Miracle-dz/UniFAR-FTUs huggingface dataset:
3DReg-i-Net: Improving Deep Learning Based 3D Registration for a Robust Real-time Alignment of Small-scale Scans

We present 3DReg-i-Net, an improved deep learning solution for pairwise registration of 3D scans, which evolves the recently proposed 3DRegNet technique by Pais et al. This is one of the very ﬁrst learning based algorithm aiming at producing the co-registration of two 3D views starting solely from a set of point correspondences, which is able to perform outlier rejection and to recover the registration matrix. We evolve the original method to face the challenging scenario of quick 3D modelling at small scales through the alignment of dense 3D views acquired at video frame-rate with a handheld scanner. We improve the system tracking robustness and alignment performance with a generalized input data augmentation. Moreover, working on suboptimal aspects of the original solution, we propose different improvements that lead to a redeﬁnition of the training loss function. When tested on the considered scenario, the proposed 3DReg-i-Net signiﬁcantly outperforms the prior solution in terms of accuracy of the estimated aligning transforms.
Pathophysiology of Protein Kinase C Isozymes in Chronic Lymphocytic Leukaemia

This chapter will review the roles of protein kinase C (PKC) isozymes in chronic lymphocytic leukaemia (CLL) cells. PKC family proteins are central to many signalling pathways within cells, and some have been implicated in the oncogenesis of numerous cancers (Benimetskaya, et al., 2001;Keenan, et al., 1999;O'Brian, 1998). In CLL, inhibitors of PKC signalling have been shown to have cytotoxic effects on the malignant cells, and the ,  and  isoforms of PKC have been shown to have pathophysiological roles (Holler, et al., 2009;Nakagawa, et al., 2006;Ringshausen, et al., 2002). The aim of this chapter is to discuss whether PKC can be considered a drug target in the treatment of this disease. We will examine how inhibitors of PKCs have been used in past preclinical studies of CLL, and will discuss the roles of various PKC isozymes (namely PKCII, PKC, PKC and PKC) in the pathology of CLL. This chapter will end with the proposal that inhibition of PKC may be useful in combination therapy through a potential role in regulating Mcl-1 expression.
Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.
Spatial and Temporal Variation of Aerosol and Water Vapour Effects on Solar Radiation in the Mediterranean Basin during the Last Two Decades

This study aims to calculate and analyse the spatial and temporal variation of aerosol optical thickness (AOT) and precipitable water vapour (PWV) and their effects on solar radiation at the surface in the Mediterranean basin, one of the maritime areas with the largest aerosol loads in the world. For the achievement of this objective, a novel and validated methodology was applied. Satellite data, specifically CERES (Clouds and the Earth’s Radiant Energy System) SYN1deg products during the period 2000–2018, were used. Results show that the spatial distribution of AOT and PWV are closely linked to the spatial distributions of its effects on solar radiation. These effects are negative, indicating a reduction of solar radiation reaching the surface due to aerosol and water vapour effects. This reduction ranges between 2% and 8% for AOT, 11.5% and 15% for PWV and 14% and 20% for the combined effect. The analysis of the temporal distribution has focused on the detection of trends from their anomalies. This study has contributed to a better understanding of AOT and PWV effects on solar radiation over the Mediterranean basin, one of the most climatically sensitive regions of the planet, and highlighted the importance of water vapour.
The intrinsic cost of causal consistency

In the last few years, causal consistency has become a popular consistency model for geo-replicated databases. The algorithms proposed to enforce causal consistency typically associate with each operation some metadata, which is used to guarantee that an operation is not executed if its execution would break causality. This may lead to the impression that causal consistency is intrinsically costly and non scalable. In this paper, we analyze the metadata costs of enforcing causal consistency and put these costs in perspective, considering the metadata that is necessary to enforce reliability. We show that by wisely ordering the propagation of operations it is possible to enforce causal consistency without any additional metadata other than the already necessary to enforce reliability.
Interactive Proofs for Verifying Machine Learning

