Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advances in nanomaterials-based optical sensors for detection of various biomarkers (inorganic species, organic and biomolecules).

This review briefly emphasizes the different detection approaches (electrochemical sensors, chemiluminescence, surface-enhanced Raman scattering), functional nanostructure materials (quantum dots, metal nanoparticles, metal nanoclusters, magnetic nanomaterials, metal oxide nanoparticles, polymer-based nanomaterials, and carbonaceous nanomaterials) and detection mechanisms. Further, this review emphasis on the integration of functional nanomaterials with optical spectroscopic techniques for the identification of various biomarkers (nucleic acids, glucose, uric acid, oxytocin, dopamine, ascorbic acid, bilirubin, spermine, serotonin, thiocyanate, Pb2+ , Cu2+ , Hg2+ , F- , peptides, and cancer biomarkers (mucin 1, prostate specific antigen, carcinoembryonic antigen, CA15-3, human epidermal growth factor receptor 2, C-reactive protein, and interleukin-6). Analytical characteristics of nanomaterials-based optical sensors are summarized in Tables, providing the insights of nanomaterials-based optical sensors for biomarkers detection. Finally, the opportunities and challenges of nanomaterials-based optical analytical approaches for the detection of various biomarkers (inorganic, organic, biomolecules, peptides and proteins) are discussed.
Guiding the training of Generative Adversarial Networks

Image generation with Artificial Neural Networks (ANNs) has been popular in the last few years by using the knowledge about objects and structures to generate dreamlike images, applying smart filters or making images appear scary. Although vivid and impressive, the generated images do not appear natural to a human viewer. Training ANNs to generate better images is a hard problem to solve with conventional machine learning techniques. Generative Adversarial Networks (GAN) is a new approach that utilizes two ANNs in one system. The first ANN is called the Discriminator and evaluates the quality of generated images from the other neural network called the Generator. These two networks partake in an adversarial battle to improve their own performance. The results of this battle is a model capable of generating new natural looking images based on unsupervised learning of an image dataset. GAN have evolved considerably since its inception, but there are still open problems. One such problem is how to objectively evaluate a GAN model. Image generation has no fixed solution, and evaluating it is a difficult as evaluating other creative works. One method allows for two GAN models to be compared by evaluating each other. This method leverage the adversarial min-max aspect of GANs, by having the Discriminator from one model classify generated samples from the other, and vice versa. This thesis will propose a custom implementation of this method that allows for comparison between two GAN models both overall and throughout the learning process. GANs have a reputation for being hard to train. One concrete problem is maintaining the balance between the Generator and Discriminator. As for humans, it is easier to rate the quality of images then it is to actually create them. A good evaluator is necessary, but it must not out power the generative model. Two main approaches will be explored to achieve a better balanced GAN model. The first method makes guided alterations to the usually random input of the Generator. The second method adds an additional Discriminator to the model. Techniques based on both of these methods were shown to effectively guide the training process and creating strong models that outperformed regular GANs when compared with the previously mentioned evaluation metric.
PEM360: a dataset of 360° videos with continuous physiological measurements, subjective emotional ratings and motion traces

From a user perspective, immersive content can elicit more intense emotions than flat-screen presentations. From a system perspective, efficient storage and distribution remain challenging, and must consider user attention. Understanding the connection between user attention, user emotions and immersive content is therefore key. In this article, we present a new dataset, PEM360 of user head movements and gaze recordings in 360° videos, along with self-reported emotional ratings of valence and arousal, and continuous physiological measurement of electrodermal activity and heart rate. The stimuli are selected to enable the spatiotemporal analysis of the connection between content, user motion and emotion. We describe and provide a set of software tools to process the various data modalities, and introduce a joint instantaneous visualization of user attention and emotion we name Emotional maps. We exemplify new types of analyses the PEM360 dataset can enable. The entire data and code are made available in a reproducible framework.
Nagare media ingest: a server for live CMAF ingest workflows

