Miracle-dz/UniFAR-FTUs huggingface dataset:
1 changes in the spontaneous combustion tendency of humic coals according to their petrographic

The article presents the analysis of the correlation between the self-ignition parameters and the ultimate and proximate analysis, as well as the petrographic properties of Polish lignite, sub-bitumi nous coal, bituminous coal and anthracite. The following coal properties were determined: the moisture, ash content, volatile matter and sulfur content, gross calorific value, net calorific value, C, O, N and H contents, total porosity, ash oxide composition, rates of spontaneous combustion in 237°C and 190°C, and activation energy. During the petrographic analysis, maceral composition and random reflectance were determined. To determine the linear correlation between the self-ignition parame ters and the analyzed coal properties, the Pearson correlation coefficient was calculated. The results show that there is no strong linear correlation between the lignite tendency to self-ignition and its petrographic properties. However, a strong negative correlation between the rate of spontaneous combustion and moisture and volatile matter content was observed. In the case of bituminous coal, strong correlations between self-combustion parameters and various coal properties were confirmed. The most noteworthy are the correlations between self-ignition parameters and the maceral composition, that is, between the content of macerals of the inertinite group in coal and content of macerals of the huminite/vitrinite group. The obtained results suggest that the spontaneous combustion tendency of coal increases with the increasing content of semifusinite and liptinite.
Activated T Cells Expression by Increasing Nuclear Factor of T Cell Priming Enhances IL-4 Gene

The repetitive activation of T cells (priming) enhances the expression of many cytokines, such as IL-4, but not others, such as IL-2. Molecular mechanisms underlying selective expression of cytokines by T cells remain poorly understood. Here we show that priming of CD4 T cells selectively enhances IL-4 expression relative to IL-2 expression by a transcriptional mechanism involving nuclear factor of activated T cells (NFAT) proteins. As detected by in vivo footprinting, priming markedly increases the activation-dependent engagement of the P0 and P1 NFAT-binding elements of the IL-4 promoter. Moreover, each proximal P element is essential for optimal IL-4 promoter activity. Activated primed CD4 T cells contain more NFAT1 and support greater NFAT-directed transcription than unprimed CD4 T cells, while activator protein 1 binding and activator protein 1-mediated transcription by both cell types is similar. Increased expression of wild-type NFAT1 substantially increases IL-4 promoter activity in unprimed CD4 T cells, suggesting NFAT1 may be limiting for IL-4 gene expression in this cell type. Furthermore, a truncated form of NFAT1 acts as a dominant-negative, reducing IL-4 promoter activity in primed CD4 T cells and conﬁrming the importance of endogenous NFAT to increased IL-4 gene expression by effector T cells. NFAT1 appears to be the major NFAT family member responsible for the initial increased expression of IL-4 by primed CD4 T cells. The Journal of Immunology, 1999, 162: 860–870. production primed CD4 T cells enhanced transcription directed by the immediate 5 9 ﬂank promoter of the IL-4 gene. We further show that both proximal P elements of the IL-4 promoter, P0 and P1, are occupied in vivo during IL-4 transcription in normal human T cells, and that both are independently required for optimal IL-4 reporter gene activity in this cell type. An analysis of nuclear protein complexes formed in vitro with an IL-4 P1 element oligonucleotide demonstrates that NFAT1 is the major NFAT protein forming a speciﬁc complex in fresh and primed CD4 T cells, and that there is a striking and direct correlation between the abundance of NFAT1 in the nucleus and IL-4 gene expression. Impor-tantly, expression of wild-type and truncated NFAT1 constructs demonstrates that NFAT1 is a limiting component for IL-4 gene transcription by fresh CD4 T cells and also plays a role in the enhanced level of IL-4 gene expression by primed T cells. Western blot analysis and IL-4 promoter-directed reporter gene studies involving transient overexpression of various NFAT family members both indicate that NFAT1, rather than other NFAT family members, contributes to increased IL-4 gene expression by primed human CD4 T cells. Together, these results suggest that an increase in NFAT1 in primed CD4 T cells is an important mechanism by which priming of T cells enhances the expression of IL-4. This mechanism, or a similar one, may also contribute to the increased expression of other cytokines by primed T cells, a critical event in T cell-mediated immunity.
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University Growing through the merger and acquisition Growing through the merger and acquisition

