Miracle-dz/UniFAR-FTUs huggingface dataset:
The provenance of coarse-grained turbidite sandstones in the Kırkgeçit Formation(western part of the Elazığ Basin-east Turkey)

and geotectonic Abstract: Modal analysis of sandstones for conglomerate rich marine basins is useful for investigating the provenance types surrounded by plate tectonics. Different kinds of siliciclastic rocks are important indicators for defining the paleogeography and geotectonic conditions of the sedimentary basin by provenance analysis. This paper, which focuses on the petrography and provenance of conglomerate rich turbidite systems, is one of the rarest studies on the sedimentary characteristics in the scientific literature. In this context, the study was carried out in the western part of the Elazığ Basin. The Middle Eocene–Oligocene basin infill called the Kırkgeçit Formation is characterised by the conglomerate rich submarine channel deposits and coarse-grained turbidite sandstones. Petrographically, the coarse-grained sandstones of the Kırkgeçit Formation are composed of litharenite and rarely feldspathic litharenite, lithic arkose and rock fragments. Mostly the components are derived from volcanic and plutonic rocks of the Elazığ Magmatites. Fossils and fossil fragments are transported from the shallow part of the western Elazığ Basin. The components transported from metamorphic rock fragments, which are rarely observed in the sandstone samples, are composed of recrystallized limestones and derived from Keban Metamorphites in the northern part of the basin. The provenance studies and petrographic analyses indicated that the conglomerate rich channels and coarse-grained sediments of turbidite sandstones in the Kırkgeçit Formation were plotted mostly to the recycled orogenic and undissected arc type source rocks. Therefore, this type of source rocks has been reflected as a back-arc basin deposit during Middle-Upper Eocene. The geotectonic evolution of the western Elazığ Basin has also supported
INCERTEZAS SOBRE A MODELAGEM MATEMÁTICA DE QUALIDADE DA ÁGUA EM CURSO DE ÁGUA URBANO

Mathematical models of water quality have been used as a way to assist managers on the control of water pollution. However, the uncertainties of the input parameters affect the model predictions and therefore the decision on the management process. So, it becomes necessary to identify the input variables that directly affect the uncertainty of the output results of the simulation model. To investigate the uncertainties involved in predicting the minimum concentration of dissolved oxygen, a sensitivity analysis was carried through from using a stochastic method on a deterministic simulation model of the watercourse of depuration in an urban watershed. For this, we used the analysis of Monte Carlo method and the results interpreted with respect to meeting the water quality standards regulated by law. We have found that most affected input variables of the model were the concentration of oxygen and re-aeration coefficient. The use of stochastic method for uncertainty analysis was fundamental in check which parameters should be more careful in its determination that the responses of the simulation model do not suffer large variations when compared to monitored data. Keywords: Water quality; Self-purification; Monte Carlo Simulation.
Leveling the Playing Field: Teacher Perception of Integrated STEM, Engineering, and Engineering Practices

The purpose of this study was to describe the perceptions and approaches of 14 thirdthrough-fifth grade Arkansan elementary teachers towards integrative engineering and engineering practices during 80 hours of integrated STEM professional development training in the summer and fall of 2014. This training was known as Project Flight. The purpose of the professional development was to learn integrated STEM content related to aviation and to write grade level curriculum units using Wiggins and McTighe’s Understanding by Design curriculum framework. The current study builds upon on the original research. Using a mixed method exploratory, embedded QUAL[quan] case study design and a nonexperimental convenience sample derived from original 20 participants of Project Flight, this research sought to answer the following question: Does professional development influence elementary teachers’ perceptions of the curriculum and instruction of integrated STEM engineering and engineering practices in a 3-to-5 grade level setting? A series of six qualitative and one quantitative sub-questions informed the research of the mixed method question. Hermeneutic content analysis was applied to archival and current qualitative data sets while descriptive statistics, independent t-tests, and repeated measures ANOVA tests were performed on the quantitative data. Broad themes in the teachers’ perceptions and understanding of the nature of integrated engineering and engineering practices emerged through triangulation. After the professional development and the teaching of the integrated STEM units, all 14 teachers sustained higher perceptions of personal self-efficacy in their understanding of Next Generation Science Standards (NGSS). The teachers gained understanding of engineering and engineering practices, excluding engineering habits of mind, throughout the professional development training and unit teaching. The research resulted in four major findings specific to elementary engineering, which included engineering as student social agency and empowerment and the emergence of the engineering design loop as a new heuristic, and three more general non-engineering specific findings. All seven, however, have implications for future elementary engineering professional development as teachers in adopting states start to transition into using the NGSS standards.
Signal processing techniques for ultra-wideband synthetic aperture radar data

