Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal feature extraction for normally distributed data

In this paper, we propose an optimal feature extraction method for normally distributed data. The feature extraction algorithm is optimal in the sense that we search the whole feature space to find a set of features which give the smallest classification error for the Gaussian ML classifier. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we compute the classification error. Then we move the feature vector slightly in the direction so that the classification error decreases most rapidly. This can be done by taking gradient. We propose two search methods, sequential search and global search. In the sequential search, if more features are needed, we try to find an additional feature which gives the best classification accuracy with the already chosen features. In the global search, we are not restricted to use the already chosen features. Experiment results show that the proposed method outperforms the conventional feature extraction algorithms.
mixtools: An R Package for Analyzing Mixture Models

The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture models. In the latter category, mixtools provides algorithms for estimating parameters in a wide range of different mixture-of-regression contexts, in multinomial mixtures such as those arising from discretizing continuous multivariate data, in nonparametric situations where the multivariate component densities are completely unspecified, and in semiparametric situations such as a univariate location mixture of symmetric but otherwise unspecified densities. Many of the algorithms of the mixtools package are EM algorithms or are based on EM-like ideas, so this article includes an overview of EM algorithms for finite mixture models.
Constraining the aggradation mode of Pleistocene river deposits based on cosmogenic radionuclide depth profiling and numerical modelling

Abstract. Pleistocene braided-river deposits commonly represent long periods of non-deposition or erosion that are interrupted by rapid and short aggradation phases. When dating these sedimentary sequences with in situ-produced cosmic radionuclides (CRNs), simple concentration depth profiling approaches often fall short, as they assume that the alluvial sedimentary sequence has been deposited with a constant and rapid aggradation rate and been exposed to cosmic radiations afterwards. Numerical modelling of the evolution of CRNs in alluvial sequences permits one to account for aggradation, non-deposition and erosion phases and can simulate which scenarios of aggradation and preservation most likely represent the river dynamics. In this study, such a model was developed and applied to a Middle Pleistocene gravel sheet (Zutendaal gravels) exposed in NE Belgium. The model parameters were optimised to the observed 10Be and 26Al concentrations of 17 sediment samples taken over a depth interval of 7 m that constitutes the top of a gravel sheet up to 20 m thick. In the studied sedimentary sequence, (at least) three individual aggradation phases that were interrupted by non-deposition or erosion can be distinguished, each interruption lasting ∼ 40 kyr. The age for the onset of aggradation of the upper 7 m of the gravel sheet was further constrained to 654-62+218 ka. This age, within error limits, does not invalidate previous correlations of the gravel sheet with the Cromerian Glacial B and Marine Isotope Stage (MIS) 16. The deposition of the entire gravel sheet likely represents more than one climatic cycle and demonstrates the importance of accounting for the depositional modes of braided rivers when applying in situ cosmogenic radionuclide techniques.
The Synergistic Hepatoprotective Activity of Rosemary Essential Oil and Curcumin: The Role of the MEK/ERK Pathway

Background: Curcumin is a natural product obtained from the rhizome of Curcuma longa. Rosemary (Rosmarinus officinalis) is a medicinal and aromatic plant that is widely spread in the Mediterranean region. Both Curcumin and rosemary essential oil are natural products of high medicinal and pharmacological significance. The hepatoprotective effect of both natural products is well-established; however, the mechanism of such action is not fully understood. Thus, this study is an attempt to explore the hepatoprotective mechanism of action of these remedies through their effect on MEK and ERK proteins. Furthermore, the effect of rosemary essential oil on the plasma concentration of curcumin has been scrutinized. Materials and methods: The major constituents of REO were qualitatively and quantitatively determined by GC/MS and GC/FID, respectively. Curcumin and rosemary essential oil were given to mice in a pre-treatment model, followed by induction of liver injury through a high dose of paracetamol. Serum liver enzymes, lipid peroxidation, antioxidant activities, the inflammatory and apoptotic biomarkers, as well as the MEK and ERK portions, were verified. The plasma levels of curcumin were determined in the presence and absence of rosemary essential oil. Results: The major constituents of REO were 1,8-cineole (51.52%), camphor (10.52%), and α-pinene (8.41%). The results revealed a superior hepatoprotective activity of the combination when compared to each natural product alone, as demonstrated by the lowered liver enzymes, lipid peroxidation, mitigated inflammatory and apoptotic biomarkers, and enhanced antioxidant activities. Furthermore, the combination induced the overexpression of MEK and ERK proteins, providing evidence for the involvement of this cascade in the hepatoprotective activity of such natural products. The administration of rosemary essential oil with curcumin enhanced the curcuminoid plasma level. Conclusion: The co-administration of both curcumin and rosemary essential oil together enhanced both their hepatoprotective activity and the level of curcumin in plasma, indicating a synergistic activity between both natural products.
Scale invariance of normalized cross-helicity throughout the inertial range of solar wind turbulence

