Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter estimation algorithms for Hammerstein time-delay systems based on the orthogonal matching pursuit scheme

This study focuses on the parameter estimation of Hammerstein non-linear systems with unknown time-delay and coloured noise. By means of the compressed sensing recovery theory and the auxiliary model identification idea, an orthogonal matching pursuit based iterative algorithm is proposed for estimating the system parameters and the time delay. The proposed algorithms can give highly accurate parameter estimates from a small number of observations. Two simulation examples are provided to test the proposed algorithms.
Distribution of low-molecular lipophilic extractives beneath the surface of air- and kiln-dried Scots pine sapwood boards

During industrial wood drying, extractives migrate towards the wood surfaces and make the material more susceptible to photo/biodegradation. The present work provides information about the distribution, quantity and nature of lipophilic substances beneath the surface in air- and kiln-dried Scots pine (Pinus sylvestris L.) sapwood boards. Samples were taken from knot-free sapwood surfaces and the composition of lipophilic extractives, phenols and low-molecular fatty/resin acids layers at different nominal depths below the surface was studied gravimetrically, by UV-spectrometry and by gas chromatography-mass spectrometry (GC-MS). The concentration of total extractives was significantly higher in kiln-dried than in air-dried samples and was higher close to the surface than in the layers beneath. The scatter in the values for the lipophilic extractives was high in both drying types, being highest for linoleic acid and slightly lower for palmitic, oleic and stearic acids. The amount of fatty acids was low in kiln-dried boards, probably due to a stronger degradation due to the high temperature employed. The most abundant resin acid was dehydroabietic acid followed by pimaric, isopimaric, and abietic acids in both drying types. It is concluded that during kiln-drying a migration front is created at a depth of 0.25 mm with a thickness of about 0.5 mm.
Deep neural network-based linear predictive parameter estimations for speech enhancement

This study presents a speech enhancement technique to improve noise corrupted speech via deep neural network (DNN)-based linear predictive (LP) parameter estimations of speech and noise. With regard to the LP coefficient estimation, an enhanced estimation method using a DNN with multiple layers was proposed. Excitation variances were then estimated via a maximum-likelihood scheme using observed noisy speech and estimated LP coefficients. A time-smoothed Wiener filter was further introduced to improve the enhanced speech quality. Performance was evaluated via log spectral distance, a composite multivariate adaptive regression splines modelling-based measure, and a segmental signal-to-noise ratio. The experimental results revealed that the proposed scheme outperformed competing methods.
Message passing detection for large-scale MIMO systems: damping factor analysis

A message passing detector based on belief propagation (BP) algorithm for Markov random fields (MRF-BP) and factor graph (FG-BP) graphical models is analysed under different large-scale (LS) multiple-input multiple-output (MIMO) scenarios, including system parameters, such as damping factor (DF), number of users and number of antennas, from N = 20 to 200 antennas. Specifically, the DF variation under different number of antennas configuration and signal-to-noise ratio (SNR) regions is extensively evaluated; bit error rate (BER) performance and computational complexity are assessed over different scenarios. Numerical results lead to a great performance gain with damped MRF-BP approach, overcoming FG-BP scheme in specific scenarios, with no extra computational complexity. Also, message damping (MD) method resulted in faster convergence of MRF-BP algorithm in LS scenarios, evidencing that, besides the performance gain, MD technique can lead to a computational complexity reduction. Specifically under low number of transmit antennas scenarios, the DF value needs to be carefully chosen. Furthermore, based on the proposed analysis, optimal value for the DF is determined considering wide LS antennas scenarios and SNR regions.
Mitophagy activation repairs Leber's hereditary optic neuropathy‐associated mitochondrial dysfunction and improves cell survival

