Miracle-dz/UniFAR-FTUs huggingface dataset:
A lock-in model for the complex Matuyama-Brunhes boundary record of the loess/palaeosol sequence at Lingtai (Central Chinese Loess Plateau)

SUMMARY In most marine sedimentary records, the Matuyama-Brunhes boundary (MBB) has been found in interglacial oxygen isotope stage 19. In the magnetostratigraphic records of most Chinese loess/palaeosol profiles the MBB is located in loess layer L8, which was deposited during a glacial period. The MBB at Lingtai (central Chinese Loess Plateau) also occurs in L8 and is characterized by multiple polarity flips. The natural remanent magnetization is mainly carried by two coexisting components. The higher coercivity (harder) component dominates in loess layers and is thought to be of detrital origin. The lower coercivity (softer) component prevails in palaeosols and was most probably formed in situ by (bio-)chemical processes. A lock-in model for the Lingtai MBB record has been developed by extending the lithologically controlled PDRM model of Bleil & von Dobeneck (1999). It assumes two lock-in zones. The NRM of the magnetically harder component is physically locked by consolidation shortly after loess deposition, whereas the softer component is formed at greater depth by pedogenesis and acquires a chemical remanent magnetization of younger age. At polarity boundaries, grains carrying reversed and normal directions may therefore occur together within a single horizon. The model uses ARM coercivity spectra to estimate the relative contributions of the two components. It is able to explain the observed rapid multiple polarity flips and low magnetization intensities as well as the stratigraphic shift of the Lingtai MBB with respect to the marine records.
Quaternary tectonics in a passive margin: Marajó Island, northern Brazil

Marajó Island is located in a passive continental margin that evolved from rifting associated with the opening of the Equatorial South Atlantic Ocean in the Late Jurassic/Early Cretaceous period. This study, based on remote sensing integrated with sedimentology, as well as subsurface and seismographic data available from the literature, allows discussion of the significance of tectonics during the Quaternary history of marginal basins. Results show that eastern Marajó Island contains channels with evidence of tectonic control. Mapping of straight channels defined four main groups of lineaments (i.e. NNE–SSW, NE–SW, NW–SE and E–W) that parallel main normal and strike‐slip fault zones recorded for the Amazon region. Additionally, sedimentological studies of late Quaternary and Holocene deposits indicate numerous ductile and brittle structures within stratigraphic horizons bounded by undeformed strata, related to seismogenic deformation during or shortly after sediment deposition. This conclusion is consistent with subsurface Bouguer mapping suggestive of eastern Marajó Island being still part of the Marajó graben system, where important fault reactivation is recorded up to the Quaternary. Together with the recognition of several phases of fault reactivation, these data suggest that faults developed in association with rift basins might remain active in passive margins, imposing important control on development of depositional systems. Copyright © 2007 John Wiley & Sons, Ltd.
Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$ -Balls

Consider the high-dimensional linear regression model <i>y</i> = <i>X</i> β<sup>*</sup> + <i>w</i>, where <i>y</i> ∈ \BBR<i>n</i> is an observation vector, <i>X</i> ∈ \BBR<i>n</i> × <i>d</i> is a design matrix with <i>d</i> >; <i>n</i>, β<sup>*</sup> ∈ \BBR<i>d</i> is an unknown regression vector, and <i>w</i> ~ <i>N</i>(0, σ<sup>2</sup><i>I</i>) is additive Gaussian noise. This paper studies the minimax rates of convergence for estimating β<sup>*</sup> in either <i>l</i><sub>2</sub>-loss and <i>l</i><sub>2</sub>-prediction loss, assuming that β<sup>*</sup> belongs to an <i>lq</i> -ball \BBB<i>q</i>(<i>Rq</i>) for some <i>q</i> ∈ [0,1]. It is shown that under suitable regularity conditions on the design matrix <i>X</i>, the minimax optimal rate in <i>l</i><sub>2</sub>-loss and <i>l</i><sub>2</sub>-prediction loss scales as Θ(<i>Rq</i> ([(log<i>d</i>)/(<i>n</i>)])<sup>1-q</sup>/<sub>2</sub>). The analysis in this paper reveals that conditions on the design matrix <i>X</i> enter into the rates for <i>l</i><sub>2</sub>-error and <i>l</i><sub>2</sub>-prediction error in complementary ways in the upper and lower bounds. Our proofs of the lower bounds are information theoretic in nature, based on Fano's inequality and results on the metric entropy of the balls \BBB<i>q</i>(<i>Rq</i>), whereas our proofs of the upper bounds are constructive, involving direct analysis of least squares over <i>lq</i>-balls. For the special case <i>q</i>=0, corresponding to models with an exact sparsity constraint, our results show that although computationally efficient <i>l</i><sub>1</sub>-based methods can achieve the minimax rates up to constant factors, they require slightly stronger assumptions on the design matrix <i>X</i> than optimal algorithms involving least-squares over the <i>l</i><sub>0</sub>-ball.
Erosion mechanisms and sediment sources in a peatland forest after ditch cleaning

