Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic bandwidth management as part of an integrated network management system based on distributed agents

We present a system for dynamic network resource configuration in environments with bandwidth reservation and path restoration mechanisms. Our focus is on the dynamic bandwidth management results, although the main goal of the system is the integration of the different mechanisms that manage the reserved paths (bandwidth, restoration, and spare capacity planning). The objective is to avoid conflicts between these mechanisms. The system is able to dynamically manage a logical network such as a virtual path network in ATM or a label switch path network in MPLS. This system has been designed to be modular in the sense that in can be activated or deactivated, and it can be applied only in a sub-network. The system design and implementation is based on a multi-agent system (MAS). We also included details of its architecture and implementation.
Facial Expression Recognition using Convolutional Neural Network through Region-based Patch Generation: Harnessing Subtle Facial Cues

The task of classifying human emotions based on facial expressions has been a challenging area of research for the past two decades. Early approaches relied on hand-crafted features such as SIFT, HOG, and LBP and classifiers trained on facial expression datasets. However, these methods failed to provide accurate results on newer, more complex datasets. Recently, the trend has shifted towards using deep learning models for facial expression classification, providing improved results. Nevertheless, there is still room for improvement in the accuracy of these models. In this research, we propose a novel model to improve facial expression classification accuracy on several benchmark datasets. Our model is based on generating nine different patches for different relevant facial regions and uses nine different streams, each for one of those micro-facial regions. We evaluated our model on the CK+, Oulu-Casia, and RAF-DB datasets and achieved state-of-the-art results, including 10-fold cross-validation accuracy of 99.8% and 91.25% for CK+ and Oulu-Casia datasets, respectively. Our proposed model demonstrates a significant improvement in facial expression classification accuracy and provides a promising approach to future research in this field.
Callosal degeneration topographically correlated with cognitive function in amnestic mild cognitive impairment and alzheimer's disease dementia

Degeneration of the corpus callosum (CC) is evident in the pathogenesis of Alzheimer's disease (AD). However, the correlation of microstructural damage in the CC on the cognitive performance of patients with amnestic mild cognitive impairment (aMCI) and AD dementia is undetermined. We enrolled 26 normal controls, 24 patients with AD dementia, and 40 single‐domain aMCI patients with at least grade 1 hippocampal atrophy and isolated memory impairment. Diffusion tensor imaging (DTI) with fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR) were measured. The entire CC was parcellated based on fiber trajectories to specific cortical Brodmann areas using a probabilistic tractography method. The relationship between the DTI measures in the subregions of the CC and cognitive performance was examined. Although the callosal degeneration in the patients with aMCI was less extended than in the patients with AD dementia, degeneration was already exhibited in several subregions of the CC at the aMCI stage. Scores of various neuropsychological tests were correlated to the severity of microstructural changes in the subregional CC connecting to functionally corresponding cortical regions. Our results confirm that CC degeneration is noticeable as early as the aMCI stage of AD and the disconnection of the CC subregional fibers to the corresponding Brodmann areas has an apparent impact on the related cognitive performance. Hum Brain Mapp 35:1529–1543, 2014. © 2013 Wiley Periodicals, Inc.
The Long-Run Behavior of Debt and Equity Underwriting Spreads

This paper is the first to look at the long-run (30-year) behavior of underwriting spreads in the markets for corporate equity and debt. Specifically, we analyze the determinants of underwriting spreads on corporate bond issues, secondary equity offerings and initial public offerings over the period 1970-2000. We explain the time-varying cross-sectional behavior of these spreads by analyzing three sets of variables or factors: macro (systematic) factors, investment banking market structure factors and issuer specific characteristics. We also analyze the relationship between the direct costs (underwriting spreads) and indirect costs (underpricing) of new issues. Among our many results we find an apparent decline in spreads over time, an increased clustering in spreads for both IPOs and SEOs, the dominance of issuer- specific characteristics in explaining spreads, and a relatively weak linkeage between the direct and indirect costs of issuance.
Frontiers in Synaptic Neuroscience Synaptic Neuroscience Spike-timing Dependent Plasticity in Inhibitory Circuits

