Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of Foreign Body Response Mitigation by Nitric Oxide Release

Implantable glucose biosensors provide real-time information about blood glucose fluctuations, but their utility and accuracy are time-limited due to the foreign body response (FBR) following their insertion beneath the skin. The slow release of nitric oxide (NO), a gasotransmitter with inflammation regulatory properties, from a sensor surface has been shown to dramatically improve sensors’ analytical biocompatibility by reducing the overall FBR response. Indeed, work in a porcine model suggests that as long as the implants (sensors) continue to release NO, even at low levels, the inflammatory cell infiltration and resulting collagen density are lessened. While these studies strongly support the benefits of NO release in mitigating the FBR, the mechanisms through which exogenous NO acts on the surrounding tissue, especially under the condition of hyperglycemia, remain vague. Such knowledge would inform strategies to refine appropriate NO dosage and release kinetics for optimal therapeutic activity. In this study, we evaluated mediator, immune cell, and mRNA expression profiles in the local tissue microenvironment surrounding implanted sensors as a function of NO release, diabetes, and implantation duration. A custom porcine wound healing-centric multiplex gene array was developed for nanoString barcoding analysis. Tissues adjacent to sensors with sustained NO release abrogated the implant-induced acute and chronic FBR through modulation of the tissue-specific immune chemokine and cytokine microenvironment, resulting in decreased cellular recruitment, proliferation, and activation at both the acute (7-d) and chronic (14-d) phases of the FBR. Further, we found that sustained NO release abrogated the implant-induced acute and chronic foreign body response through modulation of mRNA encoding for key immunological signaling molecules and pathways, including STAT1 and multiple STAT1 targets including MAPK14, IRAK4, MMP2, and CXCL10. The condition of diabetes promoted a more robust FBR to the implants, which was also controlled by sustained NO release.
Characterization of the MADS-Box Gene Family in Akebia trifoliata and Their Evolutionary Events in Angiosperms

As the largest clade of modern plants, flower plants have evolved a wide variety of flowers and fruits. MADS-box genes play key roles in regulating plant morphogenesis, while basal eudicots have an evolutionarily important position of acting as an evolutionary bridge between basal angiosperms and core eudicots. Akebia trifoliata is an important member of the basal eudicot group. To study the early evolution of angiosperms, we identified and characterized the MADS-Box gene family on the whole-genome level of A. trifoliata. There were 47 MADS-box genes (13 type I and 34 type II genes) in the A. trifoliata genome; type I genes had a greater gene length and coefficient of variation and a smaller exon number than type II genes. A total of 27 (57.4%) experienced whole or segmental genome duplication and purifying selection. A transcriptome analysis suggested that three and eight genes were involved in whole fruit and seed development, respectively. The diversification and phylogenetic analysis of 1479 type II MADS-box genes of 22 angiosperm species provided some clues indicating that a γ whole genome triplication event of eudicots possibility experienced a two-step process. These results are valuable for improving A. trifoliata fruit traits and theoretically elucidating evolutionary processes of angiosperms, especially eudicots.
The Impact of Future Sea-Level Rise on Low-Lying Subsiding Coasts: A Case Study of Tavoliere Delle Puglie (Southern Italy)

Low-lying coastal zones are highly subject to coastal hazards as a result of sea-level rise enhanced by natural or anthropogenic land subsidence. A combined analysis using sea-level data and remote sensing techniques allows the estimation of the current rates of land subsidence and shoreline retreat, supporting the development of quantified relative sea-level projections and flood maps, which are appropriate for specific areas. This study focuses on the coastal plain of Tavoliere delle Puglie (Apulia, Southern Italy), facing the Adriatic Sea. In this area, land subsidence is mainly caused by long-term tectonic movements and sediment compaction driven by high anthropogenic pressure, such as groundwater exploitation and constructions of buildings. To assess the expected effects of relative sea-level rise for the next decades, we considered the following multidisciplinary source data: (i) sea-level-rise projections for different climatic scenarios, as reported in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, (ii) coastal topography from airborne and terrestrial LiDAR data, (iii) Vertical Land Movement (VLM) from the analysis of InSAR and GNSS data, and (iv) shoreline changes obtained from the analysis of orthophotos, historic maps, and satellite images. To assess the expected evolution of the coastal belt, the topographic data were corrected for VLM values, assuming that the rates of land subsidence will remain constant up to 2150. The sea-level-rise projections and expected flooded areas were estimated for the Shared Socioeconomic Pathways SSP1-2.6 and SSP5-8.5, corresponding to low and high greenhouse-gas concentrations, respectively. From our analysis, we estimate that in 2050, 2100, and 2150, up to 50.5 km2, 118.7 km2 and 147.7 km2 of the coast could be submerged, respectively, while beaches could retreat at rates of up to 5.8 m/yr. In this area, sea-level rise will be accelerated by natural and anthropogenic land subsidence at rates of up to −7.5 ± 1.7 mm/yr. Local infrastructure and residential areas are thus highly exposed to an increasing risk of severe inundation by storm surges and sea-level rise in the next decades.
An Approach on Learning Multivariate Regression Chain Graphs from Data

