Miracle-dz/UniFAR-FTUs huggingface dataset:
On the error analysis and implementation of some eigenvalue decomposition and singular value decomposition algorithms

Many algorithms exist for computing the symmetric eigendecomposition, the singular value decomposition and the generalized singular value decomposition. In this thesis, we present several new algorithms and improvements on old algorithms, analyzing them with respect to their speed, accuracy, and storage requirements. We first discuss the variations on the bisection algorithm for finding eigenvalues of symmetric tridiagonal matrices. We then discuss a fast implementation of bisection using parallel prefix. We also discuss possible ways to stabilize it by using iterative refinement. Finally, we discuss how to use a divide-and-conquer algorithm to compute the singular value decomposition and solve the linear least squares problem and how to implement Van Loan''s algorithm for the generalized singular value decomposition using the divide-and-conquer algorithm.
Design and Implementation of a Vision Based In-Situ Defect Detection System of Automated Fiber Placement Process

In this paper, an in-situ defect detection system is proposed for automated fiber placement (AFP) process monitoring. To acquire meaningful data about the laid-up tows, the design, manufacturing and integration of a flexible three degrees of freedom vision system to the AFP machine is proposed. An image segmentation algorithm is developed to locate and isolate defects in input images. The proposed algorithm utilizes Gabor filters to extract the desired texture features which is followed by an adaptive thresholding. Successful results with four of the main defect classes namely, foreign bodies, wrinkles, gaps and bridging, were obtained. This monitoring system can reduce time-consuming and expensive efforts of manual quality inspection and will significantly increase AFP process reliability.
Resource Allocation in Multiple Backup Modes under Reliability Guarantee with Workload-Dependent Failure Probability

This paper proposes a primary and backup resource allocation model under reliability guarantees to minimize the deployment cost, which is considered as the number of activated physical nodes. We suppose that each node has a workload-dependent failure probability, which reveals a relationship between workload and node failure probability. Backup resources are allocated for the recovery of an unavailable function in a period, where the time relates to different backup modes and assigned recovery workload. We consider multiple states of pre-configuration for each function with different degrees of instantiation, initialization, synchronization, and different recovery times. We consider that the extra-assigned recovery resources can be adopted, which means that the recovery workload can be scaled, to speed up the recovery, while improving the resource efficiency to fully utilize the idle capacity for faster recovery. On the other hand, the extra-assigned recovery resources may lead to unsuccessful recovery in a specific failure configuration. We consider two reliability indicators, which are recovery time and the total unsuccessful recovery probability; each reliability indicator is restricted under a guarantee while minimizing the number of activated nodes as deployment cost. Adopting the proposed model saves on average 14% and 11% of the deployment cost when compared to baselines that do not incorporate variable backup modes and extra-assigned recovery resources, respectively, in our tests.
Multi-Robot Flocking Control Using Multi-Agent Twin Delayed Deep Deterministic Policy Gradient

This paper proposes the use of Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) to resolve the issues accompanied with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) such as the overestimation bias of a centralized critic when used to solve the multi-robot flocking control problem. A temporal difference error prioritized replay buffer is used alongside to achieve the most efficient learning process. In order to allow the robots to maintain a flock throughout a complex environment, an appropriate reward function was constructed, taking into consideration the following parameters: reaching the goal, maintaining a distance between the agents to ensure a stable connection while avoiding collision between one another, obstacle avoidance, and moving in a specific formation. Results of both algorithms are then compared to test the performance of the MATD3.
Electronic, transport, and optical properties of bulk and mono-layer PdSe2

The electronic and optical properties of bulk and monolayer PdSe2 are investigated using first-principles calculations. Using the modified Becke-Johnson potential, we find semiconductor behavior for both bulk and monolayer PdSe2 with indirect gap values of 0.03 eV for bulk and 1.43 eV for monolayer, respectively. Our sheet optical conductivity results support this observation and show similar anisotropic feature in the 2D plane. We further study the thermoelectric properties of the 2D PdSe2 using Blotzmann transport model and find interestingly high Seebeck coefficients (>200 μV/K) for both p- and n-type up to high doping level (∼2 × 1013 cm−2) with an anisotropic character in an electrical conductivity suggesting better thermoelectric performance along y direction in the plane.
Deep Network-Enabled Haze Visibility Enhancement for Visual IoT-Driven Intelligent Transportation Systems

