Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of three‐dimensional boundary shape of the Osaka sedimentary basin by waveform inversion

SUMMARY We present a method for estimating the boundary shape of a 3-D basin velocity structure by waveform inversion, and we apply the method to real seismic data observed in the Osaka sedimentary basin in Japan. The initial model is the 3-D Osaka basin velocity structure model developed by Iwata et al. (2008. Basin and crustal velocity structure models for the simulation of strong ground motions in the Kinki area, Japan. J. Seism., 2, 223–234) based on bedrock depth information obtained from various geophysical exploration surveys. We adopt representation of the boundary shape of the seismic bedrock using a 2-D cubic B-spline function. The spline coefficients are estimated by minimizing the L2-norm of the difference between the observed and synthetic waveforms. The target of the inversion is the set of velocity waveforms in the period range of 3–10 s of the real seismic data observed in the Osaka basin during two regional MW5.0 earthquakes that occurred 60–90 km to the east and southeast of the Osaka basin; they contain later phases with large amplitudes and long durations. We first perform a series of synthetic tests to validate the method and examine the effect of the choice of time window in the waveform inversion. We show that the later phases of the ground motion have a larger influence on the inversion and are affected by the structure of a wider area relative to the body-wave portion. We then apply the method to the real data in two successive steps: Step-1 involves the body-wave portion only and Step-2 involves the body-wave and later phases. We propose a multi-event inversion method in which ground motion data from two earthquakes with different azimuths are solved simultaneously. The updated basin velocity structure model obtained in our inversion analysis reproduces the ground motion better than the initial model at most of the stations. The bedrock depth of the updated model is smaller than that of the initial model in the central part of the basin, which is consistent with the bedrock depth information inferred from deep borehole data.
Efficient Discovery of the Top-K Optimal Dependency Rules with Fisher's Exact Test of Significance

Statistical dependency analysis is the basis of all empirical science. A commonly occurring problem is to find the most significant dependency rules, which describe either positive or negative dependencies between categorical attributes. For example, in medical science one is interested in genetic factors, which can either predispose or prevent diseases. The requirement of statistical significance is essential, because the discoveries should hold also in the future data. Typically, the significance is estimated either by Fisher's exact test or the $\chi^2$-measure. The problem is computationally very difficult, because the number of all possible dependency rules increases exponentially with the number of attributes. As a solution, different kinds of restrictions and heuristics have been applied, but a general, scalable search method has been missing. In this paper, we introduce an efficient algorithm for searching for the top-K globally optimal dependency rules using Fisher's exact test as a measure function. The rules can express either positive or negative dependencies between a set of positive attributes and a single consequent attribute. The algorithm is based on an application of the branch-and-bound search strategy, supplemented by several pruning properties. Especially, we prove a new lower-bound for the Fisher's p, and introduce a new effective pruning principle. The general search algorithm is applicable to other goodness measures, like the $\chi^2$-measure, as well. According to our experiments on classical benchmark data, the algorithm is well scalable and can efficiently handle even dense and high dimensional data sets. In addition, the quality of rules is significantly better than with the $\chi^2$-measure using the same search algorithm.
The Vulnerability of Science and the Science of Vulnerability

Abstract The events of September 11th shocked the nation and painfully illustrated our vulnerability to international terrorist attacks. Despite some of the most sophisticated models, monitoring systems, and science in the world, officials were unable to anticipate and predict these cascading events. The collective scientific ability to geographically represent environmental threats, map exposures, and map consequences is relatively straightforward when the threats are recognized. But what happens when we cannot recognize threats or some of their unintended consequences? This article examines the twin issues of the inadequacies in our current modes of understanding (the vulnerability of science) and the need for more integrative approaches in understanding and responding to environmental hazards (vulnerability science).
Routine interpretation of the Lugeon water-test

Summary A relatively simple routine method of calculating and interpreting the “modified” Lugeon water test is described. The method is used for assessing the need for foundation grouting at dam sites; it comprises calculations of lugeon values for each of five test runs at increasing and then decreasing pressures, followed by interpretation of the pattern of results, and hence selection of an appropriate representative permeability. The relative frequency of different behaviour patterns is indicated by reference to 811 actual tests.
CAMP-Net: Context-Aware Multi-Prior Network for Accelerated MRI Reconstruction

