacy: A Meta-Analysis

Computer self-efficacy (CSE) has been a popular and important construct in information systems research for more than two decades. Although CSE researchers have conducted extensive qualitative reviews, quantitative analyses are lacking for studies of the relationships between CSE and variables of interest. This study provides such a quantitative analysis. The authors meta-analyze 102 empirical CSE studies that reported 232 usable statistical relationships between CSE and seven correlates. Their main meta-analysis demonstrates that CSE is significantly correlated in the theoretically predicted direction with each of the seven correlates. They also quantitatively assess five study characteristics as potential moderators of the CSE-correlate relationships. The moderator analysis reveals complex patterns and indicates that more research is needed to investigate possible moderating effects.
Social optima in mean field LQG control: Centralized and decentralized strategies

We study a class of LQG control problems with N decision makers possessing different dynamics. The basic objective is to minimize a social cost function as the sum of N individual costs containing mean field coupling. For large N, the exact socially optimal solution (which also has the Pareto optimality property) requires centralized information and this leads to high computational complexity. In this paper, we develop decentralized cooperative optimization so that the strategy of each agent only uses its own state and a function which can be computed off-line; the resulting set of strategies asymptotically achieves the social optimum as N ¿ ¿. Our solution relies on a mean field approximation and the calculation of the social cost variation due to individual strategy perturbations. We also discuss the relationship between the socially optimal solution and the so-called Nash Certainty Equivalence based solution presented in previous work (see e.g. Huang, Caines and Malhame¿, IEEE Trans. Auto. Control, Sept. 2007) on mean field LQG games. This leads to insights into connections and differences between cooperative and noncooperative solutions to mean field LQG problems.
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection

Preventing the spread of misinformation is challenging. The detection of misleading content presents a significant hurdle due to its extreme linguistic and domain variability. Content-based models have managed to identify deceptive language by learning representations from textual data such as social media posts and web articles. However, aggregating representative samples of this heterogeneous phenomenon and implementing effective real-world applications is still elusive. Based on analytical work on the language of misinformation, this paper analyzes the linguistic attributes that characterize this phenomenon and how representative of such features some of the most popular misinformation datasets are. We demonstrate that the appropriate use of pertinent symbolic knowledge in combination with neural language models is helpful in detecting misleading content. Our results achieve state-of-the-art performance in misinformation datasets across the board, showing that our approach offers a valid and robust alternative to multi-task transfer learning without requiring any additional training data. Furthermore, our results show evidence that structured knowledge can provide the extra boost required to address a complex and unpredictable real-world problem like misinformation detection, not only in terms of accuracy but also time efficiency and resource utilization.
Characterizing Distributed Mobile Augmented Reality Applications at the Edge

Mobile Augmented Reality (AR) is gaining traction as a compelling application due to recent advancements in hardware and software. Previous studies have suggested that distributing AR services on an edge computing infrastructure can offer significant performance benefits, especially for consolidating concurrent clients. In this study, we shed light on several research challenges directly impacting the effective integration of distributed AR and edge computing. Specifically, we conduct extensive experiments by deploying our distributed stream processing-based AR pipeline, scAtteR, on a representative edge-cloud infrastructure managed by the Oakestra framework. We uncover several unapparent challenges that inhibit the effective marriage of distributed AR when deployed on edge and demonstrate the potential improvements through scAtteR++. We offer valuable insights and best practices to the growing AR research community, specifically those interested in leveraging edge and public cloud technologies for large-scale AR operations.
C OLLABORATION T HROUGH O PEN S UPERPOSITION : A T HEORY OF THE O PEN S OURCE W AY 1

