Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquiring Abstract Visual Knowledge of the Real-World Environment for Autonomous Vehicles

This paper considers the problem of modeling the surrounding environment of a driven car by using the images captured by a dash cam during the driving process. Inspired from a human driver’s interpretation of the car’s surrounding environment, an abstract representation of the environment is developed that can facilitate in decision-making to prevent the car’s collisions with surrounding objects. The proposed technique for modeling the car’s surrounding environment utilizes the dash cam to capture images as the car is driven facing multiple situations and obstacles. By relying on the human driver’s interpretation of various driving scenarios, the images of the car’s surrounding environment are manually grouped into classes that reflect the driver’s abstract knowledge. Grouping the images allows the formulation of knowledge transfer process from the human driver to an autonomous vehicle as a classification problem, producing a meaningful and efficient representation of models arising from real-world scenarios. The framework of convolutional neural networks (CNN) is employed to model the surrounding environment of the driven car, encapsulating the abstract knowledge of the human driver. The proposed modeling approach is applied to determine its efficacy in two experimental scenarios. In the first experiment, a highway driving scenario is considered with three classes. Alternatively, in the second experiment, a scenario of driving in a residential area is addressed with six classes. Excellent modeling performance is reported for both experiments. Comparisons conducted with alternative image classification techniques reveal the superiority of the CNN for modeling the considered driving scenarios.
A Novel Hybrid Finite Element‐Spectral Boundary Integral Scheme for Modeling Earthquake Cycles: Application to Rate and State Faults With Low‐Velocity Zones

We present a novel hybrid finite element‐spectral boundary integral (SBI) scheme that enables efficient simulation of earthquake cycles. This combined finite element‐SBI approach captures the benefits of finite elements in modeling problems with nonlinearities, as well as the computational superiority of SBI. The domain truncation enabled by this scheme allows us to utilize high‐resolution finite elements discretization to capture inhomogeneities or complexities that may exist in a narrow region surrounding the fault. Combined with an adaptive time stepping algorithm, this framework opens new opportunities for modeling earthquake cycles with high‐resolution fault zone physics. In this initial study, we consider a two‐dimensional antiplane model with a vertical strike‐slip fault governed by rate and state friction in the quasi‐dynamic limit under the radiation damping approximation. The proposed approach is first verified using the benchmark problem BP‐1 from the Southern California Earthquake Center sequence of earthquake and aseismic slip community verification effort. The computational framework is then utilized to model the earthquake sequence and aseismic slip of a fault embedded within a low‐velocity fault zone (LVFZ) with different widths and compliance levels. Our results indicate that sufficiently compliant LVFZs contribute to the emergence of subsurface events that fail to penetrate to the free surface and may experience earthquake clusters with nonuniform interseismic time. Furthermore, the LVFZ leads to slip rate amplification relative to the homogeneous elastic case. We discuss the implications of our results for understanding earthquake complexity as an interplay of fault friction and bulk heterogeneities.
Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm

Abstract Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use.
Scanned to Digital Face Images Matching With Siamese Network

Often in law enforcement and forensic application it is needed to match scanned facial image with a digital face image. This is because in many scenario, non-digital face images are obtained from the crime scene, news articles etc. that are needed to be identified. Non-digital face images are first scanned and then enhanced to match against the database. Challenges arrives because of poor quality of non-digital image, artifacts introduced in scanning process and high saturation etc. therefore matching becomes difficult. The methods used in literature involve specialized hand crafted pre-processing. In our paper, we propose an automated way of matching by using Siamese networks. The proposed method have been able to achieve an EER of 2.346% that is better than the current state-of-the-art.
Paint Relics on Middle Age Building Stones as Proxies of Commercial Routes and Artistic Exchanges: A Multi-Analytical Investigation

Fifty-four pieces out of 356 marble pieces deriving from the decorative and architectonic apparatus of the medieval monastic complex of S. Francesco of Castelletto (Genoa, Italy) preserve traces of varicolored paint layers. Microscopic samples of green, blue, red, pink, white, and yellow paint relics were collected by scalpel and analyzed by means of Scanning Electron Microscope coupled with Energy Dispersive Spectroscopy (SEM-EDS), µ-Raman, and Fourier Transform Infra-Red Spectroscopy with Attenuated Total Reflection (FTIR-ATR), to characterize pigments and binders. The combined results from the different techniques allowed verification that stone decoration in Genoa during the Middle Ages encompassed a calcite groundwork and the use of a mixture of oils and proteins (probably egg) to apply pigments. The assemblage of impurities within the pigment has been correlated with the provenance sites along the commercial continental (Hungary and France) and maritime (Sardinia, Cyprus, or Veneto) routes between the 13th and 15th centuries. Moreover, the investigation of the painted layer improved the characterization of the decorative techniques in use in Genoa during the Middle Ages.
Design of a robotic instrument for minimally invasive waterjet surgery