More importantly, plastic properties can vary remarkably between two interneurons located in close proximity in the same hippocampal area, but showing different neurochemical marker expression or different axon distribution (Lamsa et al., 2007b; Nissen et al., 2010) (Figure 1). A specific pattern of pre-and postsynaptic activity that induces plasticity in one interneuron type can fail or elicit a different plastic change in another interneuron type located in the same layer (Lamsa et al., 2005, 2007b; Nissen et al., 2010). These findings, that the interneuron plasticity is diverse and cell type-specific, have important impacts on the dynamics of the cortical networks. First, specific patterns of pre-and postsynaptic action potentials are capable of triggering plasticity in only a sub-population of inhibitory cells (Lamsa et al., 2005, 2007b). Second, plasticity is likely to be induced in different brain states in distinct interneuron types. It has been shown that the activity of interneu-rons and their glutamatergic afferents is highly specific to a cell type during neuronal oscillations related to different behavioral states (Klausberger and Somogyi, 2008). It is therefore tempting to speculate that the distinct forms of LTP and LTD discovered in interneu-rons in vitro might underlie the use-dependent dynamics reported in selective parts of the inhibitory networks in vivo Several important questions remain to be answered on plasticity in interneurons and one of the most intriguing is related to the Plasticity in excitatory afferents of inhibitory circuits Long-term potentiation (LTP) and depression (LTD) of glutama-tergic synapses onto GABAergic interneurons has been discovered in several areas of the brain suggesting that plasticity in this locus is common in the CNS. Although these studies have concentrated heavily on the circuits of the hippocampal formation, there is evidence that similar plasticity takes place in the neocortex (Sarihi et al. One of the striking features of plasticity in this locus is that it can be highly specific to an interneuron type Subpopulations of unidentified interneurons may show LTP, LTD or no plasticity at all, for stimulation of the same afferent glutama-Lamsa et al., 2007a). Recent studies on identified interneurons in the hippocampus have revealed that these subpopulations are composed of anatomically distinct interneuron types. Plasticity has been shown to be consistent in individual anatomically identified GABAergic cell Inhibitory circuits in the brain rely on GABA-releasing interneurons. For long, inhibitory circuits were considered weakly plastic in the face of patterns of neuronal activity that trigger long-term changes in …
Comparative Evaluation of Several Convolutional Neural Network Architectures in Diabetic Retinopathy Classification

The increasing global prevalence of diabetes mellitus has brought diabetic retinopathy (DR) to the forefront as a significant cause of visual impairment and blindness among adults. DR, a complication of diabetes, can be broadly categorized into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), each comprising distinct clinical stages with varying severity. The accurate and timely detection and classification of these stages are essential for preserving vision, a task that grows in importance as the worldwide diabetes epidemic continues to rise, with an estimated 693 million cases projected by 2045. In response to this healthcare challenge, the development of efficient automated systems for DR diagnosis and classification has emerged as a critical research focus. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated substantial promise in addressing this issue. This research presents a comparative evaluation of three prominent CNN architectures, namely Le-Net, AlexNet, and VGG16, in the context of DR classification, aiming to provide valuable insights into their performance. Among these architectures, VGG16 emerges as the standout performer, achieving an accuracy of 74.4%. This outcome underscores the potential of VGG16 as a robust tool for DR diagnosis, with its deeper and more intricate architecture proving adept at capturing intricate retinal features. As the research landscape evolves, future directions may encompass exploring newer model variations, incorporating more extensive and diverse datasets, fine-tuning hyperparameters, and enhancing the interpretability of deep learning models for real-world medical applications, ultimately propelling the field toward more efficient and accurate DR diagnosis and classification.
The unintended effects of political party affirmative action policies on female candidates’ nomination chances

Some state political party organizations that hold nominating conventions implement affirmative action (AA) policies to encourage the nomination of women and minority candidates. This paper assesses whether these policies help or hinder female candidates seeking statewide office. On the one hand, these policies could benefit female candidates since they demonstrate an organization's commitment to diversity. On the other hand, diversity and AA policies may have negative, unintended consequences for female candidates such as promoting gender stereotype activation or creating a stigma of incompetence for female candidates. I examine whether and how delegates’ awareness of these policies shapes candidate evaluations, gender stereotypes, and nominee choice. I test this by analyzing unique survey data from Democratic state nominating convention delegates who evaluate candidates in statewide nominations in four states. The results suggest that while evaluations of the female candidate are not downgraded, focus on AA leads Democratic delegates to inflate their views of her male opponent. Furthermore, when delegates perceive that their party focuses on AA, they are less likely to choose the female candidate, in part because this perceived focus highlights that female candidates might lack masculine strengths. The resulting implications for female candidates and political party organizations are discussed.
Exploring the impact on young people who are caring and grieving for a parent with motor neurone disease

Background: Motor Neurone Disease (MND) is a terminal neurological disease. Little attention has been paid in the literature to investigating the experiences of children and young people who provide care for a parent with the disease. Aim: To explore how providing care for a parent with MND impacts upon a young person's life. Sample: A purposive sample of seven young people who were bereaved or currently providing care for a parent with MND. Methods: A qualitative methodology with informal semi-structured interviews. Results: Five key themes were identified; initial MND diagnosis, changing family roles, implications upon education, implications upon social life and positive outcomes. Conclusion: The study suggests that young carers for a parent with MND experience substantial challenges in addition to some positive outcomes.
RoboCupRescue - Robot League Team ALCOR, Italy