We consider the following question: using a source of labeled data and interaction with an untrusted prover, what is the complexity of verifying that a given hypothesis is “approximately correct”? We study interactive proof systems for PAC verification, where a verifier that interacts with a prover is required to accept good hypotheses, and reject bad hypotheses. Both the verifier and the prover are efficient and have access to labeled data samples from an unknown distribution. We are interested in cases where the verifier can use significantly less data than is required for (agnostic) PAC learning, or use a substantially cheaper data source (e.g., using only random samples for verification, even though learning requires membership queries). We believe that today, when data and data-driven algorithms are quickly gaining prominence, the question of verifying purported outcomes of data analyses is very well-motivated. We show three main results. First, we prove that for a specific hypothesis class, verification is significantly cheaper than learning in terms of sample complexity, even if the verifier engages with the prover only in a single-round (NP-like) protocol. Moreover, for this class we prove that single-round verification is also significantly cheaper than testing closeness to the class. Second, for the broad class of Fourier-sparse boolean functions, we show a multi-round (IP-like) verification protocol, where the prover uses membership queries, and the verifier is able to assess the result while only using random samples. Third, we show that verification is not always more efficient. Namely, we show a class of functions where verification requires as many samples as learning does, up to a logarithmic factor. 1 Email: {shafi.goldwasser,shaferjo}@berkeley.edu. 2 Email: rothblum@alum.mit. edu. 3 Email: amir.yehudayoff@gmail.com. ISSN 1433-8092 Electronic Colloquium on Computational Complexity, Revision 1 of Report No. 58 (2020)
Endoscopic Artefact Detection using Cascade R-CNN based Model

Accurate detection of artefacts is a core challenge in a wide-range of endoscopic applications addressing multiple different disease areas. Our work aims to localise bounding bboxes and predict class labels of 8 different artefact classes for given frames and clinical endoscopy video clips. To solve the task, we use Cascade R -CNN[1] as network architecture and adopt ImageNet pretrained ResNet101[2] as backbone with Feature Pyramid Network (FPN) [3] structure. To improve the network performance, methods like data augmentation and multi-scale are also be adopted. In the end, we analyze the major challenge of the task.
A trio of unique alternative splicing patterns : the splicing of tandem NAGNAG acceptors, transcription-start-site-dependent and mutually dependent cassette exons

With the rapid increase in the volume of genomic and transcript data in mouse and human, a diverse set of alternative splicing patterns can be discovered. We have set out to explore in more depth some of these unique splicing patterns which include: i. ) tandem acceptor splice sites termed ’NAGNAGs’, which cause an exon length variation of three nucleotides, ii. ) the splicing of internal cassette exons, whose inclusion and exclusion are strongly associated with their transcriptional start sites, iii. ) and groups of internal cassette exons that are always observed to be either included or excluded together within a transcript. We did not ﬁnd much evidence of functional potential for the majority of variant acceptor splice sites carrying the NAGNAG motif and thus conclude that their abundance is due mostly to the stochastic behaviour of the spliceosome. We inferred that a large fraction (15-30%) of internal cassette exons in transcription units with multiple start sites are included and skipped in a transcription-start-site-dependent manner. We did not ﬁnd that this relationship is conserved in orthologous human and mouse exons. Our ﬁrst analysis has revealed several interesting trends in mutually dependent exons when compared to mutually exclusive and constitutive exons: these exons have a stronger pressure to maintain the reading frame as a group of exons rather than individually, and they generally have shorter intron lengths. Ours are the ﬁrst analyses of transcription-start-site-dependent and mutually-dependent splicing. Their mechanisms remain to be further elucidated and our results provide a good starting point for future computational and experimental studies.
Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features

Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and displacement, the 3D transformation can be easily achieved. Thus, the two point clouds are aligned together. The experimental results well demonstrate that our method can obtain high-precision registration results and is computationally very efficient. In the experimental results obtained by our method, the biggest rotation error is 0.5219°, and the biggest horizontal and vertical errors are 0.2319 m and 0.0119 m, respectively. The largest total computation time is only 713.4647 s.
Associations between Wolbachia, maize and Diabrotica virgifera virgifera

Increasing demand for ethanol is motivating growers to increase maize production reducing production of other crops. WCR is the most significant and widespread pest to maize in North America and is becoming so in parts of Europe. It has surmounted chemical and cultural control strategies. Currently, selection for native resistance to WCR is driven by phenotypic expression. The goal of this study was to identify maize genes unique to WCR feeding with the hope of locating genes specific for insect defense. A maize microarray experiment was performed to identify WCR-responsive genes and mutants of candidate genes were assayed to confirm their involvement in WCR feeding response. Surprisingly the number of commonly employed insect defense genes which were differentially expressed was low. Interestingly, genes involved in bacterial defense, chromatin remodeling, and gene silencing were among those identified as WCR-specific. Nine genes currently not associated with insect defense in plants were differentially expressed in response to WCR feeding. The results suggest a bacterium capable of altering gene expression may contribute to the WCR feeding-specific changes in maize seminal root gene expression.
Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.
A Submission Note on EAD 2020: Deep Learning based Approach for Detecting Artefacts in Endoscopy