New media ingest protocols have been presented recently. SRT and RIST compete with old protocols such as RTMP while the DASH-IF specified an HTTP-based ingest protocol for CMAF formatted media that lends itself towards delivery protocols such as DASH and HLS. Additionally, use cases of media ingest workflows can vary widely. This makes implementing generic and flexible tools for ingest workflows a hard challenge. A monolithic approach limits adoption if the implementation does not fit the use case completely. We propose a new design for ingest servers that splits responsibilities into multiple components running concurrently. This design enables flexible ingest deployments as is discussed for various use cases. We have implemented this design in the open source software Nagare Media Ingest for the new DASH-IF ingest protocol.
Coherence-based Second Chance Autoencoders for Document Understanding

The application of conventional autoencoders for textual data often leads to learning trivial and redundant representations due to the high dimensional nature of the text, sparsity, and following power-law word distribution. In order to address these challenges, we introduce a new autoencoder, termed CSCAT ( C oherence-based S econd C hance A utoencoder for T ext), which uses competitive learning to select k winning neurons in the bottleneck layer that becomes specialized in recognizing specific patterns–leading to learning semantically significant representations of the text. CSCAT introduces a new competition learning based on a measure of consistency to eliminate incoherent features. Our experiments demonstrate that CSCAT achieves outstanding performance on several tasks, including classification, topic modeling, and document visualization compared to LDA , k-sparse , KATE , NVCTM , and ProdLDA .
Linking Graph Databases and Semantic Web for Reasoning in Library Domains

The need for digital libraries to store, catalogue, and manipulate documents is growing and the requirements are increasing daily. Much research is underway to discover effective ways to store and keep track of documents, their properties and uses. In this context, we propose the use of Labeled Property Graphs (LPGs) to store digital archives. The use of these graphs allows us high efficiency since the graphs are navigable very quickly thanks to Database Management Systems (DBMSs) (such as Neo4j) especially developed to guarantee high performance. On the other hand, this technology is not natively suited to infer new knowledge automatically. In this paper, we propose a method to use Semantic Web tools to enrich our knowledge base. This way, new operations are available, not possible with only graph databases. The proposed approach has been integrated into the GraphBRAIN (GB) system, a tool able to manage ontologies.
Multilevel Operation Strategy of a Vascular Interventional Robot System for Surgical Safety in Teleoperation

Remote-controlled vascular interventional robots have great potential for use in minimally invasive vascular surgeries in recent years due to their ability to reduce the occupational risk of surgeons and improve the stability and accuracy of surgical procedures. However, blood vessels will suffer from the damage caused by collision with medical instruments to some extent even though the surgeries are very successful. Moreover, when surgeons perform unsafe operations, the unsafe operations will not only seriously affect surgical safety (or even cause serious complications) but also restrict the continuity of operation. In this article, a multilevel concept for operating force is first introduced into surgical procedures as a reference for the choice and design of operation strategies. Based on this concept, a novel multilevel operation strategy is first proposed to reduce blood vessel damage, ensure surgical safety, and allow for continuous operation. This strategy can remind surgeons about the operative conditions in real-time, reduce collision to blood vessels, and eliminate unsafe operations online. A prototype was fabricated and calibrated through calibration experiments and the performance of the multilevel operation strategy was validated through in vitro and ex vivo experiments. Experimental results demonstrate the engineering effectiveness of the proposed method and motivate the need for further in vivo studies to evaluate improvement on surgical safety.
Automation Of Roundabouts Via Consensus-based Distributed Auctions and Stochastic Model Predictive Control

This contribution presents a decentralized control strategy for the automation of roundabouts, in which autonomous vehicles are required to circulate without collisions and to safely interact with pedestrians. The inherent hierarchical controller incorporates a consensus-based auction algorithm, which allows vehicles to agree on a crossing order at each entry point of the roundabout. An on-board scenario-based model predictive controller avoids the occurrence of collisions with other vehicles and pedestrians, while optimizing performance metrics over time. We run stochastic simulations that confirm theoretical findings.
A Volume Bound for Montesinos Links

