Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-consistent parametrization of DFT + U framework using linear response approach: Application to evaluation of redox potentials of battery cathodes

The accuracy of $\mathrm{DFT}+U$ calculations, applied to the study of electronic structure and energetics of strongly correlated materials, heavily depends on $U$ parameters, chosen for adequate treatment of $d$ and $f$ states. Computational evaluation of $U$ parameters, which does not require fitting to experimental measurements or results of computationally expensive schemes, is highly desirable for the study of novel materials and even more so for materials not yet synthesized to date. Within this work, we show that the linear response method could provide $U$ parameters which can yield redox potentials of battery cathode materials in much better agreement with experiment than conventional density functional theory (DFT). In our approach, we evaluate $U$ values self-consistently, ensuring agreement between $U$ calculated using linear response with the value used for $\mathrm{DFT}+U$ calculations. We find that such self-consistency is necessary for determination of adequate values of $U$. We also studied the impact of using various PAW (projector augmented wave) potentials for transition-metal ions, that differ by the number of electrons treated as valence. We find that redox potentials are reasonably well reproduced for all cases, although a slightly higher degree of accuracy corresponds to PAW potentials with semicore electrons treated as valence. Importantly, we find that converged values of $U$ are substantially different for various PAW potentials of transition-metal ions of the same material. Overall, we find that self-consistent $\mathrm{DFT}+U$/linear response calculations provide quite accurate values of redox potentials for materials with purely ionic bonding (e.g., ${\mathrm{LiFePO}}_{4}$, ${\mathrm{LiCoPO}}_{4}$, ${\mathrm{LiCoO}}_{2}$, ${\mathrm{LiMnPO}}_{4}$, ${\mathrm{NaFePO}}_{4}$), whereas for materials with covalent $pd$ hybridization (e.g., ${\mathrm{LiNiO}}_{2}$) or conducting materials (e.g., ${\mathrm{LiTiS}}_{2}$) the agreement with experimental redox voltage is lower. This emphasizes the need for application of more advanced techniques (e.g., $\mathrm{DFT}+U+V$ method) for accurate study of partially covalent and metallic materials, which contain transition-metal ions.
Accessing High Temperature Stereolithography Photopolymers Compabilities for Antennas

—This paper presents a 2D-rectenna fabricated using a Plastronic approach for ambient Radio Frequency energy harvesting from Wi-Fi band 2.45 GHz. The first experimental vehicle is a patch antenna with a transmission line feeding method connected to a rectifier with a series topology. Measurements show a rectified voltage of 72 mV and a RF-DC efficiency of 13% with a load of 10 kΩ when operating at RF input power of -24 dBm. The device is made with a combination of Additive Manufacturing and Plastronics, which will allow in the future fabricating antennas on 3D substrates.
Largest Aftershock Nucleation Driven by Afterslip During the 2014 Iquique Sequence

Various earthquake models predict that aseismic slip modulates the seismic rupture process but actual observations of such seismic‐aseismic interaction are scarce. We analyze seismic and aseismic processes during the 2014 Iquique earthquake sequence. High‐rate Global Positioning System displacements demonstrate that most of the early afterslip is located downdip of the M 8.1 mainshock and is accompanied by decaying aftershock activity. An intriguing secondary afterslip peak is located ∼120 km south of the mainshock epicenter. The area of this secondary afterslip peak likely acted as a barrier to the propagating mainshock rupture and delayed the M 7.6 largest aftershock, which occurred 27 hr later. Interevent seismicity in this secondary afterslip area ended with a M 6.1 near the largest aftershock epicenter, kicking the largest aftershock rupture in the same area. Hence, the interevent afterslip likely promoted the largest aftershock nucleation by destabilizing its source area, favoring a rate‐dependent cascade‐up model.
Learning with Auxiliary Activation for Memory-Efficient Training

While deep learning has achieved great success in various fields, a large amount of memory is necessary to train deep neural networks, which hinders the development of massive state-of-the-art models. The reason is the conventional learning rule, backpropagation, should temporarily store input activations of all the layers in the network. To overcome this, recent studies suggested various memory-efficient implementations of backpropagation. However, those approaches incur computational overhead due to the recomputation of activations, slowing down neural network training. In this work, we propose a new learning rule which significantly reduces memory requirements while closely matching the performance of backpropagation. The algorithm combines auxiliary activation with output activation during forward propagation, while only auxiliary activation is used during backward propagation instead of actual input activation to reduce the amount of data to be temporarily stored. We mathematically show that our learning rule can reliably train the networks if the auxiliary activation satisfies certain conditions. Based on this observation, we suggest candidates of auxiliary activation that satisfy those conditions. Experimental results confirm that the proposed learning rule achieves competitive performance compared to backpropagation in various models such as ResNet, Transformer, BERT, ViT, and MLP-Mixer.
No Single Silver Bullet: Measuring the Accuracy of Password Strength Meters