The Internet of Things (IoT) has recently emerged as a revolutionary communication paradigm where a large number of objects and devices are closely interconnected to enable smart industrial environments. The tremendous growth of visual sensors can significantly promote the traffic situational awareness, traffic safety management, and intelligent vehicle navigation in intelligent transportation systems (ITSs). However, due to the absorption and scattering of light by the turbid medium in atmosphere, the visual IoT inevitably suffers from imaging quality degradation, e.g., contrast reduction, color distortion, etc. This negative impact can not only reduce the imaging quality, but also bring challenges for the deployment of several high-level vision tasks (e.g., object detection, tracking, recognition, etc.) in the ITS. To improve imaging quality under the hazy environment, we propose a deep network-enabled three-stage dehazing network (termed TSDNet) for promoting the visual IoT-driven ITS. In particular, the proposed TSDNet mainly contains three parts, i.e., multiscale attention module for estimating the hazy distribution in the RGB image domain, two-branch extraction module for learning the hazy features, and multifeature fusion module for integrating all characteristic information and reconstructing the haze-free image. Numerous experiments have been implemented on synthetic and real-world imaging scenarios. Dehazing results illustrated that our TSDNet remarkably outperformed several state-of-the-art methods in terms of both qualitative and quantitative evaluations. The high-accuracy object detection results have also demonstrated the superior dehazing performance of the TSDNet under hazy atmosphere conditions. The source code is available at https://github.com/gy65896/TSDNet.
Amorphous-Ga2O3/GaAs(100) anisotype heterojunctions with amorphous-Ga2O3 n-type films grown by magnetron sputtering

In this work, the growth and characterization of undoped amorphous gallium oxide films (Ga<inf>2</inf>O<inf>3</inf>-a) and titanium (Ti) doped films (Ga<inf>2</inf>O<inf>3</inf>-a:Ti) grown by the magnetron sputtering method are reported. The doped films resulted n-type with electron mobilities in the range of 13.68(cm<sup>2</sup>/Vs) to 35.11(cm<sup>2</sup>/Vs) with carrier concentration of 1.0x10<sup>14</sup>(cm<sup>-3</sup>) to 5.0x10<sup>19</sup>(cm<sup>-3</sup>). Both kinds of films were used in the construction + anisotype Ga<inf>2</inf>O<inf>3</inf>-a:Ti/Ga<inf>2</inf>O<inf>3</inf>-a/p GaAs (100) heterojunctions to demonstrate their potential application for the electronic device development.
Application of Different Least Square Methods for Transmission Line Parameter Estimation

Estimation of line parameters is a mathematically demanding process that depends on constructional characteristics and surrounding environment. Modern transmission systems and smart grids require the most efficient methodology for line parameters estimation where dynamic system behaviour and time-dependent parameters have to be taken into account. The least squares method is one of the most important family of methods which are widely used for processing experimentally obtained data. Different variants of least squares methods for transmission line parameters are proposed in literature. Most of the methods proposed are tested on simulated data, adding noise and/or systematic bias to measured quantities.In this paper, three different least squares methods, ordinary least squares, improved ordinary least squares and total least squares, are analysed and applied to real measurement data from two 220 kV lines. Results of parameter estimation are compared to classical measurement solutions. Only the ordinary least squares method produces satisfactory results for analysed cases.
Time- and Length Scales of Combustion in Liquid Rocket Thrust Chambers

