Miracle-dz/UniFAR-FTUs huggingface dataset:
Cryptosporidium in rivers of the world: the GloWPa-Crypto model

Diarrhoeal disease is very common around the world. Knowing more about the global burden of diarrhoeal disease and about the geographical distribution of pathogen pollution is important for decision making and water and sanitation planning. The objective of this thesis is to increase knowledge on the sources, fate and transport of Cryptosporidium in rivers worldwide using spatially explicit modelling. I present the Global Waterborne Pathogen model for Cryptosporidium (GloWPa-Crypto), the first global model of waterborne pathogen emissions to and concentrations in rivers. The model is used to provide information on pathogen concentrations in data-sparse regions, identify hotspot regions, identify the relative contribution of different sources, and in scenario analysis to study the impacts of global change or management strategies. Furthermore, the model can be applied in the analysis of risk, burden of disease and health-based treatment targets, and make a valuable contribution in meeting the Sustainable Development Goals.
Adaptive Sampling for Computer Vision-Oriented Compressive Sensing

Compressive sensing (CS) is renowned for its efficient signal data compression. However, due to its compressive nature, the accuracy of downstream computer vision (CV) tasks by reconstruction inevitably degrades as sampling rate decreases. This limitation significantly hinders the application of existing CS techniques. To overcome the drawback, this paper presents a novel CS technique that employs adaptive sampling rates based on saliency distribution. The goal of this work is to enhance the preservation of information necessary for classification while reducing the weight of non-essential information. Experimental results show the effectiveness of the proposed adaptive sampling technique, which outperforms existing sampling CS techniques on STL10 and Imagenette datasets. The average classification accuracy is maximally improved by 26.23% and 18.25%, respectively.
STUDY ON MAXGROW UNIVERSAL LIQUID FERTILIZER EFFECT ON ALFALFA (Medicago Sativa L.) FOR FORAGE AND SEED PRODUCTION

Liquid complex fertilizers find wide application in agriculture. The share of the used liquid fertilizers in the world is over 40%. The use of liquid fertilizers in Bulgaria has a very great future, due to their low price and minimum costs of application. They are convenient to dose when preparing the working solution, contain the essential macro and micro nutrients in the best possible form for assimilation by the plants. They are particularly effective for outside root additional nutrition–the so–called foliar fertilizing, which results in better absorption of nutrients (1.2). The last studies showed that the liquid fertilizers had a positive influence on the processes of foliar and root additional nutrition of plants with the purpose of increasing the yield and quality of seeds. Other authors recommended the combined application of leaf fertilizers with growth regulators, which increased the effect. The researches in our country on these matters are still insufficient and incomplete. The objective of this study was to establish the influence of the all– purpose liquid fertilizer “MaxGrow” on lucerne for forage and seed. The doses of treatment of lucerne from 0, 03 tо 0, 05 l/ha MG for seed production were of interest, since on average for the period the yield was the highest: 356.3; 321.9 and 315.6 kg/ha, respectively. The increase, as compared to the untreated control, varied from 35.4 tо 231.3 kg/ha. The highest yield of forage green mass, on average for the period of study, was produced at the dose of 0.06 l/ha MG with an yield of 12540 kg/ha, followed by the doses of treatment of 0,05 and 0,04 l/ha MG with produced green mass from 12490 tо 10330 kg/ha, respectively.
XTasks: How to Make Efficient Synchronization and Prefetching Easy

The hardware environment has changed rapidly in recent years: Many cores, multiple sockets, and large amounts of main memory have become a commodity. To benefit from these highly parallel systems, the software has to be adapted. Sophisticated latch-free data structures and algorithms are often meant to address the situation. But they are cumbersome to develop and may still not provide the desired scalability. As a remedy, we present XTasking, a task-based framework that assists the design of latch-free and parallel data structures. XTasking also eases the information exchange between applications and the operating system, resulting in novel opportunities to manage resources in a truly hardwareand application-conscious way. As such, XTasking also lays the foundation for our vision of a truly co-designed system of operating system and database management system. ACM Reference Format: . 2021. XTasks: How to Make Efficient Synchronization and Prefetching Easy. In SIGMOD ’21: International Conference on Management of Data, June 20–25, 2021, Xi’an, Shaanxi, China. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/1122445.1122456
Character Association and Path Coefficient Analysis for Yield Component Traits in Rice (Oryza sativa L.) Under Moisture Stress Condition at Vegetative Stage