To help users create stronger passwords, nearly every respectable web service adopts a password strength meter (PSM) to provide real-time strength feedback upon user registration and password change. Recent research has found that PSMs that provide accurate feedback can indeed effectively nudge users toward choosing stronger passwords. Thus, it is imperative to systematically evaluate existing PSMs to facilitate the selection of accurate ones. In this paper, we highlight that there is no single silver bullet metric for measuring the accuracy of PSMs: For each given guessing scenario and strategy, a speciﬁc metric is necessary. We investigate the intrinsic characteristics of online and ofﬂine guessing scenarios, and for the ﬁrst time , propose a systematic evaluation framework that is composed of four different dimensioned criteria to rate PSM accuracy under these two password guessing scenarios (as well as various guessing strategies). More speciﬁcally, for online guessing , the strength misjudgments of passwords with different popularity would have varied effects on PSM accuracy, and we suggest the weighted Spearman metric and consider two typical attackers: The general attacker who is unaware of the target password distribution, and the knowledgeable attacker aware of it. For ofﬂine guessing , since the cracked passwords are generally weaker than the uncracked ones, and they correspond to two disparate distributions, we adopt the Kullback-Leibler divergence metric and investigate the four most typical guessing strategies: brute-force, dictionary-based, probability-based, and a combination of above three strategies. In particular, we propose the Precision metric to measure PSM accuracy when non-binned strength feedback (e.g., probability) is transformed into easy-to-understand bins/scores (e.g., [weak, medium, strong]). We further introduce a reconciled Precision metric to characterize the impacts of strength misjudgments in different directions (e.g., weak → strong and strong → weak) on PSM accuracy. The effectiveness and practicality of our evaluation framework are demonstrated by rating 12 leading PSMs, leveraging 14 real-world password datasets. Finally, we provide three recommendations to help improve the accuracy of PSMs.
Trends in polar ozone loss since 1989: First signs of recovery in Arctic ozone column

Ozone depletion over the polar regions is monitored each year by satellite and ground-based instruments. In this study, the vortex-averaged ozone loss over the last three decades is evaluated for both polar regions using the passive ozone tracer of the chemical transport model TOMCAT/SLIMCAT and total ozone observations from Système d'Analyse par 20 Observation Zénithale (SAOZ) ground-based instruments and Multi-Sensor Reanalysis (MSR2). The passive tracer method allows us to determine the evolution of the daily rate of column ozone destruction, and the magnitude of the cumulative loss at the end of the winter. Three metrics are used to estimate the linear trend since 2000 and to assess the current situation of ozone recovery over both polar regions: 1) The maximum ozone loss at the end of the winter; 2) the onset day of ozone loss at a specific threshold and 3) the ozone loss residuals computed from the differences between annual ozone loss and ozone loss 25 values regressed with respect to sunlit volume of polar stratospheric clouds (VPSC). This latter metric is based on linear and parabolic regressions for ozone loss in the Northern and Southern Hemispheres, respectively. In the Antarctic, metrics 1
Building a Consensus on Definition and Nomenclature of Human Hepatic, Biliary and Pancreatic Organoids

Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands Department of Surgery, Erasmus MC-University Medical Center, PO box 2040, 3000 CA Rotterdam, The Netherlands HBP Organoid Consortium, Table S1 (supplementary files) ETH Zürich Inst. f. Molecular Health Sciences Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center, Uppsalalaan 8, Utrecht, 3584 CT, The Netherlands Leiden University Medical Center, Department of Medicine, Leiden, the Netherlands Université catholique de Louvain, de Duve Institute, Brussels, Belgium MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH164UU, UK Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Division of Gastroenterology, Hepatology and Nutrition, Division of Developmental Biology, and Center for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children Hospital Medical Center, Cincinnati, OH, USA and Institute of Research, Tokyo Medical and Dental University (TMDU) Department of Surgery, University of Cambridge and National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge UK These authors contributed equally Correspondence: b.spee@uu.nl (B.S.)
POWOP: Weather-based Power Outage Prediction

