Miracle-dz/UniFAR-FTUs huggingface dataset:
Rate of construction of the Black Mesa bysmalith, Henry Mountains, Utah

Abstract At shallow-crustal levels, the most efficient process for the accommodation of magma emplacement is roof lifting, which induces an upward vertical displacement of the Earth’s surface. Estimates of the rate and duration of this process have rarely been published. One of the most spectacular places where plutons constructed by such mechanisms are exposed is the Henry Mountains in Utah. In this place, Pollard & Johnson (1973) derive a time of ‘less than several weeks’ for the construction of the Black Mesa bysmalith (BMb), by coupling a mechanical approach with a model for the flow rate of Bingham magma in a tabular conduit with a constant driving pressure at the feeder. The aim of this new study of the BMb is to evaluate the maximum duration of its emplacement and propose a feasible scenario for its construction. Our study of the pluton’s internal structures suggest that the BMb is a multi-pulse pluton. We have constrained the duration of BMb emplacement by simulating the thermal evolution of the growing pluton and its wallrocks for different construction scenarios. We have adjusted the number, the thickness and the frequency of the pulses with our textural ‘time’ constraints, which are the absence of solid-state textures around internal contacts, which implies that a melted zone was maintained in the intrusion during its construction; and the absence of significant contact metamorphism or recrystallization which means that the increase of temperature in the host rock was relatively small, or short-lived, or both. In accordance with the previous estimates of Pollard & Johnson (1973), we propose that the emplacement of the BMb was a very short geological event, with a maximum duration in the order of 60 years, implying minimum vertical displacement rates of the wallrocks above the pluton of 4 metres per year. Moreover, our simulations indicate that pulses around 20 metres thick rapidly injected approximately every three months are the most consistent with the constraints from field observations.
A Meta-embedding-based Ensemble Approach for ICD Coding Prediction

International Classiﬁcation of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efﬁcient storage and retrieval of diagnostic information. The problem of automatically assigning ICD codes has been approached in literature as a multilabel classiﬁcation, us-ing neural models on unstructured data. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientiﬁc articles. Furthermore, we exploit the geometric properties of the two sets of word vectors and combine them into a common dimensional space, using meta-embedding techniques. We demonstrate the efﬁcacy of this approach for a multimodal setting, using unstructured and structured information. We empirically show that our approach improves the current state-of-the-art deep learning architectures and beneﬁts ensemble models.
Linear Convergence and Metric Selection for Douglas-Rachford Splitting and ADMM

Recently, several convergence rate results for Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) have been presented in the literature. In this paper, we show global linear convergence rate bounds for Douglas-Rachford splitting and ADMM under strong convexity and smoothness assumptions. We further show that the rate bounds are tight for the class of problems under consideration for all feasible algorithm parameters. For problems that satisfy the assumptions, we show how to select step-size and metric for the algorithm that optimize the derived convergence rate bounds. For problems with a similar structure that do not satisfy the assumptions, we present heuristic step-size and metric selection methods.
Stochastic Top K-Subset Bandits with Linear Space and Non-Linear Feedback with Applications to Social Influence Maximization

There are numerous real-world problems where a user must make decisions under uncertainty. For the problem of influence maximization on a social network, for example, the user must select a set of K influencers who will jointly have a large influence on many users. With the lack of prior knowledge about the diffusion process or even topological information, this problem becomes quite challenging. This problem can be cast as a combinatorial bandit problem, where the user can repeatedly choose a candidate set of K out of N arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. In this work, we present the first combinatorial bandit algorithm for which the only feedback is a non-linear reward of the selected K arms. No other feedback is needed. In the context of influence maximization, this means no feedback in the form of which nodes or edges were activated needs to be available, just the amount of influence. The novel algorithm we propose, CMAB-SM, is based on a divide-and-conquer strategy. It is computationally and storage efficient. Over a time horizon T, the proposed algorithm achieves a regret bound of Õ(K1/2N1/3T2/3). This bound is sub-linear in all of the parameters: T, N, and K. We empirically demonstrate our algorithm’s performance using the applications of influence maximization and product cross-selling. For influence maximization, we provide experiments on real-world social networks, showing that the proposed CMAB algorithm outperforms bandit-specific and social-influence-domain-specific algorithms in terms of empirical run-time and expected influence. For product cross-selling, we also demonstrate that the proposed CMAB algorithm outperforms considered baselines on synthetic data.
MALATYA OLİGO-MİYOSEN İSTİFİNİN BENTİK FORAMİNİFER BİYOSTRATİGRAFİSİ (DOĞU TOROSLAR, DOĞU TÜRKİYE)