This paper summarizes experimental and computational results of research on propellant mixing and combustion in high-pressure LOX/GH2 rocket combustors. Hot-fire tests and numerical experiments are utilized to get more detailed information about the physics of mixing and combustion under high-pressure (transcritical) conditions. Time- and length scales of the reacting shear layer are identified by results from numerical simulation and experiments. The comparison between numerical and experimental results show the same tendencies of the distribution of length scales within the reactive shear layder. It was ascertained that the length scales are increasing downstream of the injector face plate. Qualitative and quantitative data about the flowfield (morphology, velocity, species distribution, etc.) at different boundary conditions are presented.
Simulation of Theoretical and Experimental Parameters of Materials Used in CIGSe Thin Film Solar Cells by SCAPS software

The CIGSe thin film solar cell (TFSC) has been attracted to photovoltaic technologies for energy production due to its favorable material properties. This research work introduces the SCAPS software to model the CIGSe TFSC. From the simulated results, the solar cell performance was enhanced with an increase in thickness of the CIGSe by improving the absorption of photons. The recombination centers for the generated charge carriers were increased at higher carrier concentrations of the CIGSe. The efficiency was slightly improved when CdS thickness is increased from 20 to 100 nm. The collection of generated charge carriers was enhanced with a rise in carrier concentration of CdS, which increases the device performance. After analyzing these results, the optimized efficiency of 27.32% was noticed for CIGSe TFSC. The quantum efficiency (QE) results confirmed that the photons whose wavelength value lower than 900 nm are absorbed in the device. Moreover, the experimental results of CIGSe and CdS thin films are studied in this simulation. It was observed that the device’s performance was reduced with an increase in the thickness of the CdS. Hence, SCAPS software could be useful for designing the device experimentally.
Bandwidth Efficient Homomorphic Encrypted Matrix Vector Multiplication Accelerator on FPGA

Homomorphic Encryption (HE) is a promising solution to the increasing concerns of privacy in Machine Learning (ML) as it enables computations directly on encrypted data. However, it imposes significant overhead on the compute system and remains impractically slow. Prior works have proposed efficient FPGA implementations of basic HE primitives such as number theoretic transform (NTT), key switching, etc. Composing the primitives together to realize higher level ML computation is still a challenge due to the large data transfer overhead. In this work, we propose an efficient FPGA implementation of HE Matrix Vector Multiplication $(\mathbf{M}\times \mathbf{V})$, a key kernel in HE-based Machine Learning applications. By analyzing the data reuse characteristics and the encryption overhead of HE $\mathbf{M}\times \mathbf{V}$, we show that simply using the principles of unencrypted $\mathbf{M}\times \mathbf{V}$ to design accelerators for HE $\mathbf{M}\times \mathbf{V}$ can lead to a significant amount of DRAM data transfers. We tackle the computation and data transfer challenges by proposing a bandwidth efficient dataflow that is specially optimized for HE $\mathbf{M}\times \mathbf{V}$. We identify highly reused data entities in HE $\mathbf{M}\times \mathbf{V}$ and efficiently utilize the on-chip SRAM to reduce the DRAM data transfers. To speed up the computation of HE $\mathbf{M}\times \mathbf{V}$, we exploit three types of parallelism: partial sum parallelism, residual polynomial parallelism and coefficient parallelism. Leveraging these innovations, we demonstrate the first FPGA accelerator for HE matrix vector multiplication. Evaluation on 7 HE $\mathbf{M}\times \mathbf{V}$ benchmarks shows that our FPGA accelerator is up to $3.8\times$ (GeoMean $2.8\times$) faster compared to the 64-thread CPU implementation.
Robust grayscale image watermarking approach using DCT and DWT