Abstract Leber's hereditary optic neuropathy (LHON) is a classical mitochondrial disease caused by mutations in the mitochondrial DNA encoding complex I subunits. Oxidative stress associated with complex I defect has been implicated in developing LHON phenotype such as retinal ganglion cell (RGC) death and loss of vision. However, the mechanism of LHON pathogenesis is still not very clear and thus no effective therapies are available to date. Using cybrid models for LHON, we show that autophagy is significantly compromised in cells carrying LHON‐specific mtDNA mutations, which results in reduced clearance of dysfunctional mitochondria contributing to cell death. We further show that pharmacological activation of autophagy selectively clears the damaged mitochondria and thus repairs mitochondrial defects and improves overall cell survival in LHON cell models. Our results suggest that compromised autophagy is the missing link from oxidative stress to LHON pathogenesis. Activation of mitophagy ameliorates mitochondrial defects and exerts a protective role by improving cell survival in cells carrying LHON mutations that could be utilized as a potential therapeutic target for LHON treatment.
Entrega de Recursos Educativos Digitales Mediante Razonamiento Basado en Casos

La inteligencia artificial desde sus diferentes tecnicas contribuye a todas las areas del conocimiento, entre ellas la educacion. Dentro de las posibilidades que ofrece se encuentra la de recuperacion de informacion con algoritmos que mejoran los resultados obtenidos y dan versatilidad a las busquedas. En el caso educativo, la obtencion de recursos digitales, desde diversas fuentes a partir de requerimientos especificos de los usuarios encuentra en estas tecnologias una herramienta muy util. Este trabajo presenta una propuesta basada en la tecnica conocida como Razonamiento Basado en Casos (RBC), para la recuperacion personalizada de recursos educativos digitales, almacenados en repositorios. La validacion de la propuesta se hace mediante un caso de estudio donde se evalua la relevancia de los materiales recomendados.
Learning Multi-agent Implicit Communication Through Actions: A Case Study in Contract Bridge, a Collaborative Imperfect-Information Game

In situations where explicit communication is limited, a human collaborator is typically able to learn to: (i) infer the meaning behind their partner’s actions and (ii) balance be-tween taking actions that are exploitative given their current understanding of the state vs. those that can convey private information about the state to their partner. The ﬁrst component of this learning process has been well-studied in multi-agent systems, whereas the second — which is equally crucial for a successful collaboration — has not. In this work, we complete the learning process and introduce our novel algorithm, Policy-Belief-Iteration (“P-BIT”), which mimics both components mentioned above. A belief module models the other agent’s private information by observing their actions, whilst a policy module makes use of the inferred private information to return a distribution over actions. They are mutually reinforced with an EM-like algorithm. We use a novel auxiliary reward to encourage information exchange by actions. We evaluate our approach on the non-competitive bidding problem from contract bridge and show that by self-play agents are able to effectively collaborate with implicit communication, and P-BIT outperforms several meaningful baselines that have been considered.
Structural and Functional Brain Connectivity Changes Between People With Abdominal and Non-abdominal Obesity and Their Association With Behaviors of Eating Disorders

Abdominal obesity is important for understanding obesity, which is a worldwide medical problem. We explored structural and functional brain differences in people with abdominal and non-abdominal obesity by using multimodal neuroimaging and up-to-date analysis methods. A total of 274 overweight people, whose body mass index exceeded 25, were enrolled in this study. Participants were divided into abdominal and non-abdominal obesity groups using a waist–hip ratio threshold of 0.9 for males and 0.85 for females. Structural and functional brain differences were assessed with diffusion tensor imaging and resting-state functional magnetic resonance imaging. Centrality measures were computed from structural fiber tractography, and static and dynamic functional connectivity matrices. Significant inter-group differences in structural and functional connectivity were found using degree centrality (DC) values. The associations between the DC values of the identified regions/networks and behaviors of eating disorder scores were explored. The highest association was achieved by combining DC values of the cerebral peduncle, anterior corona radiata, posterior corona radiata (from structural connectivity), frontoparietal network (from static connectivity), and executive control network (from dynamic connectivity) compared to the use of structural or functional connectivity only. Our results demonstrated the effectiveness of multimodal imaging data and found brain regions or networks that may be responsible for behaviors of eating disorders in people with abdominal obesity.
Event-Related potentials in Externally and Internally-Driven target Selection: a Preliminary Study