—Despite promising advances in deep learning-based MRI reconstruction methods, restoring high-frequency image details and textures remains a challenging problem for accelerated MRI. To tackle this challenge, we propose a novel context-aware multi-prior network (CAMP-Net) for MRI reconstruction. CAMP-Net leverages the complementary nature of multiple prior knowledge and explores data redundancy between adjacent slices in the hybrid domain to improve image quality. It incorporates three interleaved modules respectively for image enhancement, k-space restoration, and calibration consistency to jointly learn context-aware multiple priors in an end-to-end fashion. The image enhancement module learns a coil-combined image prior to suppress noise-like artifacts, while the k-space restoration module explores multi-coil k-space correlations to recover high-frequency details. The calibration consistency module embeds the known physical properties of MRI acquisition to ensure consistency of k-space correlations extracted from measurements and the artifact-free image intermediate. The resulting low-and high-frequency reconstructions are hierarchically aggregated in a frequency fusion module and iteratively reﬁned to progressively reconstruct the ﬁnal image. We evaluated the generalizability and robustness of our method on three large public datasets with various accelerations and sampling patterns. Comprehensive experiments demonstrate that CAMP-Net outperforms state-of-the-art methods in terms of reconstruction quality and quantitative T 2 mapping.
Estimating Russia's Impact on the Economic Performance of the Commonwealth of Independent States since 1991: The Cases of the Kyrgyz Republic, Tajikistan, Armenia, Georgia and Ukraine

This paper finds that Russia’s traditional forms of influence on growth in the case-study countries have generally declined during transition (with the notable exception of Tajikistan). Countries that have integrated into the global economy and undertaken robust domestic policy and structural reforms have overcome inherited economic distortions and reduced their ties with the CIS and Russia to a greater degree. However, new forms of economic linkage with Russia are emerging, most of which could have a significant impact on the key determinants of growth.
DWRSeg: Dilation-wise Residual Network for Real-time Semantic Segmentation

Real-time semantic segmentation has played an important role in intelligent vehicle scenarios. Recently, numerous networks have incorporated information from multi-size receptive ﬁelds to facilitate feature extraction in real-time semantic segmentation tasks. However, these methods preferentially adopt massive receptive ﬁelds to elicit more contextual information, which may result in inefﬁcient feature extraction. We believe that the elaborated receptive ﬁelds are crucial, considering the demand for efﬁcient feature extraction in real-time tasks. Therefore, we propose an effective and efﬁcient architecture termed Dilation-wise Residual segmentation (DWRSeg), which possesses different sets of receptive ﬁeld sizes within different stages. The architecture involves (i) a Dilation-wise Residual (DWR) module for extracting features based on different scales of receptive ﬁelds in the high level of the network; (ii) a Simple Inverted Residual (SIR) module that uses an inverted bottleneck structure to extract features from the low stage; and (iii) a simple fully convolutional network (FCN)-like decoder for aggregating multiscale feature maps to generate the prediction. Extensive experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a state-of-the-art trade-off between accuracy and inference speed, in addition to being lighter weight. Without using pretraining
Sublethal effects of heavy metal contamination on marine sponges: Responses at different biological levels

Many antrophic activities release pollutants to the marine environment. Among them heavy metals are of great importance, since they are conservative pollutants, which can be accumulated through trophy chains. Sometimes, the liberation to the marine waters is acute and drastic, leading to massive mortalities. However, more often, the liberation is by means of low and chronic concentrations. The main objective of the present thesis is to analyze the sponge responses in face to sublethal concentrations of heavy metals and to determine, if it is possible, whether they can be used as biomonitors of such kind of pollution. First, we have studied their availability to accumulate havey metals in both, temporal and especial scales. And then, by means of the use of different biomarkers, to analyze the sublethal effects of heavy metals on sponges at different levels of biological organization, from molecules to populations. The present thesis has been performed by means of experiments in situ and at the laboratory, in order to elucidate heavy metal accumulation patterns and their effects depending on the specie and metal considered. "RESUM: TITOL DE LA TESI: "Efectes subletals de la contaminacio per metalls pesats en esponges marines: Respostes a diferents nivells dorganitzacio". TEXT: Moltes activitats antropiques alliberen contaminants en el medi. Entre els contaminants hem de destacar els metalls pesants, ja que son contaminants conservatius y que sacumulen a traves de les xarxes trofiques. En alguns casos la alliberacio dels contaminants en el medi es dona de forma aguda i drastica, el que pot provovar mortalitats massives. De totes formes, de forma general lalliberacio al medi dels metalls pesants es dona a baixes concentracions i de forma cronica. Lobjectiu principal daquesta tesis es analitzar les respostes de les esponges a concentracions subletals de metalls pesants i determinar la seva utilitat com a biomonitors daquest tipus de contaminacio. En primer lloc, sha destudiar la capacitat de les esponges per acumular metalls pesants tant a una escala temporal com espaial. En segon lloc, mitjancant lus de diferents biomarcadors, analitzar els efectes subletals dels metalls en les esponges, a diferents nivells dorganitzacio biologica, desde les molecules fins a les poblacions. Aquesta tesis sha dut a terme mitjancant experiments que convinen aproximacions in situ i en el laboratori per tal de compendre de millor maners els models dacumulacio i els seus efectes en funcio de lespecie i el metall considerat. "
Crystal structure of high cummingtonite