In this work, we implement a robust image watermarking method for grayscale images that uses two transforms, namely, discrete wavelet transform (DWT) and discrete cosine transform (DCT). During the embedding stage, 1-level 2D-DWT is first performed on the host image to yield four sub-matrices, that is to say, LL<inf>1</inf>, HL<inf>1</inf>, LH<inf>1</inf> and HH<inf>1</inf>. Afterwards, two of these sub-matrices, namely, HL<inf>1</inf> and LH<inf>1</inf> are divided into 8 x 8 non-overlapping blocks. Then, 2D-DCT operation is applied to each of the above-mentioned blocks. The watermark bits are embedded using pre-generated pseudo-random sequences PRNo or PRN<inf>1</inf> and a scaling factor δ, inside the middle-band DCT block coefficients only, in accordance with watermark binary bit value, that is, 0 or 1. Next, each altered block of HL<inf>1</inf> and LH<inf>1</inf> sub-bands are used to generate the modified HL<inf>1</inf> and LH<inf>1</inf> sub-bands, using inverse 2D-DCT transform operation. At last, the marked image is produced by means of inverse 2D-DWT. In the retrieval stage, the marked image, which may be attacked, is transformed, in a similar way, as in the embedding process, by 1-level 2D-DWT to produce four sub-matrices, LL<inf>1</inf>, modified LH<inf>1</inf>, modified HL<inf>1</inf>, and HH<inf>1</inf>. Then, forward 2D-DCT is operated on modified HL<inf>1</inf> to retrieve the modified middle-band DCT coefficients of modified HL<inf>1</inf>. The same process is repeated with the modified LH<inf>1</inf> sub-band to extract the modified middle-band DCT coefficients of modified HL<inf>1</inf>. The correlations between PRNo, PRN<inf>1</inf>, and the extracted middle-frequency coefficients of modified HL<inf>1</inf> are computed. The same process is repeated for modified LH<inf>1</inf> sub-band. Finally, the watermark is extracted using correlation values. To add more security to the watermarking mechanism, Arnold transform (AT) is applied to encrypt the watermark image prior to embedding.
Memory Sampled-Data Controller Design for Interval Type-2 Fuzzy Systems via Polynomial-Type Lyapunov–Krasovskii Functional

This study deals with the investigation of the interval type-2 (IT2) fuzzy sampled-data (SD) stabilization problem based on nonlinearities and parameter uncertainties. For the first time, a memory SD control design involving a known signal transmission delay is adapted to address the stabilization problem for IT2 fuzzy systems. New polynomial-type Lyapunov–Krasovskii functionals (LKFs) associated with the state of constant signal transmission delay are introduced to achieve less conservative stability results. To bound the derivative of such LKFs, the Jacobi–Bessel inequality is introduced. Due to this, improved delay-dependent sufficient conditions can be obtained relating to set of linear matrix inequalities (LMIs). Thus, by solving LMIs using the LMI solver in MATLAB, the closed-loop system can be stabilized. The proposed method is verified in the simulation results with a nonlinear permanent-magnet vernier generator (PMVG)-based wind energy model and a Rossler model. Also, the applicability and superiority of the derived sufficient conditions are proved when compared with the existing results.
S0031182022001469jra 32..41

Vector-borne parasites are important ecological drivers influencing life-history evolution in birds by increasing host mortality or susceptibility to new diseases. Therefore, understanding why vulnerability to infection varies within a host clade is a crucial task for conservation biology and for understanding macroecological life-history patterns. Here, we studied the relationship of avian life-history traits and climate on the prevalence of Plasmodium and Parahaemoproteus parasites. We sampled 3569 individual birds belonging to 53 species of the family Thraupidae. Individuals were captured from 2007 to 2018 at 92 locations. We created 2 phylogenetic generalized least-squares models with Plasmodium and Parahaemoproteus prevalence as our response variables, and with the following predictor variables: climate PC1, climate PC2, body size, mixed-species flock participation, incubation period, migration, nest height, foraging height, forest cover, and diet. We found that Parahaemoproteus and Plasmodium prevalence was higher in species inhabiting open habitats. Tanager species with longer incubation periods had higher Parahaemoproteus prevalence as well, and we hypothesize that these longer incubation periods overlap with maximum vector abundances, resulting in a higher probability of infection among adult hosts during their incubation period and among chicks. Lastly, we found that Plasmodium prevalence was higher in species without migratory behaviour, with mixed-species flock participation, and with an omnivorous or animal-derived diet. We discuss the consequences of higher infection prevalence in relation to life-history traits in tanagers.
Structural invariants of linear multivariable systems

