Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hydrostratigraphic Model of the Pahute Mesa - Oasis Valley Area, Nye County, Nevada

A 3-D hydrostratigraphic framework model has been built for the use of hydrologic modelers who are tasked with developing a model to determine how contaminants are transported by groundwater flow in an area of complex geology. The area of interest includes Pahute Mesa, a former nuclear testing area at the Nevada Test Site (NTS), and Oasis Valley, a groundwater discharge area down-gradient from contaminant source areas on Pahute Mesa. To build the framework model, the NTS hydrogeologic framework was integrated with an extensive collection of drill-hole data (stratigraphic, lithologic, and alteration data); a structural model; and several recent geophysical, geological, and hydrological studies to formulate a hydrostratigraphic system. The authors organized the Tertiary volcanic units in the study area into 40 hydrostratigraphic units that include 16 aquifers, 13 confining units, and 11 composite units. The underlying pre-Tertiary rocks were divided into six hydrostratigraphic units, including two aquifers and four confining units. The model depicts the thickness, extent, and geometric relationships of these hydrostratigraphic units (''layers'' in the model) along with all the major structural features that control them, including calderas and faults. The complexity of the model area and the non-uniqueness of some of the interpretations incorporated into the base model made it necessary to address alternative interpretations for some of the major features in the model. Six of these alternatives were developed so they could be modeled in the same fashion as the base model.
Mid-Day Call Auctions

In illiquid and fragmented limit order book markets, asynchronously arriving buyers and sellers have a coordination problem. This problem is particularly strong mid-day, when trading is generally thin. We evaluate a market structure reform at Nasdaq Nordic, where the continuous trading session is replaced mid-day by a five-minute call auction. We find that the mid-day call auction works as a coordination device, reducing transitory price impact. The call auction attracts end investors rather than intermediaries. Stocks with greater end investor flows show stronger benefits of the call auction. The results indicate that mid-day auctions can improve continuous markets.
ISK BOUNDS WHEN LEARNING INFINITELY MANY RESPONSE FUNCTIONS BY ORDINARY LINEAR REGRESSION

Consider the problem of learning a large number of response functions simultaneously based on the same input variables. The training data consist of a single independent random sample of the input variables drawn from a common distribution together with the associated responses. The input variables are mapped into a highdimensional linear space, called the feature space, and the response functions are modelled as linear functionals of the mapped features, with coefficients calibrated via ordinary least squares. We provide convergence guarantees on the worst-case excess prediction risk by controlling the convergence rate of the excess risk uniformly in the response function. The dimension of the feature map is allowed to tend to infinity with the sample size. The collection of response functions, although potentially infinite, is supposed to have a finite Vapnik–Chervonenkis dimension. The bound derived can be applied when building multiple surrogate models in a reasonable computing time.
Cu0.8Mg1.2Si2O6 : a copper-bearing silicate with the low-clinopyroxene structure

Abstract The Cu0.8Mg1. 2Si2O6 pyroxene has been synthesized using a soft chemistry method. Its crystal structure was determined from powder X-ray diffraction data. Cu0.8Mg1. 2Si2O6 crystallizes with the lowclinopyroxene monoclinic structure (space group P21/c). The role of the Jahn-Teller-distorted Cu2+ cation on the stability of this strongly distorted structure is investigated. Cu2+ shows a strong preference for the M2 site, attributed to a better adaptation of its JT-distorted coordination polyhedron to this already distorted and more flexible site. Comparison with previously reported compounds indicates that increasing the Cu content enhances the M2 site distortion, eventually leading to symmetry lowering from orthorhombic Pbca to monoclinic P21/c. These observations bring new insight into the mechanisms of formation and chemical composition of pyroxene minerals in the presence of JT cations.
Ju n 20 10 Circadian rhythm and cell population growth May 24 , 2021

