Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchy of Multinomial Models for Multidimensional Source Monitoring

Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information (Meiser & Broder, 2002), for partial source memory (Dodson, Holland, & Shimamura, 1998) and for several sources (Batchelder, Hu, & Riefer, 1994). The hierarchical relationship allows model comparisons and facilitates the specification of identifiability conditions. Conditions for global identifiability are discussed, and model comparisons are illustrated by reanalyses and by a new experiment on the storage and retrieval of multidimensional source information.
Multipath Congestion Control: Measurement, Analysis, and Optimization From the Energy Perspective

Multipath TCP (MPTCP) is a promising TCP extension that exploits different Internet paths between a pair of hosts to obtain high aggregate throughput, while it also brings the concern of energy consumption. There has been a lively interest in the design of energy-efficient MPTCP. The research community, however, lacks a comprehensive understanding of which components in an MPTCP congestion control algorithm play the fundamental role in energy efficiency, how various algorithms compare against each other from energy-consuming perspective, or whether there exist potentially better solutions for energy saving. This article takes the first step towards answering these questions. Through realworld MPTCP Linux kernel experiments, we show that the energy consumption is related to three major aspects: average throughput, path delay and different network scenarios. In order to bridge congestion control to the three aspects, we analyze the existing algorithms and capture the essential parameters of multipath congestion control model related to energy efficiency. Then we propose a window increase factor to shift traffic to low-delay energy-efficient paths. To further extend this design, we apply an energy-aware compensative parameter to fit the general hierarchical Internet topology. Our evaluation indicates that the enhanced congestion control module can successfully improve MPTCP energy efficiency without affecting its transmission performance.
Measurements of rotational transform due to noninductive toroidal current using motional Stark effect spectroscopy in the Large Helical Device

The change of rotational transform due to noninductive plasma current driven by a negative neutral beam, which has a value typically less than 10% of total rotational transform determined by the external current in the helical coils, is measured in the Large Helical Device with motional Stark effect spectroscopy. Radial profiles of rotational transform are derived from the radial profiles of the polarization angle of the σ component in the Hα line emitted from the high energy hydrogen atom of the beam with four sets of linear polarizers, spectrometers, and CCD detectors. The radial profile of the change in the rotational transform due to the noninductive toroidal current driven by the neutral beam is measured.
Evaluation of the effects of a honey‑based gel on blood redox biomarkers and the physiological profile of healthy adults: A pilot study

Honey is a natural product derived from the insect Apis mellifera. Approximately 200 different compounds are included, making it a complex mixture with antimicrobial, antioxidant, and antidiabetic activity. Flavonoids and phenolic acids contained in honey are associated with its antioxidant capacity via mechanisms such as hydrogen donation and metallic ion chelation, although the exact antioxidant mechanism remains unknown. The aim of the present study was to: i) Estimate the antioxidant activity of a natural honey-based gel, commercially available under the trade name of ‘Bear Strength honey gel’ and to ii) assess the physiological and redox adjustments obtained after its consumption in healthy adult participants. For this purpose, 20 healthy participants (10 men and 10 women) included in their habitual diet 70 g of the honey-based gel for 14 days in a row. Pre- and post-consumption, physiological [weight, height, body mass index, body fat, waist-to-hip ratio, resting heart rate and blood pressure (BP)] and hematological (complete blood count) data were evaluated, along with the levels of five redox biomarkers: Glutathione (GSH), catalase (CAT), total antioxidant capacity (TAC), protein carbonyls (PCARBS) and thiobarbituric reactive substances (TBARS). The results revealed that the honey-based gel decreased the diastolic and mean arterial BP, especially in women, without affecting the rest of the physiological and hematological variables. Regarding the changes observed in antioxidant status variables, GSH was increased both in the total and women's group, while TAC was increased in all groups post-consumption. No changes were detected in the levels of CAT. Regarding oxidative stress, a decrease in the levels of TBARS in the total and women's group, was observed. PCARBS levels were decreased post-consumption only in the women's group. In conclusion, the present study demonstrated the potential positive effects of a honey-based gel on BP and redox status of healthy adults in a sex-specific manner.
Organoids in high-throughput and high-content screenings