A Volume Bound for Montesinos Links Kathleen Finlinson Department of Mathematics, BYU Master of Science The hyperbolic volume of a knot complement is a topological knot invariant. Futer, Kalfagianni, and Purcell have estimated the volumes of Montesinos link complements for Montesinos links with at least three positive tangles. Here we extend their results to all hyperbolic Montesinos links.
Speed Tracking of an Electric Vehicle Using a Restricted Structure NGMV Control Algorithm

A Restricted-Structure Non-linear Generalized Minimum Variance (RS-NGMV) algorithm is applied to a scalar quasi Linear Parameter-Varying (qLPV), or State-Dependent (SD), Electric Vehicle speed tracking control problem. The model represents the longitudinal vehicle dynamics with disturbance factors from the road and the environment such as road inclination, aerodynamic drag and the rolling resistance forces. The control problem is based on the longitudinal speed tracking under the impact of these disturbances with an emphasis on the inclination. The simulation studies consider constant speed, UDDS and HWFET drive cycle scenarios as the reference speed profiles. The Restricted-Structure (RS) controller is of low order and uses NGMV optimization to calculate the feedback gains. The results show that RS-NGMV is efficient in dealing with disturbances and parameter variations, and battery State of Charge (SOC) results are also presented.
Online and Stochastic Universal Gradient Methods for Minimizing Regularized H\"older Continuous Finite Sums

Online and stochastic gradient methods have emerged as potent tools in large scale optimization with both smooth convex and nonsmooth convex problems from the classes $C^{1,1}(\reals^p)$ and $C^{1,0}(\reals^p)$ respectively. However to our best knowledge, there is few paper to use incremental gradient methods to optimization the intermediate classes of convex problems with Holder continuous functions $C^{1,v}(\reals^p)$. In order fill the difference and gap between methods for smooth and nonsmooth problems, in this work, we propose the several online and stochastic universal gradient methods, that we do not need to know the actual degree of smoothness of the objective function in advance. We expanded the scope of the problems involved in machine learning to Holder continuous functions and to propose a general family of first-order methods. Regret and convergent analysis shows that our methods enjoy strong theoretical guarantees. For the first time, we establish an algorithms that enjoys a linear convergence rate for convex functions that have Holder continuous gradients.
Parameter Estimation for a Sinusoidal Signal with a Time-Varying Amplitude

This paper addresses the parameter estimation problem of a non-stationary sinusoidal signal with a time-varying amplitude, which is given by a known function of time multiplied by an unknown constant coefficient. A robust estimation algorithm is proposed for identifying the unknown frequency and the amplitude coefficient in real-time. The estimation algorithm is constructed based on the Volterra integral operator with suitably designed kernels and sliding mode adaptation laws. It is shown that the parameter estimation error converges to zero within an arbitrarily small finite time, and the robustness against bounded additive disturbances is certified by bounded-input-bounded-output arguments. The effectiveness of the estimation technique is evaluated and compared with other existing tools through numerical simulations.
Finite Time Parameter Estimation Algorithm for Salient PMSM

The problem of parameter estimation for salient permanent magnet synchronous motors (PMSMs) is considered in this paper. It is assumed that stator currents and voltages and rotor position are measurable signals and the only known motor parameter is the number of pole pairs. The proposed estimation algorithm uses dynamic operators applied to the measurable signals to get a linear regression model with unknown motor parameters. As proven, scalar estimators that are designed for each unknown motor parameter ensure finite time convergence of estimation errors to zero. The main benefit of the proposed approach is to relax the stringent assumptions imposed on regression functions such as condition of persistent excitation (PE) and non-square integrability that are often difficult to verify in practice.
Safeguarded Anderson acceleration for parametric nonexpansive operators

