Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Intellectual Capital on Company Value in Industries Affected by Covid-19

Purpose - This study aims to examine the effect of intellectual capital on company value in industries affected by covid-19 and to examine whether there are differences in intellectual capital between the two industries affected by covid-19. Research Method - This research is quantitative research. The sample was selected based on a purposive sampling technique with certain criteria. After the sample selection process was carried out, it resulted in 62 total data samples obtained from 18 hotel, restaurant, and tourism industry companies and 13 health industry companies. The data covers the period from 2020-2021. The data analysis technique used is descriptive statistics with simple linear regression and different independent sample t-tests. Findings - The results of the simple linear regression test show that intellectual capital has a significant effect on firm value. The results of the independent sample t-test show that there are differences between intellectual capital in the hotel, restaurant, and tourism industry and the health industry. Implication - Company value is a representation of the state of a company, where potential investors will give a special assessment related to the good and bad performance of the company. Nevertheless, investors should consider the company’s intellectual capital too. Moreover, result of this research shows that the Covid-19 outbreaks make a significant difference to company value. Investors should take this into a concern in deciding investment decision. Keywords: Intellectual Capital, Company Value, Covid-19
Monetary Policy and the Gender and Racial Employment Dynamics in Brazil

Monetary policy has been historically concerned with controlling inflation, using the interest rate as its main tool. However, such policies are not gender-or race-neutral. This paper explores econometrically the effect of changes in the interest rate for female and black employment creation in Brazil. We conduct a panel data fixed effects analysis for 13 states between 2012 and 2021 to estimate the effects of changes in interest rates on unemployment, separating the data by gender and race. Our results show that the real interest rate has a positive effect on the relative unemployment of black men to white men, no effect on the relative unemployment of black women to white men, and a negative effect on the relative unemployment of white women to white men. These effects are intensified in regions where the black population ratio is lower. This paper contributes to understanding the challenges to closing gender and racial gaps, particularly in developing economies. We conclude that social stratification, if not considered, can lead to misleading policies that perpetuate unequal socioeconomic outcomes.
IFAN: An Icosahedral Feature Attention Network for Sound Source Localization

Currently, sound source localization (SSL) techniques based on deep learning mainly rely on traditional signal processing methods to generate input features. Nevertheless, the applicability of these features in various environments shows significant differences. This study proposes a new single SSL model, called the icosahedral feature attention network (IFAN), to overcome this limitation. The proposed IFAN not only uses steered response power with phase transform (SRP-PHAT) but also develops steered response power with least mean square (SRP-LMS) as inputs of the network. The IFAN network encodes spatial position information into convolution kernels by introducing icosahedral convolutions. In addition, it adaptively learns optimal feature weights based on the input acoustic environment using the sigmoid function to capture the spatial distribution information of the sound source. For single-source SSL and tracking scenarios, the proposed method on the localization and tracking (LOCATA) challenge data corpus outperforms other state-of-the-art models. Moreover, it is capable of learning complementary information even in acoustic simulations involving a wide range of reverberations. The proposed IFAN can thus enhance the robustness and performance in different environments.
Robots as Recipients: The Effect of Familiarity on Children’s Sharing Behavior in Children-Robot Interactions

Previous research has found that familiarity with the recipient influences children’s sharing behavior, but the recipient is mainly limited to humans. With the spread of artificial intelligence, our social interactions are expanding to nonhumans, such as robots. The present study investigated the effects of familiarity on children’s sharing behavior with robot recipients. Familiarity varied as a function of whether there were interactions between the child and the robot. The Dictator Game was used to measure children’s sharing behavior. Consistent with our initial predictions, children shared more stickers with the familiar robot than with the unfamiliar one. We also conducted the animism test to ensure that the participants did not make animism errors towards robots. Our findings suggest that, children may view robots as special beings with which they can build social connections, even though they know robots are not alive.
Effects of Spraying Illite and Zeolite on Litter Quality, Microflora,
 and Footpad Dermatitis in Broiler Litter