Present study was attempted with forty six F3 families to trace the associations among twelve yield component characters under moisture stress condition at vegetative growth stage in rice during 2013. Grain yield per plant showed significant positive association with productive tillers per plant, filled spikelet per panicle and harvest index and significant negative association with days to flowering and days to maturity at both genotypic (rg) and phenotypic (rp) levels. Path coefficient analysis exhibited the positive direct effect on grain yield per plant via days to flowering, productive tillers per plant, spikelet fertility and 100 grain weight. In total, four characters viz., productive tillers per plant, filled spikelets per panicle, spikelet fertility and harvest index showed highly significant positive correlation coefficients along with high direct effects on grain yield per plant indicating selection based on these characters could be very effective in selecting high yielding genotypes under moisture stress condition at vegetative stage stress in rice.
Applications Note: Reagents and Synthesis Immunogenicity of Recombinant Maltose- binding Protein (MBP)-Gonadotropin Releasing Hormone I (GnRH-I)

A recombinant fusion protein, maltose-binding protein (MBP) – gonadotropin releasing hormone I (GnRH-I), was produced by cloning a gene fragment encoding a tandem repeated GnRH-I hexmer peptide (GnRH-I6) into the pMAL-c4x vector which was subsequently expressed in E. coli TB1. MBPGnRH-I6 was affinity purified. MBP-GnRH-I6 was verified by SDS-PAGE and Western blot and immunogenicity tested in boars. Injection of the recombinant fusion protein into boars yielded a high-titer antibody specific for GnRH-I. This was followed by serum testosterone and the degeneration of spermatogenesis. These results showed that MBP-GnRH-I6 acted as a strong immunogen and could be a candidate for an immune antifertility vaccine.
Author ’ s Accepted Manuscript The Impact of Individuation on the Bases of Human Empathic Responding

While there is substantial overlap in the neural systems underlying empathy for people we know as opposed to strangers, social distance has been shown to significantly moderate empathic neural responses towards the negative experiences of others. Intriguingly however, variance in empathic neural responses towards known and unknown targets has not been reflected by behavioral differences as indexed by self-reported empathic ratings. One explanation for this disconnect is that empathic evaluations of known and unknown individuals draw on different bases (e.g. target identity/reactions) within the empathic process. To test this hypothesis, we utilized high density EEG to assess how individuating targets with personal names moderated the link between behavioral pain ratings and attentional processing oriented towards (a) initial target processing and (b) subsequent expressions target discomfort. Consistent with prior findings, no differences in pain ratings between individuated and unindividuated targets was observed. However, individual mean pain rating differences for individuated targets was strongly positively related to attentional processing levels, indexed by the P300, during the initial presentation of those targets, a relationship absent for unindividuated targets. In contrast, pain ratings for unindividuated targets was positively related to levels of attentional processing, indexed by the Late Positive Potential (LPP), during the subsequent discomfort expression stage. Furthermore, the LPP response to individuated target discomfort was positively linked to behavioral measures of emotional expressivity whereas the LPP response to unindividuated target discomfort was positively associated with cognitive appraisal. These findings suggest that individuation can significantly shift the bases of empathic responding.
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds

Event cameras can record scene dynamics with high temporal resolution, providing rich scene details for monocular depth estimation (MDE) even at low-level illumination. Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding. However, most methods directly fuse two modalities at pixel level, ignoring that the attractive complementarity mainly impacts high-level patterns that only occupy a few pixels. For example, event data is likely to complement contours of scene objects. In this paper, we discretize the scene into a set of high-level patterns to explore the complementarity and propose a Pattern-based Complementary learning architecture for monocular Depth estimation (PCDepth). Concretely, PCDepth comprises two primary components: a complementary visual representation learning module for discretizing the scene into high-level patterns and integrating complementary patterns across modalities and a refined depth estimator aimed at scene reconstruction and depth prediction while maintaining an efficiency-accuracy balance. Through pattern-based complementary learning, PCDepth fully exploits two modalities and achieves more accurate predictions than existing methods, especially in challenging nighttime scenarios. Extensive experiments on MVSEC and DSEC datasets verify the effectiveness and superiority of our PCDepth. Remarkably, compared with state-of-the-art, PCDepth achieves a 37.9% improvement in accuracy in MVSEC nighttime scenarios.
Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph

In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, presents profound challenges. This study presents LeukoGraph, a recently developed framework designed explicitly for this purpose employing graph attention networks (GATs) to navigate hierarchical classification (HC) complexities. Notably, LeukoGraph stands as a pioneering effort, marking the application of graph neural networks (GNNs) for hierarchical inference on graphs, accommodating up to one million nodes and millions of edges, all derived from flow cytometry data. LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets. The technique is more general than this example. A hallmark achievement of LeukoGraph is its F-score of 98%, significantly outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's prowess extends beyond theoretical innovation, showcasing remarkable precision in predicting both flat and hierarchical cell types across flow cytometry datasets from 30 distinct patients. This precision is further underscored by LeukoGraph's ability to maintain a correct label ratio, despite the inherent challenges posed by hierarchical classifications.
Role of In Situ Seed Desiccation in the Control of Seed Viability of Cassava ( Manihot esculenta crantz) Hybrids TMS 95/0379 and TMS 98/0505

