Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Prediction Under Multimodality with Conditional Stochastic Networks

Given a visual history, multiple future outcomes for a video scene are equally probable, in other words, the distribution of future outcomes has multiple modes. Multimodality is notoriously hard to handle by standard regressors or classifiers: the former regress to the mean and the latter discretize a continuous high dimensional output space. In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables. Such random variables are sampled from simple Gaussian distributions whose means and variances are parametrized by the output of convolutional encoders over the visual history. We introduce novel convolutional architectures for predicting future body joint trajectories that outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}. We introduce stochastic spatial transformers through optical flow warping for predicting future frames, which outperform their deterministic equivalents \cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves an intractable marginalization over stochastic variables. We compare various training schemes that handle such marginalization through a) straightforward sampling from the prior, b) conditional variational autoencoders \cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed K-best-sample loss that penalizes the best prediction under a fixed "prediction budget". We show experimental results on object trajectory prediction, human body joint trajectory prediction and video prediction under varying future uncertainty, validating quantitatively and qualitatively our architectural choices and training schemes.
Experimental Investigation on Moisture Sensing Behavior of La2O3 with La(OH)3 at Nanoscale

ABSTRACT This article reports the moisture-sensing behavior of a nanostructured La2O3 and La(OH)3. Pellets of this material were prepared and used as a sensing element after successive thermal annealing for 2 h at different temperatures of 200, 400, 600, and 800°C. After each annealing step, this sensing element was placed in a conductivity holder and exposed to humidity. Variations in resistance with relative humidity were recorded. The sensing material was characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). A close look of the scanning electron micrographs confirms that the sensing element annealed at 400°C was more porous and uniform than the other samples. The maximum average sensitivity obtained was 19 MΩ/% relative humidity (RH) and results were found to be reproducible. The crystallite size of the sensing material was found to be in the range of 30–140 nm. The mass loss of the sensing material at different temperatures was also studied.
Current and future CO 2 emissions from drained peatlands in Southeast Asia

Abstract. Forested tropical peatlands in Southeast Asia store at least 42 000 Million metric tonnes (Mt) of soil carbon. Human activity and climate change threatens the stability of this large pool, which has been decreasing rapidly over the last few decades owing to deforestation, drainage and fire. In this paper we estimate the carbon dioxide (CO2) emissions resulting from drainage of lowland tropical peatland for agricultural and forestry development which dominates the perturbation of the carbon balance in the region. Present and future emissions from drained peatlands are quantified using data on peatland extent and peat thickness, present and projected land use, water management practices and decomposition rates. Of the 27.1 Million hectares (Mha) of peatland in Southeast Asia, 12.9 Mha had been deforested and mostly drained by 2006. This latter area is increasing rapidly because of increasing land development pressures. Carbon dioxide (CO2) emission caused by decomposition of drained peatlands was between 355 Mt y−1 and 855 Mt y−1 in 2006 of which 82% came from Indonesia, largely Sumatra and Kalimantan. At a global scale, CO2 emission from peatland drainage in Southeast Asia is contributing the equivalent of 1.3% to 3.1% of current global CO2 emissions from the combustion of fossil fuel. If current peatland development and management practices continue, these emissions are predicted to continue for decades. This warrants inclusion of tropical peatland CO2 emissions in global greenhouse gas emission calculations and climate mitigation policies. Uncertainties in emission calculations are discussed and research needs for improved estimates are identified.
A scalable massive MIMO array architecture based on common modules

Massive MIMO is envisioned as one of the key enabling technologies for 5G wireless and beyond. While utilizing the spatial dimension to reduce interference and increase capacity in multi-user scenarios, massive MIMO base stations present several unique implementation challenges due to their large physical size and the high datarate generated by all the elements. To be cost-effective and energy efficient, practical designs must leverage the particular characteristics of massive MIMO to ensure scalability. Here, we propose an array architecture based on a common module which serves a small number of antennas with RF transceivers, data converters, and several support functions. Multiple chips are tiled into a grid and interconnected through a digital nearest-neighbor mesh network, avoiding the severe problems associated with analog signal distribution. Scalability across a wide range of array sizes is achieved by using distributed beamforming algorithms. It is demonstrated that by using this approach, the maximum backhaul datarate scales as the number of users rather than the number of antennas. Finally, we present a detailed accounting of the power consumption of the array and use the resulting optimization problem to show that per-element overhead limits the minimum achievable power consumption.
Lobomycosis: A case from Southeastern Europe and review of the literature.

