Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive function in multiple sclerosis and its modulation by cholinergic drugs

﻿ In order to assess cognitive function in multiple sclerosis (MS) and the effect of cholinergic modulation, experiments were conducted using functional magnetic resonance imaging (fMRI) to assess the brain activation during cognitive tasks. A study comparing the processing of verbal working memory with an N-back task found that patients showed smaller increase in activation than healthy controls with greater task difficulty, suggesting a reduced functional reserve. Controls and patients showed differences of correlations between brain regions activated. Interactions between prefrontal regions may provide an adaptive mechanism that could limit clinical expression of the disease distinct from recruitment of novel processing regions. The effect of Rivastigmine on the cognitive processing in MS patients was tested in a longitudinal study, involving serial fMRI scans. Changes in the brain activation patterns were demonstrated with drug administration, without any changes in behavioural measures. Rivastigmine may act to increase the functioning of the normal neural network reducing the need for previously recruited compensatory mechanisms in MS patients. A study on healthy subjects examined the effect of cholinergic inhibition on cognitive processing and brain activation. Changes in functional activation due to Hyoscine during verbal working memory were found analogous to that in MS patients without any changes in behavioural measures. Processes that potentially impair brain cognitive function may recruit similar compensatory functional adaptive mechanisms. Studies on rats and MS patients explored the effect of Rivastigmine on the relationship of the BOLD fMRI signal with the underlying neural activity. Rivastigmine may be influencing the cortical excitability after direct cortical stimulation but showed only a small effect on the BOLD signal under more physiological neural activity. The neural activity in response to visual stimulation is slightly increased with Rivastigmine in MS patients, a change not detected with functional imaging. These studies suggest that changes in BOLD signal do represent sufficiently large changes of underlying neural activity in the presence of Rivastigmine. The relationship of damage in MS to measures of connectivity was studied using diffusion tensor imaging (DTI). Correlation was found between measures of connectivity and callosal size, a measure of fibre loss. The distribution of lesions was spatially correlated with changes in connectivity due to MS. Thus DTI could be utilized to explore the connectivity changes associated with MS, and the relationship with changes in functional activation.
Cardiac Biomarkers

1Department of Internal Medicine III (Cardiology and Angiology), Innsbruck Medical University, 6020 Innsbruck, Austria 2Division of Cardiovascular Diseases and Internal Medicine, Mayo Clinic and Medical School, Rochester, MN 55905, USA 3Department of Laboratory Medicine and Pathology, Hennepin County Medical Center and University of Minnesota School of Medicine, Minneapolis, MN 55415, USA 4Department of Medical Sciences, Uppsala Clinical Research Center, Uppsala University, 75237 Uppsala, Sweden
Apparent Competition and Enemy-Free Space in Insect Host-Parasitoid Communities

Apparent competition is indirect competition between two or more victim species that share a natural enemy, caused by that enemy's numerical response. We review empirical examples of apparent competition in phytophagous insect hosts attacked by polyphagous parasitoids and develop models of apparent competition in host-parasitoid systems. Apparent competition is particularly likely in insect assemblages because parasitoids can limit their hosts to levels at which resource competition is unimportant. A consideration of both equilibrium and nonequilibrium models in which polyphagous parasitoids impose significant mortality on their hosts suggests that the most common outcome is the exclusion of all but one host species, which generates dynamic monophagy (i.e., a single host species persisting with a potentially polyphagous parasitoid). A crisp criterion for dominance in apparent competition is that the winning host supports the highest parasitoid density. We conclude that it is difficult for alternative hosts to coexist when the sole regulatory factor is a shared parasitoid. Yet in nature, coexisting hosts frequently do share parasitoids. We examine several mechanisms promoting host coexistence, including donor-controlled parasitoid dynamics, additional sources of host density dependence (e.g., resource limitation), spatial and temporal refuges, trophic web structure, and labile parasitoid behavior. Elucidating the mechanisms permitting the coexistence of host species confronted by effective polyphagous parasitoids deserves more attention from experimental field ecologists.
Bmc Musculoskeletal Disorders Responsiveness of the Shoulder Pain and Disability Index in Patients with Adhesive Capsulitis

Background: Instruments designed to measure the subjective impact of painful shoulder conditions have become essential in shoulder research. The Shoulder Pain and Disability Index (SPADI) is one of the most extensively used scales of this type. The objective of this study was to investigate reproducibility and responsiveness of the SPADI in patients with adhesive capsulitis.
Enhancing IoT Security Against DDoS Attacks through Federated Learning