The necessity of modeling is vital for the purpose of reasoning and diagnosing in complex systems, since the human mind might sometimes have a limited capacity and an inability to be objective. The chain graph (CG) class is a powerful and robust tool for modeling real-world applications. It is a type of probabilistic graphical models (PGM) and has multiple interpretations. Each of these interpretations has a distinct Markov property. This thesis deals with the multivariate regression chain graph (MVR-CG) interpretation. The main goal of this thesis is to implement and evaluate the results of the MVR-PC-algorithm proposed by Sonntag and Pena in 2012. This algorithm uses a constraint based approach used in order to learn a MVR-CG from data.In this study the MRV-PC-algorithm is implemented and tested to see whether the implementation is correct. For this purpose, it is run on several different independence models that can be perfectly represented by MVR-CGs. The learned CG and the independence model of the given probability distribution are then compared to ensure that they are in the same Markov equivalence class. Additionally, for the purpose of checking how accurate the algorithm is, in learning a MVR-CG from data, a large number of samples are passed to the algorithm. The results are analyzed based on number of nodes and average number of adjacents per node. The accuracy of the algorithm is measured by the precision and recall of independencies and dependencies.In general, the higher the number of samples given to the algorithm, the more accurate the learned MVR-CGs become. In addition, when the graph is sparse, the result becomes significantly more accurate. The number of nodes can affect the results slightly. When the number of nodes increases it can lead to better results, if the average number of adjacents is fixed. On the other hand, if the number of nodes is fixed and the average number of adjacents increases, the effect is more considerable and the accuracy of the results dramatically declines. Moreover the type of the random variables can affect the results. Given the samples with discrete variables, the recall of independencies measure would be higher and the precision of independencies measure would be lower. Conversely, given the samples with continuous variables, the recall of independencies would be less but the precision of independencies would be higher.
Surface Plasmon Polariton Excitation Based on Refractive Index Tunable Technique

A novel phase matching idea of surface plasmon polariton (SPP) design is presented based on changing of the prism refractive index in Kretschmann configuration. The selected prism material is a thermotropic liquid crystalline material (TLC) which has been chemically prepared in vitro and was thermally and optically characterized. The experimentally measured refractive indices of the LC material possess a linear change with the temperature at several selected wavelengths in the visible region. The resonant excitation of the SPP was theoretically confirmed by the thermal control of the prism refractive index and can be utilized in analyte refractive index sensing.
Effect of Sagittal Split Osteotomy with Medpor ® Porous Polyethylene Implant on Masticatory Reflex

The purpose of this report was to provide data regarding whether the use of alloplastic implants to the mandibular body and angle as an occasional adjunct to orthognathic surgery affects the jaw reflex. Two patients to whom sagittal split osteotomy with Medpor porous polyethylene implant was performed due to severe mandibular asymmetry were included. To elucidate alterations in reflexes, serial surface electromyographic data were collected from the masseter muscle. The latencies and durations of the silent period were measured. In both patients, massetery inhibitory reflex response on the Medpor-augmented side was not elicited at the postoperative first month. Although postoperative masticatory inhibitory reflex latencies, durations, and configurations were similar between the right and left sides in both patients, we did not elicit the first silent period response following porous polyethylene implant placement at the postoperative sixth month. In conclusion, porous polyethylene implants may decrease masticatory reflex activity. The clinical use of implants made of porous polyethylene seems to be safe; however, indications, chances, and risks should be carefully evaluated prior to application.
Modeling Alloy Clustering Limited Low-Field Electron Mobility in GaN FinFETs