The benthic foraminiferal biostratigraphy of Oligo-Miocene aged Muratli and Petekkaya formations which crop out over wide regions around Akcadag town, west of Malatya province in Eastern Taurids were revealed in this study. Systematical sampling was carried out in measured stratigraphical sections in four locations in order to perform stratigraphical and paleontological investigations. Benthic foraminifera taken from 182 hard rock samples were defined and three biozones were determined as; SBZ 21-22, belonging to Oligocene (Rupelian - Early Chattian), SBZ 23 (Late Chattian) and SBZ 25 belonging to Lower Miocene in shallow marine deposits in the region. It was stated that the assemblage of planktic foraminifer and nannoplankton which stratigraphically detected within Chattian - Burdigalian units in the succession most probably indicated Aquitanian age. Besides; Oligo-Miocene transition in the region was approved with this study based on biostratigraphical locations of benthic foraminiferal taxa
JOINT AND INDIVIDUAL VARIATION EXPLAINED ( JIVE ) FOR INTEGRATED ANALYSIS OF MULTIPLE

Research in a number of fields now requires the analysis of “multiblock” data, in which multiple high-dimensional, and fundamentally disparate, datatypes are available for a common set of objects. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general method for the integrated analysis of multi-block data. The method decomposes a multi-block dataset into a sum of three terms: a low-rank approximation capturing joint variation across datatypes, low-rank approximations for structured variation individual to each datatype, and residual noise. This decomposition can be used to quantify the amount of joint variation between datatypes, visually explore joint and individual structure, and reduce the dimensionality of the data. The proposed method represents an extension of Principal Component Analysis (PCA) and has clear advantages over popular two-block methods such as Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS). We apply JIVE to data from The Cancer Genome Atlas (TCGA), where multiple genomic technologies are available for a common set of Glioblastoma Multiforme tumor samples. Software is available at https://genome.unc. edu/jive/.
Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

The United Nations (UN) sustainable development goals (SDGs), a strategy to guide the world’s social and economic transformation, highlight the issue of urban air pollution in SDG 11. Open data, as an output of citizen science (CS), are needed to supply and improve the SDG indicator system. Therefore, we propose a CS framework to extend the paradigm of urban air pollution monitoring from particulate matter concentration levels to air quality-related health symptom load, and foster the development of a tier-3 SDG indicator (which we call indicator 11.6.3). Building this new perspective for CS contributions to the achievement of SDGs, we address the problem of crowdsourced data bias as a prerequisite for better quality open data output. The aim of this study is to propose an air pollution symptom mapping framework for citizen-driven research and to find the most robust data quality assurance system (QAs) in this field. The method includes a GeoWeb application as well as data quality assurance mechanisms based on conditional statements, in order to reduce crowdsourced data bias. A four-month crowdsourcing campaign, released in Lubelskie voivodship (Poland), resulted in 1823 outdoor reports with a rejection rate of up to 28%, depending on the applied QA system (QAs). Testing the QAs variants, we find the most robust data bias solving method in survey-based symptom mapping. The framework output is shared via GeoWeb dashboards, including the 11.6. 3 indicator evaluation. By familiarizing the public with citizen science, a city can track the progress of its SDG achievements and increase the transparency of the process through the use of GeoWeb.
Clustering strategy and method selection

Note: This paper is a chapter in the forthcoming Handbook of Cluster Analysis, Hennig et al. (2015). For definitions of basic clustering methods and some further methodology, other chapters of the Handbook are referred to. To read this version of the paper without the Handbook, some knowledge of cluster analysis methodology is required. The aim of this chapter is to provide a framework for all the decisions that are required when carrying out a cluster analysis in practice. A general attitude to clustering is outlined, which connects these decisions closely to the clustering aims in a given application. From this point of view, the chapter then discusses aspects of data processing such as the choice of the representation of the objects to be clustered, dissimilarity design, transformation and standardization of variables. Regarding the choice of the clustering method, it is explored how different methods correspond to different clustering aims. Then an overview of benchmarking studies comparing different clustering methods is given, as well as an outline of theoretical approaches to characterize desiderata for clustering by axioms. Finally, aspects of cluster validation, i.e., the assessment of the quality of a clustering in a given dataset, are discussed, including finding an appropriate number of clusters, testing homogeneity, internal and external cluster validation, assessing clustering stability and data visualization.
Breaking Sticks and Ambiguities with Adaptive Skip-gram ( supplementary material )