Deep neural network-based methods are becoming popular for disease diagnosis and treatment in Endoscopy. In this paper, we discuss our submission to Endoscopic Artefact Detection Challenge (EAD2020). The competition is part of grand challenges in Biomedical Image Analysis and consists of three sub-tasks 1 : i) Bounding box-based localisation of artefacts ii) Region-based segmentation of artefacts, and iii) Out of sample generalisation task. For the ﬁrst sub-task, we modify the Faster R-CNN object detector by integrating a powerful backbone network and a feature pyramidal module. For the second sub-task, we implemented a U-Net based autoencoder with a modiﬁed loss function to construct the semantic channels. For the third sub-task, we used ensembling techniques along with a data-augmentation technique inspired by RandAugment to boost the generalisation performance. We report a Score d of 0 . 1869 ± 0 . 1076 for the ﬁrst task, sscore of 0.5187 with a sstd of 0.2755 for the second task and mAP g of 0.2620 and a dev g of 0.0890 for the third task on the test data-set. Our method for the third task, achieves the third position on the leaderboard for the mAP g metric and also surpasses the results obtained by many methods in the previous EAD2019 challenge.
Neural Appearance Synthesis and Transfer

Appearance acquisition is a challenging problem. Existing approaches require expensive hardware and acquisition times are long. Alternative “in-the-wild” few-shot approaches provide a limited reconstruction quality. Furthermore, there is a fundamental tradeoff between spatial resolution and the physical sample dimensions that can be captured in one measurement. In this paper, we investigate how neural texture synthesis and neural style transfer approaches can be applied to generate new materials with high spatial resolution from high quality SVBRDF measurements. We perform our experiments on a new database of measured SVBRDFs. Categories and Subject Descriptors (according to ACM CCS) : I.3. 7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture
Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms.
University of Groningen Assessment of Dynamic Change of Coronary Artery Geometry and Its Relationship to Coronary Artery Disease, Based on Coronary CT Angiography

To investigate the relationship between dynamic changes of coronary artery geometry and coronary artery disease (CAD) using computed tomography (CT). Seventy-one patients underwent coronary CT angiography with retrospective electrocardiographic gating. End-systolic (ES) and end-diastolic (ED) phases were automatically determined by dedicated software. Centerlines were extracted for the right and left coronary artery. Differences between ES and ED curvature and tortuosity were determined. Associations of change in geometrical parameters with plaque types and degree of stenosis were investigated using linear mixed models. The differences in number of inflection points were analyzed using Wilcoxon signed-rank tests. Tests were done on artery and segment level. One hundred thirty-seven arteries (64.3%) and 456 (71.4%) segments were included. Curvature was significantly higher in ES than in ED phase for arteries (p = 0.002) and segments (p < 0.001). The difference was significant only at segment level for tortuosity (p = 0.005). Number of inflection points was significantly higher in ES phase on both artery and segment level (p < 0.001). No significant relationships were found between degree of stenosis and plaque types and dynamic change in geometrical parameters. Non-invasive imaging by cardiac CT can quantify change in geometrical parameters of the coronary arteries during the cardiac cycle. Dynamic change of vessel geometry through the cardiac cycle was not found to be related to the presence of CAD.
New stabilizer Cellulose Nano Rods-Zinc Oxide (CNR-ZnO) material for nanocomposite synthesis and anti-bacterial applications

Zinc oxide (ZnO) nanorods were fabricated using Cellulose Nano Rods (CNR) as a new stabilizer material. Synthesized of ZnO-CNR nanocomposites, with a molar ratio of ZnO to CNR (1/2g) were prepared in distilled water. The nanocomposites were distinguished using X-ray diffraction (XRD), ultraviolet-visible (UV-Vis), and Field Emission scanning electron microscope (FESEM) techniques. XRD data were showed, the ZnO nanorods with a hexagonal wurtzite structure such readily scattered inside CNR with an average size 20-40 nm. (FESEM) images showed the homogenous morphology of Zinc oxide rods. The optimum ratio of ZnO-CNR was selected to be the tiny size of the ZnO nanorods that yielded a good stabilizer material and antibacterial activity. The ultraviolet-visible (UV-Vis) absorption spectrum of the ZnO-CNR nanocomposites appeared absorption peaks in the ultraviolet region at (350-360 nm) wavelength attributes with the energy gap of (3.41 eV) of ZnO-CNR. The antibacterial activities of samples have been investigated against the Gram-positive ( pneumonia ) and gram-negative ( pseudomonas ). The maximum antibacterial activities against the Gram-positive ( pneumonia ) of ZnO nanorods and of ZnO- cellulose nanorods are 16 mm and 22 mm respectively. The optimum anti-bacterial activities versus the Gram-negative ( pseudomonas ) of zinc oxide nanorods and zinc oxide- cellulose nanorods are 17 mm and 19 mm respectively. The optimum anti-bacterial activities versus the Gram-negative ( pseudomonas ) of zinc oxide nanosheet and of zinc oxide- cellulose nanorods are 17 mm and 22 mm.