BACKGROUND Lobomycosis, also known as Jorge Lobo's disease, represents a rare chronic subcutaneous mycosis caused by the fungus Lacazia loboi, an organism that is found within lesions but has not been cultured to date. The natural reservoir of L. loboi is unknown but it is believed to be aquatic, or associated with soil and vegetation. More than 550 human cases have been reported, especially in patients with a history of travel or residence in endemic areas (Central and South America, particularly Brazil) or in communities along rivers. MAIN OBSERVATIONS We describe a 64-year-old Greek female farmer living in a coastal region, who presented with an erythematous plaque on her left inner thigh resembling a keloid. The diagnosis was based on the triad: 1) absence of fungal growth in cultures, 2) positive direct microscopic examination of the lesion and 3) histopathology, all consistent with lobomycosis. Particularly, skin biopsy showed deep cutaneous fungal infection with granulomatous reaction. Fungal cells were found inside giant cells. The fungi were thick-walled with some budding, isolated or in short chains. Dermal fibrosis was present. Our patient had a medical history of common variable immunodeficiency but no history of travel to South or Central America. She probably acquired this rare infection by injury during her agricultural works. CONCLUSION Our case represents probably the first documented case of human lobomycosis in Southeastern Europe. This case is unusual due to the rarity of lobomycosis in Mediterranean countries, particularly in Southeastern Europe.
Processing and Characterization of Sol-Gel Cerium Oxide Microspheres

Of interest to space exploration and power generation, Radioisotope Thermoelectric Generators (RTGs) can provide long-term power to remote electronic systems without the need for refueling or replacement. Plutonium-238 (Pu-238) remains one of the more promising materials for thermoelectric power generation due to its high power density, long half-life, and low gamma emissions. Traditional methods for processing Pu-238 include ball milling irregular precipitated powders before pressing and sintering into a dense pellet. The resulting submicron particulates of Pu-238 quickly accumulate and contaminate glove boxes. An alternative and dust-free method for Pu-238 processing is internal gelation via sol-gel techniques. Sol-gel methodology creates monodisperse and uniform microspheres that can be packed and pressed into a pellet. For this study cerium oxide microspheres were produced as a surrogate to Pu-238. The similar electronic orbitals between cerium and plutonium make cerium an ideal choice for non-radioactive work. Before the microspheres can be sintered and pressed they must be washed to remove the processing oil and any unreacted substituents. An investigation was performed on the washing step to find an appropriate wash solution that reduced waste and flammable risk. Cerium oxide microspheres were processed, washed, and characterized to determine the effectiveness of the new wash solution.
Property evaluation of 304L stainless steel fabricated by selective laser melting

Purpose – Additive manufacturing raw material cost has been recently confirmed as a significant obstacle to widespread deployment of these technologies in industry. Aiming at reducing the cost of the selective laser melting (SLM) process, the purpose of this paper is to evaluate the different properties of products fabricated by SLM using low‐cost ($10/Kg) feedstock 304L stainless steel powders. The entire process cost was also evaluated.Design/methodology/approach – Using an experimental approach, 24 samples with different shapes and sizes were fabricated with layer thickness of 30, 50 and 70 μm and laser scanning speed set at 70 and 90 mm/s. Part geometry, dimensional tolerance, surface quality, density, mechanical properties and microstructure were evaluated.Findings – Results confirmed that the SLM of low‐cost 304L powder was successful and could produce functional parts with fine details and small wall thickness. Using small layer thickness and low scanning speed improved the properties by more than .
Practical Considerations for Simulated Annealing Implementation

a mathematical model, it can be used to solve a broad range of problems. Unfortunately, mapping a real problem to the domain of simulated annealing can be difficult and requires familiarity with the algorithm. More often than not, it is possible to encode the solution (solve the problem) using several approaches. In addition, there are other factors that determine the success of failure of this algorithm. This chapter reviews how to plan the encoding of the solution, and discusses how to decide which encoding is more appropriate for each application. Several practical considerations for the proper implementation of simulated annealing are reviewed and analyzed. These include how to perturb the solution, how to decide a proper cooling schedule, and most important, how to properly implement the algorithm. Several cooling schedules are covered, including exponential, linear and temperature cycling. Additionally, the impact of random number generators is examined; how they affect the speed and quality of the algorithm. Essentially, this chapter is focused for those who want to solve real problems using simulated annealing for artificial intelligence, engineering, or research. An illustrative example is solved using simulated annealing and implemented in a popular programming language using an object-orien ted approach. This chapter offers a great opportunity to understand the power of this algorithm as well as to appreciate its limitations. Finally, it is reviewed how is possible to combine simulated annealing with other optimization algorithms (including the deterministic ones) to solve complex optimization problems. In particular, it is discussed how to train artificial neural networks using simulated annealing with gradient based algorithms.
Implementation of Technological Breakthroughs at Sector Level and Technology-Bias

