Miracle-dz/UniFAR-FTUs huggingface dataset:
2D PET Image Reconstruction Using Robust L1 Estimation of the Gaussian Mixture Model

An image or volume of interest in positron emission tomography (PET) is reconstructed from gamma rays emitted from a radioactive tracer, which are then captured and used to estimate the tracer’s location. The image or volume of interest is reconstructed by estimating the pixel or voxel values on a grid determined by the scanner. Such an approach is usually associated with limited resolution of the reconstruction, high computational complexity due to slow convergence and noisy results. This paper presents a novel method of PET image reconstruction using the underlying assumption that the originals of interest can be modelled using Gaussian mixture models. Parameters are estimated from one-dimensional projections using an iterative algorithm resembling the expectation-maximization algorithm. This presents a complex computational problem which is resolved by a novel approach that utilizes ${L_{1}}$ minimization.
DIGITAL EMULATION OF DISTORTION EFFECTS BY WAVE AND PHASE SHAPING METHODS

This paper will consider wave and phase signal shaping techniques for the digital emulation of distortion effect processing. We examine in detail how to determine the wave and phase shaping functions with harmonic amplitude data only first, and then after including the harmonic phase data. Three distortion effects units are used to provide test data. Wave and phase shaping functions for the emulation of these effects are derived with the assistance of a super-resolution frequency-domain analysis technique. In complement to this, we describe an alternative time domain method for determining phase shaping functions using Dynamic Time Warping. Finally, we propose a method for assessing the frequency dependency of distortion effects to help the design of multiband wave and phase shaping functions.
Influence of temperature and heating rates on calcination of waste cockle shells to calcium oxides

Cockle shells or Anadara granosa has been touted as an alternative for the calcium carbonate sources instead of using the limestone. Mining large quantities of rocks can contribute to the environmental damage and engage with the high cost for environmental compliance. Li et al. (2009) also proved that seashells to have higher composition of calcium oxides compared to the limestones. In this research, the shells of Malaysian blood cockle, Anadara granosa, were used as the starting material. The present study was done to evaluate the influence of operating conditions on the minerals of Anadara granosa shells by heat treatment at various temperatures and heating rates, and to subsequently use the resulting material for the formation of calcium oxide. CaO associated with carbonaceous material has been of recent interest in applications such as transesterification catalysts and sorbents for sulphur dioxide. The utilisation of the economic resources of waste bio-minerals from blood cockle shells is expected to promote the commercial application of the resulting calcium oxides. Calcination of cockle shells was analysed with TGA with different range of temperature (700, 750, 800, 850 and 900oC) and heating rates at 10oC/min and 20oC/min. Overall, the better performance is shown at the calcination temperature of 800oC for 39 minutes and heating rate of 20oC/min under N2 gas flow of 40mL/min. Then, the characterization of the cockle shells are done by the FTIR spectrophotometer which is performed in a range of 4000cm-1 to 400 cm-1 with a resolution of 2 cm-1 and averaging of 62 scans. As result, the FTIR analysis showed that the waste cockle shell from the seashore gave the peak of 1472.99cm-1
Density Evolution Analysis of Partially Information Coupled Turbo Codes on the Erasure Channel

In this paper, we investigate the performance of a class of spatially coupled codes, namely partially information coupled turbo codes (PIC-TCs) over the binary erasure channel (BEC). This class of codes enjoy flexible code rate adjustment by varying the coupling ratio. Moreover, the coupling method can be directly applied to any component codes without changing the encoding and decoding architectures of the underlying component codes. However, the theoretical performance of PIC-TCs has not been fully investigated. For this work, we consider the codes that have coupling memory $m$ and study the corresponding graph model. We then derive the exact density evolution equations for these code ensembles with any given coupling ratio and coupling memory $m$ to precisely compute their belief propagation decoding thresholds for the BEC. Our simulation results verify the correctness of our theoretical analysis and also show better error performance over uncoupled turbo codes with a variety of code rates on the BEC.
Electronic structure of superlattices of graphene and hexagonal boron nitride

We study the electronic structure of superlattices consisting of graphene and hexagonal boron nitride slabs, using ab initio density functional theory. We find that the system favors a short C–B bond length at the interface between the two component materials. A sizeable band gap at the Dirac point is opened for superlattices with single graphene layers but not for superlattices with graphene bilayers. The system is promising for applications in electronic devices such as field effect transistors and metal-oxide semiconductors.
Optical Quenching of Metastable Helium Atoms using Excitation to the 
4P
 State

