Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of transparent thin films by low-coherent diffractometry

We introduce a method to determine the refractive index (RI) and thickness of transparent thin films (TTF) simultaneously. The method is based on Fresnel diffraction from a phase step in the transmission mode. The average visibility of the three central fringes in the Fresnel diffraction pattern depends on the incident beam angle, RI, and thickness of TTF. By analyzing the visibility versus the angle of incidence, the RI and the thickness of TTF are determined simultaneously. Experimental outcomes and the theoretical model are in perfect agreement. The results show that the error in the thickness measurement is about 0.04 μm, and the uncertainty of the obtained RI is Δ N = 0.002 . Owing to its very simple setup and reliable results, the method can provide an alternative to conventional methods, e.g. interferometry.
Anonymous Privacy Preservation Based on m-Signature and Fuzzy Processing for Real-Time Data Release

The real-time data generated from various smart devices will be released and shared for public to obtain numerous benefits. However, it will lead to individual privacy leakage because of data mining or analysis. Currently, many existing privacy protection models either fail to be directly applied in real-time data release or are unsatisfactory in terms of data utility and privacy protection. Toward this end, based on <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-signature and fuzzy processing, an anonymous privacy protection model, named PMF, is proposed in this paper. Specifically, for the proposed model there are five advantages: 1) PMF defines <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-signature for making each bucket with at least <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> different sensitive values instead of generating any counterfeit tuples, which can not only resist <inline-formula> <tex-math notation="LaTeX">${h}$ </tex-math></inline-formula>-difference attack but also improve practical value; 2) the buckets satisfying <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-signature are variable over time, and this flexibility of <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>-signature can improve the efficiency of dynamic update; 3) PMF can effectively insert, delete, and modify real-time data for release; 4) PMF applies fuzzy processing to handle the tuples in the candidate list, which strikes a good balance between the utility of released data and privacy protection; and 5) PMF adopts greedy heuristic algorithm to process update operations, which greatly reduces the information loss of released data. Furthermore, PMF is obviously more secure than the existing models in the real-time data release. Finally, the results of the comparison experiments on real-world and synthetic datasets illustrate that PMF is superior to the existing models in terms of data utility and efficiency.
Development of power system reliability analysis program with consideration of system operation

This paper describes a new program which analyzes the reliability of composite power systems with consideration of power system operational aspects. It uses the sequential Monte Carlo method and calculates annual risk indices for each load point. The program deals with the system problems involved in load flow, voltage, frequency, and small‐signal stability if necessary. The distinctive feature of the program is consideration of power system operation in a sequential manner, such as generation dispatch and system restoration after equipment failure. This makes possible the evaluation of both power system planning and operation from the reliability point of view. Several new methods of reducing calculation time are developed and implemented, including the concurrent LP/load flow method, which improves solvability. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 153(4): 20–31, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20138
A remark on the GNSS single difference model with common clock scheme for attitude determination

Abstract GNSS-based attitude determination technique is an important field of study, in which two schemes can be used to construct the actual system: the common clock scheme and the non-common clock scheme. Compared with the non-common clock scheme, the common clock scheme can strongly improve both the reliability and the accuracy. However, in order to gain these advantages, specific care must be taken in the implementation. The cares are thus discussed, based on the generating technique of carrier phase measurement in GNSS receivers. A qualitative assessment of potential phase bias contributes is also carried out. Possible technical difficulties are pointed out for the development of single-board multi-antenna GNSS attitude systems with a common clock.
Performance evaluation of impulse radio UWB systems with pulse-based polarity randomization in asynchronous multiuser environments

The performance of a binary phase shift keyed random time-hopping impulse radio system with pulse-based polarity randomization is analyzed. The effects of multiple access interference are investigated for both chip-synchronous and asynchronous systems. It is shown that the performance of a chip-synchronous system is the same as that for the symbol-synchronous case studied in E. Fisher and H. V. Poor (Oct. 2-4, 2002). The asynchronous system is modelled as a chip-synchronous system with uniformly distributed timing jitter on the transmitted pulses of interfering users. This extends the analytical technique developed for the chip-synchronous case to the asynchronous case. An approximate closed-form expression for the probability of error, expressed in terms of the autocorrelation function of the transmitted pulse, is derived for the asynchronous case. The analysis shows that the chip-synchronous assumption can result in over-estimating the error probability, and hence that the system design based on this approximation will he on the safe side. The degree of over-estimation mainly depends on the autocorrelation function of the UWB pulse and signal-to-interference-plus-noise-ratio (SIR) of the system. Simulations studies support this approximate analysis.
Higgs Criticality in a Viable Motivated Model