This paper develops and illustrates the theory of collaboration through open superposition: the process of depositing motivationally independent layers of work on top of each other over time. The theory is developed in a study of community-based free and open source software (FLOSS) development, through a research arc of discovery (participant observation), replication (two archival case studies), and theorization. The theory explains two key findings: (1) the overwhelming majority of work is accomplished with only a single programmer working on any one task, and (2) tasks that appear too large for any one individual are more likely to be deferred until they are easier rather than being undertaken through structured team work. Moreover, the theory explains how working through open superposition can lead to the discovery of a work breakdown that results in complex, functionally interdependent, work being accomplished without crippling search costs. We identify a set of socio-technical contingencies under which collaboration through open superposition is likely to be effective, including characteristics of artifacts made from information as the objects being worked on. We demonstrate the usefulness of the theory by using it to analyze difficulties in learning from FLOSS in other domains of work and in the IS function of for-profit organizations.
The Role of Serendipity in User-Curated Music Playlists

In this paper, we study the role of serendipity in music playlists. Serendipity is an important construct in recommendations, and finding an indicator of serendipity in a user-created playlist can facilitate the recommendation task. In particular, we want to know how the serendipity level of playlists is affected by the creator’s ability and by the context they are created. To do so, we (1) measure the serendipity level of music playlists using a previously established Linked Open Data-based approach, (2) assess whether the ability of the creator of the playlists has an effect on the serendipity level, and (3) assess whether different contexts facilitate a higher or lower serendipity level of playlists. The serendipity level of playlists is calculated with the cosine distance between Linked Open Data Paths that connect the songs contained in the playlist. The ability of the creator to generate serendipitous recommendations is estimated by measuring his/her coping potential and assessing the genre diversity of listening history. We instrument a study using a Spotify playlists dataset. Previous results in different contexts suggest that the coping potential is a good proxy for the curiosity level of a person, and, in turn, for the diversified knowledge this person has. Our analyses confirm these findings also in the music context: we find that playlist creators with higher coping potential have a more diversified knowledge. They create a higher number of playlists that span across multiple contexts and genres. Conversely, a lower copying potential implies a lower number of less coherent playlists.
OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on Inequality

In recent years, social media platforms such as Twitter have allowed people to voice their opinions by engaging in online discussions. The availability of such discussions has garnered interest amongst researchers in analyzing the dynamics on critical topics, such as inequality. Most of the current strategies are, however, limited with respect to conveying the fine-grained opinions of users, focusing on tasks such as sentiment analysis or topic modeling that extract coarse categorizations. In this work, we address this challenge by integrating a Twitter corpus with the output of finer-grained semantic parsing for the analysis of social media discourse. To do so, we first introduce the OBservatory Integrated Ontology (OBIO) that integrates social media metadata with various types of linguistic knowledge. We then present the Observatory Knowledge Graph (OKG), a knowledge graph in terms of the ontology, populated with tweets on inequality. We lastly provide use cases showing how the knowledge graph can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse.
Feedback Capacity of a Class of Symmetric Finite-State Markov Channels

We consider the feedback capacity of a class of symmetric finite-state Markov channels. Here, symmetry (termed “quasi-symmetry”) is defined as a generalized version of the symmetry defined for discrete memoryless channels. The symmetry yields the existence of a hidden Markov noise process that depends on the channel's state process and facilitates the channel description as a function of input and noise, where the function satisfies a desirable invertibility property. We show that feedback does not increase capacity for such class of finite-state channels and that both their nonfeedback and feedback capacities are achieved by an independent and uniformly distributed (i.u.d.) input. As a result, the channel capacity is explicitly given as a difference of output and noise entropy rates, where the output is driven by the i.u. d. input.
Diversity of Rotifers of Rajapalayam Taluk, Virudhunagar District, Tamil Nadu, India

A study on the diversity of rotifers was carried out in seasonal and perennial ponds of Rajapalayam taluk, Virudhunagar district, for a period of 8 months from July 2007 to February 2008. During the study, 45 species of rotifers belonging to 10 families were documented. Among the 45 species, 11 are new records for the state and the recording of Brachionus donneri after its first description from the state is noteworthy.
Integrated modeling of groundwater–surface water interactions in a tile‐drained agricultural field: The importance of directly measured flow route contributions