Discharge and electron-impact excitation lead to the production of metastable helium atoms in two metastable states, 2$^1$S$_0$ and 2$^3$S$_1$. However, many applications require pure beams of one of these species or at least a detailed knowledge of the relative state populations. In this paper, we present the characterization of an original experimental scheme for the optical depletion of He(2$^1$S$_0$) in a supersonic beam which is based on the optical excitation of the 4$^1$P$_1 \leftarrow 2^1$S$_0$ transition at 397 nm using a diode laser. From our experimental results and from a comparison with numerical calculations, we infer a near unit depletion efficiency at all beam velocities under study (1070 m/s $\leq v \leq$ 1750 m/s). Since the technique provides a direct means to determine the singlet-to-triplet ratio in a pulsed supersonic helium beam, our results show that the intrabeam singlet-to-triplet ratio is different at the trailing edges of the gas pulse.
Improving performance measurement practices in construction organisations

Performance measurement (PM) is being practised by a variety of construction organisations and many are aware of its importance to them. It is necessary for organisations intending to extend businesses locally or globally and as an important ingredient for the strategy development process. The paper contributes to a growing body of knowledge on PM and describes a maturity model to help organisations to structure and organise the PM practices. In particular, it explores the importance of PM and potential tools such as the Balanced Scorecard (BSC) and the European Foundation for Quality Management (EFQM) Excellence Model. These two tools are widely known and the most used in all sectors including construction to measure organisations’ performance. This paper focuses on current PM practices in two countries, UK and Malaysia. Information on knowledge and understanding of PM, PM processes, criteria, tools and models used and challenges in implementing PM was gathered using semi-structured interviews with twelve large construction organisations. The purpose of interviews was to seek the organisations’ views on how they approach and conduct PM and derive benefits from it. Results indicate that organisations understand what they can gain from implementing PM. It is being practised in organisations to help improve business and gain more profits. Involvement of all staff, managerial level to bottom level, is important either directly or indirectly in the PM process. Furthermore, all organisations agreed that the appropriate use of tools and models to measure performance simplifies the process and indicates how organisations can move in future. Financial and non-financial aspects are evaluated and measured for assessing organisations’ performance. However, PM for organisations remains a challenge. It is seen that organisations face difficulties not only in understanding the PM process but also where appropriate data for measuring performance can be sourced. A critical analysis of the literature reviewed and the interview results lead to ways of helping organisations to target relevant performance measures, based on their maturity level.
Learning Restricted Boltzmann Machines with Arbitrary External Fields

We study the problem of learning graphical models with latent variables. We give the first algorithm for learning locally consistent (ferromagnetic or antiferromagnetic) Restricted Boltzmann Machines (or RBMs) with {\em arbitrary} external fields. Our algorithm has optimal dependence on dimension in the sample complexity and run time however it suffers from a sub-optimal dependency on the underlying parameters of the RBM. Prior results have been established only for {\em ferromagnetic} RBMs with {\em consistent} external fields (signs must be same)\cite{bresler2018learning}. The proposed algorithm strongly relies on the concavity of magnetization which does not hold in our setting. We show the following key structural property: even in the presence of arbitrary external field, for any two observed nodes that share a common latent neighbor, the covariance is high. This enables us to design a simple greedy algorithm that maximizes covariance to iteratively build the neighborhood of each vertex.
Learning Part Generation and Assembly for Structure-aware Shape Synthesis

Learning powerful deep generative models for 3D shape synthesis is largely hindered by the difficulty in ensuring plausibility encompassing correct topology and reasonable geometry. Indeed, learning the distribution of plausible 3D shapes seems a daunting task for the holistic approaches, given the significant topological variations of 3D objects even within the same category. Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet. The network is composed of an array of per-part VAE-GANs, generating semantic parts composing a complete shape, followed by a part assembly module that estimates a transformation for each part to correlate and assemble them into a plausible structure. Through delegating the learning of part composition and part placement into separate networks, the difficulty of modeling structural variations of 3D shapes is greatly reduced. We demonstrate through both qualitative and quantitative evaluations that PAGENet generates 3D shapes with plausible, diverse and detailed structure, and show two applications, i.e., semantic shape segmentation and part-based shape editing.
Effects of Aerosol Solubility and Regeneration on Mixed-Phase Orographic Clouds and Precipitation