In the last few years, GaN FinFETs are being considered as promising candidates for lower voltage applications over conventional HEMT devices. At the same time, GaN-based devices can operate in very low temperature environments that are relevant for superconducting and quantum computing applications. For example, due to its polarization-induced doping, GaN can overcome the carrier freeze-out challenges of other technologies such as doped silicon. This technology is not without problems, however. Structural inhomogeneity at the AlGaN/GaN hetero-interface, such as alloy clustering, becomes significant as the FinFET dimensions decrease. In this work, scattering rate due to alloy clustering is derived for Quasi-1D systems and added as an additional scattering mechanism in the in-house Monte Carlo transport kernel. Our simulation results show that alloy clustering limiting mobility is significant at lower temperatures. Therefore, any cryogenic analysis of GaN FinFETs must include the impact of alloy clustering.
Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools

Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0% to 100%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67% for two use cases and one with 59%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.
Heteroscedastic errors-in-variables models in computer vision

We have witnessed in the last ten-fifteen years a significant improvement of 3D computer vision applications such as scene reconstruction, tracking, mosaicing. The deeper understanding of the geometry underlining these tasks is one of the main reasons behind this progress. The geometry of the scene imposes various constraints on the projection of 3D objects in the image plane. For example, in uncalibrated stereo the features must lie on corresponding epipolar lines. These constraints are rigorously satisfied only in the absence of measurement errors. However, there are multiple sources of errors in the real data, ranging from quantization noise to violations of the embedded assumptions at earlier processing stages. A large number of geometric constraints encountered in computer vision are linear in the parameter of interest and depend on the measurements through relative simple nonlinear functions. For such models, an incorrect way of handling the measurement noise during the estimation process may yield parameter estimates with poor accuracy. We argue that the proper analysis of the geometric constraints when all the measurements are affected by noise is by employing the errors-in-variables (EIV) statistical model. We perform the analysis of the EIV model under the most general assumption of anisotropic and inhomogeneous, i.e. heteroscedastic, noise. The main contribution of the thesis is a novel estimation technique for the EIV model with heteroscedastic errors, the HEIV algorithm. The HEIV algorithm was successfully applied to a variety of computer vision applications: 3D rigid motion estimation, conic fitting, fundamental matrix and trifocal tensor estimation. Most often, we are interested not only in finding the parameter estimates, but also in assessing how accurate these estimates are, given a particular set of measurements. We address the issue of performance assessment in two different ways: by deriving analytical expressions for the covariance and bias of the HEIV parameters, or by doing bootstrap simulation. In the last part of the thesis we present a method for measuring the uncertainty in correlation based feature matching between images. The shape of the correlation surface models the uncertainty associated with a match and is encoded in covariance matrices. These covariance matrices are then employed in the outlier rejection and ensuing parameter estimation using either HEIV, or other optimization techniques.
Solar PV Panel Area Requirement and Performance- Specific Panel Sizing at Different Locations

One of the most popular renewable power generation technologies in use today is solar photovoltaic (PV) technology. In this essay, the efficiency of PV panels at three separate Indian locations—Mathura, Ladakh, and Bikaner—is compared for some of the same environmental variables at the same time and place. To get the necessary voltage at the load side, a boost converter is integrated with the PV panel. To test and track changes in parameters of the panel and at the load, the simulation model is simulated using simulation software for various solar irradiation and temperature. The existing panel’s efficiency will change after these numbers are obtained. The impact of high cell temperature and solar irradiation is shown based on the previously determined values of performance parameters for the panel. The values of the performance parameters will also be used to determine the size of the PV Panel and the area needed for it to generate 1 Kilo watt. Observations for various environmental circumstances will be recorded.
3FO: The Three-Frame-Only Approach for Fast and Accurate Monocular SLAM Initialization