The U. S. Army Research Laboratory (ARL), as part of its mission-funded applied research program, has been evaluating the utility of a low-frequency, ultra-wideband (UWB) imaging radar to detect tactical vehicles concealed by foliage. Measurement programs conducted at Aberdeen Proving Ground and elsewhere have yielded a significant and unique database of extremely wideband and (in some cases) fully polarimetric data. Complementary efforts have been underway to develop high-performance algorithms as well as data visualization and assessment tools to generate very high-quality, low-artifact-level imagery for use in target detection and exploitation studies. Prior reports have concentrated on discussions of emerging detection concepts. Key strategy has been to exploit frequency-dependent scattering to identify and locate likely targets-particularly the application of a "spatial affinity" technique between detection lists separately generated at VHF frequencies versus UHF. The utility of this approach is now being evaluated more broadly by the UWB radar community. A large quantity of appropriate data has been measured by two separate airborne radars-a VHF radar (Coherent All RAdio BAnd Sensing (CARABAS)) [1,2] and a UHF radar (the P-3 radar) [3,4]. In support of Defense Advanced Research Project Agency's (DARPA) national-scope initiative in foliage penetration, ARL is using tools and algorithms developed as part of our mission program to perform image formation and evaluate various spatial affinity concepts on this freshly measured data. The reader will find this paper is organized into three complementary sections. First, we define the research objectives ARL and other defense organizations share. We then report in detail on the signal processing/image formation techniques developed as part of our mission program. We conclude with a discusssion on the use of these algorithms and tools with the emerging data from major defense-wide collections. Imaging concepts are discussed and various quality metrics are provided.
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer

In Neural Machine Translation (NMT), each token prediction is conditioned on the source sentence and the target prefix (what has been previously translated at a decoding step). However, previous work on interpretability in NMT has mainly focused solely on source sentence tokens’ attributions. Therefore, we lack a full understanding of the influences of every input token (source sentence and target prefix) in the model predictions. In this work, we propose an interpretability method that tracks input tokens’ attributions for both contexts. Our method, which can be extended to any encoder-decoder Transformer-based model, allows us to better comprehend the inner workings of current NMT models. We apply the proposed method to both bilingual and multilingual Transformers and present insights into their behaviour.
A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning

Temporal-difference learning is a popular algorithm for policy evaluation. In this paper, we study the convergence of the regularized non-parametric TD(0) algorithm, in both the independent and Markovian observation settings. In particular, when TD is performed in a universal reproducing kernel Hilbert space (RKHS), we prove convergence of the averaged iterates to the optimal value function, even when it does not belong to the RKHS. We provide explicit convergence rates that depend on a source condition relating the regularity of the optimal value function to the RKHS. We illustrate this convergence numerically on a simple continuous-state Markov reward process.
Universal Dependency Treebank for Odia Language

This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia were selected from “Samantar”, the largest available parallel corpora collection for Indic languages. All the selected sentences are manually annotated following the “Universal Dependency” guidelines. The morphological analysis of the Odia treebank was performed using machine learning techniques. The Odia annotated treebank will enrich the Odia language resource and will help in building language technology tools for cross-lingual learning and typological research. We also build a preliminary Odia parser using a machine learning approach. The accuracy of the parser is 86.6% Tokenization, 64.1% UPOS, 63.78% XPOS, 42.04% UAS and 21.34% LAS. Finally, the paper briefly discusses the linguistic analysis of the Odia UD treebank.
Smart Grid: Cyber Attacks, Critical Defense Approaches, and Digital Twin

