Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Shadow Mapping

We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects.
A Roadmap To Post-Moore Era for Distributed Systems

We are reaching the limits of the von Neumann computing architectures (also called Moore's law era) as there is no free ride of the performance growth from simply shrinking the transistor features. As one of the consequences, we experience the rise of highly specialized architectures ranging from neuromorphic to quantum computing, exploiting completely different physical phenomena and demanding the development of entirely new architectures - that, however, can perform the computations within a fraction of the energy needed by the von Neumann architecture. Thus, we experience the paradigm shift from generalized architectures of the Von Neumann era to highly specialized architectures in the Post-Moore era where we expect the coexistence of multiple types of architectures specialized for different types of computation. In this paper, we discuss the implications of the post-Moore era for distributed systems.
The Lower Carboniferous (Tournaisian) Crinoids from Hook Head, County Wexford, Ireland

ABSTRACT The Lower Carboniferous (late Tournaisian) crinoid fauna from the Hook Head Formation at Hook Head, Co. Wexford, Ireland, is revised. The Hook Head crinoid fauna is composed of 43 species assigned to 25 genera plus five taxa left in open nomenclature, and it is essentially one of the same faunas studied by J. S. Miller in 1821 when he described the Crinoidea as a distinct group. Seven new genera (Borucrinus, Erincrinus, Fiannacrinus, Gaelicrinus, Iotacrinus, Maevecrinus and Thinocrinus) and 11 new species are described. Type species for some of Miller's genera are revised, which has importance for the fundamentals of the classification of the Crinoidea. Neotypes are established for eight species, including Actinocrinites triacontadactylus Miller, Cyathocrinites planus Miller, Playcrinities granulatus Miller, P. laevis Miller, and P. striatus Miller. Lectotypes are established for Fiannacrinus quinquangularis and Iotacrinus dorsatus.
Novel multilayer structural epoxy nanocomposite coating for enhanced adhesion and protection properties of steel

ABSTRACT This work is aimed to synthesize novel structural epoxy nanocomposites containing graphene oxide (GO) and 3-(trimethoxysilyl) propyl methacrylate (TMSPMA) modified W2C. The coating resistance of EP-GO/TMSPMA-W2C was additionally noticed to be over 44.9% higher than that of pure matrix. The GO/TMSPMA-W2C was enriched in the degradation products, forming an excellent passive layer at the coating/steel interface. The newly designed EP-GO/TMSPMA-W2C nanocomposite coating had improved protection against corrosion and excellent hydrophobic characteristics (WCA: 159°). The incorporation of graphene oxide enveloped W2C in the epoxy matrix resulted in improved adhesion strength, tensile strength and hardness properties.
Nanodendrite—promising nanoreinforcement for emerging next-generation nanocomposite

ABSTRACT Nanodendrite is a unique nanostructure having branched nano-extension. Nanodendrite may have different types as carbon nanodendrite, metal nanodendrite, and bimetallic nanodendrite. Nanodendrite modified graphene and graphene oxide nanostructures have also been developed. This is an exclusive overview on design, features, and applications of nanodendrite, polymer/nanodendrite, and polymer/graphene-nanodendrite nanocomposite. Polymers like poly(diallyldimethylammonium chloride), poly(3,4-ethylenedioxythiophene) polyethylenimine, polyamidoamine, nafion, and other conductive and non-conjugated polymers have been used. Amalgamation of unique nanodendrite nanoreinforcement with polymer has revealed inimitable morphology, electrical, electrochemical, thermal, mechanical, capacitance, and sensing properties. The high performance polymer/nanodendrite and polymer/graphene-nanodendrite nanocomposite have found potential for supercapacitor, fuel cell, and sensor. Graphical abstract
Jordan curve theorems with respect to certain pretopologies on Z

We discuss four quotient pretopologies of a certain basic topology on Z2. Three of them are even topologies and include the wellknown Khalimsky and Marcus-Wyse topologies. Some known Jordan curves in the basic topology are used to prove Jordan curve theorems that identify Jordan curves among simple closed ones in each of the four quotient pretopologies.
Deep Learning-Based Hybrid Analog-Digital Signal Processing in mmWave Massive-MIMO Systems