A total of 192 one-day-old Ross 308 broilers (initial body weight: 31.30±0.41 g) were used in this experiment for 28 days. Treatments were as follows: basal rice husk (CON), rice husk + 1% illite (T1), rice husk + 1% zeolite (T2), and rice husk + 0.5% illite + 0.5% zeolite (T3). The percentage of illite and zeolite was calculated on a weight of litter. Each treatment had four replicates, with 12 birds per pen. Each pen was provided with 5 kg of rice husk as litter. Litter moisture content was significantly decreased ( P <0.05) in the T1, T2, and T3 groups compared to CON group at week 4. In litter nitrogen, the T1 group showed significantly lower ( P <0.05) litter nitrogen content than the other groups at weeks 1, 2, and 3. Also, the T3 group showed a significantly lower ( P <0.05) litter nitrogen content than the CON and T2 groups at weeks 2 and 3. The counts of E. coli in the litter were significantly decreased ( P <0.05) in the T1 group compared to the CON group at weeks 2 and 3. Moreover, the counts of Salmonella in the litter were significantly decreased ( P <0.05) in the T1 group compared to the CON group at week 4. The FPD score significantly decreased ( P <0.05) in the T1 group compared to the CON group. In conclusion, spraying illite could be an ideal way to improve litter quality and decrease FPD in broilers.
Glacier thickness and volume estimation in the Upper Indus Basin using modelling and ground penetrating radar measurements

In the Himalaya, ice thickness data are limited, and field measurements are even scarcer. In this study, we employed the GlabTop model to estimate ice reserves in the Jhelum (1.9 ± 0.6 km 3 ) and Drass (2.9 ± 0.9 km 3 ) sub-basins of the Upper Indus Basin. Glacier ice thickness in the Jhelum ranged up to 187 ± 56 m with a mean of ∼ 24 ± 7 m, while the Drass showed ice thickness up to 202 ± 60 m, with a mean of ∼ 17 ± 5 m. Model results were validated using Ground Penetrating Radar measurements across four profiles in the ablation zone of the Kolahoi glacier in the Jhelum and nine profiles across the Machoi glacier in the Drass sub-basin. Despite underestimating ice-thickness by ∼ 10%, the GlabTop model effectively captured glacier ice-thickness and spatial patterns in most of the profile locations where GPR measurements were taken. The validation showed high correlation coefficient of 0.98 and 0.87, low relative bias of ∼ − 13% and ∼ − 3% and a high Nash – Sutcliffe coefficient of 0.94 and 0.93 for the Kolahoi and Machoi glaciers, respectively, demonstrating the model ’ s effectiveness. These ice-thickness estimates improve our understanding of glacio-hydrological, and glacial hazard processes over the Upper Indus Basin.
Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection

Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two data sets have shown the effectiveness of our method. In addition, a large-scale high-resolution remote sensing object detection data set is established to make up for the current situation that the existing data set is insufficient or too small. The data set is available at https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset.
Social, Spatial and Material Conditions for Mattering: Newly Arrived Young Migrants’ Possibilities to Matter in Everyday Life in a Swedish School

Mattering as a central part of well-being has not been studied in the context of newly arrived young migrants in Swedish schools. Neither have studies on mattering included material and spatial conditions. This article draws on data collected from ethnographic fieldwork to address this. The theoretical contribution is based on the combination of the concept of mattering with Ahmed’s feminist and postcolonial theory of orientation and a critical view of lived experience as social and bodily orientation devices. Combining these theoretical frameworks, we explore social, spatial and material conditions for mattering in newly arrived youths’ everyday school lives. The overall outcome of our analysis illustrates that mattering is not only a question of social relations but also related to spatial and material dimensions. A conclusion is that Swedishness as an unspoken norm of whiteness is ‘built into the walls’ of Swedish schools and that (in)directly discriminates newly arrived young migrants.
Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking

Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz), cannabis use disorder (CanUD), and ever-regular tobacco smoking (Smk) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17 - 0.62). Causal inference analyses suggested the presence of horizontal pleiotropy, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for horizontal pleiotropy. We identified 439 pleiotropic loci in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both horizontal pleiotropy and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.
Inviscid Burgers as a degenerate elliptic problem

We demonstrate the feasibility of a scheme to obtain approximate weak solutions to the (inviscid) Burgers equation in conservation and Hamilton-Jacobi form, treated as degenerate elliptic problems. We show different variants recover non-unique weak solutions as appropriate, and also specific constructive approaches to recover the corresponding entropy solutions.
On the efficiency of an autonomous dynamic Szilard engine operating on a single active particle