The listings are available in microfiche. Order from the American Geophysical Union, 1707 L Street, N.W. , Washington, D.C. 20036. Document J72-005; $1.00. Payment must accompany order. The crystal structure of high cummingtonite has been refined with X-ray diffraction data collected at 270°C, which is well above the primitive- to C-centered structural transition (100°C). The high-temperature structure is nearly identical to that of a manganoan cummingtonite and the ‘average’ primitive-cummingtonite structure. The transition of cummingtonite from primitive to high involves (1) kinking of the A chains and unkinking of the B chains until they become equivalent at the transition temperature (100°C), (2) displacement of the M4 cations away from the octahedral strip toward a position in the site more similar to the position of M4 cations in calcic amphiboles, (3) higher volumetric expansion of octahedra than tetrahedra, (4) movement of the M1 and M2 cations toward the centers of the octahedra, and (5) the suggestion of a decrease of positional disorder in the M4 site with Mn, Ca, and Mg occupying more nearly the same position in the high-temperature structure.
Reward Informed Dreamer for Task Generalization in Reinforcement Learning

A long-standing goal of reinforcement learning is that algorithms can learn on training tasks and generalize well on unseen tasks like humans, where different tasks share similar dynamic with different reward functions. A general challenge is that it is nontrivial to quantitatively measure the similarities between these different tasks, which is vital for analyzing the task distribution and further designing algorithms with stronger generalization. To address this, we present a novel metric named Task Distribution Relevance (TDR) via optimal Q functions to capture the relevance of the task distribution quantitatively. In the case of tasks with a high TDR, i.e., the tasks differ signiﬁcantly, we demonstrate that the Markovian policies cannot distinguish them, yielding poor performance accordingly. Based on this observation, we pro-pose a framework of Reward Informed Dreamer (RID) with reward-informed world models, which captures invariant latent features over tasks and encodes reward signals into policies for distinguishing different tasks. In RID, we calculate the corresponding variational lower bound of the log-likelihood on the data, which includes a novel term to distinguish different tasks via states, based on reward-informed world models. Finally, extensive experiments in DeepMind control suite demonstrate that RID can signiﬁcantly improve the performance of handling different tasks at the same time, especially for those with high TDR, and further generalize to unseen tasks effectively.
Quantification of Al-goethite from diffuse reflectance spectroscopy and magnetic methods

SUMMARY As one of the most abundant iron oxides in soils, the presence and nature of goethite is controlledbythesoilconditionsandburialhistory.Thevisiblediffusereflectancespectroscopy (DRS) is a useful tool for quantifying goethite. However, aluminium (Al) substitution for goethite is very common in soils and the effects of Al content on the DRS properties of goethite have not been fully resolved. In this study, two series of Al substituted goethites (Al-goethite) and 20 Chinese loess/palaeosol samples were investigated using both DRS and magnetic methods to test the feasibility of quantifying Al-goethite with the DRS method. Results show that the peak positions and amplitudes of the goethite DRS band are significantly influenced by Al substitution. Specifically, the goethite concentration proxy, the amplitude of the DRS band, is relatively stable only when Al substitution ranged between about 4 and 16 mol percent. Practically, in order to resolve the difficulty in measuring Al content in natural samples, the unblocking temperature (Tb) is proposed as the proxy for Al substitution of goethite. When Tb of Al-goethite was above 250K, the amplitude of DRS can be used to reliably trace the goethite concentration variation in natural samples. For example, the DRS spectra for the Chinese loess–palaeosol samples support the idea that only haematite is enhanced via pedogenesis. In contrast, the origin of goethite seems to be mostly related to the aeolian inputs.
Seismic Evidence for a Low-Velocity Zone in the Upper Crust Beneath Mount Vesuvius

A two-dimensional active seismic experiment was performed on Mount Vesuvius: Explosive charges were set off at three sites, and the seismic signal along a dense line of 82 seismometers was recorded. A high-velocity basement, formed by Mesozoic carbonates, was identified 2 to 3 kilometers beneath the volcano. A slower (P-wave velocity VP ∼ 3.4 to 3.8 kilometers per second) and shallower high-velocity zone underlies the central part of the volcano. Large-amplitude late arrivals with a dominant horizontal wave motion and low-frequency content were identified as a P to S phase converted at a depth of about 10 kilometers at the top of a low-velocity zone (VP < 3 kilometers per second), which might represent a melting zone.
The 2003 Boumerdes, Algeria earthquake: Regional moment tensor analysis