The paper studies with an endogenous growth model how the merger and acquisition (M&A) affects the aggregate growth rate. We model the M&A as a capital reallocation process, which can increase both productivity and growth rates of ﬁrms. The model is tractable and greatly consistent with patterns observed in the M&A at the micro level. Matching our model to the data, we ﬁnd that prohibiting the M&A would lead to the re- duction of the aggregate growth rate of US economy by 0.1% and the reduction of the aggregate TFP by 5%.
Utility Theory for Sequential Decision Making

The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function. We extend these axioms to increasingly structured sequential decision making settings and identify the structure of the corresponding utility functions. In particular, we show that memoryless preferences lead to a utility in the form of a per transition reward and multiplicative factor on the future return. This result motivates a generalization of Markov Decision Processes (MDPs) with this structure on the agent's returns, which we call Affine-Reward MDPs. A stronger constraint on preferences is needed to recover the commonly used cumulative sum of scalar rewards in MDPs. A yet stronger constraint simplifies the utility function for goal-seeking agents in the form of a difference in some function of states that we call potential functions. Our necessary and sufficient conditions demystify the reward hypothesis that underlies the design of rational agents in reinforcement learning by adding an axiom to the VNM rationality axioms and motivates new directions for AI research involving sequential decision making.
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting

Keyword spotting is the task of detecting a keyword in streaming audio. Conventional keyword spotting targets predefined keywords classification, but there is growing attention in few-shot (query-by-example) keyword spotting, e.g., N-way classification given M-shot support samples. Moreover, in real-world scenarios, there can be utterances from unexpected categories (open-set) which need to be rejected rather than classified as one of the N classes. Combining the two needs, we tackle few-shot open-set keyword spotting with a new benchmark setting, named splitGSC. We propose episode-known dummy prototypes based on metric learning to detect an open-set better and introduce a simple and powerful approach, Dummy Prototypical Networks (D-ProtoNets). Our D-ProtoNets shows clear margins compared to recent few-shot open-set recognition (FSOSR) approaches in the suggested splitGSC. We also verify our method on a standard benchmark, miniImageNet, and D-ProtoNets shows the state-of-the-art open-set detection rate in FSOSR.
Studying Generalization Through Data Averaging

The generalization of machine learning models has a complex dependence on the data, model and learning algorithm. We study train and test performance, as well as the generalization gap given by the mean of their difference over different data set samples to understand their ``typical"behavior. We derive an expression for the gap as a function of the covariance between the model parameter distribution and the train loss, and another expression for the average test performance, showing test generalization only depends on data-averaged parameter distribution and the data-averaged loss. We show that for a large class of model parameter distributions a modified generalization gap is always non-negative. By specializing further to parameter distributions produced by stochastic gradient descent (SGD), along with a few approximations and modeling considerations, we are able to predict some aspects about how the generalization gap and model train and test performance vary as a function of SGD noise. We evaluate these predictions empirically on the Cifar10 classification task based on a ResNet architecture.
Linear programming-based solution methods for constrained POMDPs

Constrained partially observable Markov decision processes (CPOMDPs) have been used to model various real-world phenomena. However, they are notoriously diﬃcult to solve to optimality, and there exist only a few approximation methods for obtaining high-quality solutions. In this study, we use grid-based approximations in combination with linear programming (LP) models to generate approximate policies for CPOMDPs. We consider ﬁve CPOMDP problem instances and conduct a detailed numerical study of both their ﬁnite and inﬁnite horizon formulations. We ﬁrst establish the quality of the approximate unconstrained POMDP policies through a comparative analysis with exact solution methods. We then show the performance of the LP-based CPOMDP solution approaches for varying budget levels (i.e., cost limits) for different problem instances. Finally, we show the ﬂexibility of LP-based approaches by applying deterministic policy constraints, and investigate the impact that these constraints have on collected rewards and CPU run time. Our analysis demonstrates that LP models can eﬀectively generate approximate policies for both ﬁnite and inﬁnite horizon problems, while providing the ﬂexibility to incorporate various additional constraints into the underlying model.
Finite features of quantum de Sitter space

We consider degrees of freedom for a quantum de Sitter spacetime. The problem is studied from both a Lorentzian and a Euclidean perspective. From a Lorentzian perspective, we compute dynamical properties of the static patch de Sitter horizon. These are compared to dynamical features of a black hole. We point out differences suggestive of non-standard thermal behaviour for the de Sitter horizon. We establish that geometries interpolating between an asymptotically AdS 2×S2 space and a dS4 interior are compatible with the null energy condition, albeit with a non-standard decreasing radial size of S 2. The putative holographic dual of an asymptotic AdS2 spacetime is comprised of a finite number of underlying degrees of freedom. From a Euclidean perspective we consider the gravitational path integral for fields over compact manifolds. In two-dimensions, we review Polchinski’s BRST localisation of Liouville theory and propose a supersymmetric extension of timelike Liouville theory which exhibits supersymmetric localisation. We speculate that localisation of the Euclidean gravitational path integral is a reflection of a finite number of degrees of freedom in a quantum de Sitter Universe.
The Galactic Nova Rate: Estimates from the ASAS-SN and Gaia Surveys