AbstractA detailed bin aerosol-microphysics scheme has been implemented into the Weather Research and Forecast Model to investigate the effects of aerosol solubility and regeneration on mixed-phase orographic clouds and precipitation. Two-dimensional simulations of idealized moist flow over two identical bell-shaped mountains were carried out using different combinations of aerosol regeneration, solubility, loading, ice nucleation parameterizations, and humidity. The results showed the following. 1) Pollution and regenerated aerosols suppress the riming process in mixed-phase clouds by narrowing the drop spectrum. In general, the lower the aerosol solubility, the broader the drop spectrum and thus the higher the riming rate. When the solubility of initial aerosol increases with an increasing size of aerosol particles, the modified solubility of regenerated aerosols reduces precipitation. 2) The qualitative effects of aerosol solubility and regeneration on mixed-phase orographic clouds and precipitation ar.
Perceptual Based Adversarial Audio Attacks

Recent work has shown the possibility of adversarial attacks on automatic speechrecognition (ASR) systems. However, in the vast majority of work in this area, theattacks have been executed only in the digital space, or have involved short phrasesand static room settings. In this paper, we demonstrate a physically realizableaudio adversarial attack. We base our approach specifically on a psychoacoustic-property-based loss function, and automated generation of room impulse responses, to create adversarial attacks that are robust when played over a speaker in multiple environments. We show that such attacks are possible even while being virtually imperceptible to listeners.
Leakage-Resilient Non-Malleable Secret Sharing in Non-compartmentalized Models

Non-malleable secret sharing was recently proposed by Goyal and Kumar in independent tampering and joint tampering models for threshold secret sharing (STOC18) and secret sharing with general access structure (CRYPTO18). The idea of making secret sharing non-malleable received great attention and by now has generated many papers exploring new frontiers in this topic, such as multiple-time tampering and adding leakage resiliency to the one-shot tampering model. Non-compartmentalized tampering model was first studied by Agrawal this http URL (CRYPTO15) for non-malleability against permutation composed with bit-wise independent tampering, and shown useful in constructing non-malleable string commitments. We initiate the study of leakage-resilient secret sharing in the non-compartmentalized model. The leakage adversary can corrupt several players and obtain their shares, as in normal secret sharing. The leakage adversary can apply arbitrary affine functions with bounded total output length to the full share vector and obtain the outputs as leakage. These two processes can be both non-adaptive and do not depend on each other, or both adaptive and depend on each other with arbitrary ordering. We construct such leakage-resilient secret sharing schemes and achieve constant information ratio (the scheme for non-adaptive adversary is near optimal). We then explore making the non-compartmentalized leakage-resilient secret sharing also non-malleable against tampering. We consider a tampering model, where the adversary can use the shares obtained from the corrupted players and the outputs of the global leakage functions to choose a tampering function from a tampering family F. We give two constructions of such leakage-resilient non-malleable secret sharing for the case F is the bit-wise independent tampering and, respectively, for the case F is the affine tampering functions.
Fovea-Attentive Residual Network for Extracting 3D Angiograms from Single Volumetric OCT Scan

Optical Coherence Tomography Angiography (OCTA) has drawn numerous attentions in ophthalmology. However, its data acquisition is time-consuming, because it is based on temporal-decorrelation principle thus requires multiple repeated volumetric OCT scans. In this paper, we propose a Fovea-Attentive Residual Network (FAR-Net). By combining an attention mechanism and residual learning, it is able to extract capillary-level angiograms directly from a single OCT scan. The segmentation results of the inner limiting membrane and outer plexiform layers and the central $1\times1$ mm$^2$ field of view of the fovea are employed in the fovea-attention mechanism. So the influences of large retinal vessels and choroidal vasculature on the extraction of capillaries can be minimized during the training of the FAR-Net. The residual learning is used to take advantages of the residual block's shortcut mechanism, so as to avoid the gradient vanishing and accelerate the network convergence. The quantitative and qualitative results demonstrate that the FAR-Net has the capacity to better-visualizing capillaries around the foveal avascular zone than the existing work using a U-Net architecture.
A Comparative Study on Prediction Accuracy of Statistical Models for Modeling Deterministic Output Responses

The aim of this paper is to compare the prediction accuracy between three popular statistical models named Response surface methodology (RSM), Kriging model (KRG), and Radial basis function model (RBF). These models have been extensively used for modeling the responses from computer simulated experiments (CSE) along with various classes of design. A selection of design to run CSE is critical for prediction accuracy of the approximation model. In this paper, three main classes of design namely Latin hypercube design (LHD), and two classical designs, Central composite design (CCD) and Fractional factorial design (FFD) have been used to fit the models. The prediction accuracy of each model is validated using different nonlinear test problems ranging from 2 to 10 input variables. The comparative studies are employed through the root mean square of error (RMSE) values. The results indicate that RSM performs best when the optimal Latin hypercube design is used with non-complex problem. In the case of complex problem, KRG and RBF models are superior over RSM. Furthermore, when the classical designs are used, RBF model performs best in terms of prediction accuracy.
Integrated Expert Analytical System for Assessment of Oil-and-Gas Saturation of Strata

