Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of Beach Profile Change Under Random Waves

A numerical model is developed to calculate the average net cross-shore transport rate and beach profile evolution under random waves. Cross-shore transport formulas for random waves are derived by superimposing the transport from individual waves, which belong to an ensemble that represents the random wave field. The transport relationships for individual waves are based on experiments with monochromatic waves in large wave tanks. The model is validated using beach profile data from the SUPERTANK Laboratory Data Collection Project. Three different types of profile evolution events are studied, namely equilibrium erosion with bar formation, berm flooding, and the impact of breaking waves on an offshore mound. The berm flooding tests include the erosion of a well-developed summer berm and the erosion of an artificially constructed foredune, and the offshore mound tests encompass narrow- and broad-crested mounds. The new model represents an attempt to consistently treat random waves in all components of a beach profile change numerical model. Previous approaches to model the profile evolution under random waves have typically involved using statistical wave measures in equations primarily developed for monochromatic waves.
Eutrophication-driven deoxygenation in the coastal ocean

Human activities, especially increased nutrient loads that set in motion a cascading chain of events related to eutrophication, accelerate development of hypoxia (lower oxygen concentration) in many areas of the world's coastal ocean. Climate changes and extreme weather events may modify hypoxia. Organismal and fisheries effects are at the heart of the coastal hypoxia issue, but more subtle regime shifts and trophic interactions are also cause for concern. The chemical milieu associated with declining dissolved oxygen concentrations affects the biogeochemical cycling of oxygen, carbon, nitrogen, phosphorus, silica, trace metals, and sulfide as observed in water column processes, shifts in sediment biogeochemistry, and increases in carbon, nitrogen, and sulfur, as well as shifts in their stable isotopes, in recently accumulated sediments.
Throughput Analysis of Wireless Powered Cognitive Radio Networks with Compressive Sensing and Matrix Completion

In this paper, we consider a cognitive radio network in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons (PBs). A new frame structure is proposed for the considered network. A wireless power transfer (WPT) model and a compressive spectrum sensing model are introduced. In the WPT model, a new WPT scheme is proposed, and the closed-form expressions for the power outage probability are derived. In compressive spectrum sensing model, two scenarios are considered: 1) Single SU, and 2) Multiple SUs. In the single SU scenario, in order to reduce the energy consumption at the SU, compressive sensing technique which enables sub-Nyquist sampling is utilized. In the multiple SUs scenario, cooperative spectrum sensing (CSS) is performed with adopting low-rank matrix completion technique to obtain the complete matrix at the fusion center. Throughput optimizations of the secondary network are formulated into two linear constrained problems, which aim to maximize the throughput of single SU and the CSS networks, respectively. Three methods are provided to obtain the maximal throughput of secondary network by optimizing the time slots allocation and the transmit power. Simulation results show that: 1) Multiple SUs scenario can achieve lower power outage probability than single SU scenario; and 2) The optimal throughput can be improved by implementing compressive spectrum sensing in the proposed frame structure design.
3D reconstruction with two webcams and a laser line projector

Three-dimensional (3D) reconstruction is one of the most attractive research topics in photogrammetry and computer vision. Nowadays 3D reconstruction with simple and consumable equipment plays an important role. In this paper, a 3D reconstruction desktop system is built based on binocular stereo vision using a laser scanner. The hardware requirements are a simple commercial hand-held laser line projector and two common webcams for image acquisition. Generally, 3D reconstruction based on passive triangulation methods requires point correspondences among various viewpoints. The development of matching algorithms remains a challenging task in computer vision. In our proposal, with the help of a laser line projector, stereo correspondences are established robustly from epipolar geometry and the laser shadow on the scanned object. To establish correspondences more conveniently, epipolar rectification is employed using Bouguet’s method after stereo calibration with a printed chessboard. 3D coordinates of the observed points are worked out with rayray triangulation and reconstruction outliers are removed with the planarity constraint of the laser plane. Dense 3D point clouds are derived from multiple scans under different orientations. Each point cloud is derived by sweeping the laser plane across the object requiring 3D reconstruction. The Iterative Closest Point algorithm is employed to register the derived point clouds. Rigid body transformation between neighboring scans is obtained to get the complete 3D point cloud. Finally polygon meshes are reconstructed from the derived point cloud and color images are used in texture mapping to get a lifelike 3D model. Experiments show that our reconstruction method is simple and efficient.
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in which each shared latent factor contains a novel word that is unique to that factor, a property that has come to be known as separability. Our algorithm is based on the key insight that the novel words correspond to the extreme points of the convex hull formed by the row-vectors of a suitably normalized word co-occurrence matrix. We leverage this geometric insight to establish polynomial computation and sample complexity bounds based on a few isotropic random projections of the rows of the normalized word co-occurrence matrix. Our proposed random-projections-based algorithm is naturally amenable to an efficient distributed implementation and is attractive for modern web-scale distributed data mining applications.
Ensuring valid inference for hazard ratios after variable selection