Solar wind measurements over the period from 1995 through 2006 are used to study fluctuations in the plasma bulk velocity and magnetic field over the range of magnetohydrodynamic (MHD) scales commonly referred to as the inertial range. Power spectra of the solar wind velocity, magnetic field, proton density, total energy (kinetic plus magnetic), and cross-helicity are analyzed for 176 time intervals in which the interplanetary magnetic field is restricted to a single magnetic sector. The data yield measurements of the normalized cross-helicity σc, the ratio of the cross-helicity spectrum to the energy spectrum, that span the entire inertial range at 1 AU and extend previous measurements by more than one decade in wavenumber. The results show that σc is approximately constant throughout the inertial range, independent of wavenumber, consistent with existing theoretical ideas. At the highest frequencies measured, |σc| is observed to decrease toward zero, however, this decrease is caused by measurement noise.
Imaging efficacy of a targeted imaging agent for fluorescence endoscopy

Colorectal cancer is a major cause of cancer death. A significant unmet clinical need exists in the area of screening for earlier and more accurate diagnosis and treatment. We have identified a fluorescence imaging agent targeted to an early stage molecular marker for colorectal cancer. The agent is administered intravenously and imaged in a far red imaging channel as an adjunct to white light endoscopy. There is experimental evidence of preclinical proof of mechanism for the agent. In order to assess potential clinical efficacy, imaging was performed with a prototype fluorescence endoscope system designed to produce clinically relevant images. A clinical laparoscope system was modified for fluorescence imaging. The system was optimised for sensitivity. Images were recorded at settings matching those expected with a clinical endoscope implementation (at video frame rate operation). The animal model was comprised of a HCT-15 xenograft tumour expressing the target at concentration levels expected in early stage colorectal cancer. Tumours were grown subcutaneously. The imaging agent was administered intravenously at a dose of 50nmol/kg body weight. The animals were killed 2 hours post administration and prepared for imaging. A 3-4mm diameter, 1.6mm thick slice of viable tumour was placed over the opened colon and imaged with the laparoscope system. A receiver operator characteristic analysis was applied to imaging results. An area under the curve of 0.98 and a sensitivity of 87% [73, 96] and specificity of 100% [93, 100] were obtained.
Research on the Impact of Regular Exercise Behavior of College Students on Academic Stress and Sleep Quality during the COVID-19 Pandemic

When college students face the COVID-19 pandemic and new learning challenges simultaneously, how to reduce or alleviate their own academic stress has become a topic of concern to students and their parents. The psychological and physiological benefits of regular exercise have been confirmed by related studies. This study aimed to explore the impact of college students’ regular exercise behavior on academic stress and sleep quality during the COVID-19 pandemic. This study used a purposive sampling method to collect data through online questionnaires posted to relevant college student groups in northern, central, southern, and eastern Taiwan and the outlying islands. A total of 320 questionnaires were collected, with a response rate of 91.4%; based on 304 valid questionnaires. The validity rate was 95%. The obtained data were entered in SPSS 24.0 statistical software, and the correlation between variables was analyzed with AMOS 24.0 statistical software. The results show that hypothesis 1 is established, that is, regular exercise behavior of college students during the COVID-19 pandemic has a significant negative impact on academic stress, meaning that during the COVID-19 pandemic, if college students can use their spare time to make exercise part of their life, such a regular schedule will help reduce their academic stress. In addition, the empirical results show that hypothesis 2 is established, that is, regular exercise behavior of college students during the COVID-19 pandemic has a significant positive impact on sleep quality. A possible reason is that under the COVID-19 pandemic, the efficiency of the body to absorb oxygen is increased through regular exercise, which reduces pressure and improves sleep quality. Hypothesis 3 is also confirmed, that is, the academic stress of college students during the COVID-19 pandemic has a significant negative impact on sleep quality. The reason may be that many leisure and social activities have been suspended during the COVID-19 pandemic, and thus college students exercised and studied during the time they originally intended for leisure and social activities, which reduced their academic stress, stabilized their mood, and improved their sleep quality.
Two-Layer-Based Multiarms Bilateral Teleoperation Architecture