This paper describes the design of a safeguarding scheme for Anderson acceleration to improve its practical performance and stability when used for first-order optimisation methods. We show how the combination of a non-expansiveness condition, conditioning constraints, and memory restarts integrate well with solver algorithms that can be represented as fixed point operators with dynamically varying parameters. The performance of the scheme is demonstrated on seven different QP and SDP problem types, including more than 500 problems. The safeguarded Anderson acceleration scheme proposed in this paper is implemented in the open-source ADMM-based conic solver COSMO.
Who made the paintings: Artists or artificial intelligence? The effects of identity on liking and purchase intention

Investigating how people respond to and view AI-created artworks is becoming increasingly crucial as the technology’s current application spreads due to its affordability and accessibility. This study examined how AI art alters people’s evaluation, purchase intention, and collection intention toward Chinese-style and Western-style paintings, and whether art expertise plays a role. Study 1 recruited participants without professional art experience (non-experts) and found that those who made the paintings would not change their liking rating, purchase intention, and collection intention. In addition, they showed ingroup preference, favoring Chinese-style relative to Western-style paintings, in line with previous evidence on cultural preference in empirical aesthetics. Study 2 further investigated the modulation effect of art expertise. Art experts evaluated less favorably (less liking, lower purchase, and collection intentions) AI-generated paintings relative to artist-made paintings, while non-experts showed no preference. There was also an interaction effect between the author and the art expertise and interaction between the painting style and the art expertise. Collectively, the findings in this study showed that who made the art matters for experts and that the painting style affects aesthetic evaluation and ultimate reception of it. These results would also provide implications for AI-art practitioners.
Alterations in brain networks in children with sub-threshold autism spectrum disorder: A magnetoencephalography study

Individuals with sub-threshold autism spectrum disorder (ASD) are those who have social communication difficulties but do not meet the full ASD diagnostic criteria. ASD is associated with an atypical brain network; however, no studies have focused on sub-threshold ASD. Here, we used the graph approach to investigate alterations in the brain networks of children with sub-threshold ASD, independent of a clinical diagnosis. Graph theory is an effective approach for characterizing the properties of complex networks on a large scale. Forty-six children with ASD and 31 typically developing children were divided into three groups (i.e., ASD-Unlikely, ASD-Possible, and ASD-Probable groups) according to their Social Responsiveness Scale scores. We quantified magnetoencephalographic signals using a graph-theoretic index, the phase lag index, for every frequency band. Resultantly, the ASD-Probable group had significantly lower small-worldness (SW) in the delta, theta, and beta bands than the ASD-Unlikely group. Notably, the ASD-Possible group exhibited significantly higher SW than the ASD-Probable group and significantly lower SW than the ASD-Unlikely group in the delta band only. To our knowledge, this was the first report of the atypical brain network associated with sub-threshold ASD. Our findings indicate that magnetoencephalographic signals using graph theory may be useful in detecting sub-threshold ASD.
Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles

In this article, we propose a visual-inertial navigation system that directly minimizes a photometric error without an explicit data-association. We focus on the photometric error parametrized by pose and structure parameters that is highly nonconvex due to the nonlinearity of image intensity. The key idea is to introduce an optimal intensity gradient that accounts for a projective uncertainty of a pixel. Ensembles sampled from the state uncertainty contribute to the proposed gradient and yield a correct update direction even in a bad initialization point. We present two sets of experiments to demonstrate the strengths of our framework. First, a thorough Monte Carlo simulation in a virtual trajectory is designed to reveal robustness to large initial uncertainty. Second, we show that the proposed framework can achieve superior estimation accuracy with efficient computation time over state-of-the-art visual-inertial fusion methods in a real-world UAV flight test, where most scenes are composed of a featureless floor.
Extracorporeal organ support and the kidney