Understanding the dynamics of groundwater–surface water interaction is needed to evaluate and simulate water and solute transport in catchments. However, direct measurements of the contributions of different flow routes from specific surfaces within a catchment toward the surface water are rarely available. For this study, we physically separated the tile drain discharge toward a 43.5 m ditch transect from the groundwater–plus–overland flow routes. Direct groundwater flow and ephemeral overland flow were jointly captured in three sheet pile in‐stream reservoirs, while the effluent from three tile drain outlets was collected in vessels. Our flux measurements showed that, in response to a rainfall event, the tile drain contribution to the total ditch discharge decreased from 80% to 28%. We used these flow route measurements to calibrate a field‐scale integrated water transport model. The HydroGeoSphere code was used because it simultaneously solves the flow regimes in the variably saturated domain, the tile drain domain, and the surface flow domain. This simultaneous solution is needed for a correct representation of the mutual interactions between groundwater flow, tile drain flow, and ditch water flow. Our model produced a flow distribution between the flow paths which deviated only 2% from the measured flow distribution. A sensitivity analysis showed that model parameters related to tile drain entrance resistance and to the resistance to water flow through the surface water system controlled the water flow route distribution but with little effect on groundwater levels. This indicates that a model calibration based on groundwater levels alone does not necessarily produce a correct representation of the flow route contributions.
Validation of Candlestick Patterns as a Technical Indicator for Commodity Traders, Proposal of Improved Candlestick Encoding, and Data Mining Improved Candlestick Patterns

Traders in financial markets utilize many different technical indicators. One of the most widely used technical indicators in trading is the candlestick pattern, which is the graphical representation of the price movement of a pre-determined time period. The candlestick patterns are widely used by traders to help identify trends, reversals, and signals for entry and exit for creating profitable trading strategies in financial markets. Though candlestick patterns are popular in financial circles, this study aims to use data mining approaches to further scrutinize candlestick patterns with real-world trading data. The study would first examine the effectiveness of some existing well-known candlestick patterns by checking whether the price movements would match the predicted state after the patterns occur. If a pattern is declared as a bullish pattern (e.g., HAMMER), the ratio between increase and decrease should exceed a threshold value to confirm the declaration. With the results of the existing patterns as the benchmark, the study aims to utilize data-mining techniques to search for new candlestick patterns that may perform better than the popular patterns that had been proposed. Data-mining approaches were used in which an encoding method was applied to allow more detailed candlestick patterns to be defined, and to facilitate the pattern-finding process. To validate the newly proposed candlestick patterns, the proposed candlesticks were benchmarked against the original patterns with real trading data.
Liquid embolic surface area as a predictor of chronic subdural hematoma resolution in middle meningeal artery embolization

Background Liquid embolic agents (LEAs) such as ethylene vinyl alcohol (EVOH) are utilized for middle meningeal artery embolization (MMAE) for chronic subdural hematomas (cSDH). LEAs may be advantageous for MMAE as they are permanent and can penetrate the microvasculature of the subdural membranes. LEA surface area (SA) can quantify this penetration. The segmentation of LEA SA is not described in the literature and may be of interest in refining MMAE technique. Methods We retrospectively collected computerized tomography (CT) scans from 74 patients (with 95 cSDH) who underwent first-line MMAE with EVOH. Non-contrast head CTs were acquired pre-embolization, immediately post-embolization and at 1-, 3-, and 6 month follow-up. A 3D-Slicer was used to segment hematoma volumes and the liquid embolic cast. We hypothesized that greater LEA SA would be correlated with greater improvements in cSDH volumetric resolution. Results There was significant resolution in cSDH volumes over the follow-up period compared to preoperative volume (p<0.0001). The LEA SA was significantly correlated with the rate of cSDH resolution at 3 months (R2=0.08, p=0.03), and 6 months (R2=0.14, p=0.01). Conclusions The correlation of LEA surface area with hematoma resolution at 3-months and 6-months suggests greater LEA penetration may improve radiographic outcomes. This study uniquely provides a quantitative radiological perspective on the effect of LEA penetration on cSDH resolution.
Direct and Observed Joint Attention Modulate 9-Month-Old Infants’ Object Encoding