The problem of how to best select variables for confounding adjustment forms one of the key challenges in the evaluation of exposure effects in observational studies, and has been the subject of vigorous recent activity in causal inference. A major drawback of routine procedures is that there is no finite sample size at which they are guaranteed to deliver exposure effect estimators and associated confidence intervals with adequate performance. In this work, we will consider this problem when inferring conditional causal hazard ratios from observational studies under the assumption of no unmeasured confounding. The major complication that we face with survival data is that the key confounding variables may not be those that explain the censoring mechanism. In this paper, we overcome this problem using a novel and simple procedure that can be implemented using off-the-shelf software for penalized Cox regression. In particular, we will propose tests of the null hypothesis that the exposure has no effect on the considered survival endpoint, which are uniformly valid under standard sparsity conditions. Simulation results show that the proposed methods yield valid inferences even when covariates are high-dimensional.
Openbots

Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such (semi-)automated programs, it is still unclear how sophisticated those bots actually are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo as well as trends of publicly available bot code. Our findings indicate that most of the code on collaboration platforms is of supportive nature and provides modules of automation instead of fully fledged social bot programs. Hence, the cost (in terms of additional programming effort) for building social bots with the goal of topic-specific manipulation is higher than assumed and that methods in context of machine- or deep-learning currently only play a minor role. However, our approach can be applied as multifaceted knowledge discovery framework to monitor trends in public bot code evolution to detect new developments and streams.
Aerosol size distributions retrieved from multiband transmissometer data in the southern Baltic Sea during the VAMPIRA trials

In an earlier paper [1], data from our Multi-Band Radiometer Transmissometer (MSRT) were used to compare the ratio of extinction coefficients in different spectral bands during periods of changing visibility conditions. This ratio is an indication of the characteristics and origin (eg rural or maritime) of the haze- or fog particles, present in the measurement path. In this paper we will analyze the VAMPIRA transmission data in more detail by separating the contributions due to molecular extinction, scattering and (potentially) refraction. In our analysis we take the contribution due to scattering in order to obtain the characteristics of the Particle Size Distribution (PSD). For this purpose we take the average value and the slope of the measured transmission level in two neighboring spectral bands. Via a special simulation tool, developed for Junge-type PSD's, the slope of the PSD (defined: Junge exponent) and its value at a particle diameter of 1 μm (Junge coefficient) can be determined via a set of retrieval steps. Reference is made to a similar approach [2] where in stead of a Junge distribution, three contiguous lognormal distributions are taken. The associated procedure for the Junge-type PSD is explained in detail in this paper and applied to the VAMPIRA transmission data. The versatility of the new retrieval method is demonstrated, especially when wavelengths around lμm are chosen (a somewhat higher number than the diameter of the majority of the particles, so that most of the scattering is in the so-called Rayleigh regime). It is obvious, that the method fails in conditions of dense fog, when the transmission levels (average value and slope) over the 8.6 km path approach zero. The results are compared with in-situ PSD measurements, carried out simultaneously with a PMS (Particle Measurement System) probe at the pier near the Suerendorf shore station. In many conditions different results appear due to the fact that the MSRT system delivers path integrated data, while the PMS probe measures locally in a small volume. The MSRT data, collected over an overseas path, are more relevant to be used in the data analysis of the shore based sensor systems [3], measuring simultaneously signal values of distant point targets. The MSRT system has a higher signal to noise ratio and due to the shorter time constant, rapid fluctuations in particle characteristics are observed, not measured by the PMS probe. The availability of reliable aerosol characteristics (i.e. Junge exponent and -coefficient) allows a more precise interpretation of the data from the surveillance systems.
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefficiency, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains which are known to be challenging for classical model-based controllers. We observe the robot to be able to learn walking gait consistently on all of these terrains. Finally, we evaluate our design decisions in a simulated environment.
I. Introduction and general methods