Ditch cleaning in drained peatland forests increases sediment loads and degrades water quality in headwater streams and lakes. A better understanding of the processes controlling ditch erosion and sediment transport in such systems is a prerequisite for proper peatland management. In order to relate hydrological observations to key erosion processes in headwater peatlands drained for forestry, a two‐year study was conducted in a nested sub‐catchment system (treated with ditch cleaning) and at two reference sites. The treated catchment was instrumented for continuous discharge and turbidity monitoring, erosion pin measurements of changes in ditch bed and banks and time‐integrated sampling of suspended sediment (SS) composition. The results showed that ditch cleaning clearly increased transient suspended sediment concentrations (SSCs) and suspended sediment yields (SSYs), and resulted in temporary storage of loosely deposited organic sediment in the ditch network. After exhaustion of this sediment storage, subaerial processes and erosion from ditch banks became dominant in producing sediment for transport. Recorded SSCs were higher on the rising limbs of event hydrographs throughout the study period, indicating that SS transport was limited by availability of erosion‐prone sediment. A strong positive correlation (R2 = 0.84, p < 0.001) between rainfall intensity (above a threshold of 1 mm h−1) and average SSC obtained on the rising limb of hydrographs for the sub‐catchment showed that soil detachment from ditch banks by raindrop impact can directly increase SSC in runoff. At the main catchment outlet, variation in SSC was best explained (R2 = 0.67, p < 0.05) by the linear combination of initial discharge (−), peak discharge (+) and the lag time from initial to peak discharge (−). Based on these factors, ditch cleaning slightly increased peak discharges and decreased transit times in the study catchment. The implications of the results for water pollution management in peatland forests are discussed. Copyright © 2016 John Wiley & Sons, Ltd.
Tensor rank and the ill-posedness of the best low-rank approximation problem

There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher, that parallels the Eckart-Young theorem for matrices. In this paper, we argue that the naive approach to this problem is doomed to failure because, unlike matrices, tensors of order 3 or higher can fail to have best rank-r approximations. The phenomenon is much more widespread than one might suspect: examples of this failure can be constructed over a wide range of dimensions, orders and ranks, regardless of the choice of norm (or even Bregman divergence). Moreover, we show that in many instances these counterexamples have positive volume: they cannot be regarded as isolated phenomena. In one extreme case, we exhibit a tensor space in which no rank-3 tensor has an optimal rank-2 approximation. The notable exceptions to this misbehavior are rank-1 tensors and order-2 tensors. In a more positive spirit, we propose a natural way of overcoming the ill-posedness of the low-rank approximation problem, by using weak solutions when true solutions do not exist. In our work we emphasize the importance of closely studying concrete low-dimensional examples as a first step towards more general results. To this end, we present a detailed analysis of equivalence classes of 2-by-2-by-2 tensors, and we develop methods for extending results upwards to higher orders and dimensions. Finally, we link our work to existing studies of tensors from an algebraic geometric point of view. The rank of a tensor can in theory be given a semialgebraic description; i.e., can be determined by a system of polynomial inequalities. In particular we make extensive use of the 2-by-2-by-2 hyperdeterminant.
Reducing the Influence of Skin Effects on Hydraulic Conductivity Determinations in Multilevel Samplers Installed with Direct Push Methods

Field tests were conducted to investigate skin effects in multilevel ground water samplers installed with direct push methods. Tests consisted of determining hydraulic conductivity values using slug tests in five multilevel samplers before, during, and after development. Development was conducted using a minisurge block tool designed for this study. Post‐development hydraulic conductivity values were found to be 3.2 to 9.6 times greater than predevelopment values. The largest differences were observed in multilevel samplers positioned in low permeable materials that were installed in preprobed holes advanced by hydraulic hammering and pushing as opposed to probes situated in higher permeable materials that were installed in cored holes. The presence and magnitude of the skin effects, as well as the reduction of these effects by rigorous development, were confirmed through comparisons with steady‐state pumping tests conducted in multilevel samplers and previously conducted slug tests in conventional monitoring wells. These results indicate the need for rigorous development prior to hydraulic conductivity testing in ground water samplers installed with direct push methods.
New ways to multiply 3 x 3-matrices