Organoids are self-organized three-dimensional (3D) multicellular tissue cultures which derive from cancerous and healthy stem cells, sharing a highly similarity to the corresponding in vivo organs. Since their introduction in 2009, they have emerged as a valuable model for studying early embryogenesis, organ and tissue development, as well as tools in drug screening, disease modeling and personalized therapy. Organoids can now be established for various tissues, including brain, retina, thyroid, gastrointestinal, lung, liver, pancreas, and kidney. These micro-tissues resemble the native organ in terms of gene expression, protein expression, tissue architecture and cell-cell interactions. Despite the success of organoid-based research and the advances in patient-derived organoid culture, important challenges remain. In this review, we briefly showcase the evolution from the primary 3D systems to complex, multilayered 3D structures such as assembloids, gastruloids and ETiX embryoids. We discuss current developments in organoid research and highlight developments in organoid culturing systems and analysis tools which make organoids accessible for high-throughput and high-content screening. Finally, we summarize the potential of machine learning and computational modeling in conjunction with organoid systems.
Internal dynamics of the dwarf elliptical NGC 185

Kinematic observations along the major and minor axis of the E2 dwarf elliptical galaxy NGC 185 are presented. The velocity dispersion is constant at 28 ± 8 km/s between 3″ and 40″, but may increase to about twice that value in the center. An upper limit of 10 km/s is found for the rotation along either axis, so that the velocity distribution is anisotropic. The derived M/L B is ∼3 in solar units. Analysis of the kinematic and photometric data now available shows that giant ellipticals and dwarfs fall on a continuous sequence in the (L,σ) plane
Rotation of Polarization Angle in Gamma-Ray Burst Prompt Phase

The rotations of the polarization angle (PA) with time (energy) can lead to the depolarization of the time-integrated (energy-integrated) polarization. However, we do not know how and when it will rotate. Here, we consider a magnetic reconnection model to investigate the polarizations, especially the PA rotations of gamma-ray burst (GRB) prompt emission. For a large-scale ordered aligned magnetic field configuration, we find that within T 50 PAs will rotate with time (energy) for slight off-axis observations. Our studies show that the rotations of the PAs are due to changes in the observed image of the emitting region before being averaged over the equal arrival time surface for the unresolved sources. We apply our model to the single-pulse bursts of GRB 170101A and GRB 170114A with time-resolved PA observations. We find it can interpret the violent PA variation of GRB 170101A, while the model could not predict the two 90° PA changes in GRB 170114A. A more detailed model should be considered.
Accuracy assessment on multi‐temporal land‐cover change detection using a trajectory error matrix

This study develops a practical methodology to assess the accuracy of multi‐temporal change detection using a trajectory error matrix (TEM). In this error matrix one axis represents the land‐cover change trajectory categories derived from single‐date classified images, and the other represents the land‐cover change trajectories identified from reference data. The overall accuracies of change trajectories and states of change/no‐change are used as indices for accuracy assessment. As the number of possible land‐cover change trajectories can be enormous, a practical processing flow for computing accuracy assessment indices has also been developed to avoid listing all possible change trajectories in the error matrix. A case study using this method was conducted to assess the accuracy of land‐cover change over a period with five observations in a study area in China's arid zone. This method simplifies the process of estimating overall accuracy in the change trajectory analysis, and provides a more realistic and detailed assessment of the results of multi‐temporal change detection using post‐classification comparison methods.
Application of text categorization based on improved maximum entropy means clustering algorithm

In view of the traditional text classification algorithm has the problems of the characteristics having same influence on classification results,the low rate of classification accuracy,and the increasing of the algorithm time complexity,this paper presented an improved maximum entropy C-means clustering text classification methods.This method combined the C-means clustering algorithm and the maximum entropy algorithm,set Shannon entropy as a maximum entropy model in the target function,simplified classifier forms of expression,and then used the C-means clustering algorithm to the optimal features for classification.The simulation results show that,compared with traditional text classification methods,the proposed method can fast obtain the optimal classification feature subset,greatly improve the accuracy of text classification.
Surface air temperature variability and trends in the Arctic: new amplification assessment and regionalisation