We present a stable and autonomous system based on a strong inter- action between visual cognitive inference and actions executed. Mapping, Vi- sion and Navigation are all collaborative agents that work together sharing data to accomplish to complex identification and mapping tasks. The visual process reacts to interesting features in the arena trying to approach the focused area in order to achieve more successful observations from the environment. This ac- tivity is performed coordinating the states that model the robot actions in the arena.
functions of TNF Network dynamics determine the autocrine and paracrine signaling Material

Material Supplemental http://genesdev.cshlp. org/content/suppl/2014/09/29/28.19. 2120.DC1. html References http://genesdev.cshlp. org/content/28/19/2120.full. html#ref-list-1 This article cites 65 articles, 38 of which can be accessed free at: License Commons Creative . http://creativecommons.org/licenses/by-nc/4.0/ at Creative Commons License (Attribution-NonCommercial 4.0 International), as described ). After six months, it is available under a http://genesdev.cshlp. org/site/misc/terms.xhtml six months after the full-issue publication date (see This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first
Structure of Rhodococcus equi virulence-associated protein B (VapB) reveals an eight-stranded antiparallel β-barrel consisting of two Greek-key motifs

The structure of VapB, a member of the Vap protein family that is involved in virulence of the bacterial pathogen R. equi, was determined by SAD phasing and reveals an eight-stranded antiparallel β-barrel similar to avidin, suggestive of a binding function. Made up of two Greek-key motifs, the topology of VapB is unusual or even unique.
Interactions, Computations, and Experience: Interleaved Springboards of Cognitive Emergence

Abstract This rejoinder attempts to show that Bickhard's criticism is irrelevant to our theory and epistemologically one-sided. It is irrelevant because our theory is pragmaticist and interactivist at a number of levels ignored by Bickhard. It is epistemologically one-sided because it assumes that interactivism is the only proper model of mind. Thus, we try to show that different approaches can be used to model different levels or phrases in the organization and development of the mind. The discussion focuses on how interactivism, connectionism, computationalism and experientialism may complement each other as tools for modeling cognitive architecture and change. We also try to respond to various other, less central, criticisms that Bickhard leveled against our theory.
PolDIP2 interacts with human PrimPol and enhances its DNA polymerase activities

Translesion synthesis (TLS) employs specialized DNA polymerases to bypass replication fork stalling lesions. PrimPol was recently identified as a TLS primase and polymerase involved in DNA damage tolerance. Here, we identify a novel PrimPol binding partner, PolDIP2, and describe how it regulates PrimPol's enzymatic activities. PolDIP2 stimulates the polymerase activity of PrimPol, enhancing both its capacity to bind DNA and the processivity of the catalytic domain. In addition, PolDIP2 stimulates both the efficiency and error-free bypass of 8-oxo-7,8-dihydrodeoxyguanosine (8-oxoG) lesions by PrimPol. We show that PolDIP2 binds to PrimPol's catalytic domain and identify potential binding sites. Finally, we demonstrate that depletion of PolDIP2 in human cells causes a decrease in replication fork rates, similar to that observed in PrimPol−/− cells. However, depletion of PolDIP2 in PrimPol−/− cells does not produce a further decrease in replication fork rates. Together, these findings establish that PolDIP2 can regulate the TLS polymerase and primer extension activities of PrimPol, further enhancing our understanding of the roles of PolDIP2 and PrimPol in eukaryotic DNA damage tolerance.
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition

Zara, or ‘Zara the Supergirl’ is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7% accuracy on six different emotion classes, which is an impressive 4.5% improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues.
Non‐linear hierarchical models for monitoring compliance

As biomarkers transformable by specific drug agents increasingly become available, so their usefulness also increases for monitoring compliance in clinical and prevention trials, and for subsequent monitoring in the general population if a treatment is found successful. Marker levels measured over the course of a treatment yield a longitudinal trajectory that is typically non‐linear, with varying velocities during the phase‐in and steady‐state periods of treatment, followed by decays back to normal in the presence of non‐compliance. There is often considerable between‐individual variability both in the mean parameters of the trajectory and the variability over time. An example is the biomarker mean corpuscular volume (MCV), which increases by 20 per cent from the drug zidovudine (AZT), and has been used to monitor compliance to AZT. Using MCV data from a previous AIDS clinical trial as an example, we describe a non‐linear hierarchical growth model suitable for biomarkers that exhibit sigmoidal and/or asymptotic growth behaviour and show how such models can be supplemented with a change‐point to identify potential times of non‐compliance. We perform a fully Bayesian analysis to obtain a variety of posterior summaries for the behaviour of the longitudinal trajectory and the times of non‐compliance, and describe how to obtain predictions of non‐compliance for new individuals. Copyright © 2002 John Wiley & Sons, Ltd.
Constitutive and IL‐6‐induced nuclear factors that interact with the human C‐reactive protein promoter.

