Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Shortest Path Restoration Lemmas for Multiple Edge Failures: Trade-offs Between Fault-tolerance and Subpaths

The restoration lemma is a classic result by Afek, Bremler-Barr, Kaplan, Cohen, and Merritt [PODC '01], which relates the structure of shortest paths in a graph $G$ before and after some edges in the graph fail. Their work shows that, after one edge failure, any replacement shortest path avoiding this failing edge can be partitioned into two pre-failure shortest paths. More generally, this implies an additive tradeoff between fault tolerance and subpath count: for any $f, k$, we can partition any $f$-edge-failure replacement shortest path into $k+1$ subpaths which are each an $(f-k)$-edge-failure replacement shortest path. This generalized result has found applications in routing, graph algorithms, fault tolerant network design, and more. Our main result improves this to a multiplicative tradeoff between fault tolerance and subpath count. We show that for all $f, k$, any $f$-edge-failure replacement path can be partitioned into $O(k)$ subpaths that are each an $(f/k)$-edge-failure replacement path. We also show an asymptotically matching lower bound. In particular, our results imply that the original restoration lemma is exactly tight in the case $k=1$, but can be significantly improved for larger $k$. We also show an extension of this result to weighted input graphs, and we give efficient algorithms that compute path decompositions satisfying our improved restoration lemmas.
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions

The challenge in fine-grained visual categorization lies in how to explore the subtle differences between different subclasses and achieve accurate discrimination. Previous research has relied on large-scale annotated data and pre-trained deep models to achieve the objective. However, when only a limited amount of samples is available, similar methods may become less effective. Diffusion models have been widely adopted in data augmentation due to their outstanding diversity in data generation. However, the high level of detail required for fine-grained images makes it challenging for existing methods to be directly employed. To address this issue, we propose a novel approach termed the detail reinforcement diffusion model~(DRDM), which leverages the rich knowledge of large models for fine-grained data augmentation and comprises two key components including discriminative semantic recombination (DSR) and spatial knowledge reference~(SKR). Specifically, DSR is designed to extract implicit similarity relationships from the labels and reconstruct the semantic mapping between labels and instances, which enables better discrimination of subtle differences between different subclasses. Furthermore, we introduce the SKR module, which incorporates the distributions of different datasets as references in the feature space. This allows the SKR to aggregate the high-dimensional distribution of subclass features in few-shot FGVC tasks, thus expanding the decision boundary. Through these two critical components, we effectively utilize the knowledge from large models to address the issue of data scarcity, resulting in improved performance for fine-grained visual recognition tasks. Extensive experiments demonstrate the consistent performance gain offered by our DRDM.
Leveraging Contextual Information for Effective Entity Salience Detection

In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.
Solving the Problem of Induction

This article solves the Hume's problem of induction using a probabilistic approach. From the probabilistic perspective, the core task of induction is to estimate the probability of an event and judge the accuracy of the estimation. Following this principle, the article provides a method for calculating the confidence on a given confidence interval, and furthermore, degree of confirmation. The law of large numbers shows that as the number of experiments tends to infinity, for any small confidence interval, the confidence approaches 100\% in a probabilistic sense, thus the Hume's problem of induction is solved. The foundation of this method is the existence of probability, or in other words, the identity of physical laws. The article points out that it cannot be guaranteed that all things possess identity, but humans only concern themselves with things that possess identity, and identity is built on the foundation of pragmatism. After solving the Hum's problem, a novel demarcation of science are proposed, providing science with the legitimacy of being referred to as truth.
Particle dynamics on test Papapetrou fields of vacuum spacetimes

In this paper we examine the dynamics of test particles subjected to Papapetrou fields which emerge from vacuum spacetimes isometries. Assuming that the background geometry is described by a Kerr metric whose timelike Killing vector satisfies Maxwell equations, we evaluate the electromagnetic fields -- Papapetrou fields -- by fixing a locally non-rotating (LNR) frame of reference. We also evaluate such the electromagnetic fields for a vanishing rotation parameter assuming a timelike frame of reference for a Schwarzschild background. In order to probe for the effect of the test Papapetrou fields we study the motion of charged particles in a Kerr background. Restricting ourselves to orbits in the equatorial plane with LNR initial conditions we show that there is an explicit deviation between orbits of neutral and charged particles in the case of repulsive electromagnetic configurations. For critical charge-mass ratios $\zeta_*$ test particles can be found in the Kerr retrograde photon sphere. For static configurations we show that massive/charged test particles may populate the unstable Schwarzschild photon sphere for given domain of the parameter space.
OPUS: An integrated simulation model for transport of nonpoint-source pollutants at the field scale. Volume 2, user manual