As a national critical infrastructure, the smart grid has attracted widespread attention for its cybersecurity issues. The development towards an intelligent, digital, and Internetconnected smart grid has attracted external adversaries for malicious activities. It is necessary to enhance its cybersecurity by either improving the existing defense approaches or introducing novel developed technologies to the smart grid context. As an emerging technology, digital twin (DT) is considered as an enabler for enhanced security. However, the practical implementation is quite challenging. This is due to the knowledge barriers among smart grid designers, security experts, and DT developers. Each single domain is a complicated system covering various components and technologies. As a result, works are needed to sort out relevant contents so that DT can be better embedded in the security architecture design of smart grid. In order to meet this demand, our paper covers the above three domains, i.e., smart grid, cybersecurity, and DT. Specifically, the paper i) introduces the background of the smart grid; ii) reviews external cyber attacks from attack incidents and attack methods; iii) introduces critical defense approaches in industrial cyber systems, which include device identification, vulnerability discovery, intrusion detection systems (IDSs), honeypots, attribution, and threat intelligence (TI); iv) reviews the relevant content of DT, including its basic concepts, applications in the smart grid, and how DT enhances the security. In the end, the paper puts forward our security considerations on the future development of DT-based smart grid. The survey is expected to help developers break knowledge barriers among smart grid, cybersecurity, and DT, and provide guidelines for future security design of DT-based smart grid.
On Computing Coercivity Constants in Linear Variational Problems Through Eigenvalue Analysis

In this work, we investigate the convergence of numerical approximations to coercivity constants of variational problems. These constants are essential components of rigorous error bounds for reduced-order modeling; extension of these bounds to the error with respect to exact solutions requires an understanding of convergence rates for discrete coercivity constants. The results are obtained by characterizing the coercivity constant as a spectral value of a self-adjoint linear operator; for several diﬀerential equations, we show that the coercivity constant is related to the eigenvalue of a compact operator. For these applications, convergence rates are derived and veriﬁed with numerical examples.
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks

Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Existing work adds $\ell_\infty$-bounded perturbations to the original sample so that the trained model generalizes poorly. Such perturbations, however, are easy to eliminate by adversarial training and data augmentations. In this paper, we resolve this problem from a novel perspective by perturbing only one pixel in each image. Interestingly, such a small modification could effectively degrade model accuracy to almost an untrained counterpart. Moreover, our produced \emph{One-Pixel Shortcut (OPS)} could not be erased by adversarial training and strong augmentations. To generate OPS, we perturb in-class images at the same position to the same target value that could mostly and stably deviate from all the original images. Since such generation is only based on images, OPS needs significantly less computation cost than the previous methods using DNN generators. Based on OPS, we introduce an unlearnable dataset called CIFAR-10-S, which is indistinguishable from CIFAR-10 by humans but induces the trained model to extremely low accuracy. Even under adversarial training, a ResNet-18 trained on CIFAR-10-S has only 10.61% accuracy, compared to 83.02% by the existing error-minimizing method.
Why strengthening gap junctions may hinder action potential propagation

Gap junctions are channels in cell membranes allowing ions to pass directly between cells. They are found throughout the body, including heart myocytes, neurons, and astrocytes. In cardiac tissue and throughout the nervous system, an action potential (AP) in one cell can trigger APs in neighboring cells connected by gap junctions. It is known experimentally that there is an ideal gap junction conductance for AP propagation—lower or higher conductance can lead to propagation failure. We explain this phenomenon geometrically in branching networks by analyzing an idealized model that focuses exclusively on gap junction and AP-generating currents. As expected, the gap junction conductance must be high enough for AP propagation to occur. However, if the gap junction conductance is too high, then it dominates the cell's intrinsic firing conductance and disrupts AP generation. We also identify conditions for semi-active propagation, where cells in the network are not individually excitable but still propagate action potentials.
Conceptual Design Report of DaRveX: Decay at Rest $\nu_e$ + Lead Cross Section Measurement at J-PARC MLF

DaRveX stands for “Decay at Rest ν e -Pb cross (X) section measure-ment,,. This report explains a conceptual design of DaRveX, an experiment that measures diﬀerential cross section of low energy ν e +Pb charged current interaction, for the ﬁrst time using ν e from µ + decay at rest at J-PARC MLF. So far, there has not been good ν e target to detect low energy ν e . Lead is expected to be an excellent ν e target because the cross section is expected to be very large and the delayed coincidence technique can be used using ﬁnal state neutron(s) to reduce backgrounds. However, the cross section have not been measured yet. If it is measured, it opens a new window to the future neutrino research ﬁeld, such as low energy ν e oscillation measurements, ﬂavor speciﬁc detection of the supernova explosion ν e and understanding of ν e -nucleus interactions. The DaRveX ν e detector is a remodeling of existing reactor neutrino detector PANDA (Plastic scintillator AntiNeutrino Detector Array). A thin lead sheet is sandwiched between two double-layer thin plastic scintillator strips. This thin lead-scintillators unit is sandwiched between PANDA scintillator modules and e − energy and direction will be measured. Backgrounds can be reduced by making use of very narrow pulsed beam of MLF, delayed coincidence with neutron absorption signal on Gd, and requirement of event topology of the charged current ν e +Pb reaction, together with thick neutron and γ -ray shields. With two years of data taking, the cross section is expected to be measured with 20% of precision. We performed on-site background measurements at J-PARC MLF in 2021 to understand the real backgrounds. This background measurements are also described in the appendix.
A Contrario multi-scale anomaly detection method for industrial quality inspection