Transcription of the human C‐reactive protein (CRP) gene is induced by interleukin‐6 (IL‐6) during acute inflammation. Important information for inducible CRP expression is located within the 90 bases preceding the transcriptional start site. We show that the CRP promoter contains two adjacent binding sites (beta and alpha) that interact with at least two hepatocyte‐specific nuclear proteins, H‐APF‐1 and H‐APF‐2. Point mutations that abolish or reduce binding drastically affect the level of CRP gene expression. Binding to beta is identical when extracts from uninduced or IL‐6‐induced Hep3B cells are used. On the contrary, both quantitative and qualitative changes in the alpha binding can be detected with extracts from uninduced cells or from cells treated with IL‐6 or IL‐6 + cycloheximide. A synthetic promoter based on the multimerization of the beta‐binding domain, but not of the alpha‐domain, is highly inducible when transfected in hepatoma cells. These results are discussed in relation to the structure of the promoter region of other acute phase inducible genes.
Conocimiento etnobotánico, patrones de uso y manejo de plantas útiles en la cuenca del río Cane-Iguaque (Boyacá - Colombia): una aproximación desde los sistemas de uso de la biodiversidad

The Use of Biodiversity Systems seeks being closer to the local knowledge regarding the use of biodiversity, needs and conflicts of access, identifying their role in the villagers livelihood. Semi-structured interviews, workshops with focus groups and tours to areas of use were conducted in a prioritized area of the Cane-Iguaque river basin (Boyaca - Colombia). Decline of cultural values and ethnobotanical knowledges was found. The inclusion of goods from other cultures has replaced the use of native species by species of exogenous origin, supplying the most important demands, like firewood, timber and medicinal.
A Novel Artificial Intelligence–Based Transaction

A key part of system security is making sure that people can't get in without permission and verifying their name. What the study found is that different types of biometrics are being used for mixed biometric security systems. But the review found that there hadn't been a lot of research that compared the Continuous Multimodal Biometric Authentication (CMBA) model when it came to pairings of biometric types, like fingerprint and facial recognition. The goal of this work is to make the results of cutting-edge continuous multimodal biometric identification systems official by looking at how they were designed, put into action, and evaluated. A Continuous Multimodal Biometric Authentication (CMBA) programme should be put into action and thoroughly tested on real-world data to show that such processes are possible. This will help us figure out how to use multimodal biometrics based on a transaction for a constant authentication process.
Progressive Coding for Neural Field Transmission

Neural fields are a new signal representation and are widely used now to represent various forms of multimedia. However, due to their size, they could introduce latency when delivered over the network. A way to mitigate that is to use Progressive Coding which encodes the content into a single bitstream that can be decoded at various bitrates. In this paper, we introduce a novel progressive coding framework for neural fields. We propose three progressive scanning mechanisms - first is based on the layers of the model, the second is based on the bit-planes across all layers of the model, and the third is based on the block of coefficients across all layers of the model. We leverage the fact that neural fields are continuous functions (constructed using a multi-layer perceptron (MLP)) and use that to reconstruct the entire original content and progressively improve the quality as more bits are obtained. We validate the effectiveness of the proposed method by showing results on public data sets.
The impact of standards-based reform on special education and the creation of the ‘dividual

An urban Pre-K through 5th grade school referred to as Westvale Elementary School was the focal point for this research study. Westvale was located within an urban district in New York State that was host to approximately 20,000 students. Both the school and the district were labeled as failing under the No Child Left Behind Act. Foucauldian conception of biopolitics and Deleuzian notion of the ‘dividual are the theoretical frameworks used to make meaning of qualitative data collected for this study. Interview, observation, and document data revealed how the structures of a biopolitical society hierarchized, segregated, and geographically shifted certain demographic groups of students throughout the school district based on their potential to succeed on high stakes examinations. Teachers and administrators were also linked to the demographics of the students they taught and mandates of standards-based reform (SBR) often required the turnover of school personnel, causing frustration and stigma for educators and students alike. Mandated teacher and leader evaluations were also found to increase fear of teaching students with disabilities because they were viewed as ‘dividuals within the biopolitical system that SBR exacerbated.