The worldwide energy-crisis, in particular current gas shortages, pose a critical risk for the energy-intensive process industry. Rising costs for gas lead to an intensified usage of power (e.g. for heating) in industry and private households that network operators are not prepared for. Weather-dependent energy-sources (e.g. windparks, solar panels) lead to additional fluctuations within the power grid, especially in autumn and winter seasons with potential storms and less sun hours. In worst case a simultaneous and prolonged loss of gas supply and electricity will lead to network bottlenecks, or complete network shutdowns - blackouts. For manufacturers, power outages thereby lead to severe consequences (i.e. waste, broken machines, additional costs), with only limited options to prevent them. Within this paper we highlight the implementation of POWOP, a weather-based service for POWer Outage Prediction that increases the resilience within the German process industry by an early forecast for the next 7 days including possible action recommendations. Our publicly available web-application was evaluated for 15 locations of paper manufacturers in the German region Bavaria and will be demonstrated within a screencast
Automatic exercise generation to support macro‑adaptivity in intelligent language tutoring systems

Foreign language teaching achieves best learning outcomes when individual differences of learners are taken into account. While it is difficult for teachers to support internal differentiation in the classroom, digital tools can adaptively propose individual learning paths through activities so that students can practice with appropriately challenging exercises. But how can sufficiently varied, systematically parametrized exercises be provided to enable a system to match them to individual learner needs? We present an approach for high-variability exercise generation that transforms a single specification into a multitude of exercises varying in complexity. The approach is currently being evaluated in a randomized controlled study in regular German seventh grade English classes, facilitating a systematic empirical exploration of adaptive exercise complexity in relation to learning outcomes
Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources

We propose an updated version of KarstMod, an adjustable platform dedicated to lumped parameter rainfall-discharge modeling of karst aquifers. KarstMod provides a modular, user-friendly modeling environment for educational, research and operational purposes. It also includes numerical tools for time series analysis, model evaluation and sensitivity analysis. The modularity of the platform facilitates common operations related to lumped parameter rainfall-discharge modeling, such as (i) set up and parameter estimation of a relevant model structure, and (ii) evaluation of internal consistency, 5 parameter sensitivity and hydrograph characteristics. The updated version now includes (i) external routines to better consider the input data and their related uncertainties, i.e. evapotranspiration and solid precipitation, (ii) enlargement of multi-objective calibration possibilities, allowing more flexibility in terms of objective functions as well as observation type and (iii) additional tools for model performance evaluation including further performance criteria and tools for model errors representation.
SPORTS MATCH VIDEO TO TEXT SUMMARIZATION USING NEURAL NETWORK

This project offers a deep studying approach to trendy lengthy soccer films the use of the spatiotemporal gaining knowledge of abilities of a three-D Convolutional Neural Network (three-D-CNN) and a Long Short Memory (LSTM)- Recurrent Neural Network (RNN). The method of our assignment consists of stepwise development of a Residual Network (ResNet) based totally on 3-d-Cnn, which recognizes football activities, manual annotation of 744 soccer clips from five instructions of football pastime to shape and LSTM Network education on soccer functions extracted the use of the goals 3-d-Cnn is based totally on ResNet. We combine 3-D-CN and LSTM models to determine the quality soccer moments. To summarise, a video footer triggers a version of the enter video signal as a series of video segments, whose inclusion in the composite video manufacturing is based totally on its demonstrated relevance. To examine the proposed aggregation system, 10 football motion pictures have been aggregated and then viewed with the aid of 48 members from eight countries and scored using a mean score (MOS) scale. Collectively, the linked motion pictures are rated 4 out of 5 MOS.
Kemandirian Belajar Mahasiswa Melalui Blended Learning pada Mata Kuliah Metode Numerik

This study aims to know the improvement of student self-regulated learning (SRL) through blended learning. The method used is a quasi-experimental design with nonequivalent pre-test and post-test control-group. The subjects were students of mathematics education in the University of PGRI Palembang which consists of 54 students. They were divided into two groups of samples, namely the experimental class and control class. Experiment class got blended learning treatment, while control class got conventional learning. Research instrument consists of students' initial ability (SIA) test and KBM questionnaire. Data were analyzed quantitative descriptive. The results show that the improvement of SRL who get blended learning is not better than students who get conventional learning.
Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.
Home Energy Management with Dynamic Tariffs and Tiered Peak Power Charges