It is known since the 1970s that no more than 23 multiplications are required for computing the product of two 3 x 3-matrices. It is not known whether this can also be done with fewer multiplications. However, there are several mutually inequivalent ways of doing the job with 23 multiplications. In this article, we extend this list considerably by providing more than 13 000 new and mutually inequivalent schemes for multiplying 3 x 3-matrices using 23 multiplications. Moreover, we show that the set of all these schemes is a manifold of dimension at least 17.
Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning

Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor lambda. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD(lambda) for arbitrary lambda, for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(lambda), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using lambda> 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning.
Association of optic disc parameters and glaucoma incidence based on automated segmentation, evidence from the UK Biobank

Objective: To assess the correlation between glaucoma incidence and optic disc parameters obtained through an automated deep learning (DL) algorithm segmentation. Methods and Analysis : We obtained eligible fundus photographs and corresponding participant data from the UK Biobank. To accurately assess the optic disc parameters and their relationship with glaucoma incidence using Cox proportional hazard regression models, we developed a DL algorithm that automatically segmented the optic disc and cup and calculated various parameters including the vertical cup-to-disc ratio (VCDR), ovality index, cup-to-disc area ratio, rim area, disc area, and disc rotation from the fundus photos. We performed two logistic regression models, with model A comprising sociodemographic and health covariates and model B including additional ophthalmic features. Receiver operating characteristic curves (ROC) and areas under the curve (AUC) were plotted and calculated for each model to evaluate their performance. Results: A total of 44,376 subjects with fundus photos were included in our study. After a median follow-up of 10.1 years, 354 incident glaucoma were documented. Subjects with larger VCDR had a higher risk of incident glaucoma; the HR (95% CI) was 2.05 (1.57-2.66) in the multivariable-adjusted model (p<0.001). The results remain significant in the sensitivity analysis that excluded fundus photographs with "Reject" quality. After adding the optic disc parameters into the regression model A, the AUC increased by 4.2% to 78.6%. Conclusion: The VCDR calculated by automatic optic disc segmentation model shows potential as a biomarker for evaluating the risk of glaucoma.
Finite-dimensional Gaussian approximation with linear inequality constraints

Gaussian process (GP) modulated Cox processes are widely used to model point patterns. Existing approaches require a mapping (link function) between the unconstrained GP and the positive intensity function. This commonly yields solutions that do not have a closed form or that are restricted to specific covariance functions. We introduce a novel finite approximation of GP-modulated Cox processes where positiveness conditions can be imposed directly on the GP, with no restrictions on the covariance function. Our approach can also ensure other types of inequality constraints e.g. monotonicity, convexity), resulting in more versatile models that can be used for other classes of point processes (e.g. renewal processes). We demonstrate on both synthetic and real-world data that our framework accurately infers the intensity functions. Where monotonicity is a feature of the process, our ability to include this in the inference improves results.
How can regime characteristics of catchments help in training of local and regional LSTM-based runoff models?

This paper carefully studies long short-term memory networks (LSTM) for rainfall-runoff prediction, using a large-sample of catchments in France. The key focus is on exploring local and regional models as well as the impact of the ‘lookback’ period, an important hyper-parameter of LSTM, with respect to predictive performance and physical understanding of the model results. The authors include well-thought out experiments to identify the impact of the lookback period and cases where local and regional LSTM models are best suited. The authors also benchmark LSTM with GR4J, due to its useful ability to capture ground water exchanges with aquifers and/or between catchments. The authors spend a considerable amount of effort on tying the performance of LSTM, locally and regionally, to a physical understanding of the results. Some examples include the comparison between local and regional LSTM models with GR4J in terms of a water balance exercise in Section 5.3 as well as the ability of LSTM to predict runoff in controlled catchments at a higher degree of accuracy than GR4J (in Section 5.4). This paper also presents findings (e.g., LSTM does not necessarily outperform simple conceptual rainfall-runoff models) that are counter to other recent studies on LSTM (Gauch et al., 2021; Kratzert et al., 2019; Lees et al., 2021); in all such cases, the authors take the time to carefully describe potential reasons for these differences. Overall, this paper is very strong and I could not find much to criticize. The methodology seems correct. The figures are very nice and easy to interpret and I did not find any of the content, tables, or figures to be superfluous. I suspect this paper will be very useful to other researchers interested in exploiting the general-ity of machine learning
Algorithms for nonnegative matrix factorization with the beta-divergence

