Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting Information for: ”Increased heat stress reduces future yields of three major crops in Pakistan’s Punjab region, despite intensiﬁcation of irrigation”.

1University of Kassel, Nordbahnhofstr. 1, 37213 Witzenhausen, Germany 2Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, 14473 Potsdam, Germany 3TU Darmstadt, Schnittspahnstraße 9, 64287 Darmstadt, Germany 4UFZ Helmholtz Centre for Environmental Research, Theodor-Lieser-Straße 4, 06120 Halle, Germany 5University of Agriculture Faisalabad, P.O. Box No 38040 Faisalabad, Pakistan 6Martin-Luther University Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120 Halle, Germany 7IWW Water Centre, Moritzstraße 26, 45476 Mülheim an der Ruhr, Germany 8UFZ Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany
Memristor devices for neural networks

Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfactory solutions for neural networks. Recently, several types of memristor devices have become popular alternatives because of their outstanding characteristics such as scalability, high performance, and non-volatility. To understand the characteristics of memristors, a comparison among memristors has been made, considering both maturity and performance. Magneto-resistance random access memory, phase-change random access memory, and resistive random access memory among the proposed memristors are good candidates as synaptic devices for weight storage and matrix-vector multiplication required in artificial neural networks (ANNs). Moreover, these devices play key roles as synaptic devices in research for bio-plausible spiking neural networks (SNNs) because their distinctive switching properties are well matched for emulating synaptic and neuron functions of biological neural networks. In this paper we review motivation, advantage, technology, and applications of memristor devices for neural networks from practical approaches of ANNs to futuristic research of SNNs, considering the current status of memristor technology.
Grain size dependent large rheology contrasts of halite at low deviatoric differential stress: evidence from microstructural study of naturally deformed

Constitutive laws of rRock salt cConstitutive laws are required for the prediction ofare required to predict the long-term deformation of solution mined caverns and radioactive waste repositories and solution mined cavernsin rock salt, which 15 are used for energy storage and play an important role in the energy transition. Much of this deformation is at differential stresses of a few MPa. T, while the vast majority of laboratory salt creep measurements of salt creep are at much higher
IRTCI: Item Response Theory for Categorical Imputation

Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various approaches have implications for calculating clinical scores, model building and model testing. The work showcased here offers a novel means for categorical imputation based on item response theory (IRT) and compares it against several methodologies currently used in the machine learning field including k-nearest neighbors (kNN), multiple imputed chained equations (MICE) and Amazon Web Services (AWS) deep learning method, Datawig. Analyses comparing these techniques were performed on three different datasets that represented ordinal, nominal and binary categories. The data were modified so that they also varied on both the proportion of data missing and the systematization of the missing data. Two different assessments of performance were conducted: accuracy in reproducing the missing values, and predictive performance using the imputed data. Results demonstrated that the new method, Item Response Theory for Categorical Imputation (IRTCI), fared quite well compared to currently used methods, outperforming several of them in many conditions. Given the theoretical basis for the new approach, and the unique generation of probabilistic terms for determining category belonging for missing cells, IRTCI offers a viable alternative to current approaches.
Development and evaluation of processes affecting simulation of diel fine particulate matter variation in the GEOS-Chem model

Abstract. The capability of chemical transport models to represent fine particulate matter (PM2.5) over the course of a day is of vital importance for air quality simulation and assessment. In this work, we used the nested GEOS-Chem model at 0.25∘×0.3125∘ resolution to simulate the diel (24 h) variation in PM2.5 mass concentrations over the contiguous United States (US) in 2016. We evaluate the simulations with in situ measurements from a national monitoring network. Our base case simulation broadly reproduces the observed morning peak, afternoon dip, and evening peak of PM2.5, matching the timings of these features within 1–3 h. However, the simulated PM2.5 diel amplitude in our base case was 106 % biased high, relative to observations. We find that temporal resolution of emissions, subgrid vertical gradient between surface model-level center and observations, and biases in boundary layer mixing and aerosol nitrate are the major causes for this inconsistency. We applied an hourly anthropogenic emission inventory, converted the PM2.5 mass concentrations from the model-level center to the height of surface measurements by correcting for aerodynamic resistance, adjusted the boundary layer heights in the driving meteorological fields using aircraft observations, and constrained nitrate concentrations using in situ measurements. The bias in the PM2.5 diel amplitude was reduced to −12 % in the improved simulation. Gridded hourly emissions rather than diel scaling factors applied to monthly emissions reduced biases in simulated PM2.5 overnight. Resolving the subgrid vertical gradient in the surface model level aided the capturing of the timings of the PM2.5 morning peak and afternoon minimum. Based on the improved model, we find that the mean observed diel variation in PM2.5 for the contiguous US is driven by (1) building up of PM2.5 by 10 % in early morning (04:00–08:00 local time, LT), due to increasing anthropogenic emissions into a shallow mixed layer; (2) decreasing PM2.5 by 22 % from mid-morning (08:00 LT) through afternoon (15:00 LT), associated with mixed-layer growth; (3) increasing PM2.5 by 30 % from mid-afternoon (15:00 LT) though evening (22:00 LT) as emissions persist into a collapsing mixed layer; and (4) decreasing PM2.5 by 10 % overnight (22:00–04:00 LT) as emissions diminish.
HetroTraffSim: A Simulator for Heterogeneous Traffic Flow

