Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-perturbative ⟨ϕ⟩, ⟨ϕ2 ⟩ and the dynamically generated scalar mass with Yukawa interaction in the inflationary de Sitter spacetime

We consider a massless minimally coupled self interacting quantum scalar field coupled to fermion via the Yukawa interaction, in the inflationary de Sitter background. The fermion is also taken to be massless and the scalar potential is taken to be a hybrid, V(ϕ) = λϕ 4/4! + βϕ 3/3! (λ>0). The chief physical motivation behind this choice of V(ϕ) corresponds to, apart from its boundedness from below property, the fact that shape wise V(ϕ) has qualitative similarity with standard inflationary classical slow roll potentials. Also, its vacuum expectation value can be negative, suggesting some screening of the inflationary cosmological constant. We choose that ⟨ϕ⟩∼ 0 at early times with respect to the Bunch-Davies vacuum, so that perturbation theory is valid initially. We consider the equations satisfied by ⟨ϕ (t) ⟩ and ⟨ϕ 2(t) ⟩, constructed from the coarse grained equation of motion for the slowly rolling ϕ. We then compute the vacuum diagrammes of various relevant operators using the in-in formalism up to three loop, in terms of the leading powers of the secular logarithms. For a closed fermion loop, we have restricted ourselves here to only the local contribution. These large temporal logarithms are then resummed by constructing suitable non-perturbative equations to compute ⟨ϕ⟩ and ⟨ϕ 2 ⟩. ⟨ϕ⟩ turns out to be at least approximately an order of magnitude less compared to the minimum of the classical potential, -3β/λ, owing to the strong quantum fluctuations. For ⟨ϕ 2 ⟩, we have computed the dynamically generated scalar mass at late times, by taking the appropriate purely local contributions. Variations of these quantities with respect to different couplings have also been presented.
Optimal Dorfman Group Testing For Symmetric Distributions (preprint)

We study Dorfman's classical group testing protocol in a novel setting where individual specimen statuses are modeled as exchangeable random variables. We are motivated by infectious disease screening. In that case, specimens which arrive together for testing often originate from the same community and so their statuses may exhibit positive correlation. Dorfman's protocol screens a population of n specimens for a binary trait by partitioning it into nonoverlapping groups, testing these, and only individually retesting the specimens of each positive group. The partition is chosen to minimize the expected number of tests under a probabilistic model of specimen statuses. We relax the typical assumption that these are independent and indentically distributed and instead model them as exchangeable random variables. In this case, their joint distribution is symmetric in the sense that it is invariant under permutations. We give a characterization of such distributions in terms of a function q where q(h) is the marginal probability that any group of size h tests negative. We use this interpretable representation to show that the set partitioning problem arising in Dorfman's protocol can be reduced to an integer partitioning problem and efficiently solved. We apply these tools to an empirical dataset from the COVID-19 pandemic. The methodology helps explain the unexpectedly high empirical efficiency reported by the original investigators.
Evaluating an integrated system supporting multiple information-seeking strategies

Many studies have demonstrated that people engage in a variety of different information behaviors when engaging in information seeking. However, standard information retrieval systems such as Web search engines continue to be designed to support mainly one such behavior, specified searching. This situation has led to suggestions that people would be better served by information retrieval systems which support different kinds of information-seeking strategies. This article reports on an experiment comparing the retrieval effectiveness of an integrated interactive information retrieval (IIR) system which adapts to support different information-seeking strategies with that of a standard baseline IIR system.The experiment, with 32 participants each searching on eight different topics, indicates that using the integrated IIR system resulted in significantly better user satisfaction with search results, significantly more effective interaction, and significantly better usability than that using the baseline system.
A CONTINUOUS MULTIPLE HYPOTHESIS TESTING FRAMEWORK FOR OPTIMAL EXOPLANET DETECTION BY NATHAN