The Szilard engine stands as a compelling illustration of the intricate interplay between information and thermodynamics. While at thermodynamic equilibrium, the apparent breach of the second law of thermodynamics was reconciled by Landauer and Bennett's insights into memory writing and erasure, recent extensions of these concepts into regimes featuring active fluctuations have unveiled the prospect of exceeding Landauer's bound, capitalizing on information to divert free energy from dissipation towards useful work. To explore this question further, we investigate an autonomous dynamic Szilard engine, addressing the thermodynamic consistency of work extraction and measurement costs by extending the phase space to incorporate an auxiliary system, which plays the role of an explicit measurement device. The nonreciprocal coupling between active particle and measurement device introduces a feedback control loop, and the cost of measurement is quantified through excess entropy production. The study considers different measurement scenarios, highlighting the role of measurement precision in determining engine efficiency.
Are James Webb Space Telescope Observations Consistent with Warm Dark Matter?

We compare observed with predicted distributions of galaxy stellar masses $M_*$ and galaxy rest-frame ultra-violet luminosities per unit bandwidth $L_{UV}$, in the redshift range $z = 2$ to 13. The comparison is presented as a function of the comoving warm dark matter free-streaming cut-off wavenumber $k_{fs}$. For this comparison the theory is a minimal extension of the Press-Schechter formalism with only two parameters: the star formation efficiency, and a proportionality factor between the star formation rate per galaxy and $L_{UV}$. These two parameters are fixed to their values obtained prior to the James Webb Space Telescope (JWST) data. The purpose of this comparison is to identify if, and where, detailed astrophysical evolution is needed to account for the new JWST observations.
Invertibility of local geodesic transverse and mixed ray transforms I: basic cases

Consider a compact Riemannian manifold in dimension $n\geq 3$ with strictly convex boundary. We show that the transverse ray transform of $1$ tensors and the mixed ray transform of $1+1$ tensors are invertible, up to natural obstructions, near a boundary point. When the manifold admits a strictly convex function, this local invertibility result leads to a global result by a layer stripping argument.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars. In this paper, we undertake a comprehensive study of CD-FSS and uncover two crucial insights: (i) the necessity of a fine-tuning stage to effectively transfer the learned meta-knowledge across domains, and (ii) the overfitting risk during the na\"ive fine-tuning due to the scarcity of novel category examples. With these insights, we propose a novel cross-domain fine-tuning strategy that addresses the challenging CD-FSS tasks. We first design Bi-directional Few-shot Prediction (BFP), which establishes support-query correspondence in a bi-directional manner, crafting augmented supervision to reduce the overfitting risk. Then we further extend BFP into Iterative Few-shot Adaptor (IFA), which is a recursive framework to capture the support-query correspondence iteratively, targeting maximal exploitation of supervisory signals from the sparse novel category samples. Extensive empirical evaluations show that our method significantly outperforms the state-of-the-arts (+7.8\%), which verifies that IFA tackles the cross-domain challenges and mitigates the overfitting simultaneously. The code is available at: https://github.com/niejiahao1998/IFA.
Efficient slot labelling

Slot labelling is an essential component of any dialogue system, aiming to find important arguments in every user turn. Common approaches involve large pre-trained language models (PLMs) like BERT or RoBERTa, but they face challenges such as high computational requirements and dependence on pre-training data. In this work, we propose a lightweight method which performs on par or better than the state-of-the-art PLM-based methods, while having almost 10x less trainable parameters. This makes it especially applicable for real-life industry scenarios.
Hot quark matter and merger remnants

This work investigates hot quark matter under the thermodynamic conditions characteristic of a binary neutron star (BNS) merger remnants. We use the density-dependent quark mass model (DDQM) to access the microscopic nuclear equation of state (EoS). The strange quark matter (SQM) is studied at finite temperature and entropy in the presence of electrons and muons and their neutrinos to simulate the BNS merger conditions. We observe that as the entropy of the SQM increases, the merger remnant becomes more massive, and increases in size whereas the neutrino population also increases. In the fixed temperature case, on the other hand, we observe that the entropy spreads from the surface towards the center of the remnant. We determine the particle distribution in the remnants' core, the remnant's structure, the temperature profile, sound velocity, and the polytropic index and discuss their effects. The strange quark (SQ) remnants satisfy the $2\,{\rm M_\odot}$ mass constraint associated with neutron stars (NS).
Canvil: Designerly Adaptation for LLM-Powered User Experiences