In this article, we propose a novel bilateral teleoperation architecture for a multiarms system based on the two-layer approach. Exploiting the concept of shared energy tank, a passivity layer guarantees the passivity of the overall architecture with respect to destabilizing factors such as time delays in the communication channel. The desired behavior can then be freely designed in the transparency layer. The formulation of the energy tank is first revised, allowing a more efficient use of energy, and then extended, allowing explicitly the use of both admittance and impedance causality robots. A novel framework capable of combining the use of teleoperated and autonomous robots is proposed. The architecture has been tested and validated on a multiarms system in a realistic surgical scenario with the da Vinci research kit (dVRK) and an autonomous arm holding the endoscope.
Epidemiology and Clinical Outcomes of Non-HACEK Gram-Negative Infective Endocarditis

Abstract Background The objectives of this study were to describe the changing epidemiology of gram-negative infective endocarditis (GNIE) and to identify factors associated with treatment failure and death. Methods Adult patients with GNIE were included if they met modified Duke criteria for definitive infective endocarditis (IE) between April 2010 and December 2021. Patients were identified using Boolean search terms. Clinical failure was a defined as a composite of all-cause 42-day mortality or microbiologic failure. All analyses were performed using Stata, version 15.1. Results One-hundred twenty-three patients were included. The most common pathogens were Serratia spp. (43%), Pseudomonas aeruginosa (21%), and Klebsiella spp. (14%). Fifty-two percent of cases were among persons who injection drugs (PWID; n = 64), for whom Serratia spp. (70%) was the most common cause of GNIE. Overall, patients infected with P. aeruginosa had higher microbiologic failure rates than other patients (23% vs 6%; P = .004). Patients who received combination therapy (n = 53) had comparable median lengths of stay (23 vs 19.5 days; P = .412), microbiologic failure rates (11.3% vs 7.1%; P = .528), clinical failure rates (18.9% vs 22.9%; P = .592), and 90-day mortality rates (13.2% vs 25.7%; P = .088) as those treated with monotherapy. After applying stepwise logistic regression, male gender, Pitt Bacteremia Score, and not receiving surgical intervention despite a surgical indication were associated with clinical failure. Conclusions This is the first study to identify Serratia spp. as the most common etiology of GNIE, which was particularly true among PWID. Microbiologic failures occurred most commonly among P. aeruginosa, and use of combination antimicrobial therapy did not improve clinical outcomes.
Young stellar objects in the Monoceros OB1 molecular cloud

Detailed results of an IRAS survey of a large portion of the Monoceros OB1 molecular cloud for discrete far-IR sources are presented. IRAS spectral energy distributions are constructed for 27 of the 30 sources identified in the region. Many are bright class I energy distributions, indicating that star formation is still occurring in the complex which produced the visible cluster, NGC 2264. Far-IR luminosities are calculated for each source, and the bolometric luminosity functions for class I and class II sources in the Mon OB1 cloud are found to be significantly different. Of all class I and II sources more luminous than 5 solar, 50 percent of class I sources are more luminous than 60 solar while only 4 percent of class II sources are. An attempt is made to determine the type of young stellar object in the cloud with which molecular outflows are associated. 35 refs.
Sensorimotor Confidence for Tracking Eye Movements

For successful interactions with the world, we often have to evaluate our own performance. Such metacognitive evaluations have been studied with perceptual decisions, but much less with respect to the evaluation of our own actions. While eye movements are one of the most frequent actions we perform, we are typically unaware of them. Here, we investigated if there is any evidence for metacognitive sensitivity for the accuracy of eye movements. Participants tracked a dot cloud as it followed an unpredictable sinusoidal trajectory for six seconds, and then reported if they thought their performance was better or worse than their average tracking performance. Our results show above chance identification of better tracking behavior across all trials and also for repeated attempts of the same target trajectories. While the sensitivity in discriminating performance between better and worse trials was stable across sessions, for their judgements participants relied more on performance in the final seconds of each trial. This behavior matched previous reports when judging the quality of hand movements, although overall metacognitive sensitivity for eye movements was significantly lower. Together, these results provide an additional piece of evidence for sensorimotor confidence, and open interesting questions about why it differs across actions and how it could be related to other instances of confidence. Statement of Relevance In everyday life, it is often critical to be able to evaluate the quality of our own cognitive decisions and actions. However, one of our most frequent actions often does not even reach our awareness: eye movements. We investigated whether observers were able to successfully judge the accuracy of their eye movements when tracking a cloud of dots that followed an unpredictable trajectory. We found that observers were able to distinguish good from bad trials, although sensitivity was lower compared to equivalent previous reports when judging the quality of hand movements. These results add an item to the growing list of our metacognitive abilities, but critically for eye movements that we are typically unaware of. They lead to important novel questions about why metacognitive abilities differ across decisions or different types of actions, and what the potential components of metacognitive abilities might be.
Synchronization of Switched Neural Networks via Attacked Mode-Dependent Event-Triggered Control and Its Application in Image Encryption