Different goods are produced by different sectors in an economy. The fact that sectors use different production technologies is named technology-bias. The technology-bias is well documented and has important theoretical implications for economic growth and unemployment. We provide a theoretical model that explains the technology-bias and its development. We provide empirical evidence on the development of the technology-bias and explain this development by using our model-results. Last not least, we discuss the implications of our findings for the existing growth literature and structural change literature.
Massive MIMO Beam-forming for High Speed Train Communication: Directivity vs Beamwidth

High-mobility adaption and massive Multiple-input Multiple-output (MIMO) application are two primary evolving objectives for the next generation high speed train communication system. In this paper, we consider how to design a location-aware beam-forming for the massive MIMO system.We first analyze the tradeoff between beam directivity and beamwidth, based on which we present the sensitivity analysis of positioning accuracy. Then, we derive the maximum beam directivity and corresponding beamwidth under the restriction of diverse positioning accuracies to guarantee a high efficient transmission. Finally, we present a low-complexity beam-forming design with positioning robustness utilizing location information, which requires neither eigen-decomposing (ED) the uplink channel covariance matrix (CCM) nor ED the downlink CCM (DCCM). Numerical simulation indicates that a massive MIMO system with less than a certain positioning error can guarantee a required performance with satisfying transmission efficiency in the high-mobility scenario.
Development of Electrically Conductive Polymers

Conducting composites based on graphite, carbon black and polypropylene have been prepared and the effect of composition on the flexural modulus and electrical conductivity has been studied. The conductivity of polymers containing only one kind of filler did not increase significantly, their modulus of elasticity was the highest for graphite filling, and the lowest for nanotube filling. The conductivity of dual filler hybrids increased significantly due to the synergetic interaction between the two fillers. At lower graphite contents, because of the better dispersion of graphite, the material became more flexible, but at higher carbon black contents the stiffness increased significantly. In the case of triple filler hybrids, if keeping the graphite content at a certain level and varying only the carbon black/nanotube ratio, the conductivity and the flexural modulus varied according to exponential and linear rules respectively.
Neck Circumference as a Predictive Indicator of CKD for High Cardiovascular Risk Patients

Background. Neck circumference (NC) is an anthropometric measure of obesity for upper subcutaneous adipose tissue distribution which is associated with cardiometabolic risk. This study investigated whether NC is associated with indicators of chronic kidney disease (CKD) for high cardiometabolic risk patients. Methods. A total of 177 consecutive patients who underwent the outpatient departments of cardiology were prospectively enrolled in the study. The patients were aged >20 years with normal renal function or with stages 1–4 CKD. A linear regression was performed using the Enter method to present an unadjusted R 2, standardized coefficients, and standard error, and the Durbin-Watson test was used to assess residual independence. Results. Most anthropometric measurements from patients aged ≧65 were lower than those from patients aged <65, except for women's waist circumference (WC) and waist hip ratio. Female NC obtained the highest R 2 values for 24 hr CCR, uric acid, microalbuminuria, hsCRP, triglycerides, and HDL compared to BMI, WC, and hip circumference. The significances of female NC with 24 hr CCR and uric acid were improved after adjusted age and serum creatinine. Conclusions. NC is associated with indicators of CKD for high cardiometabolic risk patients and can be routinely measured as easy as WC in the future.
CRITICAL STRAINS DETERMINING THE YIELD BEHAVIOR OF s-PP*