The Opus (not an acronym) model simulates interaction of water movement with the application, transformation, and movement of nonpoint-source pollutants in an agricultural field. The modeled processes include hydrology, erosion, weather, crop growth, agricultural management, nutrient cycling and transport, and pesticide fate. The field size is limited to catchments with a single raingage record and a single soil profile. Time scales vary by process and conditions: from fractions of a second in some hydrologic components to years in annual management cycles. Many processes proceed on a daily time step. The document describes the use of the computer program. Input variables and parameters are described, and relationships among some are explained.
An analysis of the Galveston Hurricane using the 20CR data set

The Twentieth Century Reanalysis (20CR) is an atmospheric dataset consisting of 56 ensemble members, which covers the entire globe and reaches back to 1871. To assess the suitability of this dataset for studying past extremes, we analysed a prominent extreme event, namely the Galveston Hurricane, which made landfall in September 1900 in Texas, USA. The ensemble mean of 20CR shows a track of the pressure minimum with a small standard deviation among the 56 ensemble members in the area of the Gulf of Mexico. However, there are systematic differences between the assimilated “Best Track” from the International Best Track Archive for Climate Stewardship (IBTrACS) and the ensemble mean track in 20CR. East of the Strait of Florida, the tracks derived from 20CR are located systematically northeast of the assimilated track while in the Gulf of Mexico, the 20CR tracks are systematically shifted to the southwest compared to the IBTrACS position. The hurricane can also be observed in the wind field, which shows a cyclonic rotation and a relatively calm zone in the centre of the hurricane. The 20CR data reproduce the pressure gradient and cyclonic wind field. Regarding the amplitude of the wind speeds, the ensemble mean values from 20CR are significantly lower than the wind speeds known from measurements.
A Study of ERS-1 SAR and Landsat-4 TM Synergism for Forest Cover Studies

Merging SAR data with imagery from different parts of the electromagnetic spectrum provides an almost infinite range of possible combinations to be explored. We examined the synergism between radar and optical dataset using various digital image processing techniques in a test site of Western Ghats, India. This study evaluated the advantages of combining traditional spaceborne optical data (Landsat4 TM) from the visible and infrared wavelengths with the longer wavelengths of radar (ERS1 SAR). The analysis involved speckle suppression of SAR (C-VV) data using Gamma MAP filter, optimum band selection for extraction of forest cover information, supervised classification, principal component analysis and IHS transformation. SAR data, when subjected to 9x9 Gamma MAP filters provided more meaningful joint distribution of grey level values. The triplet combination of blue, NIR and SAR possessed highest Optimum Index Factor of 31.8. Most of the forest type classes could be distinguished on TM images and the addition of SAR image ameliorated the accuracy. Improved classification accuracy for degraded forest (1%) and tropical semi-evergreen forest (0.7%) was observed could be that could be attributed to the incorporation of the structural details into the classification exercise. PC1 and PC2 were found to be highly correlated with optical and radar channels respectively. Physiographic and hydrographic features were significantly highlighted when SAR data was transformed as intensity and saturation components; supporting the fact that SAR provides more terrain details. It is apparent from the preliminary study that the effect of operations in terms of feature identification and interpretation would depend on the combinations of radar and optical systems.
ICT contribution to growth; the case of tunisia

The aim of this paper is to examine the impact of Information and Communication Technologies (ICT) on the growth of developping countries and especially on the Tunisian economy. The first part of the article summerizes the relationship between growth, productivity and ICT. We show that under some necessary conditions ICT may boost the growth in the developping countries. In the second part of the article, we estimate the contribution of ICT to the growth of Tunisia due to ICT diffusion and adoption. We compare our econometric estimation to the results obtained in France and United States.
The Interplay Between Blood Inflammatory Markers, Symptom Domains, and Severity of ADHD Disorder in Children

Objective: ADHD has a multi-factorial etiology among which the inflammatory theory presupposition. We aimed to explore the interplay between blood inflammatory markers; neutrophil lymphocyte ratios (NLR), platelet\lymphocyte ratio (PLR), mean platelet volume (MPV), symptom domains, and severity of ADHD. Method: A total of 50 children with ADHD and 50 healthy controls were recruited. Children were assessed using the mini-international neuropsychiatric interview for children and adolescents (MINI-KID), the Wechsler intelligence scale for children, the Conners’ parent rating scale. Then, NLR, PLR, and MPV were measured. Results: There was a statistically significant difference between children with ADHD and the control group with regard to all blood inflammatory markers (except platelet count). The subtypes and severity of ADHD had no significant relation with NLR, PLR, and MPV. Conclusion: blood inflammatory markers are significantly high in children with ADHD but could not predict specific symptom domains or severity.
A fast algorithm for short term electric load forecasting by a hidden semi-markov process