Using a pressurized waterjet for cutting or abrasing tissue is a well established method in surgery. This paper presents a robotic tool for minimally invasive waterjet surgery with two degrees of freedom, integrated suction and an optional splash protection. The function of the tool and the effect of the splash protection on the suction of the applicated water was successfully evaluated in tests with ballistic gelatine. Based on this evaluated design, a concept for further increasing the oscillation frequency of the instrument tip is introduced and the results of preliminary tests of a simplified mockup are shown.
Controls on the Thermomechanical Evolution of Hyperextended Lithosphere at Magma‐Poor Rifted Margins: The Example of Espirito Santo and the Kwanza Basins

High‐quality, long offset seismic data from many distal rifted margins show evidence for hyper‐extended, <10‐km‐thick crust. Direct observation of such domains is challenging as they lie, at great water depth, buried beneath thick sedimentary sequences and formed by rock‐assemblages that are hydrated and geophysically indistinguishable. Only a few drill holes have penetrated basement at ultradistal rifted margins. These observations, together with outcrops of preserved analogs exposed in collisional orogens, suggest that the complex interaction of detachment faults rooted in a subhorizontal shear zone in the hyperextended crust or, in the serpentinized mantle controls the formation of the ocean continent transition. While depth‐dependent thinning controls the early phases of rifting conforming to classical rift models, we still have a superficial understanding of how normal faults and subhorizontal shear zones form and evolve during rifting and lithospheric breakup. Here we develop a rheological parameterization to simulate the formation of, and slip‐on, large offset normal faults rooted in growing brittle to ductile shear zones. The evolution of these structures leads to the creation of a hyperextended crust and eventually exhumed serpentinized mantle. We also propose a simplified formulation to simulate magmatic underplating and seafloor spreading. The resulting numerical models provide a self‐consistent picture for the evolution of magma‐poor rifted margins from initiation of rifting to seafloor spreading. The model results are compared with first‐order observations of the Kwanza and Espirito Santo conjugate margins in the South Atlantic as well as of magma‐poor margins globally.
Determinants of out of pocket healthcare expenditures: The case of Mugla province in Turkey

As the share of health care financing from public funds was increasing in Turkey, the utilization of the health care services has also increased, dramatically. Despite of universal health coverage, the result of this trend causes to increase the incidence of making out of pocket expenditures. The aim of this study is to evaluate the determinants of households’ health expenditures in Mugla province of Turkey. A total of 204 households living in the central district of Mugla were surveyed and questioned both for their total consumption and health expenditures, as well as their health status, demographic and socio-economic characteristics. Ordinary least square method was used for the multiple regression analysis to identify the factors that affect the out of pocket health care expenditures. In addition to other empirical studies in Turkey, the effects of relative poverty and types of income and occupation on oop expenditures were estimated. Results identify that consumption expenditure of the household, poverty, wage/income status, education, household size, having chronic disease and having elderly in the households have significant effects on the amount of out of pocket (oop) health expenditure of the households. Keywords: Health care financing; Out of pocket payments; Ordinary least square method; Turkey
Distributed Parallel Sparse Multinomial Logistic Regression

Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification. However, it cannot meet the time and memory requirements for processing large-scale data. We have reinvestigated the classification accuracy and running efficiency of the algorithm for solving SMLR problems using the Alternating Direction Method of Multipliers (ADMM), which is called fast SMLR (FSMLR) algorithm in this paper. By reformulating the optimization problem of FSMLR, we transform the serial convex optimization problem to the distributed convex optimization problem, i.e., global consensus problem and sharing problem. Based on the distributed optimization problem, we propose two distribute parallel SMLR algorithms, sample partitioning-based distributed SMLR (SP-SMLR), and feature partitioning-based distributed SMLR (FP-SMLR), for a large-scale sample and large-scale feature datasets in big data scenario, respectively. The experimental results show that the FSMLR algorithm has higher accuracy than the original SMLR algorithm. The big data experiments show that our distributed parallel SMLR algorithms can scale for massive samples and large-scale features, with high precision. In a word, our proposed serial and distribute SMLR algorithms outperform the state-of-the-art algorithms.
Laparoscopic living donor hepatectomy for liver transplantation in porcine