Video-controlled tensile deformation experiments giving true stressstrain curves were carried out on samples of s-PP. Dividing the total tensile deformation into elastic and plastic parts shows three critical strains (A, B, C) for which the differential compliance and the recovery properties change. It was found that all critical strains remain constant on varying the crystallite thickness or the testing temperature. Point A marks the end of the linearelastic range. The yield point, as given by the maximum on the engineering stress-strain curve for a necking sample, essentially corresponds to the second critical point, although the yield point is shifted relative to B to higher strains and varies with temperature. It is associated with the collective onset of inter- and intralamellar slip processes. At the third critical strain (C), the lamellae become destroyed, and fibrils are formed. The texture changes accompanying the drawing determined by a simultaneous measurement of wide-angle X-ray scattering (WAXS) patterns indicate a common activity of intra- and interlamellar slip processes setting in at A and a dominance of intralamellar block slips at moderate deformations above B. The Young's modulus and the yield stress show a large decrease with temperature, whereas there are only minor changes in the crystallinity. We understand it as resulting from a change of coupling, both between the crystalline lamellae and of the blocks within the lamellae. *Dedicated to Prof. Francisco J. Baltá Calleja on the occasion of his 65th birthday.
Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction

We proposed using Conditional Random Fields with adaptive data reduction for the classification of 3D point clouds acquired from a Riegl Terrestrial laser scanner. The training and inference of the acquired large outdoor urban data can be time consuming. We approach the problem by computing an adaptive support region for each data point using 3D scale theory. For training and inference of the discriminative Conditional Random Fields, smaller set of data samples that contains relevant information within the support region is selected instead of using all point cloud data. We tested the algorithm on synthetically generated data and urban point clouds data acquired from the laser scanner. The computed support region is also used in feature extraction for urban point clouds data. The results showed improvement in the training and inference rate while maintaining comparable classification accuracy.
Knowledge-Based Anisotropic Diffusion of Vector-Valued 4-Dimensional Cardiac MR Images

We present a general formulation for a new knowledge-based approach to anisotropic diiusion of multi-feature and multi-dimensional images, with an illustrative application to cardiac MRI. We incorporate all available information through a more complete deenition of the conductance function which diiers from previous approaches in two aspects. First, we model the conductance as an explicit function of the position and not only of the diierential geometry of the image data. Inherent properties of the system (such as geometrical features or non-homogeneous data sampling) can therefore be taken into account by allowing the conductance values to depend on the location in the spatial and temporal coordinate space. Secondly, by deening the conductance as a second rank tensor, the non-homogeneous diiusion equation gains a truly anisotropic character which is essential to emulate and handle certain aspects of complex data systems. We demonstrate the eeciency of the proposed framework using density and velocity encoded cine volumetric MR images of the left ventricle. In this example we incorporate into the diiusion process spatial and temporal knowledge about the shape and dynamics of the heart. The method presented is suitable for image enhancement and also for segmentation. We compare our results to those obtained with other anisotropic diiusion methods.
A case study for optics: The solid immersion microscope

Microscopes are natural objects of study in introductory and upper level courses that cover optics because they are used in most science and engineering disciplines. The solid immersion microscope has been developed to study a variety of physical systems with high resolution and we suggest its inclusion in upper level optics courses. We briefly describe the solid immersion microscope in the context of geometrical optics and a desktop demonstration. We use the angular spectrum representation to calculate the focal fields produced by a conventional microscope and a solid immersion microscope. We also suggest a simple model for lens aberration and perform numerically the focal field calculations with and without aberrations to enable users to compare the performance of conventional and solid immersion microscopes. These calculations can help users develop intuition about the sensitivity of microscope performance to real-world manufacturing tolerances and to the limitations and capabilities of microscopy.
Factors Influencing Behavior of Selecting Touch Screen Mobile Phones

The main aim of this chapter is to examine the influence of factors affecting the behavioural intention of customers. In this chapter, perceived ease of use, perceived usefulness, and social influence are considered as predictors while behavioral intention is employed as dependent variable. A total of 260 participants were involved in this study. The participants were selected through non-probability sampling technique, namely Snow Ball. In order to achieve the objective of this study, three research hypotheses were constructed. The proposed hypotheses were tested by using multiple regression analysis. The findings demonstrate that three hypotheses are supported. The findings show that three factors are playing a significant role in developing the behavioral intention. This study will be a millstone for the potential researchers.
A Comparative Study of Biomimetic Coatings on Titanium and Stainless Steel

Titanium and stainless steel are examples of biomaterials widely used in dental and orthopedic implants owing to their properties of good corrosion resistance and excellent biocompatibility. This paper reports on a study of the biomimetic method applied to titanium (cp-Ti) and 316L stainless steel. The method consists in immersing the metal substrate in a synthetic solution of SBF (simulated body fluid) whose composition, pH and temperature resemble those of human blood plasma. The coating on the two metals was effective for obtaining hydroxyapatite, which was confirmed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR).