Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring ecosystem degradation using spatial data and the R package spatialwarnings

Some ecosystems show nonlinear responses to gradual changes in environmental conditions, once a threshold in conditions—or critical point—is passed. This can lead to wide shifts in ecosystem states, possibly with dramatic ecological and economic consequences. Such behaviours have been reported in drylands, savannas, coral reefs or shallow lakes for example. Important research effort of the last decade has been devoted to identifying indicators that would help anticipate such ecosystem shifts and avoid their negative consequences. Theoretical and empirical research has shown that, as an ecosystem approaches a critical point, specific signatures arise in its temporal and spatial dynamics; these changes can be quantified using relatively simple statistical metrics that have been referred to as “early warning signals” (EWS) in the literature. Although tests of those EWS on experiments are promising, empirical evidence from out‐of‐laboratory datasets is still scarce, in particular for spatial EWS. The recent proliferation of remote‐sensing data provides an opportunity to improve this situation and evaluate the reliability of spatial EWS in many ecological systems. Here, we present a step‐by‐step workflow along with code to compute spatial EWS from raster data such as aerial images, test their significance compared to permutation‐based null models, and display their trends, either at different time steps or along environmental gradients. We created the R‐package spatialwarnings (MIT license) to help achieve all these steps in a reliable and reproducible way, and thereby promote the application of spatial EWS to empirical data. This software package and associated documentation provides an easy entry point for researchers and managers into spatial EWS‐based analyses. By facilitating a broader application, it will leverage the evaluation of spatial EWS on real data, and eventually contribute to providing tools to map ecosystems’ fragility to perturbations and inform management decisions.
Load Plus Communication Balancing of Contiguous Sparse Matrix Partitions.

We partition to parallelize multiplication of one or more dense vectors by a sparse matrix (SpMV or SpMM). We consider contiguous partitions, where the rows (or columns) of a sparse matrix with $N$ nonzeros are split into $K$ parts without reordering. We propose exact and approximate contiguous partitioners that minimize the maximum runtime of any processor under a diverse family of cost models that combine work and hypergraph communication terms in symmetric or asymmetric settings. This differs from traditional partitioning models which minimize total communication, or from traditional load balancing models which only balance work. One can view our algorithms as optimally rounding one-dimensional embeddings of direct $K$-way noncontiguous partitioning problems. Our algorithms use linear space. Our exact algorithm runs in linear time when $K^2$ is $O(N^C)$ for $C < 1$. Our $(1 + \epsilon)$-approximate algorithm runs in linear time when $K\log(c_{\text{high}}/(c_{\text{low}}\epsilon))$ is $O(N^C)$ for $C < 1$, where $c_{\text{high}}$ and $c_{\text{low}}$ are upper and lower bounds on the optimal cost. We also propose a simpler $(1 + \epsilon)$-approximate algorithm which runs in a factor of $\log(c_{\text{high}}/(c_{\text{low}}\epsilon))$ from linear time, but is faster in practice. We empirically demonstrate that all of our algorithms efficiently produce high-quality contiguous partitions.
Express method for determining of residual oil zones at a late stage of field development

Presents the method of rapid analysis to identify wild (or little produced) zones of oil deposits at a late stage of development. The method was tested on the example of horizon P-1+2 Gnidyntsi field. The most of oil fields of PJSC "Ukrnafta" are at the late or final development stage. The deposits are characterized by high water product content. The average water content in most of them is 83-88%. The main effective method of oil field development of PJSC "Ukrnafta" is the process of maintaining reservoir pressure (MRP) by flooding, which is widely used for a long time in order to provide for high levels of oil production. In the process of oil displacement by stratal and injection water field zones are left that are not covered by drainage. It is important to identify these areas to maximize the production of inventories.
A Novel Efficient Certificateless Signature Scheme for the Prevention of Content Poisoning Attack in Named Data Networking-Based Internet of Things