Molecular circadian clocks, that are found in all nucleated cells of mammals, are known to dictate rhythms of approximately 24 hours (circa diem) to many physiological processes. This includes metabolism (e.g., temperature, hormonal blood levels) and cell proliferation. It has been observed in tumor-bearing laboratory rodents that a severe disruption of these physiological rhythms results in accelerated tumor growth. The question of accurately representing the control exerted by circadian clocks on healthy and tumour tissue proliferation to explain this phenomenon has given rise to mathematical developments, which we review. The main goal of these previous works was to examine the influence of a periodic control on the cell division cycle in physiologically structured cell populations, comparing the effects of periodic control with no control, and of different periodic controls between them. We state here a general convexity result that may give a theoretical justification to the concept of cancer chronotherapeutics. Our result also leads us to hypothesize that the above mentioned effect of disruption of circadian rhythms on tumor growth enhancement is indirect, that, is this enhancement is likely to result from the weakening of healthy tissue that are at work fighting tumor growth. keywords : mathematical biology, partial differential equations, physiologically structured models, growth processes, eigenvalues , circadian rhythms, cancer INRIA, projet BANG, Domaine de Voluceau, BP 105, 78156 Le Chesnay Cedex France INSERM U 776, Hôpital Paul-Brousse, 14, Av. Paul-Vaillant-Couturier F94807 Villejuif cedex INRIA Saclay – Île-de-France, projet MAXPLUS CMAP, Ecole Polytechnique, 91128 Palaiseau Cedex, France UPMC Univ Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005, Paris, France.
Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach

Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user from the received signal strength (RSS) of a small number of Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the RSS measurements of the users to be localized, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps. The proposed method does not require generating RSS fingerprints of each specific area where the localization task is performed and is suitable for real-time applications. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and made publicly available for the research community. By using these datasets, we also provide a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario and show numerically that LocUNet outperforms all the compared methods.
Two 6d origins of 4d SCFTs: class $\mathcal{S}$ and 6d (1,0) on a torus

We consider all 4d $\mathcal{N}=2$ theories of class $\mathcal{S}$ arising from the compactification of exceptional 6d $(2,0)$ SCFTs on a three-punctured sphere with a simple puncture. We find that each of these 4d theories has another origin as a 6d $(1,0)$ SCFT compactified on a torus, which we check by identifying and comparing the central charges and the flavor symmetry. Each 6d theory is identified with a complex structure deformation of $(\mathfrak{e}_n,\mathfrak{e}_n)$ minimal conformal matter, which corresponds to a Higgs branch renormalization group flow. We find that this structure is precisely replicated by the partial closure of the punctures in the class $\mathcal{S}$ construction. We explain how the plurality of origins makes manifest some aspects of 4d SCFTs, including flavor symmetry enhancements and determining if it is a product SCFT. We further highlight the string theoretic basis for this identification of 4d theories from different origins via mirror symmetry.
Amalgamated Geometric Structure of the Local Multiverse

We consider multiverses as time-amalgamated multiply warped products of Lorentzian (Einstein) manifolds. We define the Local Multiverse as timely-connected component of our physical (3+1)-spacetime. It is a collection of ``parallel universes"with (mutually) synchronized timelines. Metaphysical considerations suggest that the Local Multiverse could be an extremely complex agglomeration with, at least, several hundred parallel universes in the Solar neighbourhood (and many thousands in galaxy bulks). In this paper we study a simplified time-almagamated globally hyperbolic model. Our picture implies the multiversality of elementary particles which are, actually, transcosmic (super)strings with multiple endpoints on parallel universes considered as D-branes.
Sampling with Mirrored Stein Operators

We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries. Stein Variational Mirror Descent and Mirrored Stein Variational Gradient Descent minimize the Kullback-Leibler (KL) divergence to constrained target distributions by evolving particles in a dual space defined by a mirror map. Stein Variational Natural Gradient exploits non-Euclidean geometry to more efficiently minimize the KL divergence to unconstrained targets. We derive these samplers from a new class of mirrored Stein operators and adaptive kernels developed in this work. We demonstrate that these new samplers yield accurate approximations to distributions on the simplex, deliver valid confidence intervals in post-selection inference, and converge more rapidly than prior methods in large-scale unconstrained posterior inference. Finally, we establish the convergence of our new procedures under verifiable conditions on the target distribution.
The Neurally-Guided Shape Parser: A Monte Carlo Method for Hierarchical Labeling of Over-segmented 3D Shapes

Many learning-based 3D shape semantic segmentation methods assign labels to shape atoms (e.g. points in a point cloud or faces in a mesh) with a single-pass approach trained in an end-to-end fashion. Such methods achieve impressive performance but require large amounts of labeled training data. This paradigm entangles two separable subproblems: (1) decomposing a shape into regions and (2) assigning semantic labels to these regions. We claim that disentangling these subproblems reduces the labeled data burden: (1) region decomposition requires no semantic labels and could be performed in an unsupervised fashion, and (2) labeling shape regions instead of atoms results in a smaller search space and should be learnable with less labeled training data. In this paper, we investigate this second claim by presenting the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign semantic labels to regions of an over-segmented 3D shape. We solve this problem via MAP inference, modeling the posterior probability of a labeling assignment conditioned on an input shape. We employ a Monte Carlo importance sampling approach guided by a neural proposal network, a search-based approach made feasible by assuming the input shape is decomposed into discrete regions. We evaluate NGSP on the task of hierarchical semantic segmentation on manufactured 3D shapes from PartNet. We ﬁnd that NGSP delivers signiﬁcant performance improvements over baselines that learn to label shape atoms and then aggregate predictions for each shape region, especially in low-data regimes. Finally, we demonstrate that NGSP is robust to region granularity, as it maintains strong segmentation performance even as the regions undergo signiﬁcant corruption.
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise

Advances in neural architecture search, as well as explainability and interpretability of connectionist architectures, have been reported in the recent literature. However, our understanding of how to design Bayesian Deep Learning (BDL) hyperparameters, specifically, the depth, width and ensemble size, for robust function mapping with uncertainty quantification, is still emerging. This paper attempts to further our understanding by mapping Bayesian connectionist representations to polynomials of different orders with varying noise types and ratios. We examine the noise-contaminated polynomials to search for the combination of hyperparameters that can extract the underlying polynomial signals while quantifying uncertainties based on the noise attributes. Specifically, we attempt to study the question that an appropriate neural architecture and ensemble configuration can be found to detect a signal of any n-th order polynomial contaminated with noise having different distributions and signal-to-noise (SNR) ratios and varying noise attributes. Our results suggest the possible existence of an optimal network depth as well as an optimal number of ensembles for prediction skills and uncertainty quantification, respectively. However, optimality is not discernible for width, even though the performance gain reduces with increasing width at high values of width. Our experiments and insights can be directional to understand theoretical properties of BDL representations and to design practical solutions.
Comparing the Structures and Characteristics of Different Game Social Networks - The Steam Case

In most games, social connections are an essential part of the gaming experience. Players connect in communities inside or around games and form friendships, which can be translated into other games or even in the real world. Recent research has investigated social phenomena within the player social network of several multiplayer games, yet we still know very little about how these networks are shaped and formed. Specifically, we are unaware of how the game type and its mechanics are related to its community structure and how those structures vary in different games. This paper presents an initial analysis of Steam users and how friendships on Steam are formed around 200 games. We examine the friendship graphs of these 200 games by dividing them into clusters to compare their network properties and their specific characteristics (e.g., genre, game elements, and mechanics). We found how the Steam user-defined tags better characterized the clusters than the game genre, suggesting that how players perceive and use the game also reflects how they connect in the community. Moreover, team-based games are associated with more cohesive and clustered networks than games with a stronger single-player focus, supporting the idea that playing together in teams more likely produces social capital (i.e., Steam friendships).
Querying RDF Databases with Sub-CONSTRUCTs

Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL. The latter are object of study in the present paper. For this purpose, a core graph query language GrAL is defined with focus on CONSTRUCT queries. Queries in GrAL form the final step of a recursive process involving so-called GrAL patterns. By evaluating a query over a graph one gets a graph, while by evaluating a pattern over a graph one gets a set of matches which involves both a graph and a table. CONSTRUCT queries are based on CONSTRUCT patterns, and sub-CONSTRUCT patterns come for free from the recursive definition of patterns. The semantics of GrAL is based on RDF graphs with a slight modification which consists in accepting isolated nodes. Such an extension of RDF graphs eases the definition of the evaluation semantics, which is mainly captured by a unique operation called Merge. Besides, we define aggregations as part of GrAL expressions, which leads to an original local processing of aggregations.
On Matrix Factorizations in Subspace Clustering

This article explores subspace clustering algorithms using CUR decompositions, and examines the effect of various hyperparameters in these algorithms on clustering performance on two real-world benchmark datasets, the Hopkins155 motion segmentation dataset and the Yale face dataset. Extensive experiments are done for a variety of sampling methods and oversampling parameters for these datasets, and some guidelines for parameter choices are given for practical applications.
Space-time Non-local multi-continua upscaling for parabolic equations with moving channelized media