We used regional broadband seismograms to determine seismic moment tensors for the destructive May 21, 2003 Boumerdes (Algeria) Mw = 7.0 earthquake and its larger aftershocks. Fully automatic inversions using near‐real time data provided solutions for seven Mw ≥ 4.7 events within 90 minutes after event occurrence. After adding off‐line data, we manually obtained 30 solutions (Mw ≥ 3.8) from May 2003 to January 2004. All have shallow source depths (6–21 km). The median P‐axis orientation (338°) of 24 thrust and four strike‐slip events is consistent with Africa‐Eurasia plate motion (330°). The main shock hypocenter at 8–10 km depth at the coastline and its shallow southward dip (25° ± 5°) puts the fault surface trace 15–20 km offshore, consistent with documented seafloor deformation at the base of the continental slope. A main shock rupture length of about 50 km is deduced from first day aftershocks and location of strike‐slip events. The strike‐slip events probably define the western rupture end and indicate a left‐step of main convergence. Fault strike variability of thrust events suggests fault orientation changes and possibly fault segmentation.
Deep Learning with Non-Linear Factor Models: Adaptability and Avoidance of Curse of Dimensionality

In this paper, we connect deep learning literature with non-linear factor models and show that deep learning estimation makes a substantial improvement in the non-linear factor model literature. We provide bounds on the expected risk and show that these upper bounds are uniform over a set of multiple response variables by extending Schmidt-Hieber (2020) theorems. We extend our results to an additive model setting and show its connection to non-linear factor models for financial applications. Compared to traditional factor models which assume rigid linear relations between the factors and the underlying observed variables, our deep neural network factor model (DNN-FM) offers major improvements in modeling flexibility. Also we provide a uniform sample prediction error bound for unknown functions of factors, and the upper bound does not depend on the number of factors. In order to construct a covariance matrix estimator for asset returns, we develop a novel datadependent estimator of the error covariance matrix in deep neural networks. The estimator refers to a flexible adaptive thresholding technique which is robust to outliers in the innovations. We prove that the estimator is consistent in spectral norm. Then using that result, we show consistency and rate of convergence of covariance matrix and precision matrix estimator for asset returns. The rate of convergence in both results do not depend on the number of factors, hence ours is a new result in the factor model literature due to the fact that number of factors are impediment to better estimation and prediction. Except from the precision matrix result, all our results are obtained even with number of assets are larger than the time span, and both quantities are growing. Various Monte Carlo simulations confirm our large sample findings and reveal superior accuracies of the DNN-FM in estimating the true underlying functional form which connects the factors and observable variables, as well as the covariance and precision matrix compared to competing approaches. Moreover, in an out-of-sample portfolio forecasting application it outperforms in most of the cases alternative portfolio strategies in terms of out-of-sample portfolio standard deviation and Sharpe ratio.
Pore Pressure Generation Models for Sands and Silty Soils Subjected to Cyclic Loading

This paper discusses the applicability of two simple models for predicting pore water pressure generation in nonplastic silty soil during cyclic loading. The first model was developed by Seed et al. in the 1970s and relates the pore pressure generated to the cycle ratio, which is the ratio of the number of applied cycles of loading to the number of cycles required to cause liquefaction. The second model is the Green-Mitchell-Polito model proposed by Green et al. in 2000, which relates pore pressure generation to the energy dissipated within the soil. Based upon the results of approximately 150 cyclic triaxial tests, the writers show that both models are applicable to silty soils. A nonlinear mixed effects model was used for regression analyses to develop correlations for the necessary calibration parameters. The results show that the trends in both α and pseudoenergy capacity calibration parameters for the Seed et al. and Green et al. pore pressure generation models, respectively, differ significantly for.
Causal screening in dynamical systems

Many classical algorithms output graphical representations of causal structures by testing conditional independence among a set of random variables. In dynamical systems, local independence can be used analogously as a testable implication of the underlying data-generating process. We suggest some inexpensive meth-ods for causal screening which provide output with a sound causal interpretation under the assumption of ancestral faithfulness. The popular model class of linear Hawkes processes is used to provide an example of a dynamical causal model. We argue that for sparse causal graphs the output will often be close to complete. We give examples of this framework and apply it to a challenging biological system.
Density mapping from gravity data using the Walsh transform

One of the main purposes of geophysical mapping is the identification of units that can be related to known geology. On a regional scale, aeromagnetic and gravity maps are the most useful tools presently available, although other techniques such as conductivity mapping (Palacky, 1986) or remote sensing (Watson, 1985) are very helpful in locating lithologic boundaries. Interpretation now makes extensive use of enhanced maps: susceptibility maps for magnetic data, density maps for gravity data, first and second vertical derivative, and horizontal gradient maps for both types of data. The objective of susceptibility and density mapping is to transform the potential field data into a physical property map. For physical property mapping, some hypotheses and simplifications are made. The earth model is assumed to consist of right rectangular prisms of finite (gravity) or infinite (magnetics) depth extent. For ease of data processing, the potential field is interpolated onto a regular rectangular array, so that .