In this study we investigated the cue-locked (P300 and later event-related potentials components) and response-locked electroencephalography (EEG) phenomena associated to externally and internally-driven target selection. For that we designed a novel paradigm, that aimed to separate the selection of motor goals according to the respective task rules from the actual programming of the upcoming motor response. Our paradigm also made possible the estimation of the onset of a self-paced reach-and-grasp movement imagination for better capturing the associated movement-related cortical potentials (MRCPs). Our preliminary results indicate that differences between the externally and internally-driven conditions are present in the cue-locked event-related potentials, but not in the response-locked MRCPs. Our study contributes for a better understanding of the neurophysiological signature of movement-related processes, including both perception and actual motor planning, which are so extensively used in brain-computer interfaces (BCIs).
Adaptive Fraud Detection System Using Dynamic Risk Features

eCommerce transaction frauds keep changing rapidly. This is the major issue that prevents eCommerce merchants having a robust machine learning model for fraudulent transactions detection. The root cause of this problem is that rapid changing fraud patterns alters underlying data generating system and causes the performance deterioration for machine learning models. This phenomenon in statistical modeling is called "Concept Drift". To overcome this issue, we propose an approach which adds dynamic risk features as model inputs. Dynamic risk features are a set of features built on entity profile with fraud feedback. They are introduced to quantify the fluctuation of probability distribution of risk features from certain entity profile caused by concept drift. In this paper, we also illustrate why this strategy can successfully handle the effect of concept drift under statistical learning framework. We also validate our approach on multiple businesses in production and have verified that the proposed dynamic model has a superior ROC curve than a static model built on the same data and training parameters.
Flower Species Classification using Statistical and Gist Descriptors

This Paper aims at classification of flower images by means of Gist descriptor and statistical features using SVM classifier. With advances in digital image processing, automated classification of flower images over large categories of dataset is possible by knowing the name of the flower. In case of unknown species name, classification of flower image can be performed by its visual content. The purpose of the paper is to identify different species of flower with the help of visual content. Here hundred and two categories of flower dataset are used and it is obtained from Visual Geometry Group, university of Oxford. In this paper ten classes are taken from dataset and classified. The Gist descriptor is combined with statistical features such as mean, standard deviation, skewness, Kurtosis are given to SVM classifier. It uses multiple kernel frame work to classify the images. The recognition rate is 79.36%.
A Review on the Influence of Social Attachment on Human Mobility During Crises

Human behaviour during crisis evacuations is social in nature. In particular, social attachment theory posits that proximity of familiar people, places, objects, etc. promotes calm and a feeling of safety, while their absence triggers panic or flight. In closely bonded groups such as families, members seek each other and evacuate as one. This makes attachment bonds necessary in the development of realistic models of mobility during crises. In this paper, we present a review of evacuation behaviour, theories on social attachment, crises mobility, and agent-based models. We found that social attachment influences mobility in the different stages of evacuation (pre, during and post). Based on these findings, we intend to develop a multi-agent model of mobility during seismic crises, using the belief, desire and intention (BDI) agent architecture.
B Chromosomes in Populations of Mammals Revisited