Seeds from mature cassava (Manihot esculenta Crantz) fruits exhibit low seed viability (one viable seed/fruit), possess hard seed coat and germinate poorly. Since the formation of hard seed coat is associated with the desiccation/last stage of seed development, we wondered whether cassava seed viability was related to seed moisture content. Therefore, the objectives of this study were: 1) to quantify the viability of seeds of two cassava varieties (TMS 98/0505 and TMS 95/0379) collected prior to the formation of hard coat, 2) to determine the relationship between seed viability and seed moisture content, and 3) to determine the suitability of top soil, moist filter paper, fine sand as growth medium for germinating young cassava seeds. Stem cuttings of the two varieties were cultivated on the field for seed production. Fruits were sampled two times at 6 and 10 weeks after first anthesis. Seeds extracted from the fruits had either thin, colorless seed coats or moderately thickened, light brownish seed coat. The seeds were subjected to flotation and tetrazolium chloride (TCL) tests for viability, evaluated for moisture content and subjected for germination in three growth media (top soil, fine sand and moist filter paper). Seed viability increased significantly (p<0.01) from approx. one seed/fruit (0.50) at 6 WAA to approx. all seeds per fruit (0.92) at 10 WAA in TMS 98/0505. In TMS 95/0379, seed viability was 0 at 6 WAA. Flotation test was found to overestimate seed viability at 6 WAA in both varieties. In TMS 98/0505, moisture content per seed significantly (p<0.01) declined from 0.80 g/seed to 0.50 g/seed as collection date was delayed from 6 WAA to 10 WAA. Moisture content was generally higher in TMS 95/0379 than TMS 98/0505. There was no interaction effect. The relationship between seed moisture content and seed viability was significant (p<0.001), and strongly linear (R²=0.89) and negative. There was no record of germination in the three-growth media which suggest that greater than one viable seed/fruit can be obtained before the formation of hard seed coat in TMS 98/0505 but methods that can germinate them needs to be developed.
Approximations of Symbolic Substitution Systems in One Dimension

Periodic approximations of quasicrystals are a powerful tool in analyzing spectra of Schrödinger operators arising from quasicrystals, given the known theory for periodic crystals. Namely, we seek periodic operators whose spectra approximate the spectrum of the limiting operator (of the quasicrystal). This naturally leads to study the convergence of the underlying dynamical systems. We treat dynamical systems which are based on one‐dimensional substitutions. We first find natural candidates of dynamical subsystems to approximate the substitution dynamical system. Subsequently, we offer a characterization of their convergence and provide estimates for the rate of convergence. We apply the proposed theory to some guiding examples.
Mixing Times for the Facilitated Exclusion Process

The facilitated simple exclusion process (FEP) is a one-dimensional exclusion process with a dynamical constraint. We establish bounds on the mixing time of the FEP on the segment, with closed boundaries, and the circle. The FEP on these spaces exhibits transient states that, if the macroscopic density of particles is at least $1/2$, the process will eventually exit to reach an ergodic component. If the macroscopic density is less than $1/2$ the process will hit an absorbing state. We show that the symmetric FEP (SFEP) on the segment $\{1,\ldots,N\}$, with $k>N/2$ particles, has mixing time of order $N^{2}\log(N-k)$ and exhibits the pre-cutoff phenomenon. For the asymmetric FEP (AFEP) on the segment, we show that there exists initial conditions for which the hitting time of the ergodic component is exponentially slow in the number of holes $N-k$. In particular, when $N-k$ is large enough, the hitting time of the ergodic component determines the mixing time. For the SFEP on the circle of size $N$, and macroscopic particle density $\rho \in(1/2,1)$, we establish bounds on the mixing time of order $N^{2}\log N$ for the process restricted to its ergodic component. We also give an upper bound on the hitting time of the ergodic component of order $N^{2}\log N$ for a large class of initial conditions. The proofs rely on couplings with exclusion processes (both open and closed boundaries) via a novel lattice path (height function) construction of the FEP.
Saturation Dependence of Non‐Fickian Transport in Porous Media

