Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of Crop Water Productivity in the Nile Delta over Three Decades (1985–2015)

Estimating crop water productivity (CWP) for spatially variable climatic conditions in Egypt is important for the redistribution of crop planting to optimize production per unit of water consumed. The current paper aims to estimate maximum CWP trends under conditions of the Northern Nile Delta over three decades to choose crops that exhibit a higher productivity per unit of water and positive trends in the CWP. The Kafr El Sheikh Governorate was selected to represent the Northern Nile Delta Region, and mean monthly weather data for the period of 1985 to 2015 were collected to calculate standardized reference evapotranspiration and crop water use for a wide array of crops grown in the region using the CROPWAT8.0 model. The CWP was then calculated by dividing crop yield by seasonal water consumption. The CWP data range from 0.69 to 13.79 kg·m−3 for winter field crops, 3.40 to 10.69 kg·m−3 for winter vegetables, 0.29 to 6.04 kg·m−3 for summer field crops, 2.38 to 7.65 kg·m−3 for summer vegetables, 1.00 to 5.38 kg·m−3 for nili season crops (short-season post summer), and 0.66 to 3.35 kg·m−3 for orchards. The crops with the highest CWP values (kg·m−3) over three decades in descending order are: sugar beet (13.79), potato (w2) (10.69), tomato (w) (10.58), eggplant (w) (10.05), potato (w1) (9.98), cucumber (w) (9.81), and cabbage (w) (9.59). There was an increase in CWP of 41% from the first to the second and 22% from the second to the third decade. The CWP increase is attributed to a small decrease in water consumption and to a considerable increase in crop yield. The yield increases are attributed mainly to the planting of higher yielding varieties and/or the application of better agronomic practices.
NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data

Abstract Motivation Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. Results NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. Availability and implementation Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.
High-resolution fleezers reveal duplex opening and stepwise assembly by an oligomer of the DEAD-box helicase Ded1p

DEAD-box RNA helicases are ubiquitous in all domains of life where they bind and remodel RNA and RNA-protein complexes. DEAD-box helicases unwind RNA duplexes by local opening of helical regions without directional movement through the duplexes and some of these enzymes, including Ded1p from Saccharomyces cerevisiae, oligomerize to effectively unwind RNA duplexes. Whether and how DEAD-box helicases coordinate oligomerization and unwinding is not known and it is unclear how many base pairs are actively opened. Using high-resolution optical tweezers and fluorescence, we reveal a highly dynamic and stochastic process of multiple Ded1p protomers assembling on and unwinding an RNA duplex. One Ded1p protomer binds to a duplex-adjacent ssRNA tail and promotes binding and subsequent unwinding of the duplex by additional Ded1p protomers in 4-6 bp steps. The data also reveal rapid duplex unwinding and rezipping linked with binding and dissociation of individual protomers and coordinated with the ATP hydrolysis cycle.
Cell-penetrating peptides stimulate protein transport on the Twin-arginine translocation pathway

The Tat pathway is essential for photosynthetic protein transport across plant thylakoid membranes and is also ubiquitous throughout prokaryotes and archaea. The Tat pathway is unique amongst protein translocation pathways as it specializes in transporting folded proteins driven by a proton motive force. Mechanistic details of the actual translocation step (s) of the pathway remain elusive. Here, we show that membrane thinning stimulates Tat transport and, conversely, membrane strengthening abolishes Tat transport. We draw parallels from the Tat transport mechanism to that of cell penetrating peptides and propose that the Tat pore could be toroidal in shape and lined by lipids, as in those formed by cell penetrating peptides. Significance Statement Protein translocation across membranes is a significant cellular activity in both prokaryotes and eukaryotes. The Tat pathway for protein translocation operates in bacteria, archaea, chloroplasts, and plant mitochondria. Its mechanism of action has been difficult to decipher, but recent evidence suggests it does not use a conical proteinaceous transport channel. Instead, it has been suggested to translocate proteins through lipid-lined toroidal pores set up by membrane thinning. This work supports that hypothesis by showing that membrane-thinning cell-penetrating peptides stimulate the Tat pathway in both chloroplasts and bacterial plasma membranes, and that membrane stabilization blocks the pathway. We believe this is the most direct evidence to date of the toroidal pore mechanism operating in the Tat pathway.
Effects of input image size on the accuracy of fish identification using deep learning