Abstract Historically, after successful establishment of pasture on forest soils after burning, reversion to low quality swards occurred as fertility declined. This decline in pasture quality could be averted by correction of nutrient deficiencies and introduction of clovers, which were shown to fix large quantities of atmospheric N. Little subsequent work has examined the role of clover in the N cycle of high fertility pasture. The relevant literature is briefly reviewed and trials are introduced in which N fixation rates and associated pasture parameters were measured at nine sites throughout New Zealand.
GANspection

Generative Adversarial Networks (GANs) have been used extensively and quite successfully for unsupervised learning. As GANs don't approximate an explicit probability distribution, it's an interesting study to inspect the latent space representations learned by GANs. The current work seeks to push the boundaries of such inspection methods to further understand in more detail the manifold being learned by GANs. Various interpolation and extrapolation techniques along with vector arithmetic is used to understand the learned manifold. We show through experiments that GANs indeed learn a data probability distribution rather than memorize images/data. Further, we prove that GANs encode semantically relevant information in the learned probability distribution. The experiments have been performed on two publicly available datasets - Large Scale Scene Understanding (LSUN) and CelebA.
Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections

Motivation: The comparison of diverse genomic datasets is fundamental to understanding genome biology. Researchers must explore many large datasets of genome intervals (e.g., genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect: that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features is crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring the significance of relationships between sets of genomic intervals.
Experimental studies of turbulent Taylor-Couette flows: single phase and liquid-liquid dispersions

We study liquid-liquid dispersions in a turbulent Taylor-Couette flow, produced between two counterrotating coaxial cylinders. In pure Water and in counterrotation, Reynolds numbers up to 1.4 10^5 are reached. The liquids we use are a low-viscous Oil and pure Water or a Sodium Iodide solution with a refractive index matched to that of Oil, in order to get transparent dispersions. We first characterize the single-phase flow, in terms of threshold for transition to turbulence, scaling of the torque and measurements of the mean flow and of the Reynolds stress by stereoscopic PIV. We then study the increase of the dissipation in the two-phase flows and find that the torque per unit mass can be twice the torque for a single-phase flow. Long-time behaviours are also reported.
Application of a continous time cluster algorithm to the Two-dimensional Random Quantum Ising Ferromagnet

A cluster algorithm formulated in continuous (imaginary) time is presented for Ising models in a transverse field. It works directly with an infinite number of time-slices in the imaginary time direction, avoiding the necessity to take this limit explicitly. The algorithm is tested at the zero-temperature critical point of the pure two-dimensional (2d) transverse Ising model. Then it is applied to the 2d Ising ferromagnet with random bonds and transverse fields, for which the phase diagram is determined. Finite size scaling at the quantum critical point as well as the study of the quantum Griffiths-McCoy phase indicate that the dynamical critical exponent is infinite as in 1d.
NUTRIENTS AND THE PRODUCTIVITY OF ESTUARINE AND COASTAL MARINE ECOSYSTEMS

SUMMARY Recent research on estuarine and coastal marine systems has revealed two particularly interesting things about nutrients and productivity. First is the observation that these areas are among the most intensively fertilized environments on earth. Second is the common finding that much of the characteristically high primary productivity of these shallow waters is supported by nutrients released or recycled by pelagic and benthic microheterotrophs. Since nutrient inputs to coastal areas have probably been increasing and are likely to continue to do so, it is particularly important to understand the relationship between nutrient loading and nutrient cycling and the extent to which their interactions may set the levels of primary and secondary production in coastal systems. That some direct relationship exists between the input of nutrients and the productivity of higher trophic levels has been a principle of marine ecology since the turn of the century. It is surprisingly difficult, however, to find q.
An Analysis of broken $P_1$-Nonconforming Finite Element Method For Interface Problems