This task evaluates Word Sense Induction systems by performing fuzzy clustering comparison, i.e. in the gold standard each context could be assigned to several meanings with some score indicating confidence of the assignment. Two metrics were used for comparing such fuzzy clusterings: Fuzzy Normalized Mutual Information and FuzzyB-Cubed which are introduced in (Jurgens & Klapaftis, 2013). Fuzzy-NMI measures the alignment of two clustering and it is independent of the cluster sizes. It is suitable to measure how well the model captures rare senses. On the contrary Fuzzy-B-Cubed is sensitive to the cluster sizes. So it reflects the performance of the system on a dataset where the clusters have almost the same frequency. Results of MSSG, NP-MSSG and AdaGram models are shown in Table 3.
Programmatic Concept Learning for Human Motion Description and Synthesis Supplementary Materials

1. Details of Data Filtering and Model Fitting As mentioned in Section 3.5 of the main paper, we use the alignments obtained from the description model to extract individual repetitions for all motion concepts from the training dataset. For each motion concept c, we obtain a dataset of occurrences Dc = {d1, d2, · · · } where each element di corresponds to a small segment composed of a sequence of spline curves di = {si , si , · · · s li i }. Since this dataset Dc has been obtained from description model predictions, we perform two steps of filtering to generate cleaner data for learning our synthesis model.
QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models for General Sensing Matrices

In realistic compressed sensing (CS) scenarios, the obtained measurements usually have to be quantized to a ﬁnite number of bits before transmission and/or storage, thus posing a challenge in recovery, especially for extremely coarse quantization such as 1-bit sign measurements. Recently Meng & Kabashima (2023) proposed an efﬁcient quantized compressed sensing algorithm called QCS-SGM using the score-based generative models as an implicit prior. Thanks to the power of score-based generative models in capturing the rich structure of the prior, QCS-SGM achieves remarkably better performances than previous quantized CS methods. However, QCS-SGM is restricted to (approximately) row-orthogonal sensing matrices since otherwise the likelihood score becomes intractable. To address this challenging problem, in this paper we propose an improved version of QCS-SGM, which we call QCS-SGM+, which also works well for general matrices. The key idea is a Bayesian inference perspective of the likelihood score computation, whereby an expectation propagation algorithm is proposed to approximately compute the likelihood score. Experiments on a variety of baseline datasets demonstrate that the proposed QCS-SGM+ outperforms QCS-SGM by a large margin when sensing matrices are far from row-orthogonal.
East Greenland tetrapods are Devonian in age

Palynological dates unambiguously resolve the stratigraphic age of the East Greenland sedimentary rocks containing the earliest well-preserved tetrapod remains. This is the first time that spore samples have been discovered in the sedimentary succession that has yielded Acanthostega and Ichthyostega , two tetrapods that are regarded as critically important taxa for our understanding of the origin and early evolution of the tetrapods. These palynological assemblages conclusively show that the rocks are Devonian in age. The evidence resolves a 60-year-old dispute regarding the age of these rocks and contradicts a recent controversial study suggesting a much younger (Carboniferous, Visean) age for these tetrapods. Spore samples bracketing the in situ occurrences of both tetrapod genera place them securely within the Famennian Age of the Devonian Period and at least as old as Famennian 2b. The ages of all known Devonian tetrapods are reviewed and related to a common palynological standard. This review places Ichthyostega and Acanthostega as the earliest of the Famennian tetrapods.
Animal Recognition System Based On Convolutional Neural Network

—Efficient and accurate object recognition has been an important topic in the field of computer vision systems. Object recognition is concerns with determining the identity of an object being observed in the image from a set of known labels. With the advent of deep learning with neural network techniques, the accuracy for object detection has increased drastically. Animal recognition system is implemented using Convolution Neural Network (CNN) for classification. CNN model of the system is based on our own architecture. Kaggle animal-10 and monkey species dataset with 10 animals as cat, chicken , cow , dog, elephant, horse , monkey ,sheep, spider, squirrel are used in this system. The system achieved 77.2 % accuracy during testing.
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages SUPPLEMENTARY MATERIAL A MEDIAN HEURISTIC FOR GAUSSIAN KERNEL ON MEAN EMBEDDINGS