The rapid proliferation of the Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Nonetheless, the escalating threat of Distributed Denial of Service (DDoS) attacks jeopardizes the integrity and reliability of IoT networks. Conventional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems, potentially compromising data privacy. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning that allows multiple IoT devices or edge nodes to collaboratively build a global model while preserving data privacy and minimizing communication overhead. The research aims to investigate Federated Learning's effectiveness in detecting and mitigating DDoS attacks in IoT. Our proposed framework leverages IoT devices' collective intelligence for real-time attack detection without compromising sensitive data. This study proposes innovative deep autoencoder approaches for data dimensionality reduction, retraining, and partial selection to enhance the performance and stability of the proposed model. Additionally, two renowned aggregation algorithms, FedAvg and FedAvgM, are employed in this research. Various metrics, including true positive rate, false positive rate, and F1-score, are employed to evaluate the model. The dataset utilized in this research, N-BaIoT, exhibits non-IID data distribution, where data categories are distributed quite differently. The negative impact of these distribution disparities is managed by employing retraining and partial selection techniques, enhancing the final model's stability. Furthermore, evaluation results demonstrate that the FedAvgM aggregation algorithm outperforms FedAvg, indicating that in non-IID datasets, FedAvgM provides better stability and performance.
NeoNeXt: Novel neural network operator and architecture based on the patch-wise matrix multiplications

Most of the computer vision architectures nowadays are built upon the well-known foundation operations: fully-connected layers, convolutions and multi-head self-attention blocks. In this paper we propose a novel foundation operation - NeoCell - which learns matrix patterns and performs patchwise matrix multiplications with the input data. The main advantages of the proposed operator are (1) simple implementation without need in operations like im2col, (2) low computational complexity (especially for large matrices) and (3) simple and flexible implementation of up-/down-sampling. We validate NeoNeXt family of models based on this operation on ImageNet-1K classification task and show that they achieve competitive quality.
Training Comprehensiveness: Construct Development and Relation with Role Behaviour.

Purpose – This study aims to develop the scale for perception of training comprehensiveness and attempts to examine the influence of perception of training comprehensiveness on role behaviour: teachers’ efficacy as a mediator and job autonomy as a moderator. Design/methodology/approach – Through the steps for a generation, refinement, purification and validation of the scale, the measures of training comprehensiveness are defined, followed by an exploratory factor analysis. In all, 961 primary school teachers and 323 principals participated in the study. Teachers rated their perception of training comprehensiveness, self-efficacy and job autonomy, while principals rated the role behaviour of teachers, to avoid self-reporting biases. Findings – Regression analysis showed that training comprehensiveness affects teachers’ self-efficacy, higher teachers’ efficacy increases the teachers’ role behaviour, training comprehensiveness indirectly influences role behaviour and job autonomy moderated the channel of te.
Classification using Markov blanket for feature selection

Selecting relevant features is in demand when a large data set is of interest in a classification task. It produces a tractable number of features that are sufficient and possibly improve the classification performance. This paper studies a statistical method of Markov blanket induction algorithm for filtering features and then applies a classifier using the Markov blanket predictors. The Markov blanket contains a minimal subset of relevant features that yields optimal classification performance. We experimentally demonstrate the improved performance of several classifiers using a Markov blanket induction as a feature selection method. In addition, we point out an important assumption behind the Markov blanket induction algorithm and show its effect on the classification performance.
Plasticity-dependent, full detonation at hippocampal mossy fiber–CA3 pyramidal neuron synapses

Mossy fiber synapses on CA3 pyramidal cells are 'conditional detonators' that reliably discharge postsynaptic targets. The 'conditional' nature implies that burst activity in dentate gyrus granule cells is required for detonation. Whether single unitary excitatory postsynaptic potentials (EPSPs) trigger spikes in CA3 neurons remains unknown. Mossy fiber synapses exhibit both pronounced short-term facilitation and uniquely large post-tetanic potentiation (PTP). We tested whether PTP could convert mossy fiber synapses from subdetonator into detonator mode, using a recently developed method to selectively and noninvasively stimulate individual presynaptic terminals in rat brain slices. Unitary EPSPs failed to initiate a spike in CA3 neurons under control conditions, but reliably discharged them after induction of presynaptic short-term plasticity. Remarkably, PTP switched mossy fiber synapses into full detonators for tens of seconds. Plasticity-dependent detonation may be critical for efficient coding, storage, and recall of information in the granule cell–CA3 cell network. DOI: http://dx.doi. org/10.7554/eLife.17977. 001
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation

Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target goal by executing a set of low-level actions, following a series of natural language instructions. All VLN-CE methods in the literature assume that language instructions are exact. However, in practice, instructions given by humans can contain errors when describing a spatial environment due to inaccurate memory or confusion. Current VLN-CE benchmarks do not address this scenario, making the state-of-the-art methods in VLN-CE fragile in the presence of erroneous instructions from human users. For the first time, we propose a novel benchmark dataset that introduces various types of instruction errors considering potential human causes. This benchmark provides valuable insight into the robustness of VLN systems in continuous environments. We observe a noticeable performance drop (up to -25%) in Success Rate when evaluating the state-of-the-art VLN-CE methods on our benchmark. Moreover, we formally define the task of Instruction Error Detection and Localization, and establish an evaluation protocol on top of our benchmark dataset. We also propose an effective method, based on a cross-modal transformer architecture, that achieves the best performance in error detection and localization, compared to baselines. Surprisingly, our proposed method has revealed errors in the validation set of the two commonly used datasets for VLN-CE, i.e., R2R-CE and RxR-CE, demonstrating the utility of our technique in other tasks. Code and dataset will be made available upon acceptance at https://intelligolabs.github. io/R2RIE-CE
CCN2 modulates hair follicle cycling in mice

Mesenchymal cells play a role in controlling the number of hair follicles. However, the precise molecules involved are unclear. Absence in mesenchymal cells of the expression of the secreted matricellular protein CTGF/CCN2 results in an increased number of hair follicles, concomitant with increased β-catenin activity.
Fast and Flexible Large-Scale Clone Detection with CloneWorks

Clone detection in very-large inter-project repositories has numerous applications in software research and development. However, existing tools do not provide the flexibility researchers need to explore this emerging domain. We introduce CloneWorks, a fast and flexible clone detector for large-scale clone detection experiments. CloneWorks gives the user full control over the representation of the source code before clone detection, including easy plug-in of custom source transformation, normalization and filtering logic. The user can then perform targeted clone detection for any type or kind of clone of interest. CloneWorks uses our fast and scalable partitioned partial indexes approach, which can handle any input size on an average workstation using input partitioning. CloneWorks can detect Type-3 clones in an input as large as 250 million lines of code in just four hours on an average workstation, with good recall and precision as measured by our BigCloneBench.
On Linear Threshold Policies for Continuous-Time Dynamic Yield Management

We study the finite-horizon continuous-time yield management problem with stationary arrival rates and two customer classes. We consider a class of linear threshold policies proposed by Hodge (2008), in which each online (i.e., less desirable) customer is accepted if and only if the remaining inventory at the customer's arrival time exceeds a threshold that linearly decreases over the selling horizon. Using a discrete-time Markov chain representation of sample paths over inventory-time space, we show that a range of such linear threshold policies achieve uniformly bounded regret. We then generalize this result to analogous policies for the same problem with arbitrarily many customer classes. Numerical simulations demonstrate linear threshold policies' competitiveness with existing heuristics and illustrate the effects of the linear threshold's slope.
Node Spatial Correlation Aware Routing Metrics for User-Centric Environments

Node mobility affects routing in terms of robustness and efficiency, as it may lead to frequent link breaks that are not always automatically detected at a protocolar level. In user-centric environments, nodes are characterized by dynamic mobility behavior because they are devices carried or controlled by humans. We propose routing metrics aimed at making routing more robust through consideration of node spatial correlation for successor node. We have validated our metrics in Ad hoc On-Demand Distance Vector (AODV) routing protocol using Network Simulator 2. Results obtained show that our metrics increase multi-hop routing robustness in terms of path re-computation reduction.
Wall confinement effects on the dynamics of cavitation bubbles in thin tubes

Cavitation is a common phenomenon in nature and has numerous applications. In contrast to a cavitation bubble in a free domain, a cavitation bubble in a thin tube is restricted by the tube wall, which is expected to significantly affect bubble evolution but its mechanism is still unclear. In this study, the dynamics of a cavitation bubble in a thin circular tube is studied by numerical simulation, focusing on the confinement effects of the tube. The results show that besides affecting the size and lifetime of the bubble, the confinement effects of the tube lead to the generation of counter jets and a ring jet during the contraction process of the bubble, and the curvature of the two counter jets determines the ring jet's peak velocity. When the bubble deviates from the midpoint of the tube in the axial direction, the two sides of the bubble along the axial direction show asymmetric behaviors, which results in the bubble migrating toward the midpoint. The tube diameter, tube length, liquid viscosity, and initial bubble position, can significantly influence the degree of confinement effects, which can be characterized by the variations of several key indicators, such as bubble size, lifetime, degree of deformation, counter jet velocity, ring jet velocity, and axial migration of the bubble.
Zooming by in the CARPoolGP lane: new CAMELS-TNG simulations of zoomed-in massive halos

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, particularly for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method, CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to include a set of 768 zoom-in simulations of halos in the mass range of $10^{13} - 10^{14.5} M_\odot\,h^{-1}$ that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton $Y-M$, black hole mass-halo mass, and metallicity-mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of the $Y-M$ relation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev-Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package.
Presence and Global Presence in Genres of Self-Presentation: A Framework for Comparative Analysis

We review Perelman and Olbrechts-Tyteca's original formulation of presence as a technique of argument associated primarily with the selection of individual rhetorical elements, and the recent extension of the notion by Gross and Dearin, where presence is understood as a second-order effect that denotes the systematic expression and inhibition of patterns of rhetorical elements across an entire text or rhetorical artifact. We argue for an additional extension to this more global notion of presence, one that makes it not only global within a text or class of texts, but also comparative, allowing the analyst to make rigorous comparisons of expressed and inhibited rhetorical patterns across different texts, or different classes of texts, including different rhetorical genres. A return to the original conception of presence allows us to make this extension, and we illustrate global presence within this newly proposed comparative framework by analyzing two genres of self-presentation in classroom practice: the cover letter and the self-portrait. We show the close ties between global presence and genre as ways of theorizing deep similarities across texts.
Semi-automated detection and quantification of aortic atheromas from three-dimensional transesophageal echocardiography

Quantification of descending aorta atheromas (AT) with 2D transesophageal echocardiography (TEE) is time-consuming and underestimates plaque burden, while 3D TEE can provide the basis for analysis of AT 3D dimensions and volume. We developed a novel semi-automated algorithm to detect aortic AT in 3D TEE, to quantify their severity and volume and tested its accuracy compared to an expert gold standard. The 3D TEE datasets acquired from 58 consecutive patients were analyzed. Results showed a good accuracy in comparison with expert analysis, with an agreement of 95% in absolute presence/absence of AT per patient, 89% in AT number and location and 85% in patient risk due to AT severity classification. AT volumes were highly correlated (R2=0.98), with a slight underestimation (9%) compared to manual measurements. The proposed semi-automated algorithm is rapid, feasible and accurate in analyzing AT from 3D TEE descending aorta datasets.
Learning statistically efficient features for speaker recognition

We apply independent component analysis for extracting an optimal basis to the problem of finding efficient features for a speaker. The basis functions learned by the algorithm are oriented and localized in both space and frequency, bearing a resemblance to Gabor functions. The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by a basis, which is calculated so that each component should be independent upon others on the given training data. The speaker distribution is modeled by the basis functions. To assess the efficiency of the basis functions, we performed speaker classification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features, in that they can obtain a higher classification rate.
Results of a sensing system for an autonomous mobile robot based on the paraconsistent artificial neural network

This paper shows the results of a sensing system for an autonomous mobile robot. The sensing system is based on the paraconsistent neural network. The type of artificial neural network used in this work is based on the paraconsistent evidential logic (Eτ). The objective of the sensing system is to inform the other robot components the obstacle position. The reached results have been satisfactory.
Optimizing energy to minimize errors in dataflow graphs using approximate adders

Approximate arithmetic is a promising, new approach to low-energy designs while tackling reliability issues. We present a method to optimally distribute a given energy budget among adders in a dataflow graph so as to minimize expected errors. The method is based on new formal mathematical models and algorithms, which quantitatively characterize the relative importance of the adders in a circuit. We demonstrate this method on a finite impulse response filter and a Fast Fourier Transform. The optimized energy distribution yields 2.05X lower error in a 16-point FFT and images with SNR 1.42X higher than those achieved by the best previous approach.