The detection of exoplanets is hindered by the presence of complex astrophysical and instrumental noises. Given the difficulty of the task, it is important to ensure that the data are exploited to their fullest potential. In the present work, we search for an optimal exoplanet detection criterion. We adopt a general Bayesian multiple hypothesis testing framework, where the hypotheses are indexed by continuous variables. This framework is adaptable to the different observational methods used to detect exoplanets as well as other data analysis problems. We describe the data as a combination of several parametrized patterns and nuisance signals. We wish to determine which patterns are present, and for a detection to be valid, the parameters of the claimed pattern have to correspond to a true one with a certain accuracy. We search for a detection criterion minimizing false and missed detections, either as a function of their relative cost, or when the expected number of false detections is bounded. We find that if the patterns can be separated in a technical sense, the two approaches lead to the same optimal procedure. We apply it to the retrieval of periodic signals in unevenly sampled time series, emulating the search for exoplanets in radial velocity data. We show on a simulation that, for a given tolerance to false detections, the new criterion leads to 15 to 30% more true detections than other criteria, including the Bayes factor.
On the variability and dependence of human leg stiffness across strides during running and some consequences for the analysis of locomotion data

Typically, animal locomotion studies involve consecutive strides, which are frequently assumed to be independent with parameters that do not vary across strides. This assumption is often not tested. However, failing in particular to account for dependence across strides may cause an incorrect estimate of the uncertainty of the measurements and thereby lead to either missing (overestimating variance) or over-evaluating (underestimating variance) biological signals. In turn, this impacts replicability of the results because variability is accounted for differently across experiments. In this paper, we analyse the changes of a couple of measures of human leg stiffness across strides during running experiments, using a publicly available dataset. A major finding of this analysis is that the time series of these measurements of stiffness show autocorrelation even at large lags and so there is dependence between individual strides, even when separated by many intervening strides. Our results question the practice in biomechanics research of using each stride as an independent observation or of sub-selecting strides at small lags. Following the outcome of our analysis, we strongly recommend caution in doing so without first confirming the independence of the measurements across strides and without confirming that sub-selection does not produce spurious results.
Black-tailed prairie dog status and future conservation planning

The black-tailed prairie dog is one of five prairie dog species estimated to have once occupied up to 100 million ha or more in North America. The area occupied by black-tailed prairie dogs has declined to approximately 2% of its former range. Conversion of habitat to other land uses and widespread prairie dog eradication efforts combined with sylvatic plague, Yersinia pestis, have caused significant reductions. Although, the species itself is not in imminent jeopardy of extinction, its unique ecosystem is jeopardized by continuing fragmentation and isolation.
Time Series and Machine Learning methods for Demand Forecasting: a case study of machine seller in prefabricated concrete industry

In this research, Time series and Machine learning methods are conducted to find a suitable monthly demand forecasting models and parameters for a medium-scaled machine seller company in Thailand, who is currently using Simple Moving Average as the current method. Historical demand data are collected for a half decade and used as a training set and cross validation set for different forecasting models (Exponential Smoothing, Autoregressive Integrated Moving Average and Long-Short term memory). The history of demand indicates that the patterns are composed of many types of time series components, including stationary, trend and seasonality. The performance of models is measured by Relative Total Absolute Error (RTAE) because some months contain zero value of demand. The results show that Long-Short term memory is the most suitable method as compared to others.
Pflegende Angehörige – erkennen, Probleme identifizieren, Lösungen finden

Bei Multimorbiden, auch mit psychischen Komorbiditäten, werden bei Verschlechterung ambulante bzw. sogar stationäre pflegerische Maßnahmen erforderlich. Komplexe Situationen und oft sich verschlechternde Bedingungen für Pflegebedürftige und deren Angehörige benötigen individuelle Entlastungs- und Unterstützungsangebote. Hausärztliche Praxisteams kennen die schwierigen Betreuungs- bzw. Pflegesituationen und können so sinnvoll agieren.
Stimulating Online Consumers’ Purchase Intention through Social Media Marketing: Examining the Role of Brand Trust Building

With the rapid development of the digital economy, social media has become an important channel for enterprises to conduct marketing behaviors. However, massive online information with uneven quality hinders consumers from building brand trust, which poses challenges for enterprises to implement efficient marketing strategies. Drawing on the relevant theories of brand trust, we explore the determinants of brand trust and its effect on consumers’ purchase intention. Using survey data collected from Douyin users, we identify several main findings. First of all, information accuracy positively affects brand trust, whereas information timeliness and perceived risk have negative effects on it. In addition, enterprises with higher brand trust will receive more purchase intention from consumers. Our findings provide new insights into social media literature and management implications for enterprises to formulate suitable marketing strategies.
Gap‐coupled designs of star shape microstrip antennas for dual band and wide band circular polarized response