We study some numerical methods for solving second order elliptic problem with interface. We introduce an immersed interface finite element method based on the `broken' $P_1$-nonconforming piecewise linear polynomials on interface triangular elements having edge averages as degrees of freedom. This linear polynomials are broken to match the homogeneous jump condition along the interface which is allowed to cut through the element. We prove optimal orders of convergence in $H^1$ and $L^2$-norm. Next we propose a mixed finite volume method in the context introduced in \cite{Kwak2003} using the Raviart-Thomas mixed finite element and this `broken' $P_1$-nonconforming element. The advantage of this mixed finite volume method is that once we solve the symmetric positive definite pressure equation(without Lagrangian multiplier), the velocity can be computed locally by a simple formula. This procedure avoids solving the saddle point problem. Furthermore, we show optimal error estimates of velocity and pressure in our mixed finite volume method. Numerical results show optimal orders of error in $L^2$-norm and broken $H^1$-norm for the pressure, and in $H(\Div)$-norm for the velocity.
An Integrated Approach to the Generalization of Geological Maps

Abstract The British Geological Survey is recognizing the consumer's need for customized 'built-for-purpose' geoscientific data sets. It is witnessing a move away from being merely producers of standard scale cartographic products. This research proposes an automated tool for the generalization of geological maps, which may assist the organization in meeting these demands. The research shows that in order to derive a coarse scale geological map from a fine scale geological database there is a requirement for contextual sensitivity. To achieve an appropriate result the interaction between the themes represented on the map must be considered and should reflect the interaction of geological objects in the real world. A case study is presented which illustrates how standard cartographic generalization operations can be combined, using a rule base, into a partially automated process to generalize a geological data set from 1:50 000 to 1:250 000 scale. Independent geoscientific professionals evaluated the results from the case study. The tool provides consistency and tracabilty in the generation of multiscale geoscientific data. The evaluation suggests that, with further research, higher levels of autonomous generalization are achievable. In its current form it is a highly useful tool as a testbed for defining generalization requirements in geological mapping, an aid to the production of standard scale maps and a basis for creating customer specific data sets.
SPATIOTEMPORAL CHARACTERISTICS OF EXTREME RAINFALL EVENTS OVER JAVA ISLAND, INDONESIA

The patterns and trends of extreme daily rainfall within period of 1981 – 2010 have been analyzed for Java Island, Indonesia particularly East Java Province. A set of extreme indices recommended by WMO were calculated using high quality data from 84 rain stations to express the frequency and intensity of those events. The spatial patterns was identified by mapping climatological mean of indices while temporal trends was assessed using the nonparametric Mann-Kendal test. The study found that the spatial pattern of extreme rainfall events over East Java Province is generally characterized by low frequency and intensity in the coastal area, and high frequency and intensity in the mountainous area. The dominant finding from trend assessment is not-significant trend. However, the consistently significant trend was observed in some districts. Rain stations in District of Ponorogo, Ngawi, Bojonegoro, Gresik and Sumenep showed significant negative trend for almost all indices whereas significant positive trend was found in District of Surabaya and Pasuruan.
Structural basis of lipoprotein recognition by the bacterial Lol trafficking chaperone LolA

Significance Lipoproteins in gram-negative bacteria underpin the formation and maintenance of the outer membrane that constitutes a vital protective barrier against antibiotics and other noxious molecules. An essential transport system comprising the LolABCDE proteins is required to traffic lipoproteins to the outer membrane. Following maturation on the inner membrane and extraction by the LolCDE transporter, lipoproteins are passed to the chaperone LolA that carries them across the periplasm prior to insertion into the outer membrane by the LolB receptor. Here, we report the molecular details of lipoprotein interaction with the chaperone LolA, a key intermediate located at the heart of the Lol pathway. The structure provides valuable insights into this important system and could be exploited to develop new antimicrobials.