This paper describes algorithms for nonnegative matrix factorization (NMF) with the beta-divergence (beta-NMF). The beta-divergence is a family of cost functions parametrized by a single shape parameter beta that takes the Euclidean distance, the Kullback-Leibler divergence and the Itakura-Saito divergence as special cases (beta = 2,1,0, respectively). The proposed algorithms are based on a surrogate auxiliary function (a local majorization of the criterion function). We first describe a majorization-minimization (MM) algorithm that leads to multiplicative updates, which differ from standard heuristic multiplicative updates by a beta-dependent power exponent. The monotonicity of the heuristic algorithm can however be proven for beta in (0,1) using the proposed auxiliary function. Then we introduce the concept of majorization-equalization (ME) algorithm which produces updates that move along constant level sets of the auxiliary function and lead to larger steps than MM. Simulations on synthetic and real data illustrate the faster convergence of the ME approach. The paper also describes how the proposed algorithms can be adapted to two common variants of NMF : penalized NMF (i.e., when a penalty function of the factors is added to the criterion function) and convex-NMF (when the dictionary is assumed to belong to a known subspace).
Smallest singular value of a random rectangular matrix

We prove an optimal estimate of the smallest singular value of a random sub‐Gaussian matrix, valid for all dimensions. For an N × n matrix A with independent and identically distributed sub‐Gaussian entries, the smallest singular value of A is at least of the order √N − √n − 1 with high probability. A sharp estimate on the probability is also obtained. © 2009 Wiley Periodicals, Inc.
Biological Inspirations 2 . 1 . Summary of Scientistic Advances

Image aesthetics assessment has been challenging due to its subjective nature. Being extensively inspired by the scientific advances in the human visual perception and neuroaesthetics, we design the Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings often vary somewhat from observer to observer. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to effective data augmentation approaches. Experimental results on the AVA dataset show that our biological inspired, task-specific BDN model leads to significantly improved performances, compared to other state-of-the-art models with the same or higher parameter capacity.
Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor

The first observations of chlorophyll fluorescence from space for the west coast of Canada, using the U.S. moderate resolution imaging spectrometer (MODIS), show that the signals should provide a useful new tool for studying chlorophyll biomass and primary productivity. We compare MODIS fluorescence and sea-viewing wide field-of-view sensor (SeaWiFS) chlorophyll data, using a simple theoretical model of the expected variation of fluorescence emission with variations in chlorophyll concentration. The results show good agreement with the model and appear to allow separation of water masses according to fluorescence yield. As additional MODIS data come available it will be possible to study data for the full range of seasonal conditions. Additional data from the European medium-resolution imaging spectrometer (MERIS) sensor are available from 2002.
Optimal Las Vegas Approximate Near Neighbors in ℓp

We show that approximate near neighbor search in high dimensions can be solved in a Las Vegas fashion (i.e., without false negatives) for ℓp (1≤ p≤ 2) while matching the performance of optimal locality-sensitive hashing. Specifically, we construct a data-independent Las Vegas data structure with query time O(dnρ) and space usage O(dn1+ρ) for (r, c r)-approximate near neighbors in Rd under the ℓp norm, where ρ = 1/cp + o(1). Furthermore, we give a Las Vegas locality-sensitive filter construction for the unit sphere that can be used with the data-dependent data structure of Andoni et al. (SODA 2017) to achieve optimal space-time tradeoffs in the data-dependent setting. For the symmetric case, this gives us a data-dependent Las Vegas data structure with query time O(dnρ) and space usage O(dn1+ρ) for (r, c r)-approximate near neighbors in Rd under the ℓp norm, where ρ = 1/(2cp - 1) + o(1). Our data-independent construction improves on the recent Las Vegas data structure of Ahle (FOCS 2017) for ℓp when 1 < p≤ 2. Our data-dependent construction performs even better for ℓp for all pε [1, 2] and is the first Las Vegas approximate near neighbors data structure to make use of data-dependent approaches. We also answer open questions of Indyk (SODA 2000), Pagh (SODA 2016), and Ahle by showing that for approximate near neighbors, Las Vegas data structures can match state-of-the-art Monte Carlo data structures in performance for both the data-independent and data-dependent settings and across space-time tradeoffs.