The monocular simultaneous localization and mapping (SLAM) system is one of the most important among all visual or visual-inertial SLAM (VSLAM or VISLAM) systems due to its low cost, easy calibration and identification. Initialization is always crucial to bootstrap the monocular SLAM system. With the rapid growth of some quick-start required SLAM applications, e.g., augmented reality (AR) and unmanned aerial vehicles (UAVs), devising faster and more accurate initialization has become a central problem. Traditional initialization uses the first two frames to create landmarks and then computes camera poses for the subsequential frames, using a 3D-to-2D perspective-n-points (PnP) mechanism. In this paper, we propose a novel three-frame-only (3FO) initialization approach for the monocular SLAM system, which consists of two steps. In the first step, we use the first two frames to preinitialize poses and landmarks, and in the second step, we use the second and third frames to improve the preinitialization by using the scale consistency of the landmarks generated in the first step to filter out outliers and using inliers to generate more robust landmarks. In both steps, we use the pretrained multilayer perceptron (MLP) combined with homotopy continuation to solve the essential matrices. Finally, we perform a global bundle adjustment (BA) to refine the three camera poses and all the created landmarks. The proposed 3FO initialization approach is evaluated experimentally on the EuRoC benchmark data set with the initialization time and trajectory metrics. The results show that, compared to the traditional ORB-SLAM2 initialization, the 3FO approach reduces the initialization time by 5 times and improves the accuracy by 36.7% on average.
Tripartite Graph Aided Tensor Completion For Sparse Network Measurement

Network measurements provide critical inputs for a wide range of network management. Existing network-wide monitoring methods face the challenge of incurring a high measurement cost. Some recent studies show that network-wide measurement data such as end-to-end latency and flow traffic, have hidden spatio-temporal correlations and thus low-rank features. Taking advantage of the low-rank feature, enlightened by tensor model's strong capability of information representation and extracting, this paper studies a novel sparse measurement scheduling problem which selects a proportion of Origin and Destination (OD) pairs to take measurements in the future time slots, while ensuring the data of the remaining un-measured OD pairs be accurately inferred through tensor completion. It is challenging to find the optimal sampling points (OD pairs) without knowing the structure of the future data and also infer the un-measured data in the presence of noise in the measurement samples. To conquer the challenges, we propose several techniques: a tripartite graph to illustrate the relationship between sample locations and tensor factorization, a graph-based sample selection algorithm, and a graph-based robust tensor completion algorithm. We have conducted extensive experiments based on two real network latency monitoring traces (PlanetLab and Harvard) and two other network monitoring traces (including a traffic trace Abilene and a throughput trace WS-Dream). Our results demonstrate that, even with a sampling ratio of less than 5%, our scheme can accurately obtain the complete network-wide monitoring data by inferring the missing ones based on the samples taken. To achieve similar recovery performance, the best peer tensor completion algorithm needs a significantly larger number of samples, with the sampling ratio up to 25-150 times ours.
Narrow gap TIG arc weld process monitoring

Joining processes play a critical role across several industries and manufacturing activities. One such activity is narrow gap TIG arc welding, present in a number of high value manufacturing domains the quality and outcomes are highly dependent on the operating parameters and the evolving state of the weld. Human supervision, and the application of vision systems to allow the operator to monitor the process as it is being undertaken is commonplace, however monitoring, characterisation and feedback in narrow gap use cases is less so. The work undertakes a comparative study into two different approaches to process monitoring of TIG arc welding. The first through more traditional machine vision (MV) methods, and the second through more modern deep neural network methods. Results show both methods developed are able to perform the role of process monitoring and defect detection with accuracy of 82% and 93% for the traditional and DNN methods respectively.
Performance evaluation of High Bandwidth Memory for HPC Workloads

Recent advances in 3D integrated fabrication have allowed the development of 3D stacked memory. The technology presents itself as a viable solution to the memory wall problem. The 3D memory has stacked DRAM layers over the logic die connected through Through Silicon Vias (TSVs). In this paper, we study the performance of latency and energy of one such 3D stacked memory, namely the High Bandwidth Memory (HBM). We quantify the performance improvement of HBM2 against its predecessor technology, GDDR5, and competing mature technology, GDDR6. We have integrated the DRAMSim3 simulator with the Structural Simulation Toolkit simulator and have studied the High-Performance Computing Workloads from Rodinia Benchmarks Suite (RBS). In our paper, we carry out the evaluation of HBM2 and GDDR6 technology by varying the following metrics of the synthetic benchmarks- the number of instructions, and the read to write ratio. We observe the total execution time, memory cycles, and energy consumed for the RBS benchmarks. Our evaluation for synthetic benchmarks reveals that the read write latency performance of HBM2 is the orders of magnitude better than the GDDR6 technology. For real-world benchmarks, the execution time for HBM2 provides ∼ 30% improvement compared to GDDR6 and energy savings of ∼ 23% from GDDR6 to HBM2.