Objective:To study the techniques and feasibility of laparoscopic living donor hepatectomy.Methods:Two pigs were studied and laparoscopic left hepatic lobectomy performed.The procedure was conferred to human.The vital signs were monitered.Time of operation,dissection of left heptic blood vessels and heptic duct,dissection of liver parenchyma,and warm ischemia was recorded,blood loss and the left liver was measured.Results:(1)The vital signs were stable and safe,and the operations were performed successfully with a blood loss of 200ml. (2) The lengths of left hepatic artery were 1.8cm and 1.6cm,left portal vein 1.0cm and 0.8cm,hepatic duct 0.8cm and 0.6cm,hepatic vein 0.3cm and 0.2cm,and the perfusion of left livers was satisfied.The warm ischemia times were smin and 4min.Conclusions:The porcine laparoscopic living donor hepatectomy is feasible from a technical perspective.
Stein Point Markov Chain Monte Carlo

An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising a Stein discrepancy between the empirical measure and the target and, hence, require the solution of a non-convex optimisation problem to obtain each new point. This paper removes the need to solve this optimisation problem by, instead, selecting each new point based on a Markov chain sample path. This significantly reduces the computational cost of Stein Points and leads to a suite of algorithms that are straightforward to implement. The new algorithms are illustrated on a set of challenging Bayesian inference problems, and rigorous theoretical guarantees of consistency are established.
QoS Aware Channel Selection and Power Allocation for Device-to-Device Communication

Device-to-Device (D2D) communication has proven as a candidate solution for offloading the data traffic and improving spectrum efficiency of cellular communication systems. It allows the terminals to communicate directly without increasing the load on the base station (BS). Underlay D2D is adopted in our work because it is more spectrum efficient due to channel sharing among cellular user (CU) and D2D pair. This kind of sharing is beneficial only when appropriate resource management is done otherwise it causes harmful interferences too. This work introduces an efficient and simple method to find multiple CUs as reuse partners for each D2D pair. After the selection of channels, power control is applied by means of convex optimization for D2D pairs to control the interference at the BS. The objective of the work is to maximize the D2D sum-rate without affecting CU's performance. The proposed scheme is then compared with existing pairing algorithms named as Hungarian also known as Kuhn-Munkres (KM) and IPPO algorithms and performance gain is verified through simulation results.
Optimal Generation Scheduling for Wind, Solar and Thermal Power Considering Grid Security Constraints

This research aims to propose a methodology for short-term generation scheduling of thermal, wind and solar generating units considering the security constraints. The study model considers a stochastic nature of renewable sources as well as deviation charges caused due to the disparity between actual and scheduled generations. With an aim to diminish the total operating cost, RANDIW-PSO has been employed while satisfying system operational and security constraints. In order to substantiate the efficacy of our proposed algorithm, four realistic generation scheduling scenarios have been considered. Each of which has been tested on a modified IEEE-24 bus reliability test system. The consequences of transmission line flow restrictions on generation scheduling have also been verified. The results confirm a significant reduction in total operating cost with secured hourly generation schedules.
Improvements in seismic resolution and current limitations in the Global Seismographic Network

Station noise levels play a fundamental limitation in our ability to detect seismic signals. These noise levels are frequency-dependent and arise from a number of physically different drivers. At periods greater than 100 s, station noise levels are often limited by the self-noise of the instrument as well as the sensitivity of the instrument to non-seismic noise sources. Recently, station operators in the Global Seismographic Network (GSN) have deployed several Streckeisen STS-6A very broad-band borehole seismometers. These sensors provide a potential replacement for the no-longer-produced Streckeisen STS-1 seismometer and the GeoTech KS-54 000 borehole seismometer. Along with showing some of the initial observational improvements from installing modern very broad-band seismometers at depth, we look at current limitations in the seismic resolution from earth tide periods 100 000 s (0.01 mHz) to Nyquist at most GSN sites (0.02 s or 50 Hz). Finally, we show the potential for improved observations of continuously excited horizontal Earth hum as well as the splitting of very long-period torsional modes. Both of these observations make use of the low horizontal noise levels which are obtained by installing very broad-band borehole seismometers at depth.
Biomass Conversion to Fuels and Value-Added Chemicals: A Comprehensive Review of the Thermochemical Processes