An extension of the Standard Model with three right-handed neutrinos and a simple invisible axion model can account for all experimentally confirmed signals of new physics (neutrino oscillations, dark matter and baryon asymmetry) in addition to solving the strong CP problem, stabilizing the electroweak vacuum and satisfying all current observational bounds. We show that this model can also implement critical Higgs inflation, which corresponds to the frontier between stability and metastability of the electroweak vacuum. This leads to a value of the non-minimal coupling between the Higgs and the Ricci scalar that is much lower than the one usually quoted in Higgs inflation away from criticality. Then, an advantage is that the scale of perturbative unitarity breaking on flat spacetime can be very close to the Planck mass, where anyhow new physics is required. The higher dimensional operators are under control in this inflationary setup. The dependence of the cutoff on the Higgs background is also taken into account as appropriate when the Higgs is identified with the inflaton. Furthermore, critical Higgs inflation enjoys a robust inflationary attractor that makes it an appealing setup for the early universe. In the proposed model, unlike in the Standard Model, critical Higgs inflation can be realized without any tension with the observed quantities, such as the top mass and the strong coupling.
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits

Abstract An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of features extracted by recurrent neural networks with a contextual linear bandit algorithm based on posterior sampling. Our experiments on a diverse selection of contextual and noncontextual nonstationary problems show that our recurrent approach consistently outperforms its feedforward counterpart, which requires handcrafted historical contexts, while being more widely applicable than conventional nonstationary bandit algorithms. Although it is very difficult to provide theoretical performance guarantees for our new approach, we also prove a novel regret bound for linear posterior sampling with measurement error that may serve as a foundation for future theoretical work.
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization

First-order methods for stochastic optimization gained un-deniable relevance, in particular due to their pivotal role in machine learning. These methods blindly accept noisy gradients provided by an oracle, which may be inconsistent with structural properties of the underlying objective function. We exploit gradient co-coercivity of 𝐿 -smooth convex objectivefunctionstoobtainmoreaccurategradientestimates. Themethodintroducedinthisthesisisthencoinedasthe co-coercivity(COCO)denoiser. OurdenoiserisajointMaximumLikelihood(ML)estima-torforthegradients,constrainedbypairwiseco-coercivity conditions.AlthoughMLleadstoaQuadraticallyConstrained QuadraticProblem,weintroduceanefficientfirst-orderal-gorithmforCOCO,whichisbasedontheFastDualProximal Gradientmethod.Forthedenoiserthatdealswithasingle pairofgradients,wederivetheclosed-formsolutionforthe MLproblem,whichisrelevantinpractice,sinceourexper-imentshaveshownthateventhissimplescenarioleadsto variancereductiongainsinstochasticoptimization. Wecarryoutatheoreticalanalysisthatprovidesinsight intoparametertuningandestimatorresults,showinginpar-ticularwhyCOCOnecessarilyimproveswithrespecttothe noisyoracle.Theexperimentalanalysiscorroboratesthese resultsandshowsthattheCOCOestimator,althoughnot unbiased,leadstoareductionofthemeansquarederrorof thefunctiongradients.Toillustratetheimpactinstochas-ticoptimization,weusebothsyntheticdataandarealon-linelearningtask.OurexperimentsshowthatCOCOleads toimprovementsinvariancereductionwithrespecttobase-linealgorithms.
GC-MS Analysis and Antimicrobial Properties of Methanolic Extracts of the Marine Algae Skeletonema costatum and Chaetoceros spp