Traffic simulation software (TSS) is employed for planning, designing, and managing road networks. Among existing TSS, only SUMO and HETROSIM can be used for heterogeneous traffic. The objective of this work is to develop new software for heterogeneous traffic simulation which is effective and efficient. This is motivated by the fact that traffic in developing countries is typically heterogeneous. The HetroTraffSim TSS was developed using Unity3D and is based on a recently developed macroscopic traffic flow model. A 360 m section of University Road which is a two-lane arterial road in Peshawar, Pakistan, is used to evaluate the performance using real traffic data. The results obtained show that an increase in density decreases the velocity. Further, HetroTraffSim can be used to characterize and predict heterogeneous traffic behavior.
MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion

This study investigates the use of masked autoencoders (MAE) to address the challenge of filling gaps in high-resolution (1km) sea surface temperature (SST) fields caused by cloud cover, which often results in gaps in the SST data and/or blurry imagery in blended SST products. Our study demonstrates that MAE, a deep learning model, can efficiently learn the anisotropic nature of small-scale ocean fronts from numerical simulations and reconstruct the artificially masked SST images. The MAE model is trained and evaluated on synthetic SST fields and tested on real satellite SST data from VIIRS sensor on 5 Suomi-NPP satellite. It is demonstrated that the MAE model trained on numerical simulations can provide a computationally-efficient alternative for filling gaps in satellite SST. MAE can reconstruct randomly occluded images with a root mean squared error (RMSE) of under 0.2 ◦ C for masking ratios of up to 80%. It has exceptional efficiency, requiring three orders of magnitude (a factor of 5000) less time. The ability to reconstruct high-resolution SST fields under cloud cover has important implications for understanding and predicting global and regional climates, and detecting small-scale SST fronts that play a crucial role in 10 the exchange of heat, carbon, and nutrients between the ocean surface and deeper layers. Our findings highlight the potential of deep learning models such as MAE to improve the accuracy and resolution of SST data at kilometer scales. It presents a promising avenue for future research in the field of small-scale ocean remote sensing analyses.
Probabilistic forecasting of seasonal time series Combining clustering and classification for forecasting

In this article, we propose a framework for seasonal time series probabilistic forecasting. It aims at forecasting (in a probabilistic way) the whole next season of a time series, rather than only the next value. Probabilistic forecasting consists in forecasting a probability distribution function for each future position. The proposed framework is implemented combining several machine learning techniques 1) to identify typical seasons and 2) to forecast a probability distribution of the next season. This framework is evaluated using a wide range of real seasonal time series. On the one side, we intensively study the alternative combinations of the algorithms composing our framework (clustering, classi-ﬁcation), and on the other side, we evaluate the framework forecasting accuracy. As demonstrated by our experiences, the proposed framework outperforms competing approaches by achieving lower forecasting errors.
Indoor localization by attenuation maps: model -based interpolation for random medium

In this paper we propose a novel approach to include stochastic behavior in ray-tracing modeling of the propagation in an indoor environment to be used to interpolate measurements in localization method by attenuation maps. Complex propagation environment is modeled as a combination of deterministic modeling and stochastic component properly optimized. Preliminary results from validation against measurements show that a reasonable RMSE can be achieved from few optimization steps.
EXTRINSIC AND INTRINSIC

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Observations of strong turbulence and mixing impacting water exchange between two basins in the Baltic Sea