It is challenging to synchronize switched time-delay systems when some modes are uncontrolled and the dwell time (DT) of controlled mode is very small. Therefore, in this article, global exponential synchronization almost surely (GES a.s.) in a cluster of switched neural networks (NNs) with hybrid delays (time-varying delay and infinite-time distributed delay) is investigated, where transition probability (TP)-based random mode-dependent average DT (MDADT) switching is considered. A novel mode-dependent pinning event-triggered controller with nonidentical deception attacks is proposed to save the communication resource and derive less conservative results. The two necessary and restrictive conditions in existing papers that the value of the Lyapunov–Krasovskii functional (LKF) before switching instants should be smaller than that after corresponding instant and the DT of each switching mode is restricted by the sampling intervals of the event trigger are moved. Sufficient conditions in terms of linear matrix inequalities (LMIs) are given to guarantee the GES a.s. , even though both synchronizing and nonsynchronizing modes coexist and maybe the minimum DT of synchronizing modes is very small. Numerical examples, including image encryption, are provided to demonstrate the merits of the new technique.
Recursive eigenstructure assignment in linear systems

In Arbel and Rath (1985) an iterative eigenvalue assignment algorithm was presented. This algorithm has the shortcomings of clustering most of the closed-loop eigenvalues on the real axis. A general method is given here for recursive eigenstructure assignment in linear systems. The method eliminates the shortcomings of the algorithm of Arbel and Rath. It is shown that the right and left eigenvectors of the closed-loop system matrix can be determined in terms of those of a small-dimension matrix A∗c . It is also shown that the results of Arbel and Tse (1980) are special cases of those of the proposed method. Moreover, the arbitrary parameters, beyond eigenvalue assignment, are shown to be embedded in the choice of a certain arbitrary invertible matrix S. Furthermore, computer-oriented steps are outlined for recursive eigenstructure assignment in large-scale systems. Numerical examples are worked out to illustrate the generality and feasibility of the proposed method.
Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition

This paper presents an unsupervised feature learning approach based on 3D-skeleton data for human action and human discrete emotion recognition. Relying on the time series of skeleton data analysis to perform such tasks is effective and important to preserve the individual’s privacy better. Besides, such methods can represent a viable alternative to emotion recognition applications, in which most works use frontal or profile facial images disclosing the subject’s appearance. On the other hand, current unsupervised methods are able to encode the high variety of contexts and nature of the data, but often at the expense of a higher model complexity or longer computational time. To lessen these shortcomings, this paper proposes a convolutional residual autoencoder that models the skeletal geometry across the temporal dynamics of the data without relying on computationally expensive recurrent architectures. Our approach also implements a Graph Laplacian Regularization leveraging upon the implicit skeleton joints connectivity, further improving the robustness of the feature embeddings learned without using action or emotion labels. It was validated on large-scale datasets, having variability in the domain, the input skeleton data (e.g. the number of joints, adjacency matrices), and sensor technology. The results show its effectiveness by notably surpassing the performance of the state-of-the-art unsupervised methods while also achieving better recognition scores compared to the several fully supervised approaches. Extensive experimental analysis proves the usefulness of the proposed method under various evaluation protocols with observed higher-quality feature representations, even if when it is trained with fewer data. The results highlight the proposed method’s remarkable transfer-ability across various domains, and its faster inference time.
Hepatic GATA4 regulates cholesterol and triglyceride homeostasis in collaboration with LXRs

In this study, Bideyan et al. investigated role of GATA4 in adult liver metabolism and, using high-throughput sequencing technologies, identified GATA4 as a transcriptional regulator of metabolism in the liver. Their results show that hepatic GATA4 contributes to the transcriptional control of hepatic and systemic lipid homeostasis.