We present the first estimate of the Galactic nova rate based on optical transient surveys covering the entire sky. Using data from the All-Sky Automated Survey for Supernovae (ASAS-SN) and Gaia—the only two all-sky surveys to report classical nova candidates—we find 39 confirmed Galactic novae and 7 additional unconfirmed candidates discovered from 2019 to 2021, yielding a nova discovery rate of ≈14 yr−1. Using accurate Galactic stellar mass models and three-dimensional dust maps and incorporating realistic nova light curves, we have built a sophisticated Galactic nova model to estimate the fraction of Galactic novae discovered by these surveys over this time period. The observing capabilities of each survey are distinct: the high cadence of ASAS-SN makes it sensitive to fast novae, while the broad observing filter and high spatial resolution of Gaia make it more sensitive to highly reddened novae across the entire Galactic plane and bulge. Despite these differences, we find that ASAS-SN and Gaia give consistent Galactic nova rates, with a final joint nova rate of 26 ± 5 yr−1. This inferred nova rate is substantially lower than found by many other recent studies. Critically assessing the systematic uncertainties in the Galactic nova rate, we argue that the role of faint, fast-fading novae has likely been overestimated, but that subtle details in the operation of transient alert pipelines can have large, sometimes unappreciated effects on transient recovery efficiency. Our predicted nova rate can be directly tested with forthcoming red/near-infrared transient surveys in the southern hemisphere.
Constraining the Planet Occurrence Rate around Halo Stars of Potentially Extragalactic Origin

The search for planets orbiting other stars has recently expanded to include stars from galaxies outside the Milky Way. With the Transiting Exoplanet Survey Satellite (TESS) and Gaia surveys, photometric and kinematic information can be combined to identify transiting planet candidates of extragalactic origin. Here, 1080 low-luminosity red-giant branch stars observed by Gaia and TESS with kinematics suggesting a high likelihood of extragalactic origin were searched for planet transits. Transit injection-recovery tests were performed to measure the sensitivity of the TESS data and completeness of the transit search. Injected signals of planets larger than Jupiter with orbital periods of 10 days or less were recovered in ≈44% of cases. Although no planet transits were detected in this sample, we find an upper limit on planet occurrence of 0.52% for hot Jupiters, consistent with previous studies of planet occurrence around similar host stars. As stars in the halo tend to be lower metallicity, and short-period giant planet occurrence tends to be strongly correlated with stellar metallicity, we predict that relative to the Galactic disk population, a smaller fraction of halo stars will host planets detectable by transit surveys. Thus, applying the known planet occurrence trends to potential planet detection around halo stars, we predict ≳13,000 stars must be searched with similar cadence and precision as the stars studied here before a detection of a planet of extragalactic origin is likely. This may be possible with future data releases from the TESS and Gaia missions.
LIMFAST. I. A Seminumerical Tool for Line Intensity Mapping

We present LIMFAST, a seminumerical code for simulating high-redshift galaxy formation and cosmic reionization as revealed by multitracer line intensity mapping (LIM) signals. LIMFAST builds upon and extends the 21cmFAST code widely used for 21 cm cosmology by implementing state-of-the-art models of galaxy formation and evolution. The metagalactic radiation background, including the production of various star formation lines, together with the 21 cm line signal tracing the neutral intergalactic medium (IGM), is self-consistently described by photoionization modeling and stellar population synthesis coupled to the galaxy formation model. We introduce basic structure and functionalities of the code, and demonstrate its validity and capabilities by showing broad agreements between the predicted and observed evolution of cosmic star formation, IGM neutral fraction, and metal enrichment. We also present the LIM signals of 21 cm, Lyα, Hα, Hβ, [O ii], and [O iii] lines simulated by LIMFAST, and compare them with results from the literature. We elaborate on how several major aspects of our modeling framework, including models of star formation, chemical enrichment, and photoionization, may impact different LIM observables and thus become testable once applied to observational data. LIMFAST aims at being an efficient and resourceful tool for intensity mapping studies in general, exploring a wide range of scenarios of galaxy evolution and reionization and frequencies over which useful cosmological signals can be measured.
Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns

— Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are strongly distorted or hidden by certain inﬂuential data attributes. This paper presents a feature learning framework, FEALM, designed to generate an optimized set of data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are signiﬁcantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, called neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM’s effectiveness through experiments using synthetic datasets and multiple case studies on real-world datasets. UMAP i , UMAP on the UMAP proximities the similarities of the corresponding UMAP results. belonging each
The role of unobservable characteristics in friendship network formation

Inbreeding homophily is a prevalent feature of human social networks with important individual and group-level social, economic, and health consequences. The literature has proposed an overwhelming number of dimensions along which human relationships might sort, without proposing a unified empirically-grounded framework for their categorization. We exploit rich data on a sample of University freshmen with very similar characteristic - age, race and education- and contrast the relative importance of observable vs. unobservables characteristics in their friendship formation. We employ Bayesian Model Averaging, a methodology explicitly designed to target model uncertainty and to assess the robustness of each candidate attribute while predicting friendships. We show that, while observable features such as assignment of students to sections, gender, and smoking are robust key determinants of whether two individuals befriend each other, unobservable attributes, such as personality, cognitive abilities, economic preferences, or socio-economic aspects, are largely sensible to the model specification, and are not important predictors of friendships.
Correlation of the dynamic contact angle with the capillary number and its hysteresis

The moving contact-line problem is of both theoretical and practical interest. The dynamic contact angle changes with the capillary number defined by the contact-line speed, and the correlation also depends on the equilibrium contact angle measured at the static state. This correlation is usually required as an input to the traditional solvers based on the Navier-Stokes-like equations, but it is simulated as an output in the current study using the lattice Boltzmann method (LBM) in a displacement process of two-immiscible fluids. The macroscopic theory and the molecular dynamics (MD) simulation had shown a linear scaling law for the cosine of dynamic contact angle, which is also observed in the previous LBM study in a short range of small capillary numbers and for two neutral wetting conditions. However, our study shows that this linear scaling law holds in the whole range of capillary numbers and is universal for all wetting conditions. In a special case of complete wetting (spreading) with a zero equilibrium contact angle, a thin film of the wetting fluid occurs when the wettability is very strong, which leads to a hysteresis that substantial capillary number is required to initiate the deviation of the dynamic contact angle from its equilibrium state. This observation is consistent with the previous report on a new mechanism for the static contact angle hysteresis due to the presence of free liquid films. With an increasing capillary number, the fluid-fluid interface starts oscillating before fingering. Different fingering patterns are observed for cases with different equilibrium contact angles.
Spin-foams as semiclassical vertices: Gluing constraints and a hybrid algorithm

Numerical methods in spin-foam models have significantly advanced in the last few years, yet challenges remain in efficiently extracting results for amplitudes with many quantum degrees of freedom. In this paper we sketch a proposal for a ``hybrid algorithm'' that would use both the full quantum amplitude and its asymptotic approximation in the relevant regimes. As a first step towards the algorithm, we derive a new representation of the partition function where each spin-foam vertex possesses its own coherent data, such that it can be individually asymptotically approximated. We do this through the implementation of gluing constraints between vertices, which we study numerically. We further derive an asymptotic expression for the constraints for arbitrary boundary data, including data for which there are no critical points. From this new representation we conjecture an intermediate quasi-geometric spin-foam regime describing a superposition of semi-classical vertices glued in a non-matching way via the gluing constraints.
SINC: Service Information Augmented Open-Domain Conversation

Generative open-domain dialogue systems can beneﬁt from external knowledge, but the lack of external knowledge resources and the difﬁ-culty in ﬁnding relevant knowledge limit the development of this technology. To this end, we propose a knowledge-driven dialogue task using dynamic service information. Specifically, we use a large number of service APIs that can provide high coverage and spatiotemporal sensitivity as external knowledge sources. The dialogue system generates queries to request external services along with user information, get the relevant knowledge, and generate responses based on this knowledge. To implement this method, we collect and release the ﬁrst open domain Chinese service knowledge dialogue dataset DuS-inc. At the same time, we construct a base-line model PLATO-SINC, which realizes the automatic utilization of service information for dialogue. Both automatic evaluation and human evaluation show that our proposed new method can signiﬁcantly improve the effect of open-domain conversation, and the session-level overall score in human evaluation is improved by 59.29% compared with the dialogue pre-training model PLATO-2. The dataset and benchmark model will be open sourced.