Abstract. Turbulent diapycnal mixing is important for the estuarine circulation between basins of the Baltic Sea as well as for its local ecosystems, in particular with regard to eutrophication and anoxic conditions. While the interior of the basins is overall relatively calm, stratified flow over steep bathymetric features is known as a source of strong turbulent mixing. Yet, current in situ observations often cannot capture the spatio-temporal development of dynamic and intermittent turbulent mixing related to overflows over rough bathymetry. We present observational oceanographic data together with openly accessible high-resolution bathymetry from a prototypical sill and an adjacent deep channel in the sparsely sampled Southern Quark located in the Åland Sea, connecting the northern Baltic Proper with the Bothnian Sea. Our data were acquired during two 1-week cruises on R/V Electra in February–March 2019 and 2020. We collected high-resolution broadband acoustic observations of turbulent mixing together with in situ microstructure profiler measurements, and current velocities from acoustic Doppler current profilers. We found that a temporally reversing non-tidal stratified flow over the steep bathymetric sill created a dynamic and extremely energetic environment. The observed flow reversed during both cruises on timescales of a few days. Saltier, warmer, and less oxygenated deep water south of the sill was partly blocked, the reversing flow was at times hydraulically controlled with hydraulic jumps occurring on both sides of the sill, and high spatial variability occurred in the surface layer on small scales. Dissipation rates of turbulent kinetic energy, vertical turbulent diffusivities, and vertical salt flux rates were increased by 3–4 orders of magnitude in the entire water column in the vicinity of the sill compared to reference stations not directly influenced by the overflow with average dissipation rates near the sill between 10−7 and 10−6 W kg−1, average vertical diffusivities of 0.001 m2 s−1 in the halocline and up to 0.1 m2 s−1 below the halocline, and average vertical salt flux rates around 0.01 g m−2 s−1 in the halocline and between 0.1 and 1 g m−2 s−1 below the halocline. We suggest, based on acoustic observations and in situ measurements, that the underlying mechanism for the highly increased mixing across the halocline is a combination of shear and topographic lee waves breaking at the halocline interface. We anticipate that the resulting deep- and surface-water modification in the Southern Quark directly impacts exchange processes between the Bothnian Sea and the northern Baltic Proper and that the observed mixing is likely important for oxygen and nutrient conditions in the Bothnian Sea.
DP-Select: Improving Utility and Privacy in Tabular Data Synthesis with Differentially Private Generative Adversarial Networks and Differentially Private Selection

This thesis proposes DP-Select, a novel approach to tabular data synthesis that combines DP-GAN and differentially private selection. We develop a mutual information-based selection method that is flexible and scalable for high-dimensional data and large numbers of marginals while being differentially private. We evaluate DP-Select on various datasets and demonstrate its effectiveness and utility compared to existing DP-GAN methods. Our results indicate that DP-Select significantly enhances the utility of synthesized data while maintaining strong privacy guarantees, making it a promising extension of DP-GANs for privacy-preserving data synthesis in terms of differential privacy. We also show that DPSelect performs better for smaller privacy budgets, making it an attractive option for scenarios with limited privacy budgets.
on Extracting Named Entities from

This article aims to describe the annotation process of a multi-institutional corpus of clinical texts in the oncology specialty and to train models for the Recognition of Named Entities. We use the annotated corpus to train models with different amounts of data and compare the model result with the amount of data used in training. The training of the models was done from the fine-tuning of the Bidirectional Encoder Representations from Transformers adapted to the medical-biological domain of the Portuguese language (BioBERTpt). To compare model behavior with increasing training data, models were trained with incremental amounts of data. As a result, we found that models trained with smaller but fully revised datasets performed better than models trained with larger datasets with little revision.
Three Lessons From Threema: Analysis of a Secure Messenger

We provide an extensive cryptographic analysis of Threema, a Swiss-based encrypted messaging application with more than 10 million users and 7000 corporate customers. We present seven different attacks against the protocol in three different threat models. We discuss impact and remediations for our attacks, which have all been responsibly disclosed to Threema and patched. Finally, we draw wider lessons for developers of secure protocols.
Decoding Job Candidates: Forecasting Personas Using Resume/Curriculum Vitae Analysis.