The length composition of catches by species is important for stock assessment. However, length measurement is performed manually, jeopardizing the future of continuous measurement because of likely labor shortages. We focused on applying deep learning to estimate length composition by species from images of fish caught for sustainable management. In this study, input image sizes were varied to evaluate the effect of input image size on detection and classification accuracy, as a method for improving the accuracy. The images (43,226 fish of 85 classes) were captured on conveyor belts to sort set-net catches. Fish detection and classification were performed using Mask R-CNN. The effect of input image size on accuracy was examined using three image sizes of 1333×888, 2000×1333, and 2666×1777 pixels, achieving an mAP50-95 of 0.580 or higher. The accuracy improved with increasing image size, attaining a maximum improvement of 4.3% compared to the smallest size. However, increasing the image size too far from the default size may not improve the accuracy of models with fine-tuning. Improvements in accuracy were primarily observed for the species with low accuracy at the smallest image size. Increasing image size would be a useful and simple way to improve accuracy for these species.
Within-host adaptation of a foliar pathogen, Xanthomonas, on pepper in presence of quantitative resistance and ozone stress

The evolving threat of new pathogen variants in the face of global environmental changes poses a risk to the plant health and can impact the efficacy of resistance-based disease management. Here, we studied short-term eco-evolutionary response of the pathogen, Xanthomonas perforans, on quantitative resistant and susceptible pepper during a single growing season in open-top chambers under the influence of elevated Ozone (O3). We observed increased disease severity, accompanied by higher variation on resistant cultivar under elevated O3, with no apparent change on the susceptible cultivar. This altered resistance response under elevated O3 is linked to altered eco-evolutionary dynamics of pathogen. While a single pathogen genotype remained prevalent on susceptible cultivar, resistant cultivar supported heterogenous pathogen population, with the evidence of short- term evolutionary modifications seeded by de novo parallel mutations. Altered O3 levels led to strain turnover on resistant cultivar with higher within-host polymorphism containing higher proportion of random de novo mutations lacking parallelism. Population heterogeneity is a mechanism of pathogen adaptation in response to the stressors. While parallel mutations in response to quantitative resistance may provide clues to predicting long-term pathogen evolution, high proportion of transient mutations suggest less predictable pathogen evolution under climatic alterations.
Specifying the orthographic prediction error for a better understanding of efficient visual word recognition in humans and machines

Recent evidence suggests that readers optimize low-level visual information following the principles of predictive coding. Based on a transparent neurocognitive model, we postulated that readers optimize their percept by removing redundant visual signals, which allows them to focus on the informative aspects of the sensory input, i.e., the orthographic prediction error (oPE). Here, we test alternative oPE implementations by assuming all-or-nothing signaling units based on multiple thresholds and compare them to the original oPE implementation. For model evaluation, we implemented the comparison based on behavioral and electrophysiological data (EEG at 230, 430 ms). We found the highest model fit for the oPE with a 50% threshold integrating multiple prediction units for behavior and the late EEG component. The early EEG component was still explained best by the original hypothesis. In the final evaluation, we used image representations of both oPE implementations as input to a deep-neuronal network model (DNN). We compared the lexical decision performance of the DNN in two tasks (words vs. consonant strings; words vs. pseudowords) to the performance after training with unaltered word images and found better DNN performance when trained with the 50% oPE representations in both tasks. Thus, the new formulation is adequate for late but not early neuronal signals and lexical decision behavior in humans and machines. The change from early to late neuronal processing likely reflects a transformation in the representational structure over time that relates to accessing the meaning of words.
The proximal point method for locally Lipschitz functions in multiobjective optimization

This paper studies the constrained multiobjective optimization problem of finding Pareto critical points of vector-valued functions. The proximal point method considered by Bonnel et al. (SIAM J. Optim., 4 (2005), pp. 953-970) is extended to locally Lipschitz functions in the finite dimensional multiobjective setting. To this end, a new approach for convergence analysis of the method is proposed where the first-order optimality condition of the “scalarized” problem is replaced by a necessary condition for weakly Pareto points of a multiobjective problem.
How Will Climate Change Affect Aviation