In two‐phase flow through porous media, the percolating pathways can be hydrodynamically split into the flowing and stagnant regions. The highly variable velocity field in the pore space filled by the carrier fluid leads to significant differences in the transport time scales in the two regions that cannot be explained by the Fickian (Gaussian) advection‐dispersion equation. In contrast with the Darcy‐scale studies, up to now, relatively limited pore‐scale studies have been devoted to the characterization of transport properties in two‐phase flow. In this paper, we report on the results of computer simulation of advection‐dispersion transport in steady state two‐phase flow through porous media using a pore network model, employed as an upscaling tool. The simulation results are upscaled to directly estimate the Darcy‐scale transport coefficients and properties, namely, stagnant saturation, the mass transfer coefficient between the flowing and stagnant regions, and the longitudinal dispersion in the flowing regions. The mobile‐immobile model, one of the most commonly used models for simulating non‐Fickian transport in porous media, is used to estimate the transport properties using the inverse modeling of effluent concentration profiles. The disagreement between the directly estimated parameters and those obtained by the mobile‐immobile‐based inverse modeling implies fundamental shortcomings of the latter for describing transport in two‐phase flow. The simulation results indicate that the relative permeabilities may be used to obtain accurate estimates of the stagnant saturation, which link two‐phase Darcy's law and transport.
A single-particle energy-conserving dissipative particle dynamics approach for simulating thermophoresis of nanoparticles in polymer networks

Thermophoresis is an effective method to drive the motion of nanoparticles in fluids. The transport of nanoparticles in polymer networks has significant fundamental and applied importance in biology and medicine, and can be described as Brownian particles crossing entropic barriers. This study proposes a novel extension of dissipative particle dynamics (DPD), called the single-particle energy-conserving dissipative particle dynamics (seDPD), which combines the features of single-particle dissipative particle dynamics (sDPD) and energy-conserving dissipative particle dynamics (eDPD) to simulate the thermophoresis of nanoparticles under temperature gradients. The reliability of the seDPD method is verified by considering the viscosity, thermal diffusivity, and hydrodynamic drag force on the nanoparticles. Using this method, the transport of nanoparticles driven by the thermophoretic force across the polymer network is simulated. The results show that the nanoparticles exhibit the phenomenon of giant acceleration of diffusion (GAD) in the polymer network, indicating that Brownian particles can exhibit GAD when crossing entropic barriers.
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery

Sparse basis recovery is a classical and important statistical learning problem when the number of model dimensions $p$ is much larger than the number of samples $n$. However, there has been little work that studies sparse basis recovery in the Federated Learning (FL) setting, where the client data's differential privacy (DP) must also be simultaneously protected. In particular, the performance guarantees of existing DP-FL algorithms (such as DP-SGD) will degrade significantly when $p \gg n$, and thus, they will fail to learn the true underlying sparse model accurately. In this work, we develop a new differentially private sparse basis recovery algorithm for the FL setting, called SPriFed-OMP. SPriFed-OMP converts OMP (Orthogonal Matching Pursuit) to the FL setting. Further, it combines SMPC (secure multi-party computation) and DP to ensure that only a small amount of noise needs to be added in order to achieve differential privacy. As a result, SPriFed-OMP can efficiently recover the true sparse basis for a linear model with only $n = O(\sqrt{p})$ samples. We further present an enhanced version of our approach, SPriFed-OMP-GRAD based on gradient privatization, that improves the performance of SPriFed-OMP. Our theoretical analysis and empirical results demonstrate that both SPriFed-OMP and SPriFed-OMP-GRAD terminate in a small number of steps, and they significantly outperform the previous state-of-the-art DP-FL solutions in terms of the accuracy-privacy trade-off.
Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation

Although 3D shape matching and interpolation are highly interrelated, they are often studied separately and applied sequentially to relate different 3D shapes, thus resulting in sub-optimal performance. In this work we present a unified framework to predict both point-wise correspondences and shape interpolation between 3D shapes. To this end, we combine the deep functional map framework with classical surface deformation models to map shapes in both spectral and spatial domains. On the one hand, by incorporating spatial maps, our method obtains more accurate and smooth point-wise correspondences compared to previous functional map methods for shape matching. On the other hand, by introducing spectral maps, our method gets rid of commonly used but computationally expensive geodesic distance constraints that are only valid for near-isometric shape deformations. Furthermore, we propose a novel test-time adaptation scheme to capture both pose-dominant and shape-dominant deformations. Using different challenging datasets, we demonstrate that our method outperforms previous state-of-the-art methods for both shape matching and interpolation, even compared to supervised approaches.
Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging

Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guaranteed. Detecting out-of-distribution (OOD) samples is a crucial step towards building a safe classifier. Following a previous study, showing that it is possible to classify breast cancer in point-of-care ultrasound images, this study investigates OOD detection using three different methods: softmax, energy score and deep ensembles. All methods are tested on three different OOD data sets. The results show that the energy score method outperforms the softmax method, performing well on two of the data sets. The ensemble method is the most robust, performing the best at detecting OOD samples for all three OOD data sets.