- Persona of an individual strikes out as a very crucial part in the development and growth of an organization as well as one’s individual growth. Out of many, one of the stereotypical strategies of speculating an individual’s personality is either by the usual general inspection or by inspecting an individual’s Curriculum Vitae. The traditional method for the recruitment procedure of a candidate is non-automatic (manual) pre-selection of the individuals resume trying to seek job with respect to the prerequisite specified by the organization. With this work, the goal primarily is to design a system that that carries out the operation of separating candidates based on eligibility criteria and persona estimation in a recruitment process automatically. Hence to satisfy the requirements of the work proposed above, a webpage that operates online is advanced for the enrolment of candidate’s information and investigation of an individual’s personality via a persona questionnaire in the form of an online multiple-choice questions test. With respect to all of this the proposed system then inspects proficient aptness by analyzing the datasets that are trained of the CV/Resumes uploaded by the applicants. The indicated work incorporates two machine learning algorithms which are “Logistic Regression” and “Random Forest Classifier” which fairly help to select a candidate for the recruitment procedure. Consequently, the outcomes of the persona questionnaires are to be sent to the candidate as well as the governor of the indicated system respectively.
Box-Level Active Detection – Supplementary Material

Mengyao Lyu Jundong Zhou Hui Chen Yijie Huang Dongdong Yu Yaqian Li Yandong Guo Yuchen Guo Liuyu Xiang5∗ Guiguang Ding* Tsinghua University BNRist Hangzhou Zhuoxi Institute of Brain and Intelligence OPPO Research Institute Beijing University of Posts and Telecommunications {mengyao.lyu,jundong.zhou}@outlook.com {huangyijie,yudongdong,liyaqian,guoyandong}@oppo.com {jichenhui2012,yuchen.w. guo}@gmail.com xiangly@bupt.edu. cn dinggg@tsinghua.edu. cn
Studying the epoch of hydrogen reionisation in redshifted 21-cm radiation

The measurement of the spatial distribution of neutral hydrogen in the high-redshift intergalactic medium, through its 21-cm hyperfine transition, will revolutionise our understanding of the period in the evolution of the early Universe known as the cosmic dark ages. This period began once hot ionised gas, formed in the Big Bang, combined to form neutral gas, and lasted until the first stars, galaxies and quasars reionised most of the neutral hydrogen sometime between 400 000 to 700 million years after the Big Bang. The epoch of reionisation, driven by these first sources of light, is arguably the least understood, but one of the most important, periods in the evolution of the Universe. This thesis is an attempt to explain and explore some of the techniques we may employ in order to advance our understanding of this period and prepare for the first observations to come from a new generation of low-frequency instruments. Using a new efficient semi-numerical ionisation model to create simulations of the ionisation state of the intergalactic medium during the epoch of reionisation, we analyse the impact a percolating intergalactic medium has on redshifted 21-cm observations of high-luminosity quasar-generated regions of ionised hydrogen. We also investigate how a population of quasars modifies the 21-cm power spectrum during this period. The study of the reionisation history of hydrogen and, indirectly, the first galaxies will be limited not by raw sensitivity to the signal, but rather, by bright foreground radiation from Galactic and extragalactic radio sources and the Galactic continuum. We analyse the effect of non-polarised foreground subtraction on redshifted 21-cm observations, and demonstrate a method for removing polarised foregrounds using Faraday tomography.
A latent logistic model to uncover overlapping clusters in networks

Résumé : It is now widely accepted that knowledge can be learnt from networks by clustering their vertices according to connection proﬁles. Many deterministic and probabilistic methods have been developed. Given a network, almost all them partition the vertices into disjoint clusters. However, recent studies have shown that these methods were too restrictive and that most of the existing networks contained overlapping clusters. To tackle this issue, we present in this paper a latent logistic model, that allows each vertex to belong to multiple clusters, as well as an efﬁcient approximate inference procedure based on global and local variational techniques. We show the results that we obtained on a transcriptional regulatory network of yeast. Mots-clés : Networks, Clustering methods, Overlapping clusters, Global and lo-cal variational approaches.
Towards Accessible Fabrication of Proprioceptive Soft Robotic Tentacles Without External Sensors

Soft and flexible robotic manipulators can easily adapt to a variety of environments due to their compliance. Although this flexibility can be a huge advantage in many situations, soft robots have infinite degrees of freedom and are difficult to control. In addition, few soft manipulators have been created that are capable of proprioception without external sensing, and those that have this capability often require very precisely manufactured sensors. This work presents an analysis of four easily manufactured or purchased sensors, design and fabrication of a soft tentacle actuator, and the integration of embedded sensors into the soft robotic system with the goal of proprioceptive capabilities.