Anomalies can be defined as any non-random structure which deviates from normality. Anomaly detection methods reported in the literature are numerous and diverse, as what is considered anomalous usually varies depending on particular scenarios and applications. In this work we propose an a contrario framework to detect anomalies in images applying statistical analysis to feature maps obtained via convolutions. We evaluate filters learned from the image under analysis via patch PCA, Gabor filters and the feature maps obtained from a pre-trained deep neural network (Resnet). The proposed method is multi-scale and fully unsupervised and is able to detect anomalies in a wide variety of scenarios. While the end goal of this work is the detection of subtle defects in leather samples for the automotive industry, we show that the same algorithm achieves state-of-the-art results in public anomalies datasets.
On statistic alignment for domain adaptation in structural health monitoring

The practical application of structural health monitoring is often limited by the availability of labelled data. Transfer learning – specifically in the form of domain adaptation (DA) – gives rise to the possibility of leveraging information from a population of physical or numerical structures, by inferring a mapping that aligns the feature spaces. Typical DA methods rely on nonparametric distance metrics, which require sufficient data to perform density estimation. In addition, these methods can be prone to performance degradation under class imbalance. To address these issues, statistic alignment (SA) is discussed, with a demonstration of how these methods can be made robust to class imbalance, including a special case of class imbalance called a partial DA scenario. Statistic alignment is demonstrated to facilitate damage localisation with no target labels in a numerical case study, outperforming other state-of-the-art DA methods. It is then shown to be capable of aligning the feature spaces of a real heterogeneous population, the Z24 and KW51 bridges, with only 220 samples used from the KW51 Bridge. Finally, in scenarios where more complex mappings are required for knowledge transfer, SA is shown to be a vital pre-processing tool, increasing the performance of established DA methods.
Deep Reinforcement Learning for Multi-class Imbalanced Training

With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes.
Inference of a Rumor's Source in the Independent Cascade Model

We consider the so-called Independent Cascade Model for rumor spreading or epidemic processes popularized by Kempe et al.\ [2003]. In this model, a small subset of nodes from a network are the source of a rumor. In discrete time steps, each informed node"infects"each of its uninformed neighbors with probability $p$. While many facets of this process are studied in the literature, less is known about the inference problem: given a number of infected nodes in a network, can we learn the source of the rumor? In the context of epidemiology this problem is often referred to as patient zero problem. It belongs to a broader class of problems where the goal is to infer parameters of the underlying spreading model, see, e.g., Lokhov [NeurIPS'16] or Mastakouri et al. [NeurIPS'20]. In this work we present a maximum likelihood estimator for the rumor's source, given a snapshot of the process in terms of a set of active nodes $X$ after $t$ steps. Our results show that, for cycle-free graphs, the likelihood estimator undergoes a non-trivial phase transition as a function $t$. We provide a rigorous analysis for two prominent classes of acyclic network, namely $d$-regular trees and Galton-Watson trees, and verify empirically that our heuristics work well in various general networks.
Lutma: A Frame-Making Tool for Collaborative FrameNet Development

This paper presents Lutma, a collaborative, semi-constrained, tutorial-based tool for contributing frames and lexical units to the Global FrameNet initiative. The tool parameterizes the process of frame creation, avoiding consistency violations and promoting the integration of frames contributed by the community with existing frames. Lutma is structured in a wizard-like fashion so as to provide users with text and video tutorials relevant for each step in the frame creation process. We argue that this tool will allow for a sensible expansion of FrameNet coverage in terms of both languages and cultural perspectives encoded by them, positioning frames as a viable alternative for representing perspective in language models.