Gap‐coupled designs of star‐shape microstrip antennas for dual band and wide band circular polarized response are proposed. An angular displacement between fed and parasitic patches yields the optimum separation between patch orthogonal resonant modes to yield circular polarized response. In dual band design, axial ratio bandwidth of 8% is obtained whereas wideband design gives axial ratio bandwidth of nearly 29%. Proposed configurations exhibit broadside pattern with peak gain of more than 7 dBi. Resonant length formulations for star shape patch and for their dual and wide band designs are presented. They provide guidelines for redesigning similar antenna at different frequency.
Smart Services Implementation in Smart Cities: A Comprehensive Review of State-of-the-art Technologies

This paper discusses comprehensively the advanced technologies used in the implementation of smart services, which are key elements of a smart city. Smart cities are becoming eco-friendly, livable, and sustainable to deliver essential services to their citizens by leveraging internet and communication technology advancements. Starting from a short literature review about smart cities and smart services, a recent trend in the technology used to implement smart services is collected. Smart service can be defined as a service conveyed using intelligent products that possess awareness and connectivity. All kinds of smart services can be connected in smart cities to offer citizens comprehensive solutions. Pre-emptive services, self-services, or highly interactive services, for example, have the potential to be implemented using current technology such as the internet of things, artificial intelligence, cloud computing, big data, machine learning, machine-to-machine communications, and the digital twin. Therefore, there is scope for research and implementation of other technology for smart services in the future.
The Influence of Footwear Midsole Thickness on Running Economy and Frontal Plane Ankle Stability

Abstract Despite being an intricate part of advanced footwear technology, the impact of midsole thickness is not well understood. While some individuals have speculated an increased midsole thickness might bequeath an unfair performance advantage, others have suggested it may have a detrimental impact on frontal plane ankle stability. Therefore, the purpose of this study was to determine the influence of midsole thickness on running economy, and frontal plane ankle stability. Twenty one recreational runners (ten female, and eleven male) participated in this study, performing five-minute treadmill running trials in four footwear conditions. The footwear conditions were nearly identical except for their midsole thicknesses which ranged from 35- to 50-mm in increments of 5-mm. Midsole thickness was not found to influence the energetic cost of running however, it was found to decrease frontal plane ankle stability. Given these results World Athletic restrictions on midsole thickness may be unnecessary and worthy of reconsideration.
Artificial Intelligence with Python

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated and revised to Python 3.x, and TensorFlow 2 Seven new chapters that include AI on the cloud, RNNs and DL models, feature engineering, the machine learning data pipeline, and more New author with 25 years of experience in artificial intelligence across multiple industries and enterprise domains Book Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x and TensorFlow 2. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Understand RNNs and various DL models Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
Robust Out-of-distribution Detection in Neural Networks

Detecting anomalous inputs is critical for safely deploying deep learning models in the real world. Existing approaches for detecting out-of-distribution (OOD) examples work well when evaluated on natural samples drawn from a sufﬁciently different distribution than the training data distribution. However, in this paper, we show that existing detection mechanisms can be extremely brittle when evaluating on inputs with minimal adversarial perturbations which don’t change their semantics. Formally, we introduce a novel and challenging problem, Robust Out-of-Distribution Detection , and propose an algorithm that can fool existing OOD detectors by adding small perturbations to the inputs while preserving their semantics and thus the distributional membership. We take a ﬁrst step to solve this challenge, and propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples. Our method can be ﬂexibly combined with, and render existing meth-ods robust. On common benchmark datasets, we show that ALOE substantially improves the robustness of state-of-the-art OOD detection, with 58.4% AUROC improvement on CIFAR-10 and 46.59% improvement on CIFAR-100. Finally, we provide theoretical analysis for our method, underpinning the empirical results above.
Inferring the Hoxa1 Gene Regulatory Network in Mouse Embryonic Stem Cells: Time-Series RNA-seq Data and Computational Modeling Approach