In this paper, we consider a parabolic problem with time-dependent heterogeneous coefficients. Many applied problems have coupled space and time heterogeneities. Their homogenization or upscaling requires cell problems that are formulated in space-time representative volumes for problems with scale separation. In problems without scale separation, local problems include multiple macroscopic variables and oversampled local problems, where these macroscopic parameters are computed. These approaches, called Non-local multi-continua, are proposed for problems with complex spatial heterogeneities in a number of previous papers. In this paper, we extend this approach for space-time heterogeneities, by identifying macroscopic parameters in space-time regions. Our proposed method space-time Non-local multi-continua (space-time NLMC) is an efficient numerical solver to deal with time-dependent heterogeneous coefficients. It provides a flexible and systematic way to construct multiscale basis functions to approximate the solution. These multiscale basis functions are constructed by solving a local energy minimization problems in the oversampled space-time regions such that these multiscale basis functions decay exponentially outside the oversampled domain. Unlike the classical time-stepping methods combined with full-discretization technique, our space-time NLMC efficiently constructs the multiscale basis functions in a space-time domain and can provide a computational savings compared to space-only approaches as we discuss in the paper. We present two numerical experiments, which show that the proposed approach can provide a good accuracy.
Small tile sets that compute while solving mazes

We ask the question of how small a self-assembling set of tiles can be yet have interesting computational behaviour. We study this question in a model where supporting walls are provided as an input structure for tiles to grow along: we call it the Maze-Walking Tile Assembly Model. The model has a number of implementation prospects, one being DNA strands that attach to a DNA origami substrate. Intuitively, the model suggests a separation of signal routing and computation: the input structure (maze) supplies a routing diagram, and the programmer's tile set provides the computational ability. We ask how simple the computational part can be. We give two tiny tile sets that are computationally universal in the Maze-Walking Tile Assembly Model. The first has four tiles and simulates Boolean circuits by directly implementing NAND, NXOR and NOT gates. Our second tile set has 6 tiles and is called the Collatz tile set as it produces patterns found in binary/ternary representations of iterations of the Collatz function. Using computer search we find that the Collatz tile set is expressive enough to encode Boolean circuits using blocks of these patterns. These two tile sets give two different methods to find simple universal tile sets, and provide motivation for using pre-assembled maze structures as circuit wiring diagrams in molecular self-assembly based computing.
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales

Pre-trained language models achieves high performance on machine reading comprehension (MRC) tasks but the results are hard to explain. An appealing approach to make models explainable is to provide rationales for its decision. To investigate whether human rationales can further improve current models and to facilitate supervised learning of human rationales, here we present PALRACE (Pruned And Labeled RACE), a new MRC dataset with human labeled rationales for 800 passages selected from the RACE dataset. We further classified the question to each passage into 6 types. Each passage was read by at least 26 human readers, who labeled their rationales to answer the question. It is demonstrated that models such as RoBERTa-large outperforms human readers in all 6 types of questions, including inference questions, but its performance can be further improved when having access to the human rationales. Simpler models and pre-trained models that are not fine-tuned based on the task benefit more from human rationales, and their performance can be boosted by more than 30% by rationales. With access to human rationales, a simple model based on the GloVe word embedding can reach the performance of BERT-base.
Non zero θ 13 and δ CP in a realistic neutrino mass model with discrete A 4 family symmetry and perturbation to Tri-bimaximal mixing via z 2 × z 2 invariant perturbation in the neutrino sector

In this work, a flavour theory, of a neutrino mass model implementing an A4 family symmetry is proposed. This scheme provides a simple way to derive tribimaximal mixing in the neutrino sector via spontaneous breaking of A4 symmetric model leading to acceptable values of θ13 and maximal CP violation. A z2× z2 invariant perturbations in this model is introduced in the neutrino sector which leads to testable predictions of θ13 and CP violation. By changing the magnitudes of perturbations in neutrino sector, one can generate viable values of δCP and neutrino oscillation parameters.
Vision-based Behavioral Recognition of Novelty Preference in Pigs

Behavioral scoring of research data is crucial for extracting domain-specific metrics but is bottlenecked on the ability to analyze enormous volumes of information using human labor. Deep learning is widely viewed as a key advancement to relieve this bottleneck. We identify one such domain, where deep learning can be leveraged to alleviate the process of manual scoring. Novelty preference paradigms have been widely used to study recognition memory in pigs, but analysis of these videos requires human intervention. We introduce a subset of such videos in the form of the 'Pig Novelty Preference Behavior' (PNPB) dataset that is fully annotated with pig actions and keypoints. In order to demonstrate the application of state-of-the-art action recognition models on this dataset, we compare LRCN, C3D, and TSM on the basis of various analytical metrics and discuss common pitfalls of the models. Our methods achieve an accuracy of 93% and a mean Average Precision of 96% in estimating piglet behavior. We open-source our code and annotated dataset at https://github.com/AIFARMS/NOR-behavior-recognition