Recently, Named Data Networking (NDN) has emerged as a popular and active Internet architecture that addresses the issues of current host-centric communication. NDN is well suited for Internet of Things (IoT) which possesses massive applications that dominate the Internet today. It intends to provide named-based routing, in-networking caching, built-in mobility and multicast support as part of its design which leads to a substantial improvement in content delivery/retrieval. Though, this new architecture aches from some new challenges in terms of security. In this article, we seek our attention towards Content Poisoning Attack (CPA). The purpose of CPA is to inject poisoned content with an invalid signature into the NDN-based IoT networks. Unfortunately, none of the existing proposals work effectively when malicious attackers compromise the caches of NDN routers. To prevent this, we proposed a certificateless signature scheme for the preservation of CPA in NDN-based IoT networks. The proposed scheme is formally secure under the security hardness of Hyperelliptic Curve Discrete Logarithm Problem (HCDLP) with a security simulation/validation in “Automated Validation of Internet Security Protocols and Applications (AVISPA). ” Besides, the formal proof we also compared the designed scheme with some existing solutions to show the cost-efficiency in terms of communication overhead and computation cost. To conclude, a robust deployment on NDN-based IoT networks is shown.
Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.
Recycling of Waste Rubber by Thermo-Mechanical Treatment in a Twin-Screw Extruder

The recycling of waste tires is a significant environmental and economic issue. One of the leading recycling routes is the shredding of tires, resulting in the generation of ground tire rubber. This material can be easily introduced into various polymer matrices as a filler, reducing the use of conventionally applied petroleum-based materials. In such cases, it is essential to ensure sufficient interfacial compatibility, which can be achieved by the proper modification of the rubber surface. Different treatments of ground tire rubber aim to activate its surface and introduce functional groups, providing the possibility for interfacial interactions and the incorporation of significant amounts of recycled material. Therefore, in the presented paper, we examined the impact of thermo-mechanical treatment in a twin-screw extruder on the appearance and chemical structure of ground tire rubber. Moreover, for each set of process parameters, the specific mechanical energy required for processing was calculated, providing essential insights for the potential industrial application of the analyzed process. The energy demand should be considered as a very important issue during the development of “greener” processes and materials.
Suprasegmentals in dysarthric speech in cases of cerebral palsy. The research project

The article presents the research project “Suprasegmentals in Dysarthric Speech in Cases of Cerebral Palsy. Acoustic Analyses in Experimental Logopedic Studies on Polish-Speaking Children Aged 6 to 15 Years”. The objective of these planned studies is to describe and compare the acoustic parameters of suprasegmentals in the speech of dysarthric children with CP – in the experimental group (30 persons) and in the control group (30 children in whom no speech disorders and CP have been diagnosed). The analysis of the suprasegmental level of the phonetic sequence will include intonation, word and sentence stress, rhythm of utterances, rate of speech, length of phrases, and respiratory, phonatory and articulatory synchronization, nasal resonance, and voice quality. Statistical analyses of the results of acoustic experiments will also enable an evaluation of the relationships holding between selected suprasegmental phenomena in dysarthric speech. Moreover, we are planning to conduct studies with the participation of speech therapists as competent raters, whose task will be to conduct an auditory evaluation of particular phenomena manifesting themselves at the suprasegmental level of utterances of children with CP-related dysarthria, as well as an evaluation of the degree of intelligibility of their pronunciation. A comparison of the results of an acoustic analysis with the results of a perceptual analysis will make it possible to indicate the relationship between the individual suprasegmentals and between the realization of suprasegmentals and the intelligibility of CP-related dysarthric speech.
Consequences of past climate change and recent human persecution on mitogenomic diversity in the arctic fox

Ancient DNA provides a powerful means to investigate the timing, rate and extent of population declines caused by extrinsic factors, such as past climate change and human activities. One species probably affected by both these factors is the arctic fox, which had a large distribution during the last glaciation that subsequently contracted at the start of the Holocene. More recently, the arctic fox population in Scandinavia went through a demographic bottleneck owing to human persecution. To investigate the consequences of these processes, we generated mitogenome sequences from a temporal dataset comprising Pleistocene, historical and modern arctic fox samples. We found no evidence that Pleistocene populations in mid-latitude Europe or Russia contributed to the present-day gene pool of the Scandinavian population, suggesting that postglacial climate warming led to local population extinctions. Furthermore, during the twentieth-century bottleneck in Scandinavia, at least half of the mitogenome haplotypes were lost, consistent with a 20-fold reduction in female effective population size. In conclusion, these results suggest that the arctic fox in mainland Western Europe has lost genetic diversity as a result of both past climate change and human persecution. Consequently, it might be particularly vulnerable to the future challenges posed by climate change. This article is part of a discussion meeting issue ‘The past is a foreign country: how much can the fossil record actually inform conservation? ’
Robust channel-wise illumination estimation

Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches operate directly on the input image. In this paper, we argue that this problem can be decomposed into three channel-wise independent and symmetric sub-problems and propose a novel CNN-based illumination estimation approach based on this decomposition. The proposed method substantially reduces the number of parameters needed to solve the task while achieving competitive experimental results compared to state-of-the-art methods. Furthermore, the practical application of illumination estimation techniques typically requires identifying the extreme error cases. This can be achieved using an uncertainty estimation technique. In this work, we propose a novel color constancy uncertainty estimation approach that augments the trained model with an auxiliary branch which learns to predict the error based on the feature representation. Intuitively, the model learns which feature combinations are robust and are thus likely to yield low errors and which combinations result in erroneous estimates. We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Joint Particle Detection and Analysis by a CNN and Adaptive Norm Minimization Approach

Optical flow cytometry is used as the gold standard in single cell function diagnostics with the drawback of involving high complexity and operator costs. Magnetic flow cytometers try to overcome this problem by replacing optical labeling with magnetic nanoparticles to assign each cell a magnetic fingerprint. This allows operators to obtain rich cell information from a biological sample with minimal sample preparation at near in-vivo conditions in a decentralized environment. A central task in flow cytometry is the determination of cell concentrations and cell parameters, e.g. hydrodynamic diameter. For the acquisition of this information, signal processing is an essential component. Previous approaches mainly focus on the processing of one-cell signals, leaving out superimposed signals originating from cells passing the magnetic sensors in close proximity. In this work, we present a framework for joint cell/particle detection and analysis, which is capable of processing one-cell as well as multi-cell signals. We employ deep learning and compressive sensing in this approach, which involves the minimization of an adaptive norm. We evaluate our method on simulated and experimental signals, the latter being obtained with polymer microparticles. Our results show that the framework is capable of counting cells with a relative error smaller than 2 %. Inference of cell parameters works reliably at both low and high noise levels.
Neural stem/progenitor cell 3-D differentiation for repair of central nervous system injuries

Significance High-throughput-screening approaches, where hundreds of thousands of compounds are evaluated in micro-amounts for their activity against certain targets, can regularly result in hit rates that are only a fraction of a percent. Here, we take a previously-developed machine-learning classification model (with the Signature molecular descriptor) used to identify active compounds against Factor XIa and experimentally verify the virtual screening model predictions. Of 21 predicted compounds tested, 7 show activity against Factor XIa, a 33% hit rate. Introduction PubChem is a database of molecules maintained by the National Institutes of Health. Among other information, the database contains biological activity data for about 50 million compounds from thousands of primary and confirmatory bioassays (these numbers seemingly grow every day as more results are uploaded). [328] This activity data is regularly obtained in a two-step process: high-throughput screening (HTS), called a primary screen, of hundreds of thousands compounds followed by a more-detailed ∗ This study has been published. H Li, DP Visco, ND Leipzig. “Confirmation of Predicted Activity for Factor XIa Inhibitors from a Virtual High-Throughput-Screening Approach. ” AIChE Letter, August 2014, Volume 60, Issue 8, pp 2741-2746.
Bridging the potato variety gap: a review of the Kenya - The Netherlands potato project (2012–2015): its success, challenges and opportunities