The class {∑} of all linear multivariable systems is partitioned into equivalence classes by the group consisting of all basis, all state feedback and all output injection transformations. This paper provides a new derivation of a set, of complete invariants and a corresponding canonical form first given by Morse. Strong reachability and strong observability concepts are the key tools. The method yields relatively simple proofs and economical matrix algorithms.
Working Paper Series COMPLEXITY, CONVENTIONS AND INSTABILITY: THE ROLE OF MONETARY POLICY

Ever since the 2008 financial crisis, there has been both a widespread recognition that the mainstream approach on financial markets has failed to anticipate and to justify the crisis and on the need of ex ante and ex post adequate economic policies to cope with such phenomena. The aim of our paper is to provide a theoretical and methodological analysis of the role of conventions as emergent phenomena in financial markets, the latter being thought of as dynamically complex systems. Drawing upon the notion of ‘dynamic complexity’ and Keynes’ view of financial markets, we claim that social conventions can only provisionally stabilize the system, but they will eventually lead to financial instability and crisis. Then, we adopt this framework to investigate the implications for monetary policy to stabilize the system by virtue of the role of central bank to intervene, and thus shape, a convention. In this respect, we consider the credibility of the monetary authority and how it can be exerted through ‘moral suasion’ to control the financial fragility of investors’ balance-sheet positions as well as to affect the convention around the long-term interest rate.
Magnetic fabric analyses of basin inversion: a sandbox modelling approach

Abstract. A magnetic fabric analysis is a useful tool to display deformation in nature and in models. In this study, three sandbox models represent basin inversion above a velocity discontinuity (base plate). After complete deformation of each model, samples were taken in different parts of the models (along faults and areas away from faults) for magnetic fabric analysis. Model I, which simulates basin formation during extension, shows two kinds of magnetic fabric: an “undeformed”/initial fabric in areas away from faults and a normal fault-induced fabric with a magnetic foliation that tends to align with the fault surface. Models II and III were extended to the same stage as Model I but were subsequently shortened/inverted by 1.5 cm (Model II) and 4 cm (Model III). Both inverted models developed “thrusts” during inversion. The thrusts show an alignment of magnetic foliation parallel to the fault surfaces that depends on the maturity of the thrust. Our results highlight that thrusting is more efficient in aligning the magnetic fabric along them compared to normal faults. Moreover, models II and III reveal a magnetic fabric overprint towards a penetrative strain-induced fabric (magnetic lineation perpendicular to shortening direction) with increasing strain in areas away from thrusts. Such overprint shows a gradual transition of a magnetic fabric to a penetrative strain-induced fabric and further into a thrust-induced fabric during shortening/inversion. In contrast, extension (Model I) developed distinct magnetic fabrics without gradual overprint. In addition, pre-existing normal faults are also overprinted to a penetrative strain-induced fabric during model inversion. They define weak zones within the main pop-up imbricate and steepen during model inversion. Steepening influences the magnetic fabric at the faults and, in general, the strain propagation through the model during inversion. The magnetic fabric extracted from the models presented here reflect the different stages of basin development and inversion. This study is a first attempt of applying magnetic fabric analyses on models simulating inverted basins. This study illustrates the possibility of applying a robust tool, i.e. magnetic fabric analyses, to sandbox models, whose initial, intermediate, and final stages are well documented, to understand fabric development in inverted tectonic regimes.
When Is a Multistage Stochastic Programming Problem Well-Defined?

Certain measure-theoretic issues raise the possibility that (a) the optimal value of a multistage stochastic programming problem may be ill-defined and (b) the recursion defining the problem may fail, so the problem itself is not even defined. The first difficulty is illustrated by example. A rigorous definition of multistage stochastic programming with fixed linear recourse is shown to avoid this difficulty. In the context of the new definition, certain measurability, convexity, and lower-semicontinuity assumptions on the objective function preclude the second possibility.