In the proposed KJIT, there are two kernels: the inner kernel k for computing mean embeddings, and the outer Gaussian kernel κ defined on the mean embeddings. Both of the kernels depend on a number of parameters. In this section, we describe a heuristic to choose the kernel parameters. We emphasize that this heuristic is merely for computational convenience. A full parameter selection procedure like cross validation or evidence maximization will likely yield a better set of parameters. We use this heuristic in the initial mini-batch phase before the actual online learning.
C C ] 1 4 Ju n 20 04 Theory of One Tape Linear Time Turing Machines

A theory of one-tape linear-time Turing machines is quite different from its polynomial-time counterpart since one-tape linear-time Turing machines are closely related to finite state automata. This paper discusses structural-complexity issues of one-tape Turing machines of various types (deterministic, nondeterministic, reversible, alternating, probabilistic, counting, and quantum Turing machines) that halt in linear time, where the running time of a machine is defined as the height of its computation tree. We clarify how the machine types affect the computational patterns of one-tape linear-time Turing machines.
A new approach to the Stein-Tikhomirov method: with applications to the second Wiener chaos and Dickman convergence

In this paper, we propose a general means of estimating the rate at which convergences in law occur. Our approach, which is an extension of the classical Stein-Tikhomirov method, rests on a new pair of linear operators acting on characteristic functions. In principle, this method is admissible for any approximating sequence and any target, although obviously the conjunction of several favorable factors is necessary in order for the resulting bounds to be of interest. As we briefly discuss, our approach is particularly promising whenever some version of Stein's method applies. We apply our approach to two examples. The first application concerns convergence in law towards targets $F_\infty$ which belong to the second Wiener chaos (i.e. $F_{\infty}$ is a linear combination of independent centered chi-squared rvs). We detail an application to $U$-statistics. The second application concerns convergence towards targets belonging to the generalized Dickman family of distributions. We detail an application to a theorem from number theory. In both cases our method produces bounds of the correct order (up to a logarithmic loss) in terms of quantities which occur naturally in Stein's method.
MaMaDroid: detecting Android malware by building Markov chains of behavioral models (exten...

As Android has become increasingly popular, so has malware targeting it, thus pushing the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a static-analysis based system that abstracts the API calls performed by an app to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure two years after training). We also show that MaMaDroid remarkably outperforms DroidAPIMiner, a state-of-the-art detection system that relies on the frequency of (raw) API calls. Aiming to assess whether MaMaDroid? s effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps.
On the degrees of freedom achievable through interference alignment in a MIMO interference channel

Consider a K-user flat fading MIMO interference channel where the k-th transmitter (or receiver) is equipped with Mk (respectively Nk) antennas. If a large number of statistically independent channel extensions are allowed either across time or frequency, the recent work [1] suggests that the total achievable degrees of freedom (DoF) can be maximized via interference alignment, resulting in a total DoF that grows linearly with K even if Mk and Nk are bounded. In this work we consider the case where no channel extension is allowed, and establish a general condition that must be satisfied by any degrees of freedom tuple achievable through linear interference alignment. When Mk = M and Nk = N for all k, this condition implies that the total achievable DoF cannot grow linearly with K, and is in fact no more than M + N −1. If, in addition, all users have the same DoF d = 1, then this upper bound on the total DoF is actually tight for almost all MIMO interference channels.
Interpretable Complex-Valued Neural Networks for Privacy Protection - ML Reproducibility Challenge 2020

The authors of the original work do not supply any code for their work. Therefore, our goal is to validate the main claims 3 of the paper with our own implementation. The claims that we try to verify are whether the proposed complex-valued 4 neural networks perform similar to traditional real-valued networks in classification tasks and whether the introduced 5 method provides better protection against privacy attacks. 6
TOPIC : Large Text Corpora , Word Sense Disambiguation WORD COUNT : 3980 Submitted to Coling ’ 96 and ACL ' 96

This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use of the widecoverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiments have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus.
Adversarial Example Detection for DNN Models: A Review

Deep learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance system, autonomous vehicles and health-care. Such safety-critical applications have to draw its path to success deployment once they have the capability to overcome safety-critical challenges. Among these challenges are the defense against or/and the detection of the Adversarial Examples (AEs). Adversary can carefully craft small, often imperceptible, noise called perturbations, to be added to the clean image to generate the AE. The aim of AE is to fool the