The concept of extracorporeal organ support (ECOS) encompasses kidney, respiratory, cardiac and hepatic support. In an era of increasing incidence and survival of patients with single or multiple organ failure, knowledge on both multiorgan crosstalk and the physiopathological consequences of extracorporeal organ support have become increasingly important. Immerse within the cross-talk of multiple organ failure (MOF), Acute kidney injury (AKI) may be a part of the clinical presentation in patients undergoing ECOS, either as a concurrent clinical issue since the very start of ECOS or as a de novo event at any point in the clinical course. At any point during the clinical course of a patient with single or multiple organ failure undergoing ECOS, renal function may improve or deteriorate, as a result of the interaction of multiple factors, including multiorgan crosstalk and physiological consequences of ECOS. Common physiopathological ways in which ECOS may influence renal function includes: 1) multiorgan crosstalk (preexisting or de-novo 2)Hemodynamic changes and 3) ECOS-associated coagulation abnormalities and 3) Also, cytokine profile switch, neurohumoral changes and toxins clearance may contribute to the expected physiological changes related to ECOS. The main objective of this review is to summarize the described mechanisms influencing the renal function during the course of ECOS, including renal replacement therapy, extracorporeal membrane oxygenation/carbon dioxide removal and albumin dialysis. Graphical Abstract Extracorporeal organ support (ECOS) was developed in order to support a failing organ and to prevent and/or reverse secondary multiorgan failure. ECOS provide extensive physiological benefits. However, both multiorganic crosstalk and patient interaction with the extracorporeal support may have renal repurcussions. Kidney vulnerability is the result of both kidney frailty (i.e. preexisting kidney/organic damage) and the summatory of adverse renal events during the clinical course (i.e hipoxia+nephrotoxicity+Hypervolemia). The present manuscript emphasizes on both the shared physiopathological pathways and individual differences to be taken into account for different to be taken into account for different kind of ECOS (renal replacement theraphy, extracorporeal membrane oxygenation, and albumim dialysis) as well as kidney vulnerabilty points to be taken into account in order to reach favorable clinical outcomes during ECOS. No, nitro oxide; ROS, reactive oxygen species; ECMO, Extracorporeal Membrane Oxigenation; RRT, Renal Replacement Theraphy; BNP, Brain Natriuretic peptide.
Editorial: Nanomaterials for targeted delivery of therapeutic and imaging agents

Nanomedicine has been a growing field over the last couple of decades, and the development of new platforms for improving disease diagnosis, therapies, and drug formulations has been constantly evolving. The rapid clinical translation of the current Covid-19 mRNA vaccines is also in part due to the application of nanotechnology mediated gene delivery. Nanomaterials, while helping to develop targeted delivery of therapeutics, also serve as imaging agents/contrasts for monitoring the disease status in vivo using various imaging modalities. Nano-agents like gold nanoparticles, nanodiamonds, and carbon nanotubes work as excellent drug carriers while helping in intracellular tracking of delivered agents. To highlight the application of nanomaterials in therapy and diagnostic imaging (theragnostic), we invited articles related to this topic from established investigators. We received a total of seven manuscripts, including four original research manuscripts and three review articles, among of which six of them that were related to nanomaterials in theranostic applications were accepted and assembled as a special issue. As shown in the figure (Figure 1), the original research manuscript by De Canha et al. highlighted the preparation of gold nanoparticles by incorporating extract of Helichrysum odoratissimum as a potential nanoformulation for effective inhibition of Cutibacterium acnes, a commensal that causes acne in human skin, adhesion without showing any antibacterial activity (De Canha et al.). This serves as a useful formulation for preventing bacterial disease, and at the same time overcomes the possibility of developing a drug resistant bacterium, a common problem associated with the use of antibiotics for treating bacterial diseases. Similarly, Naqvi et al., has developed a polyethyleneimine (PEI) stabilized human serum albumin (HSA) nanoparticles for simultaneous delivery of a small molecule antioxidant sulforaphane (SF) and an antioxidant responsive SOD1 gene for improving the OPEN ACCESS