Abstract Potato is Kenya’s second most important staple crop which contributes immensely to the food security and income of smallholder farmers in the Kenya Highlands. Despite its importance however, crop yields are very low (7.7 to 9.5 t/ha) against a potential of over 40 t/ha. Many reasons are cited to explain this including inadequate good quality seed of suitable varieties, seed recycling, low soil fertility, limited use of agro-chemicals and leaf diseases. Potatoes were introduced in the late 19th Century from Britain and later Germany. During the 1980’s several Dutch varieties were brought in for evaluation but the process could not be completed as the test sites were allocated to other uses. This review highlights progress made with new varieties in terms of evaluation, registration and the associated technology identifies challenges and solutions. Until 2012 only 15 varieties had been in commercial use, mostly originating from temperate countries with a few local releases in collaboration with the International potato center (CIP). With increased demand for processed potato products driven by the urban middle -income population, and its preference for fast foods, there was need for high yielding varieties. Since 2012 over 100 Dutch and local varieties have been evaluated under conventional performance trials at seven locations spread across different potato-growing counties. Normal agronomic practices were followed and several yield parameters were measured. Several high-potential Dutch varieties failed due to their susceptibility to low management practices. From these, 33 varieties have been released from seven Dutch companies and local breeders and two parallel evaluation procedures have been conducted since 2013 one conventional and one intensive. Consequently, 44% and 56% of the released varieties came through conventional and intensive channels respectively. In terms of usage, those released consist of 61% for table use and 39% for processing. Mean tuber yields of controls under conventional and intensive evaluations when compared, indicates 191% increase. Accelerated availability of superior varieties can be achieved through collaborations with countries with well-developed potato value chain. Local varieties under good management can increase tuber yield substantially.
On Non-Linear Quantum Mechanics and the Measurement Problem II. The Random Part of the Wavefunction

In the first paper of this series, I introduced a non-linear, Hamiltonian, generalization of Schroedinger's theory that blocks formation of macroscopic dispersion ("cats"). But that theory was entirely deterministic, and so the origin of random outcomes in experiments such as Stern-Gerlach or EPRB was left open. Here I propose that Schroedinger's wavefunction has a random component and demonstrate that such an improvised stochastic theory can violate Bell's inequality. Repeated measurements and the back-reaction on the microsystem are discussed in a toy example. Experiments that might falsify the theory are described.
High Fidelity Physics-Based Simulation of a 512-Channel 4D-Radar Sensor for Automotive Applications

Radar has emerged as a core sensing technology for many active-safety and comfort related advanced driver assistance systems (ADAS) being deployed in today’s vehicles. Using radar technology, the ego vehicle can simultaneously detect the range and velocity of multiple targets. With multiple-input, multiple-output (MIMO) arrays, it is also possible to detect the angle of arrival of targets in azimuth and elevation. 4D automotive radar sensors can determine the range, velocity, azimuth and elevation angles-of-arrival (AoA) of targets in a traffic scene. Currently, radar-returns from traffic scenes have been mainly obtained through measurement. While measurement is valuable, it can be costly, time consuming, restrictive due to practicality issues and also unsafe in some corner-case traffic scenarios. Simulation has emerged as a potential alternative source of synthetic radar-returns that can be used to develop, test and refine signal processing techniques and detection algorithms. A key challenge in simulation has been to retain an accurate representation of actors in full-scale traffic scenes while still being able to solve electrically large problems efficiently. The introduction of MIMO arrays further increases the complexity and computational load demands of such simulations. In this paper, we present a computationally efficient, high fidelity, physics-based simulation workflow for a 77 GHz frequency-modulated continuous-waveform (FMCW)-based, 512-channel MIMO radar sensor. We demonstrate how the synthetic radar returns obtained from full-scale traffic scene simulations can be used to create 4D-radar point clouds. The accuracy of the synthetic radar returns is then evaluated by overlaying the resulting 4D-radar point clouds on 4 corresponding full-scale traffic scenes with varying levels of complexity. Results from this study demonstrate how accurate radar returns obtained from simulation can be used to develop next-generation radar sensors for autonomous vehicles.
Trust Region Method for Equilibrium Network Design Problem

This paper addresses a mathematical program with equilibrium constraints (MPEC) for network design problem with respect to link capacity expansions and signal settings, where stochastic user equilibrium constraints are expressed as variational inequality problem. The gradient of object function is received by sensitivity analysis of parametric variational inequality and a trust region method is presented for MPEC. Finally, numerical calculations are conducted and promising results have shown potential of the proposed method in solving network design problem. Streszczenie. W artykule opisano algorytm optymalizacji MPEC do tworzenia sieci, biorący pod uwage mozliwą ilośc linkow i ustawienia sygnalu. W celu uwzglednienia rownowazności uzytkownikow stochastycznych, wykorzystano zagadnienie nierowności wariacyjnej. Poprzez analizy nierowności otrzymano gradient funkcji obiektu oraz obszar ufności dla metody MPEC. Przedstawione wyniki obliczen wykazują obiecujące efekty dzialania proponowanej optymalizacji. (Metoda obszaru ufności w projektowaniu sieci rownowaznej).