ABSTRACT The paper proposes an alternative algorithm to implement the current manual choosing mechanism of energy companies whose dedicated department has a library of electric load models and one best model is chosen manually everyday for daily forecast. The proposed algorithm is a combination of an estimation of change point, and a fast ECM algorithm based on the empirical probability function, as well as methods of a hidden markov chain. We train parameters of the proposed algorithm based on a historical dataset consisting of loads, exogenous information such as temperature, and the daily recommended best model which is unavailable sometimes. Simulations and a test on a real-world dataset show that compared with other state-of-art algorithms, the proposed algorithm is fast and efficient for short-term electric load forecasting. An implement to the proposed algorithm written in Matlab is provided in supplement file.
Intensifying a crop–fallow system: impacts on soil properties, crop yields, and economics

Abstract Intensifying crop–fallow systems could address increased weed control costs, increased land or rental costs, reduced crop diversity, and degraded soil properties in water-limited environments. One strategy to intensify such systems could be the insertion of a short-season crop during fallow. But, how this strategy affects soils, crop production, and farm economics needs further research. Thus, we studied the impacts of replacing fallow in a winter wheat (Triticum aestivum L)–corn (Zea mays L.)–fallow system with a short-season spring crop [field pea (Pisum sativum L.)] on crop yields and economics from 2015 to 2019 and 5-yr cumulative effects on soil properties using an experiment in the west-central US Great Plains. After 5 yr, replacing fallow with field pea increased microbial biomass by 294 nmol g−1 and plant available water by 0.08 cm3 cm−3, and reduced bulk density by 0.1 g cm−3 and cone index by 0.73 MPa in the 0–5 cm depth. It had, however, no effect on other soil properties. Field pea yield averaged 2.24 Mg ha−1. Field pea reduced subsequent crop yield by 15–25% in two of three crops compared with fallow. However, economic analysis showed replacing fallow with field pea may improve net income by $144–303 ha−1, although income across the 5 yr differed by $65 ha−1 in favor of fallow. Replacing fallow in winter wheat–corn–fallow rotation with a short-season spring crop offers promise to improve some near-surface soil properties while increasing net economic return during fallow under the conditions of this study.
Aminergic and peptidergic modulation of Insulin-Producing Cells in Drosophila

Insulin plays a critical role in maintaining metabolic homeostasis. Since metabolic demands change momentarily, insulin release needs to be constantly adjusted. These adjustments are mediated via various pathways, most prominently the blood glucose level, but also by feedforward signals from motor circuits and different neuromodulatory pathways. Here, we analyzed how neuromodulatory inputs control the activity of the main source of insulin in Drosophila – a population of Insulin-Producing Cells (IPCs) located in the brain. IPCs are functionally analogous to mammalian pancreatic beta cells, but their location makes them accessible for in vivo recordings in intact animals. We characterized the functional inputs to IPCs using single-nucleus RNA sequencing analysis, anatomical receptor expression mapping, and an optogenetics-based “intrinsic pharmacology” approach. Our results showed that the IPC population expressed a variety of receptors for neuro-modulators and classical neurotransmitters. Interestingly, individual IPCs exhibited heterogeneous receptor profiles, suggesting that they could be modulated differentially. This was supported by electrophysiological recordings of IPCs that we performed while activating different populations of modulatory neurons. Our analysis revealed that some modulatory inputs had heterogeneous effects on the IPC activity, such that they inhibited one subset of IPCs, while exciting another. Monitoring calcium activity across the IPC population uncovered that these heterogeneous responses occurred simultaneously. Certain neuromodulatory populations shifted the IPC population activity towards an excited state, while others shifted the IPCs towards inhibition. Taken together, we provide a comprehensive, multi-level framework of neuromodulation in the insulinergic system of Drosophila.
The puzzling persistence of the intellectual property right/climate change relationship

At the 2011 United Nations Framework Convention on Climate Change (UNFCCC) Conference of the Parties in Durban, India sought to introduce a number of late amendments to the negotiations, including those on technology transfer. Specifically, they wished for the inclusion of the agenda item ‘Accelerated access to critical mitigation and adaptation technologies and related intellectual property rights’. Why? They explained that:
Ensembling Unets, sparse representation and low dimensional visualization for rare chromosomal aberration detection in light microscopy images