Arctic amplification of temperature change is theorised to be an important feature of the Earth's climate system. For observational assessment and understanding of mechanisms of this amplification, which remain uncertain, thorough and detailed analyses of surface air temperature (SAT) variability and trends in the Arctic are needed. Here we present an analysis of Arctic SAT variability in comparison with mid-latitudes and the Northern Hemisphere (NH), based on an advanced SAT dataset – NansenSAT. We define an index for the Arctic amplification as the ratio between absolute values of the Arctic (65–90°N) and NH 30-yr running linear SAT trends. It is demonstrated that the temperature amplification in the Arctic is characteristic not only for the recent warming but also the early 20th century warming (ETCW) and subsequent cooling. The amplification appears to be weaker during the recent warming than in the ETCW, simply because the index values reflect the more pervasive nature of the recent warming that reflects the background of anthropogenic global warming. We also produced a new Arctic regionalisation created from hierarchical cluster analysis, which identifies six major natural regions in the Arctic that reflect SAT variability. Statistical comparison with several climate indices shows that the Atlantic Multidecadal Oscillation (AMO) is the mode of variability that is most significantly associated with the amplified warming–cooling in the Arctic, with a stronger correlation during the ETCW and recent warming than during the intermediate period. Regionally, differences are identified in terms of annual and seasonal rates of change and in their correlations with modes of variability.
A reciprocity approach for calculating the far-field radiation patterns of a center-fed helical microstrip antenna mounted on a dielectric-coated circular cylinder

This paper presents an efficient approach based on reciprocity for calculating the far-field radiation pattern of a center-fed helical microstrip antenna mounted on a circular cylinder. The reciprocity theorem is used to find a completely analytical solution to the problem assuming that the helical microstrip is sufficiently thin with a sinusoidal current distribution. Two degenerate cases of the helical microstrip antenna, i.e., an axially and an azimuthally fed half-wave cylindrical-rectangular patch, are examined and compared to known results in order to provide validation for the general analytical solution. Further comparisons are made between the analytical results and the results obtained using a numerically rigorous method of moments (MoM) computer code.
The Lattice Stabilities and Electronic Structure of Ba x Sr1 − x TiO3 Solid Solutions by First-Principles Calculations

The structural, electronic, and optical properties of Ba x Sr 1 − x TiO 3 (BST, x = 0.125, 0.250, 0.500, 0.750 and 0.875) solid solutions were investigated by first-principles calculations within generalized gradient approximation based on density-functional theory. Stable lattice structures of cubic paraelectric phase are obtained in finding ground states. The energy bands structures, density of states and complex dielectric function are calculated from the stable structures. It is found that the lattice constants showed a linearly increase with x, which is consistent with Vegard's law. The analyses of the density of states and electron density difference conclude that the hybridization between Ba d and O p is stronger than that between Sr d and O p. From further analysis of the complex dielectric function, we get the static dielectric constant. The calculated results show good agreement with the available experimental and theoretical data.
Coupled EFGM and FFEM for unbounded problems

This paper presents a coupling technique for integrating the element-free Galerkin method (EFGM) with the fractal finite element method (FFEM) to analyze unbounded problems in the half-space. FFEM is adopted to model the far field of an unbounded domain and EFGM is used in the near field. In the transition region interface elements are employed. The proposed method combines the best features of EFGM and FFEM, in the sense that no structured mesh or special enriched basis functions are necessary. The numerical results show that the proposed method performs extremely well converging rapidly to the analytical solution.
Air plasma effect on dental disinfection

A nonthermal low temperature air plasma jet is characterized and applied to study the plasma effects on oral pathogens and biofilms. Experiments were performed on samples of six defined microorganisms’ cultures, including those of gram-positive bacteria and fungi, and on a cultivating biofilm sample of Streptococcus mutans UA159. The results show that the plasma jet creates a zone of microbial growth inhibition in each treated sample; the zone increases with the plasma treatment time and expands beyond the entire region directly exposed to the plasma jet. With 30s plasma treatment twice daily during 5 days of biofilm cultivation, its formation was inhibited. The viability of S. mutans cells in the treatedbiofilms dropped to below the measurable level and the killed bacterial cells concentrated to local regions as manifested by the fluorescence microscopy via the environmental scanning electron microscope. The emission spectroscopy of the jet indicates that its plasma effluent carries an abundance of reactive atomic oxygen, providing catalyst for the observed plasma effect.
The effects of combining classifiers with the same training statistics using Bayesian decision rules