Fossil fuels have fueled the world economy for decades. However, given their limited nature, fluctuating prices and the escalating environmental concerns, there is an urgent need to develop and valorize cheaper, cleaner and sustainable alternative energy sources to curb these challenges. Biomass represents a valid alternative to fossil fuels, especially for fuel and chemical production as it represents the only natural organic renewable resource with vast abundance. A vast array of conversion technologies is used to process biomass from one form to another, to release energy, high-value products or chemical intermediates. This paper extensively reviews the thermochemical processing of biomass to fuels and high-value chemicals, with an emphasis on the process performance, conditions, and weaknesses. Technologies with great future prospects as well as those with possible linkage to CO2 capture and sequestration are highlighted. The important chemical compositions of biomass feedstock, their conversion technologies and most importantly, the role of catalysis in their conversion to fuels, fuel additives, based chemicals, and added-value chemicals are also discussed. Special attention is given to biofuel production for transportation as this sector is responsible for the highest global greenhouse gas emissions, and has an emerging market with promising future prospects for sustainable large-scale biomass processing. The processes involved in the purification and upgrading of biomass-derived products into higher-value products are equally discussed and reviewed.
Analysis of a Fourth Order Step-Down Converter

In this paper, a fourth-order buck converter with higher capability to step down supply voltage is proposed. Owing to low voltage gain that ideally altercates from minimum to a maximum value near unity, this converter becomes a suitable choice for interfacing high voltage DC-supply with a low voltage application at the output. Key features of this topology include lesser switching elements with minimal switch voltage stress. The steady-state analysis of converter is discussed for continuous conduction mode of inductor current and design criteria for selecting circuit components are established. In order to regulate its load voltage, a single-loop voltage mode controller has been designed making use of the state-space average model obtained using equivalent ON and OFF switching circuits. A converter prototype test circuit is designed for 42 to 12 V, 28 W rating to verify the steady-state and dynamic performance of converter using simulation and experimental studies.
Dating Deep‐Sea Sediments With 230Th Excess Using a Constant Rate of Supply Model

We present a dating method for deep‐sea sediments that uses the natural radionuclide 230Th (half‐life 75,380 years) in analogy to 210Pb with the constant rate of supply (CRS) model. Using an example from the western Indian sector of the Southern Ocean, we demonstrate how sets of values of 230Th, 232Th, and U isotopes activities can supply absolute age information for the last ~450,000 years, given a sufficient precision, resolution, and depth coverage of the analytical data in a suitable core. An assessment of age uncertainties resulting from analytical errors using a Monte Carlo approach and an analytical solution for error propagation shows good agreement. We also investigate errors due to a violation of model assumptions by variable focusing of deep‐sea sediments by means of a simulated core. Finally, we use real examples from independently dated sediment cores containing carbonate, using previously existing 230Th data, to test the approach. The consideration of the systematic errors and the examples suggests that the uncertainties are smallest in the central part of the record and that the variability of focusing conditions controls the accuracy of the 230Th CRS dates. Our own example demonstrates an excellent agreement of the 230Th CRS method with independent age constraints, adding an important tool for dating marine records that does not depend on the presence of carbonate. The obtained values are also suitable to calculate 230Th‐normalized preserved vertical rain rates of various sedimentary compounds, permitting an improved quantitative comparison of marine paleorecords with other archives like ice cores.
Filling a void: Analysis of early tropical soil and vegetative recovery under leguminous, post‐coal mine reforestation plantations in East Kalimantan, Indonesia

Land degradation in Borneo, one of the world's richest biodiversity hotspots, is extensive. In East Kalimantan, 5,000,000 ha of land are zoned for surface‐mined coal. Deforestation from this mining threatens biodiversity and results in soil degradation, erosion, and polluted runoff, all directly impacting human populations. Revegetation methods developed for temperate forests are commonly used globally for mine rehabilitation. However, few empirical studies of native forest restoration as part of mine rehabilitation exist from wet tropical regions. Here, a chronosequence was established to observe forest succession under leguminous plantations at the PT Singlurus Pratama coal mine in East Kalimantan, Indonesia. Soil and natural regeneration data were recorded from samples of ten 20 × 60‐m plots randomly located in plantings aged 2, 7, and, 9 years postmining. Linear models (LMEMs) did not reveal greater soil pH, woody plant diversity, or soil phosphorus and nitrogen in older plantings. Rather, they showed higher soil carbon in older plantings, whereas nitrogen and pH were positively correlated with woody species diversity and abundance. Graminoids were less abundant, but ferns were more abundant in older sites in an ordination analysis. The implications are exotic tree plantations shade‐out competitive understory herbaceous species (such as graminoids), opening growing space for other vegetation. However, the establishment of woody species is spatially limited possibly by differences in soil degradation among sites. Our results suggest that planting leguminous trees alone may not be sufficient to restore native forests, and future management should conserve and facilitate the establishment of tropical forest topsoil.