Advancements in large language models (LLMs) are poised to spark a proliferation of LLM-powered user experiences. In product teams, designers are often tasked with crafting user experiences that align with user needs. To involve designers and leverage their user-centered perspectives to create effective and responsible LLM-powered products, we introduce the practice of designerly adaptation for engaging with LLMs as an adaptable design material. We first identify key characteristics of designerly adaptation through a formative study with designers experienced in designing for LLM-powered products (N=12). These characteristics are 1) have a low technical barrier to entry, 2) leverage designers' unique perspectives bridging users and technology, and 3) encourage model tinkering. Based on this characterization, we build Canvil, a Figma widget that operationalizes designerly adaptation. Canvil supports structured authoring of system prompts to adapt LLM behavior, testing of adapted models on diverse user inputs, and integration of model outputs into interface designs. We use Canvil as a technology probe in a group-based design study (6 groups, N=17) to investigate the implications of integrating designerly adaptation into design workflows. We find that designers are able to iteratively tinker with different adaptation approaches and reason about interface affordances to enhance end-user interaction with LLMs. Furthermore, designers identified promising collaborative workflows for designerly adaptation. Our work opens new avenues for collaborative processes and tools that foreground designers' user-centered expertise in the crafting and deployment of LLM-powered user experiences.
Towards a Complete Local-Global Principle

Ahlswede and Cai proved that if a simple graph has nested solutions (NS) under the edge-isoperimetric problems, and the lexicographic (lex) order produces NS for its second cartesian power,then the lex order produces NS for any finite cartesian power. Under very general assumptions, we prove that if a graph and its second cartesian power have NS,then so does any finite cartesian power. Harper asked if this is true without any restriction. We also conjecture that it is. All graphs studied in the literature for which the lex order is optimal are regular. This lead Bezrukov and Els\"asser to conjecture that if the lex order is optimal for the second cartesian power, then the original graph is regular. A counterexample to this conjecture is provided.
Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges

Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to offer personalized education resources due to the challenge of addressing the diverse obstacles students encounter throughout their learning journey. Recently, the emergence of large language models (LLMs), such as ChatGPT, offers the possibility for resolving this issue by comprehending individual requests. Although LLMs have been successful in various fields, creating an LLM-based education system is still challenging for the wide range of educational skills required. This paper reviews the recently emerged LLM researches related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering, with the aim to explore their potential in constructing the next-generation intelligent education system. Based on the current development status, we further outline two approaches for an LLM-based education system: a unified approach and a mixture-of-expert (MoE) approach. Finally, we explore the challenges and future directions, providing new research opportunities and perspectives on adapting LLMs for education.
The entry heating and abundances of basaltic micrometeorites

Basaltic micrometeorites (MMs) derived from HED‐like parent bodies have been found among particles collected from the Antarctic and from Arctic glaciers and are to date the only achondritic particles reported among cosmic dust. The majority of Antarctic basaltic particles are completely melted cosmic spherules with only one unmelted particle recognized from the region. This paper investigates the entry heating of basaltic MMs in order to predict the relative abundances of unmelted to melted basaltic particles and to evaluate how mineralogical differences in precursor materials influence the final products of atmospheric entry collected on the Earth's surface. Thermodynamic modeling is used to simulate the melting behavior of particles with compositions corresponding to eucrites, diogenites, and ordinary chondrites in order to evaluate degree of partial melting and to make a comparison between the behavior of chondritic particles that dominate the terrestrial dust flux and basaltic micrometeroids. The results of 120,000 simulations were compiled to predict relative abundances and indicate that the phase relations of precursor materials are crucial in determining the relative abundances of particle types. Diogenite and ordinary chondrite materials exhibit similar behavior, although diogenite precursors are more likely to form cosmic spherules under similar entry parameters. Eucrite particles, however, are much more likely to melt due to their lower liquidus temperatures and small temperature interval of partial melting. Eucrite MMs, therefore, usually form completely molten cosmic spherules except at particle diameters <100 μm. The low abundance of unmelted basaltic MMs compared with spherules, if statistically valid, is also shown to be inconsistent with a low velocity population (12 km s−1) and is more compatible with higher velocities which may suggest a near‐Earth asteroid source dominates the current dust production of basaltic MMs.