The homeotic gene Hoxa1 plays a pivotal role in regulating embryonic pattern formation and morphogenesis during mouse embryogenesis. However, despite the identification of a number of putative Hoxa1 target genes, the intricate regulatory relationships between these targets remain largely elusive. Leveraging the advancements in high-throughput technologies and sophisticated computational methods, we aimed to infer the Gene Regulatory Networks (GRNs) governed by Hoxa1 that direct cellular function, morphology, and/or differentiation. To achieve this, we generated time-series RNA-seq data from Retinoic Acid (RA)-treated Wild Type versus Hoxa1-null mouse ES cells, enabling the construction of the Hoxa1 GRN. To create this GRN, we employed NARROMI, a published technique known for its noise reduction capabilities and improved accuracy in gene-regulation inference. Using this technique, we identified putative direct and indirect connections between Hoxa1 and a set of genes with known relevance in embryonic development. Validation through qPCR confirmed the Hoxa1-dependence on mRNA expression for selected genes, both within the immediate vicinity (direct) and in secondary interactions (indirect). Furthermore, by mapping the candidate genes to relevant Gene Ontology (GO) networks, we verified their involvement in processes likely regulated by Hoxa1. Our findings provide compelling evidence supporting the accuracy of the NARROMI analysis in generating a hierarchical network of genes under the transcriptional control of Hoxa1 Transcription Factor (TF), specifically in mouse ES cells. This network reveals a pool of promising candidate genes that may function as direct targets of Hoxa1. However, further investigations, including the characterization of Hoxa1 protein interactions with target loci DNA, are necessary to confirm their direct regulatory relationship with this TF. Moreover, the time-series RNA-seq data from Wild Type ES cells, coupled with the methodology employed in this study, hold the potential for constructing GRNs for additional TFs activated by RA. This comprehensive approach can shed further light on the intricate regulatory networks governing cellular function, morphology, and differentiation, advancing our understanding of embryonic development and gene regulation processes.
Influence of functional and phylogenetic dissimilarity on exotic plant invasion depends on spatial scale

Darwin’s theory of natural selection provides two seemingly contradictory hypotheses for explaining the success of introduced species: 1) the pre-adaptation hypothesis posits that introduced species that are closely related to native species will be more likely to succeed than distantly related invaders because they already possess relevant characteristics; 2) the limiting-similarity hypothesis posits that invaders that are more similar to resident species will be less likely to succeed due to competitive exclusion. Previous studies assessing this conundrum show mixed results, possibly stemming from variation in study spatial scales and lack of both functional and phylogenetic information. We used species abundances compiled in a 33-year grassland successional survey based at Cedar Creek Ecosystem Science Reserve (USA) to assess the support for the pre-adaptation and limiting similarity hypotheses at two different spatial scales (neighbourhood scale of 0.5m2, site scale of ~40m2). We combined compositional surveys of 303 vascular plant taxa (256 native, 47 introduced) taken across 2700 plots in a chronosequence of abandonment from agriculture with species functional dissimilarities, phylogenetic distances, environmental covariates and information on species origin. Our results consistently supported the pre-adaptation hypothesis at the site scale but diverged at neighbourhood scale, with functional dissimilarity supporting the limiting similarity hypothesis and phylogenetic distance supporting the pre-adaptation hypothesis. Introduced species with low leaf dry matter content (LDMC), low height and high seed mass tended to be most abundant than rest of species, while relationships between species abundance and specific leaf area (SLA) varied with scale. Introduced species were more abundant than natives at higher concentrations of soil N but were less abundant than natives over time. Synthesis: Our study highlights the importance of environmental filtering on grassland community assembly at the scale of a site – here 40 m2, a spatial resolution that is usually considered “local”. This influence of environmental filtering might mask effects of limiting similarity at small “local” scales. Our results demonstrate the importance of accounting for both phylogenetic and functional dissimilarity when examining the complex interaction between species biogeographic origin, functional strategies and evolutionary history as considering one alone can lead to different conclusions.