In biological dosimetry, a radiation dose is estimated using the average number of chromosomal aberration per peripheral blood lymphocytes. To achieve an adequate precision in the estimation of this average, hundreds of cells must be analyzed in 2D microscopy images. Currently, this analysis is performed manually, as conventional computer vision techniques struggle with the wide variety of shapes showcased by chromosomes. The false discovery rate of current automated detection systems is high and variable, depending on small variations in data quality (chromosome spread, illumination variations …), which makes using it in a fully automated fashion impossible. Automating chromosomal aberration is needed to reduce diagnosis time. Furthermore, an automated system can process more images, which improves confidence intervals around the estimated radiation dose. We build an object detection model to automate chromosomal aberration detection using recent advances in deep convolutional neural networks and statistical learning. We formulated the problem of rare aberration detection as a heatmap regression problem requiring the minimization of a sparsity-promoting loss to reduce the false alarm rate. Our Unet-based approach is analoguous to a one-stage object detector, and keeps the number of hyperparameters to a minimum. Finally, we demonstrate large performance improvements using an ensemble of checkpoints collected during a single run of training. A PCA-based strategy is used to provide cues for interpretation of our deep neural network-based model. The methodology is demonstrated on real, large, and challenging datasets depicting rare chromosomal aberrations and is favorably compared to a reference dosimetry technique.
On the Black-Box Separation Between Ring Signatures and Public Key Encryptions

In this paper, we show that it is impossible to construct a public key encryption scheme (PKE) from a ring signature scheme in a black-box fashion in the standard model. Such an impossibility is highly non-trivial because, to the best of our knowledge, known generic constructions of ring signature scheme are based on public key cryptosystems or in the random oracle model. Technically, we introduce a new cryptographic primitive named indistinguishable multi-designated verifiers signature (IMDVS), and prove that (i) IMDVS is equivalent to PKE, and (ii) it is impossible to construct IMDVS from a ring signature scheme in a generic way. Our result suggests an essential gap between ring signature and group signature, as it is known that group signature implies PKE.
Hybrid enriched bidiagonalization for discrete ill‐posed problems

The regularizing properties of the Golub–Kahan bidiagonalization algorithm are powerful when the associated Krylov subspace captures the dominating components of the solution. In some applications the regularized solution can be further improved by enrichment, that is, by augmenting the Krylov subspace with a low‐dimensional subspace that represents specific prior information. Inspired by earlier work on GMRES, we demonstrate how to carry these ideas over to the bidiagonalization algorithm, and we describe how to incorporate Tikhonov regularization. This leads to a hybrid iterative method where the choice of regularization parameter in each iteration also provides a stopping rule.
De la caractérisation des fluides aurifères par rayonnement synchroton à la géodynamique archéenne : étude des interactions fluides-roches dans un point triple de foliation archéen (Le synclinal de Warrawoona, Australie occidentale)

The Warrawoona Syncline (Western Australia) is a key area for the understanding of Archaean tectonic and gold mineralization processes. In this study, we use a combination of field and geochemical analysis to elucidate the relationship between deformation and ore concentration. Two stages of fluid-rock interactions were recognized. The first stage involved early to syn-tectonic infiltration of seawater at low temperature and the second stage resulted in the infiltration of a mixed fluid phase of magmatic and metamorphic origin. These results indicate that ore concentration in the Warrawoona syncline is best achieved by a protracted and polyphased fluid infiltration history. Thermomechanical simulations indicate that the plumbing system associated with such a polyphased fluid-rock interaction history is best approximated in terms of a gravitational instability inducing sagduction of greenstone rocks and exhumation of granitic domes. This implies that Archaean tripe foliation junction such as the Warrawoona Syncline may be of significant importance for future gold exploration.
Segmentation and Classification of Polarimetric SAR Data Using Spectral Graph Partitioning

A new approach for segmentation and classification of polarimetric synthetic aperture radar (POLSAR) data is proposed based on spectral graph partitioning. Since automated analysis techniques are often challenged due to the noisy properties of POLSAR data, human experts are employed to aid in the interpretation of such data in an operational setting. Humans can improve the performance of segmentation and classification of POLSAR data, because their vision system can apply cognitive skills that are not easy to incorporate into an automated system. The motivation for this paper is to incorporate some of these human perceptual skills into the computer algorithms. A framework that has recently emerged in computer vision for solving grouping problems with perceptually plausible results-spectral graph partitioning-is customized for POLSAR data. Segmentation is performed using the contour information in a region-based setting with the aid of spatial proximity. This is followed by a classification step performed through graph partitioning based on similarities of the mean coherence matrices obtained for each segment. Using the proposed approach, the results achieved are superior to the Wishart classifier. Automated parameter selection procedures are under development. This framework also suggests a way to accommodate different representations of polarimetric data and combine them with other information sources (e.g., optical imagery and digital elevation models).