Hybrid analog-digital signal processing (HSP) is an enabling technology to harvest the potential of millimeter-wave (mmWave) massive-MIMO communications. In this paper, we present a general deep learning (DL) framework for efficient design and implementation of HSP-based massive-MIMO systems. Exploiting the fact that any complex matrix can be written as a scaled sum of two matrices with unit-modulus entries, a novel analog deep neural network (ADNN) structure is first developed which can be implemented with common radio frequency (RF) components. This structure is then embedded into an extended hybrid analog-digital deep neural network (HDNN) architecture which facilitates the implementation of mmWave massive-MIMO systems while improving their performance. In particular, the proposed HDNN architecture enables HSP-based massive-MIMO transceivers to approximate any desired transmitter and receiver mapping with arbitrary precision. To demonstrate the capabilities of the proposed DL framework, we present a new HDNN-based beamformer design that can achieve the same performance as fully-digital beamforming, with reduced number of RF chains. Finally, simulation results are presented confirming the advantages of the proposed HDNN design over existing hybrid beamforming schemes.
Cooperative Target Tracking by Multiagent Camera Sensor Networks via Gaussian Process

In this paper, we present learning-based robust cooperative control for camera sensor networks. The dynamics of each camera agent with the pan and tilt mechanism is modeled by the Euler-Lagrange equation. In the target tracking problem, the modeling errors caused by high nonlinearity of camera system dynamics and changes in the tracking environment may incur significant deterioration of tracking performance. To address this, we propose a distributed control method based on the Gaussian process regression to compensate for the model uncertainty of the dynamics of the camera sensor network. We consider the error dynamics of the multiagent Euler-Lagrange system to evaluate the tracking errors of the camera agents. We show that the ultimate boundedness of the error dynamics is achieved by the proposed learning-based method. A numerical example confirms that the proposed control law enables each camera agent to track the targets with a bounded tracking error in the presence of model uncertainty.
SGPT: A Generative Approach for SPARQL Query Generation From Natural Language Questions

SPARQL query generation from natural language questions is complex because it requires an understanding of both the question and underlying knowledge graph (KG) patterns. Most SPARQL query generation approaches are template-based, tailored to a specific knowledge graph and require pipelines with multiple steps, including entity and relation linking. Template-based approaches are also difficult to adapt for new KGs and require manual efforts from domain experts to construct query templates. To overcome this hurdle, we propose a new approach, dubbed SGPT, that combines the benefits of end-to-end and modular systems and leverages recent advances in large-scale language models. Specifically, we devise a novel embedding technique that can encode linguistic features from the question which enables the system to learn complex question patterns. In addition, we propose training techniques that allow the system to implicitly employ the graph-specific information (i.e., entities and relations) into the language model’s parameters and generate SPARQL queries accurately. Finally, we introduce a strategy to adapt standard automatic metrics for evaluating SPARQL query generation. A comprehensive evaluation demonstrates the effectiveness of SGPT over state-of-the-art methods across several benchmark datasets.
Fault-tolerant Control of Automated Guided Vehicle under Centroid Variation

Aiming at stability and safety problems caused by the actuator failure of the automated guided vehicle under the condition of centroid position change. The four-wheel independent drive four-wheel independent steer (4WID/4WIS) AGV is studied for fault-tolerant control of single-wheel drive actuators under the centroid position variation condition. First, 3-DOF vehicle model, drive-wheel dynamics model, HSRI tire model, and the actuator loss of effectiveness model established. Second, a hierarchical controller is designed with an optimal input controller based on Model Predictive Control (MPC) theory of the upper layer, a desired yaw moment solver based on fuzzy theory of the middle layer, and a torque reconstruction controls distributor based on the control gain in the lower layer. Then, the AGV is simulated and analyzed in straight line and double line change conditions. The simulation results show that the yaw rate and the centroid sideslip angle can closely track the theoretical value. The difference between the theoretical value is kept within 5%. Finally, the 4WID/4WIS AGV prototype vehicle was developed and tested for straight-line and double-lane change condition. The difference between simulation and experiment was within 4%. The experimental results show that the designed controller is effective.
Selective Detection of Misfolded Tau From Postmortem Alzheimer’s Disease Brains

Tau aggregates are present in multiple neurodegenerative diseases known as “tauopathies,” including Alzheimer’s disease, Pick’s disease, progressive supranuclear palsy, and corticobasal degeneration. Such misfolded tau aggregates are therefore potential sources for selective detection and biomarker discovery. Six human tau isoforms present in brain tissues and both 3R and 4R isoforms have been observed in the neuronal inclusions. To develop selective markers for AD and related rare tauopathies, we first used an engineered tau protein fragment 4RCF as the substrate for ultrasensitive real-time quaking-induced conversion analyses (RT-QuIC). We showed that misfolded tau from diseased AD and other tauopathy brains were able to seed recombinant 4RCF substrate. We further expanded to use six individual recombinant tau isoforms as substrates to amplify misfolded tau seeds from AD brains. We demonstrated, for the first time to our knowledge, that misfolded tau from the postmortem AD brain tissues was able to specifically seed all six full-length human tau isoforms. Our results demonstrated that RT-QuIC analysis can discriminate AD and other tauopathies from non-AD normal controls. We further uncovered that 3R-tau isoforms displayed significantly faster aggregation kinetics than their 4R-tau counterparts under conditions of both no seeding and seeding with AD brain homogenates. In summary, our work offers potential new avenues of misfolded tau detection as potential biomarkers for diagnosis of AD and related tauopathies and provides new insights into isoform-specific human tau aggregation.
Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures

We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous–discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.
A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types

Objective To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax. Results The ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion The parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.
Telemedicine: Benefits for Cardiovascular Patients in the COVID-19 Era

The recent pandemic with SARS-CoV-2 raises questions worldwide regarding telemedicine for housebound patients, including those with cardiovascular conditions. The need for further investigation, monitoring and therapeutic management are advancing practical issues which had not been identified for consideration prior to the pandemic. Using the marketing assessment, we identified the needs of the patients and evaluated the future steps necessary in the short term to meet them. The research found progress made via telemedicine in monitoring and conducting minor decisions (like up-titrating the doses of different medication regimens) in patients with several cardiovascular diseases (heart failure, atrial fibrillation, high blood pressure), as there is a worldwide trend to develop new telemonitoring biosensors and devices based on implantable delivered transcatheter. The worldwide telemedicine trend encourages a switch from small and hesitating steps to a more consistent assessment of the patients, based on high technology and Interventional Cardiology. Cardiovascular telemedicine, although made a sustainable effort in managing patients' health, has many obstacles to overcome before meeting all their needs. Data security, confidentiality and reimbursement are the top priorities in developing remote Cardiology. The regulatory institutions need to play an integrative role in leading the way for defining the framework of future telemedicine activities. The SARS-CoV-2 outbreak with all its tragedy served to reinforce the message that telemedicine services can be life-saving for cardiovascular patients. Once the Covid-19 era will fade away, telemedicine is likely to remain a complementary service of standard care. There is still room to improve the remote identification and investigation of heart disease, provide an accurate diagnosis and therapeutic regimen, and update regulations and guidelines to the new realities of technological progress in the field.
Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system. Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function. Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions. Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.
Suppression of yolk formation, oviposition and egg quality of locust (Locusta migratoria manilensis) infected by Paranosema locustae

Locusta migratoria manilensis is one of the most important agricultural pests in China. The locust has high fecundity and consumes large quantities of food, causing severe damage to diverse crops such as corn, sorghum, and rice. Immunity against pathogens and reproductive success are two important components of individual fitness, and many insects have a trade-off between reproduction and immunity when resources are limited, which may be an important target for pest control. In this study, adult females L. migratoria manilensis were treated with different concentrations (5 × 106 spores/mL or 2 × 107 spores/mL) of the entomopathogenic fungus Paranosema locustae. Effects of input to immunity on reproduction were studied by measuring feeding amount, enzyme activity, vitellogenin (Vg) and vitellogenin receptor (VgR) production, ovary development, and oviposition amount. When infected by P. locustae, feeding rate and phenol oxidase and lysozyme activities increased, mRNA expression of Vg and VgR genes decreased, and yolk deposition was blocked. Weight of ovaries decreased, with significant decreases in egg, length and weight.Thus, locusts used nutritive input required for reproduction to resist invasion by microsporidia. This leads to a decrease in expression of Vg and VgR genes inhibited ovarian development, and greatly decreased total fecundity. P. locustae at 2 × 107 spores/mL had a more obvious inhibitory effect on the ovarian development in migratory locusts. This study provides a detailed trade-off between reproduction and immune input of the female, which provides a reliable basis to find pest targets for biological control from those trade-off processes.
Automatic Test and Evaluation of Autonomous Systems

Physical test and evaluation (T&E) of autonomous systems in actual settings is resource intensive and time exhaustive. Simulation-based testing, however, can reduce the testing cost and time, allowing for testing the whole operation envelope through a massive set of scenarios. In this research, a novel automatic simulation-based testing is proposed that simultaneously amalgamates the knowledge of domain experts and the operating and environmental parameters of autonomous systems. The proposed method employs fuzzy logic to replace the exhaustive activities of the tester and leverages the tester’s role to review root-cause reasoning reports about the System Under Test (SUT) and focus only on the inspection of flagged scenarios rather than inspecting all instants of all scenarios. The proposed method uses Type-2 fuzzy logic to provide more robust handling of data uncertainties involved in the testing process. An integrated configurable software tool and a user-friendly Graphical User Interface (GUI) are developed that allow for testing a single and/or batches of large number scenarios to generate test reports along with the root-cause analysis. The developed software tool has been successfully applied for testing the perception system of Unmanned Aerial Systems (UAS) in the Collaborative Unmanned Systems Technology Demonstrator (CUSTD).