The study of B chromosomes (Bs) started more than a century ago, while their presence in mammals dates since 1965. As the past two decades have seen huge progress in application of molecular techniques, we decided to throw a glance on new data on Bs in mammals and to review them. We listed 85 mammals with Bs that make 1.94% of karyotypically studied species. Contrary to general view, a typical B chromosome in mammals appears both as sub- or metacentric that is the same size as small chromosomes of standard complement. Both karyotypically stable and unstable species possess Bs. The presence of Bs in certain species influences the cell division, the degree of recombination, the development, a number of quantitative characteristics, the host-parasite interactions and their behaviour. There is at least some data on molecular structure of Bs recorded in nearly a quarter of species. Nevertheless, a more detailed molecular composition of Bs presently known for six mammalian species, confirms the presence of protein coding genes, and the transcriptional activity for some of them. Therefore, the idea that Bs are inert is outdated, but the role of Bs is yet to be determined. The maintenance of Bs is obviously not the same for all species, so the current models must be adapted while bearing in mind that Bs are not inactive as it was once thought.
Monitoring reperfused myocardial infarction with delayed left ventricular systolic dysfunction in rabbits by longitudinal imaging.

Background An experimental imaging platform for longitudinal monitoring and evaluation of cardiac morphology-function changes has been long desired. We sought to establish such a platform by using a rabbit model of reperfused myocardial infarction (MI) that develops chronic left ventricle systolic dysfunction (LVSD) within 7 weeks. Methods Fifty-five New Zeeland white (NZW) rabbits received sham-operated or 60-min left circumflex coronary artery (LCx) ligation followed by reperfusion. Cardiac magnetic resonance imaging (cMRI), transthoracic echocardiography (echo), and blood samples were collected at baseline, in acute (48 hours or 1 week) and chronic (7 weeks) stage subsequent to MI for in vivo assessment of infarct size, cardiac morphology, LV function, and myocardial enzymes. Seven weeks post MI, animals were sacrificed and heart tissues were processed for histopathological staining. Results The success rate of surgical operation was 87.27%. The animal mortality rates were 12.7% and 3.6% both in acute and chronic stage separately. Serum levels of the myocardial enzyme cardiac Troponin T (cTnT) were significantly increased in MI rabbits as compared with sham animals after 4 hours of operation (P<0.05). According to cardiac morphology and function changes, 4 groups could be distinguished: sham rabbits (n=12), and MI rabbits with no (MI_NO_LVSD; n=10), moderate (MI_M_LVSD; n=9) and severe (MI_S_LVSD; n=15) LVSD. No significant differences in cardiac function or wall thickening between sham and MI_NO_LVSD rabbits were observed at both stages using both cMRI and echo methods. cMRI data showed that MI_M_LVSD rabbits exhibited a reduction of ejection fraction (EF) and an increase in end-systolic volume (ESV) at the acute phase, while at the chronic stage these parameters did not change further. Moreover, in MI_S_LVSD animals, these observations were more striking at the acute stage followed by a further decline in EF and increase in ESV at the chronic stage. Lateral wall thickening determined by cMRI was significantly decreased in MI_M_LVSD versus MI_NO_LVSD animals at both stages (P<0.05). As for MI_S_LVSD versus MI_M_LVSD rabbits, the thickening of anterior, inferior and lateral walls was significantly more decreased at both stages (P<0.05). Echo confirmed the findings of cMRI. Furthermore, these in vivo outcomes including those from vivid cine cMRI could be supported by exactly matched ex vivo histomorphological evidences. Conclusions Our findings indicate that chronic LVSD developed over time after surgery-induced MI in rabbits can be longitudinally evaluated using non-invasive imaging techniques and confirmed by the entire-heart-slice histomorphology. This experimental LVSD platform in rabbits may interest researchers in the field of experimental cardiology and help strengthen drug development and translational research for the management of cardiovascular diseases.
The CHA2DS2-VASc Score Predicts Major Bleeding in Non-Valvular Atrial Fibrillation Patients Who Take Oral Anticoagulants