Multiple classifier systems (MCS) are attracting increasing interest in the field of pattern recognition and machine learning. Recently, MCS are also being introduced in the remote sensing field where the importance of classifier diversity for image classification problems has not been examined. In this article, Satellite Pour l'Observation de la Terre (SPOT) IV panchromatic and multispectral satellite images are classified into six land cover classes using five base classifiers: contextual classifier, k-nearest neighbour classifier, Mahalanobis classifier, maximum likelihood classifier and minimum distance classifier. The five base classifiers are trained with the same feature sets throughout the experiments and a posteriori probability, derived from the confusion matrix of these base classifiers, is applied to five Bayesian decision rules (product rule, sum rule, maximum rule, minimum rule and median rule) for constructing different combinations of classifier ensembles. The performance of these classifier ensembles is evaluated for overall accuracy and kappa statistics. Three statistical tests, the McNemar's test, the Cochran's Q test and the Looney's F-test, are used to examine the diversity of the classification results of the base classifiers compared to the results of the classifier ensembles. The experimental comparison reveals that (a) significant diversity amongst the base classifiers cannot enhance the performance of classifier ensembles; (b) accuracy improvement of classifier ensembles can only be found by using base classifiers with similar and low accuracy; (c) increasing the number of base classifiers cannot improve the overall accuracy of the MCS and (d) none of the Bayesian decision rules outperforms the others.
Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization

Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks does not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. To jointly discover omitted diffusion activities and hidden network structures, we develop a probabilistic generative model called “DiffStru. ” The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled with low-dimensional latent factors. Besides inferring unseen data, latent factors such as community detection may also aid in network classification problems. We tested different missing data scenarios on simulated independent cascades over LFR networks and real datasets, including Twitter and Memetracker. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.
Cyclical Compound Domain Test-time Adaptation via Continual Domain-Matching Algorithm

—Test-time adaptation (TTA), a key component of lifelong learning in edge devices, refers to the ability of a pre-trained model to adapt itself to new environments during test time. Due to its practical ability, TTA has attracted signiﬁcant attention and experienced a rapid performance boost these days. In this paper, we present an under-explored yet more realistic TTA scenario and provide a strong baseline favorable to this scenario, named cyclical compound domain (CCD). The CCD represents the real-world scenario in which the target domain contains multiple sub-target domains ( i . e ., compound domain due to weather or time change) and the sub-target domains are likely to rise cyclically. Unfortunately, existing works do not faithfully account for this plausible scenario, only focusing on adapting to the current sub-target domain while discarding the past knowledge acquired from repeated sub-target domains. Therefore, we ﬁrst propose a lightweight domain-matching algorithm that allows the TTA model to manage knowledge from the compound domain. This algorithm identiﬁes the type of domain among sub-target domains by continuously matching the current image’s distribution with reference domain points. Moreover, our newly proposed regularization method compares the present distribution with source one in order to regularize the adaptation pace according to each data in sub-target domains. Qualitatively, we demonstrate that our simple-yet-effective approach improves the adaptation performance on various benchmarks, including image classiﬁcation on ImageNet-C and semantic segmentation on GTA5, C-driving datasets, and Cityscapes with corruptions.
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills

Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, and the considerable technical expertise required for implementation. In this work, we investigate the utility of Koopman operator theory in alleviating these limitations. Koopman operators are simple yet powerful control-theoretic structures to represent complex nonlinear dynamics as linear systems in higher dimensions. Motivated by the fact that complex nonlinear dynamics underlie dexterous manipulation, we develop a Koopman operator-based imitation learning framework to learn the desired motions of both the robotic hand and the object simultaneously. We show that Koopman operators are surprisingly effective for dexterous manipulation and offer a number of unique benefits. Notably, policies can be learned analytically, drastically reducing computation burden and eliminating sensitivity to initialization and the need for painstaking hyperparameter optimization. Our experiments reveal that a Koopman operator-based approach can perform comparably to state-of-the-art imitation learning algorithms in terms of success rate and sample efficiency, while being an order of magnitude faster. Policy videos can be viewed at https://sites.google. com/view/kodex-corl.
Relativistic quantum communication between harmonic oscillator detectors

We propose a model of communication employing two harmonic oscillator detectors interacting through a scalar field in a background Minkowski spacetime. In this way, the scalar field plays the role of a quantum channel, namely a Bosonic Gaussian channel. The classical and quantum capacities of the communication channel are found, assuming that the detectors' spatial dimensions are negligible compared to their distance. In particular, we study the evolution in time of the classical capacity after the detectors-field interaction is switched on for various detectors' frequencies and coupling strengths with the field. As a result, we find a finite value of these parameters optimizing the communication of classical messages. Instead, a reliable communication of quantum messages turns out to be always inhibited.