In this report, we describe the antimicrobial potential and phytochemical constituents of two Nigerian marine algae; Skeletonema costatum and Chaetoceros spp. The minimum inhibitory concentration (MIC) of the methanolic extract of the algae spp. was determined against 7 clinical isolates (Enterococcus faecalis, Staphylococcus aureus ATCC 25923, Bacillus species, Klebsiella pneumonia, Salmonella typhi, Escherichia coli ATCC 25922 and Candida albicans) using broth dilution method. The volatile constituents of the extracts were analyzed using the Gas ChromatographyMass Spectrometry technique. The results showed that S. costatum inhibited all isolates except Salmonella typhi and ATCC E.coli while Chaetoceros spp. had no inhibitory effect on Candida albicans. The MIC values of the two algae extract was least; < 2.0 mg/ml and highest; 10 mg/ml for the test isolates. A total of 42 and 25 volatile compounds which includes 9Octadecenamide, (Z) as the major constituent were obtained in S. costatum and Chaetoceros spp. respectively. Other compounds identified were known antimicrobial compounds; 1-Hexacosanol, Eugenol, Benzenesulfonamide, 4-methyl-N-phenyland Thiazole, 4-(4-aminophenyl)-2-methylaminoas well as some phytochemicals such as flavonoids, tannins, terpenoids, steroids and phenols. These may account for the antibiotic property of the algae extracts. This study showed that S. costatum and Chaetoceros spp. possess antimicrobial compounds which may be explored for therapeutic purposes.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up Baselines

Sentiment analysis is proven to be very useful tool in many applications regarding social media. This has led to a great surge of research in this field. Hence, in this paper, we compile the baselines for such research. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field. We draw a comparison among the methods using empirical data, obtained from the experiments. In the future, we plan to focus on extracting semantics from visual features, cross-modal features and fusion.
Adaptive sliding mode trajectory tracking control for wheeled mobile robots

ABSTRACT This paper discusses the problem of adaptive sliding mode trajectory tracking control for wheeled mobile robots in the presence of external disturbances and inertia uncertainties. A new fast nonsingular terminal sliding mode surface without any constraint is proposed, which not only avoids singularity, but also retains the advantages of sliding mode control. In order to implement the trajectory tracking mission, the error dynamic system is divided into a second-order subsystem and a third-order one. First, an adaptive fast nonsingular terminal sliding mode control law of the angular velocity is constructed for stabilising the second-order subsystem in finite time. Then, another adaptive fast nonsingular terminal sliding mode control law of the linear velocity is designed to guarantee the stability of the third-order subsystem. Finally, a simulation example is provided to demonstrate the validity of the proposed control scheme.
Simplification of I-Vector Extraction for Speaker Identification

The identity vector (i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the so-called total variability space represented with a thin and tall rectangular matrix. This paper introduces a novel algorithm to improve the computational and memory requirements for the application. In our method, the series of symmetric matrices can be represented by diagonal expression, sharing the same dictionary, which to some extent is analogous to eigen decomposition, and we name this algorithm Eigen decomposition like factorization (EDLF). Similar algorithms are listed for comparison, in the same condition, our method shows no disadvantages in identification accuracy.
VERIFICATION OF THE DISCRETIZATION ERROR OF NUMERICAL SOLUTIONS OF FLOWS USING UNSTRUCTURED MESHES

The present work evaluates the discretization error estimator named Grid Convergence Index (GCI), which is based on the Richardson Extrapolation, taking into account: (1) a two-dimensional laminar flow inside a square cavity that has a known analytical solution; (2) unstructured meshes to discretize the problem domain; (3) The Element based Finite Volume Method (EbFVM); (4) The CFX CFD code to get the numerical solutions; and (5) two variables of interest, the mass flux in the cavity and the force applied by the top boundary on the fluid in the cavity. The error estimates obtained with GCI are reliable for all meshes and both variables but their accuracy can be considered satisfactory.
Combined Robust Design and Robust Control of an Electric DC Motor

System performance can significantly benefit from optimally integrating the design and control of engineering systems. To improve the robustness properties of systems, this paper introduces an approach that combines robust design with robust control and investigates the coupling between them. However, the computational cost of improving this robustness can often be high due to the need to solve a resulting minimax design and control optimization problem. To reduce this cost, sequential and iterative strategies are proposed and compared to an all-in-one (Aio) strategy for solving the minimax problem. These strategies are then illustrated for a case study: Robust design and robust control (RDRC) of a dc motor. Results show that the resulting strategies can improve the robustness properties of the dc motor. In addition, the coupling strength between the robust design and robust control tends to increase as the applied level of uncertainty increases.
Numerical simulation of destabilizing heterogeneous suspensions at vanishing Reynolds numbers