Aircraft do not fly through a vacuum, but through an atmosphere whose meteorological characteristics are changing because of global warming. The impacts of aviation on climate change have long been recognised, but the impacts of climate change on aviation have only recently begun to emerge. These impacts include intensified turbulence, increased take-off weight restrictions, sea-level rise at coastal airports, and more extreme weather. Here we focus on the influence of climate change on flight routes and journey times. We feed synthetic atmospheric wind fields generated from climate model simulations into a routing algorithm of the type used operationally by flight planners. We focus on transatlantic flights between London and New York, and how they change when the atmospheric concentration of carbon dioxide is doubled. The methodology and results are described in full by Williams (2016).
Fungal Communities in Boreal Forest Soils: The Effect of Disturbances, Seasons and Soil Horizons

15/2017 Pär Davidsson Oligogalacturonide Signalling in Plant Innate Immunity 16/2017 Kean-Jin Lim Scots Pine (Pinus sylvestris L.) Heartwood Formation and Wounding Stress: A View from the Transcriptome 17/2017 Marja Rantanen Light and Temperature as Developmental Signals in Woodland Strawberry and Red Raspberry 18/2017 Sara Kovanen Molecular Epidemiology of Campylobacter jejuni in the Genomic Era 19/2017 Johanna Muurinen Antibiotic Resistance in Agroecosystems 20/2017 Johanna Laakso Phosphorus in the Sediment of Agricultural Constructed Wetlands 21/2017 Sadegh Mansouri Plant Biomass-Acting Enzymes Produced by the Ascomycete Fungi Penicillium subrubescens and Aspergillus niger and Their Potential in Biotechnological Applications 22/2017 Anna Salomaa Actors’ Roles and Perceptions on the Opportunities to Increase Nature Conservation Effectiveness: a Study of Interaction Between Knowledge and Policy Process 23/2017 Anniina Le Tortorec Bioluminescence of Toxic Dinoflagellates in the Baltic Sea from Genes to Models 24/2017 Tanja Paasela The Stilbene Biosynthetic Pathway and Its Regulation in Scots Pine 1/2018 Martta Viljanen Adaptation to Environmental Light Conditions in Mysid Shrimps 2/2018 Sebastián Coloma Ecological and Evolutionary Effects of Cyanophages on Experimental Plankton Dynamics 3/2018 Delfia Isabel Marcenaro Rodriguez Seedborne Fungi and Viruses in Bean Crops (Phaseolus vulgaris L.) in Nicaragua and Tanzania 4/2018 Elina Kettunen Diversity of Microfungi Preserved in European Palaeogene Amber 5/2018 Jonna Emilia Teikari Toxic and Bloom-forming Baltic Sea Cyanobacteria under Changing Environmental Conditions 6/2018 Juha Immanen Cytokinin Signaling in Hybrid Aspen Cambial Development and Growth 7/2018 Sanna Mäntynen Anaerobic Microbial Dechlorination of Polychlorinated Dibenzo-p-dioxins and Dibenzofurans in Contaminated Kymijoki River Sediments 8/2018 Johannes Cairns Low Antibiotic Concentrations and Resistance in Microbial Communities 9/2018 Samia Samad Regulation of Vegetative and Generative Reproduction in the Woodland Strawberry 10/2018 Silviya Korpilo An Integrative Perspective on Visitor Spatial Behaviour in Urban Green Spaces: Linking Movement, Motivations, Values and Biodiversity for Participatory Planning and Management 11/2018 Hui Zhang Responses of Arctic Permafrost Peatlands to Climate Changes over the Past Millennia Y E B
Incommensurate Phase in Ferroelectrics: The Urbach Energy Remains Unchanged

Temperature invariance of the Urbach energy and parallel red shift of optical absorption edge in the incommensurate phase of Sn2Р2Se6 ferroelectric is analyzed. The observed effect is explained by the presence of dynamic structural disordering of the crystal lattice caused by a wave of structure modulation which is formed by the shift of tin atoms.
On the Origins of Linear Representations in Large Language Models

Recent works have argued that high-level semantic concepts are encoded"linearly"in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights.
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems

This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy, 2015] and, more specifically, on the rate-distortion analysis from [Dong and Van Roy, 2020], where they proved a bound with regret rate of $O(d\sqrt{T \log(T)})$ for the $d$-dimensional linear bandit setting. We focus on bandit problems with a metric action space and, using a chaining argument, we establish new bounds that depend on the metric entropy of the action space for a variant of Thompson-Sampling. Under suitable continuity assumption of the rewards, our bound offers a tight rate of $O(d\sqrt{T})$ for $d$-dimensional linear bandit problems.
Backfiring Bosonisation

For a fermionic quantum field theory in $d=1+1$ dimensions, there is a subtle difference between summing over spin structures and gauging $(-1)^F$. If the gravitational anomaly vanishes mod 16, then both operations are equivalent and yield a bosonic theory. But if the gravitational anomaly only vanishes mod 8, then only gauging $(-1)^F$ is allowed, and the result is a fermionic theory. Our goal is to understand in detail how this happens, despite the fact $(-1)^F$ is defined in terms of shifting the spin structure, which would na\"ively suggest that both operations are equivalent. We do this from three perspectives: an abstract view in terms of anomalies, explicit CFT calculations, and a Symmetry TFT perspective. To conclude, we illustrate our results using the heterotic string and the famous self-triality of 8 Majorana-Weyl fermions.
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1

Identifying flood affected areas in remote sensing data is a critical problem in earth observation to analyze flood impact and drive responses. While a number of methods have been proposed in the literature, there are two main limitations in available flood detection datasets: (1) a lack of region variability is commonly observed and/or (2) they require to distinguish permanent water bodies from flooded areas from a single image, which becomes an ill-posed setup. Consequently, we extend the globally diverse MMFlood dataset to multi-date by providing one year of Sentinel-1 observations around each flood event. To our surprise, we notice that the definition of flooded pixels in MMFlood is inconsistent when observing the entire image sequence. Hence, we re-frame the flood detection task as a temporal anomaly detection problem, where anomalous water bodies are segmented from a Sentinel-1 temporal sequence. From this definition, we provide a simple method inspired by the popular video change detector ViBe, results of which quantitatively align with the SAR image time series, providing a reasonable baseline for future works.
Bayesian Generalized Distributed Lag Regression with Variable Selection

Distributed Lag Models (DLMs) and similar regression approaches such as MIDAS have been used for many decades in econometrics, and more recently in the study of air quality and its impact on human health. They are useful not only for quantifying accumulating and delayed effects, but also for estimating the lags that are most susceptible to these effects. Among other things, they have been used to infer the period of exposure to poor air quality which might negatively impact child birth weight. The increased attention DLMs have received in recent years is reflective of their potential to help us understand a great many issues, particularly in the investigation of how the environment affects human health. In this paper we describe how to expand the utility of these models for Bayesian inference by leveraging latent-variables. In particular we explain how to perform binary regression to better handle imbalanced data, how to incorporate negative binomial regression, and how to estimate the probability of predictor inclusion. Extra parameters introduced through the DLM framework may require calibration for the MCMC algorithm, but this will not be the case in DLM-based analyses often seen in pollution exposure literature. In these cases, the parameters are inferred through a fully automatic Gibbs sampling procedure.
Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information

We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of unseen space, data used to train a graph neural network to predict the goodness of temporally-extended exploratory actions. Our learning-augmented model-based planning approach predicts the expected value of information of revealing unseen space and is capable of using these predictions to actively seek information and so improve long-horizon navigation. Across two simulated office-like environments, our planner outperforms competitive learned and non-learned baseline navigation strategies, achieving improvements of up to 63.76% and 36.68%, demonstrating its capacity to actively seek performance-critical information.
Realizability of matroid quotients

We characterize the realizability of a quotient of matroids, over an infinite field $K$, in terms of the realizability over $K$ of a single matroid associated to it, called the Higgs major. This result extends to realizability of flag matroids. Further, we provide some applications to the relative realizability problem for Bergman fans in tropical geometry.
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation

Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources remains a practically important approach. While prior work using single-source knowledge has shown a clear positive correlation between the performances of knowledge selection and response generation, there are no existing multi-source datasets for evaluating support knowledge retrieval. Further, prior work has assumed that the knowledge sources available at test time are the same as during training. This unrealistic assumption unnecessarily handicaps models, as new knowledge sources can become available after a model is trained. In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms.WoW) for evaluating multi-source dialogue knowledge selection and response generation. Unlike existing datasets, it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. We further propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources in a zero-shot fashion.