The present research deals with the problem of development of an integrated expert-analytical system for optimum selection of calculated oil-field-geophysical parameters of oil and gas deposits with the purpose of increasing the accuracy of assessment of the reserves of oil and gas deposits. The purpose of the system is to make current adequate decisions on determining of oil-and-gas saturation of strata and future identification of the most significant methods for that, with these methods forming the foundation of knowledge bases for oil-and-gas deposits of the Apsheron peninsula of Azerbaijan. The system architecture allows for expanding the system with its subsequent transformation into a cluster of expert-analytical systems. A logical model of the proposed system is presented. The paper contains a detailed description of the mechanism of operation of the system as a whole and of its individual blocks. Mathematical and formal-logical bases of the intelligent system are explained. The system is equipped with a tool for dynamic statistical analysis of decisions made by it, with representation of the results in real-time mode. The results of the system testing on specific oil-and-gas deposit of the Apsheron peninsula of Azerbaijan in 2013 are given.
Computation of intrinsic spin Hall conductivities from first principles using maximally localized Wannier functions

This work was supported by the Creative-Pioneering Research Program through Seoul National University and Korean NRF No2016R1A1A1A05919979 (J.H.R. and C.-H.P.), and by Grant No. FIS2016-77188-P from the Spanish Ministerio de Economia y Competitividad (I.S.). Computational resources were provided by KISTI Supercomputing Center (KSC-2018-CHA-0051).
CHANGE ANALYSIS OF MULTI-TEMPORAL IMAGES

Image fusion and subsequent scene analysis are important for studying Earth surface conditions from remotely sensed imagery. The fusion of the same scene using satellite data taken with different sensors or acquisition times is known as multi-sensor or multi-temporal fusion, respectively. The purpose of this study is to investigate the effects of the multisensor, multi-temporal fusion process when a pan-sharpened scene is produced from low spatial resolution multispectral (MS) images and a high spatial resolution panchromatic (PAN) image. It is found that the component substitution (CS) fusion method provides better performance than the multi-resolution analysis (MRA) scheme. Quantitative analysis shows that the CS-based method gives a better result in terms of spatial quality (sharpness), whereas the MRA-based method yields better spectral quality, i.e., better color fidelity to the original MS images. Key Words— Multi-sensor, Multi-temporal fusion, Component substitution, Multi-resolution analysis
Technical Report: Optimizing Human Involvement for Entity Matching and Consolidation

An end-to-end data integration system requires human feedback in several phases, including collecting training data for entity matching, debugging the resulting clusters, confirming transformations applied on these clusters for data standardization, and finally, reducing each cluster to a single, canonical representation (or "golden record"). The traditional wisdom is to sequentially apply the human feedback, obtained by asking specific questions, within some budget in each phase. However, these questions are highly correlated; the answer to one can influence the outcome of any of the phases of the pipeline. Hence, interleaving them has the potential to offer significant benefits. In this paper, we propose a human-in-the-loop framework that interleaves different types of questions to optimize human involvement. We propose benefit models to measure the quality improvement from asking a question, and cost models to measure the human time it takes to answer a question. We develop a question scheduling framework that judiciously selects questions to maximize the accuracy of the final golden records. Experimental results on three real-world datasets show that our holistic method significantly improves the quality of golden records from 70% to 90%, compared with the state-of-the-art approaches.
An irregular filter model

In this paper we introduce a new filter model, which is of a kind that escaped investigation up to now: it is induced by an intersection type theory generated in a non-standard way, by a preorder which puts into relation an atom with an arrow type, without equating them. We study the domain-theoretic implications of this choice, that are not trivial: in order to describe this filter model a new category is introduced and a special purpose functor defined. The filter model is then characterized as the initial algebra of the functor.
On the Locally Lipschitz Robustness of Bayesian Inverse Problems

In this note we consider the robustness of posterior measures occuring in Bayesian inference w.r. t. perturbations of the prior measure and the log-likelihood function. This extends the well-posedness analysis of Bayesian inverse problems. In particular, we prove a general local Lipschitz continuous dependence of the posterior on the prior and the log-likelihood w.r. t. various common distances of probability measures. These include the Hellinger and Wasserstein distance and the Kullback-Leibler divergence. We only assume the boundedness of the likelihoods and measure their perturbations in an $L^p$-norm w.r. t. the prior. Our results indicate an increasing sensitivity of Bayesian inference as the posterior becomes more concentrated, e.g., due to more or more accurate data. This confirms and extends previous observations made in the sensitivity analysis of Bayesian inference.