Background: Patients with atrial fibrillation (AF) are at a substantial risk of ischemic stroke. The CHA2DS2-VASc score predicts the risk of thromboembolism, but its role in predicting major bleeding in patients taking oral anticoagulants is unclear. Methods: We used the National Health Insurance Research Database (NHIRD) of Taiwan to identify patients with AF from 2010 to 2016. They were divided into four groups according to the oral anticoagulants. The outcomes were ischemic stroke/systemic thromboembolism, and major bleeding. Results: A total of 279,776 patients were identified. Ischemic stroke or systemic embolism events were observed in 1.73%, 3.62%, 4.36%, and 5.02% of the patients in the apixaban, rivaroxaban, dabigatran, and warfarin groups, respectively. Major bleeding was recorded in 1.18%, 2.66%, 3.23%, and 4.70% of the patients in the apixaban, rivaroxaban, dabigatran, and warfarin groups, respectively. The highest rates for both ischemic stroke and bleeding events occurred in the patients with a CHA2DS2-VASc score of five or more. Conclusion: Non-valvular AF patients with high CHA2DS2-VASc scores are susceptible to both systemic thromboembolism and major bleeding. The trend was consistently observed in patients who took non-vitamin K oral anticoagulants (NOACs) or warfarin. NOACs might be potentially more effective in reducing overall events.
Cannabidiol affects circadian clock core complex and its regulation in microglia cells

Cannabis is often used by consumers for sleep disorders. Studies show that circadian rhythm could be affected by a misuse of cannabis. Recent research has connected the role of microglial cells with psychiatric disorders such as substance abuse. The aim was to show the effect of two major components of cannabis on circadian genes regulation in microglial cells. In BV‐2 microglial cells, cannabidiol (CBD) induces a deregulation of circadian genes with (P‐value = 0.039) or without (P‐value = 0.0015) lipopolisaccharides stimulation. CBD up regulated Arntl (P = 9.72E‐5) and down regulated Clock (P = 0.0034) in BV‐2 cells. Temporal expression of Arntl (light and dark P = 0.0054) and Clock (light and dark P = 0.047) was confirmed to have 24 hours light and dark rhythmic regulation in dissected suprachiasmatic nucleus as well as of Cb1 cannabinoid receptor (light and dark P = 0.019). In BV‐2 microglia cells, CBD also up regulated CRY2 (P = 0.0473) and PER1 (P = 0.0131). Other nuclear molecules show a deregulation of circadian rhythm in microglial cells by CBD, such as RORA, RevErbα, RORB, CREBBP, AFT4, AFT5 and NFIL3. Our study suggests that circadian rhythm in microglial cells is deregulated by CBD but not by THC. It is consistent with clinical observations of the use of therapeutic cannabis to treat insomnia.
Neural stem cell lineage-specific cannabinoid type-1 receptor regulates neurogenesis and plasticity in the adult mouse hippocampus

Abstract Neural stem cells (NSCs) in the adult mouse hippocampus occur in a specific neurogenic niche, where a multitude of extracellular signaling molecules converges to regulate NSC proliferation as well as fate and functional integration. However, the underlying mechanisms how NSCs react to extrinsic signals and convert them to intracellular responses still remains elusive. NSCs contain a functional endocannabinoid system, including the cannabinoid type-1 receptor (CB1). To decipher whether CB1 regulates adult neurogenesis directly or indirectly in vivo, we performed NSC-specific conditional inactivation of CB1 by using triple-transgenic mice. Here, we show that lack of CB1 in NSCs is sufficient to decrease proliferation of the stem cell pool, which consequently leads to a reduction in the number of newborn neurons. Furthermore, neuronal differentiation was compromised at the level of dendritic maturation pointing towards a postsynaptic role of CB1 in vivo. Deteriorated neurogenesis in NSC-specific CB1 knock-outs additionally resulted in reduced long-term potentiation in the hippocampal formation. The observed cellular and physiological alterations led to decreased short-term spatial memory and increased depression-like behavior. These results demonstrate that CB1 expressed in NSCs and their progeny controls neurogenesis in adult mice to regulate the NSC stem cell pool, dendritic morphology, activity-dependent plasticity, and behavior.