This work deals with the numerical investigation of destabilizing suspensions, which are governed by two basic processes: Clustering and sedimentation. After laying the foundation to an efficient numerical simulation based on the Stokesian Dynamics method, hydrodynamic clustering and clustering due to non-hydrodynamic interactions are investigated. It is shown that multi-particle simulations need parallelization and an efficient post-processing to yield reliable results within a reasonable time.
Linear, Second order and Unconditionally Energy Stable schemes for a phase-field moving contact line Model

In this paper, we consider the numerical approximations for solving a hydrodynamics coupled phase field model consisting of incompressible Navier-Stokes equations with generalized Navier boundary conditions, and the Cahn-Hilliard equation with dynamic moving contact line boundary conditions. The main challenging issue for solving this model numerically is the time marching problem, i.e., how to develop suitable higher order temporal schemes while preserving the unconditional energy stability at the discrete level. We solve this issue by developing two linear, second-order schemes based on the "Invariant Energy Quadratization" method for the nonlinear terms in the bulk and on the boundary, the projection method for the Navier-Stokes equations, and a subtle implicit-explicit treatment for the stress and convective terms. Rigorous proofs of the well-posedness of the linear system and the unconditional energy stabilities are provided. A spectral-Galerkin spatial discretization is implemented and various numerical results are presented to verify the second order accuracy and the efficiency of the proposed schemes.
Understanding the factors driving the opioid epidemic using machine learning

—In recent years, the US has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths. Research ﬁnds such overdose deaths are linked to neighborhood-level traits, thus providing opportunity to identify effective interventions. Typically, techniques such as Ordinary Least Squares (OLS) or Maximum Likelihood Estimation (MLE) are used to document neighborhood-level factors signiﬁcant in explaining such adverse outcomes. These techniques are, however, less equipped to ascertain non-linear relationships between confounding factors. Hence, in this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware and explore the correlation of these factors using Shapley Additive explanations (SHAP). We discovered that the factors related to neighborhoods’ environment, followed by education and then crime, were highly correlated with higher opioid risk. We also explored the change in these correlations over the years to understand the changing dynamics of the epidemic. Furthermore, we discovered that, as the epidemic has shifted from legal (i.e., prescription opioids) to illegal (e.g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased signiﬁcantly while the correlation of economic and socio-demographic variables has decreased. The correlation of education related factors has been higher from the start and has increased slightly in recent years suggesting a need for increased awareness about the opioid epidemic.
Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

In this section, we present the top-1 errors (%) of various methods for ImageNet-LT, Places-LT, and iNaturalist 20181. As the experiment setups of the existing works vary by network initialization, the sampling strategy of minibatches, losses, trainable layers of a network, etc., it is hard to have a fair comparison by the end results. Hence, besides their top-1 errors, we also report the experiment setups for each method. Tables 2, 3, and 4 show the results of the different methods on ImageNet-LT, Places-LT, and iNaturalist 2018, respectively. Our approach outperforms the classbalanced weighting scheme for both the cross-entropy loss and the focal loss, as we observed in the main paper. Moreover, our results are on par with best reported ones except on ImageNet-LT. Finally, we stress that almost all existing methods employ a class-balanced weighting or sampling strategy no matter what their main techniques are to tackle the long-tailed problem. Hence, given our consistent improvements over the class-balanced weighting, we expect
Distributed Nonconvex Optimization: Oracle-free Iterations and Globally Optimal Solution

Distributed optimization is concerned with using local computation and communication to realize a global aim of optimizing the sum of local objective functions. It has gained wide attention for a variety of applications in networked systems. This paper addresses a class of constrained distributed nonconvex optimization problems involving univariate objective functions, aiming to achieve global optimization with a simple iteration rule not requiring local oracle queries (i.e., evaluations of gradients or function values). We propose a novel algorithm named CPCA, exploiting the notion of combining Chebyshev polynomial approximation, average consensus and polynomial optimization. The proposed algorithm is i) able to yield $\epsilon$ globally optimal solutions for any arbitrarily small given tolerance $\epsilon$, ii) efficient in terms of both oracle complexities and inter-agent communication costs, and iii) distributed terminable when the specified precision requirement is met. The key insight is to use polynomial approximations to substitute for general objectives, and turn to solve an easier approximate version of the original problem. Due to the nice analytic properties owned by polynomials, this approximation not only facilitates efficient global optimization, but also allows the proposed algorithm's consensus-based iteration structure free from local oracle queries. We provide a comprehensive analysis of the accuracy and complexities of the proposed algorithm.