Abstract Sharing joint visual attention to an object with another person biases infants to encode qualitatively different object properties compared to a parallel attention situation lacking interpersonal sharedness. This study investigated whether merely observing joint attention amongst others shows the same effect. In Experiment 1 (first-party replication experiment), N = 36 9-month-old German infants were presented with a violation-of-expectation task during which they saw an adult looking either in the direction of the infant (eye contact) or to the side (no eye contact) before and after looking at an object. Following an occlusion phase, infants saw one of three different outcomes: the same object reappeared at the same screen position (no change), the same object reappeared at a novel position (location change), or a novel object appeared at the same position (identity change). We found that infants looked longer at identity change outcomes (vs. no changes) in the “eye contact” condition compared to the “no eye contact” condition. In contrast, infants’ response to location changes was not influenced by the presence of eye contact. In Experiment 2, we found the same result pattern in a matched third-party design, in which another sample of N = 36 9-month-old German infants saw two adults establishing eye contact (or no eye contact) before alternating their gaze between an object and their partner without ever looking at the infant. These findings indicate that infants learn similarly from interacting with others and observing others interact, suggesting that infant cultural learning extends beyond infant-directed interactions.
Social and economic factors associated with child unintentional injury mortality in high-income countries

Introduction Recognising the connection between country-level social determinants of health, and child unintentional injury mortality can contribute to better resource allocation for child safety. This cross-sectional country-level study aims to investigate such a link where the role of income inequality (Gini Index) is examined alongside education expenditure, current health expenditure and gross national income (GNI) per capita. Methods A total of 49 high-income countries were studied, using the WHO Global Health Estimates 2016, the World Bank’s World Development Indicators for education and GNI per capita, and the standardised world income inequality database to compile estimates of child unintentional injury mortality rates and selected socioeconomic characteristics. Results A wide range of childhood mortality rates from unintentional injury was observed (1.3–10.0 deaths per 100 000 children). Such risk is strongly associated with income inequality (0.50), GNI per capita (–0.35) and education expenditure (–0.01) (mediated by income inequality). No association was found to current health expenditure. The results explain 52% of the variance in child unintentional injury mortality. Conclusions In countries with higher overall economic activity and lower-income inequality, child mortality from unintentional injuries is lower. Allocation of education expenditure is one contributor to reducing income inequality; other factors need further exploration.
Endovascular thrombectomy for large infarcts in acute ischemic stroke: does size still matter?

Endovascular thrombectomy (EVT) for large vessel occlusion in acute ischemic stroke is the standard of care when initiated within 24 hours of stroke onset or last known well. 1 2 Current guidelines strongly recommend EVT in a broad range of patients with large vessel occlusion, within 6 hours after stroke onset, and more restricted groups with a small ischemic core volume and clinical or perfusion mismatch according to the DAWN (Clinical Mismatch in the Triage of Wake up and Late Presenting Strokes Undergoing Neurointervention with Trevo Thrombec-tomy Procedure) or DEFUSE-3 (Endovas-cular Therapy Following Imaging Evaluation for Ischaemic Stroke 3) criteria, in the late window (6 to 24 hours). 1 2 Although a small proportion of patients with a ‘large ischemic core’ (Alberta Stroke Program Early CT Score (ASPECTS) of 5 or less) were included and appeared to benefit from EVT treatment in the landmark randomized controlled trials in 2015, the overall modest pooled sample size in the HERMES collaboration prevented definitive conclusions being made about the net treatment benefit and safety in this patient cohort. 3 Recently, the final ‘large ischemic core’ trials, TESLA and LASTE, were presented (although not yet published) in addition to TENSION, which was recently published, after the publication of the RESCUE Japan LIMIT, SELECT 2, and ANGEL-ASPECT trials. 4–9 These trials which broadly included adult patients with an ASPECTS of 5 or less on computerized tomography (CT) or magnetic resonance (MR) imaging, or with an ischemic core volume of larger