We consider a simple home energy system consisting of a (net) load, an energy storage device, and a grid connection. We focus on minimizing the cost for grid power that includes a time-varying usage price and a tiered peak power charge that depends on the average of the largest $N$ daily powers over a month. When the loads and prices are known, the optimal operation of the storage device can be found by solving a mixed-integer linear program (MILP). This prescient charging policy is not implementable in practice, but it does give a bound on the best performance possible. We propose a simple model predictive control (MPC) method that relies on simple forecasts of future prices and loads. The MPC problem is also an MILP, but it can be solved directly as a linear program (LP) using simple enumeration of the tiers for the current and next months, and so is fast and reliable. Numerical experiments on real data from a home in Trondheim, Norway, show that the MPC policy achieves a cost that is only $1.7\%$ higher than the prescient performance bound.
Echo Tunnels: Polarized News Sharing on Reddit Runs Narrow but Deep

Online social platforms afford users vast digital spaces to share and discuss current events. However, scholars have concerns both over their role in segregating information ex- change into ideological echo chambers, and over evidence that these echo chambers are nonetheless over-stated. In this work, we investigate news-sharing patterns across the entirety of Reddit and find that the platform appears polarized macro- scopically, especially in politically right-leaning spaces. On closer examination, however, we observe that the majority of this effect originates from small, hyper-partisan segments of the platform accounting for a minority of news shared. We further map the temporal evolution of polarized news sharing and uncover evidence that, in addition to having grown drastically over time, polarization in hyper-partisan communities also began much earlier than 2016 and is resistant to Reddit’s largest moderation event. Our results therefore suggest that social polarized news sharing runs narrow but deep online. Rather than being guided by the general prevalence or absence of echo chambers, we argue that platform policies are better served by measuring and targeting the communities in which ideological segregation is strongest.
Energy Complexity Model for Convolutional Neural Networks

The energy eﬃciency of hardware implementations of convolutional neural networks (CNNs) is critical to their widespread de-ployment in low-power mobile devices. Recently, a plethora of methods have been proposed providing energy-optimal mappings of CNNs onto diverse hardware accelerators. Their estimated power consumption is related to speciﬁc implementation details and hardware parameters, which does not allow for machine-independent exploration of CNN energy measures. In this paper, we introduce a simpliﬁed theoretical energy complexity model for CNNs, based on only two-level memory hierarchy that captures asymptotically all important sources of power consumption of diﬀerent CNN hardware implementations. We calculate energy complexity in this model for two common dataﬂows which, according to statistical tests, ﬁts asymptotically very well the power consumption estimated by the Time/Accelergy program for convolutional layers on the Simba and Eyeriss hardware platforms. The model opens the possibility of proving principal limits on the energy eﬃciency of CNN hardware accelerators.
De-augmenting Visually-Augmented Reality

: We anticipate a future in which people frequently have virtual content displayed in their (cid:28)eld of view to augment reality. Situations where this virtual content interferes with users’ perception of the physical world will thus be more frequent, with consequences ranging from mere annoyance to serious injuries. We argue for the need to give users agency over virtual augmentations, discussing the concept of de-augmenting augmented reality by selectively removing virtual content from the (cid:28)eld of view. De-augmenting lets users target what actually interferes with their perception of the environment while keeping what is of interest. We contribute a framework that captures the di(cid:27)erent facets of de-augmentation. We discuss what it entails in terms of technical realization and interaction design, and end with three scenarios to illustrate what the user experience could be in a sample of domestic and professional situations.
Analysis of Geographic/Delay-Tolerant Routing in Mobile Wireless Networks

A mobile wireless delay-tolerant network (DTN) model is proposed and analysed, in which nodes move on an infinite plane along straight lines. Each node selects a new direction of travel from an arbitrary distribution, changing direction at time instances forming a Poisson process; all nodes maintain constant speed. A single information packet is travelling towards a given direction using both wireless transmissions and sojourns on node buffers, according to a routing rule in a broad class of possible protocols. For this model, we compute the long-term averages of (i) the speed with which the packet travels towards its destination, and~\emph{(ii)} the rate with which the wireless transmission cost accumulates. Because of the complexity of the problem, we employ two intuitive, simplifying approximations; simulations verify that the approximation error is typically small. Our results quantify the fundamental trade-off that exists in mobile wireless DTNs between the packet speed and the packet delivery cost. The general framework developed here is both general and versatile, and can be used as a starting point for further investigation.