Reviewerly/exHarmony huggingface dataset:
Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion

This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage. The metadata related to every image contains the WGS84 coordinates for allocating the images. Also, the homography matrices between every image and the orthoimage are included in the dataset. The images and homographies are compatible with the well-known assessment protocol for detection and description proposed by Mikolajczyk and Schmid [1].
Image Deblurring Utilizing Inertial Sensors and a Short-Long-Short Exposure Strategy

Image blur caused by camera movement is common in long-exposure photography. A recent approach to address image blur is to record camera motion via inertial sensors in imaging equipment such as smartphones and single-lens reflex (SLR) cameras. However, because of device performance limitations, directly estimating a blur kernel from sensor data is infeasible. Previous works that have attempted to correct blurry image content via sensor data have also been susceptible to theoretical defects. Here, we propose a novel method of deblurring images that uses inertial sensors and a short-long-short (SLS) exposure strategy. Assisted short-exposure images captured before and after the formal long-exposure image are employed to correct the sensor data. A half-blind deconvolution algorithm is proposed to refine the estimated kernel. An extra smoothing filter is integrated into the framework to address the coarse initial kernel. Hence, we propose a fast solution for optimization that uses the iteratively reweighted least squares (IRLS) method in the frequency domain. We evaluate these methods via several blind deconvolutions. Quantitative indicators and the visual performance of the image deblurring results show that our method performs better than previous methods in terms of image quality restoration and computational time cost. This method will increase the feasibility of applying deblurring to imaging devices.
A Method for Correcting Frequency and RoCoF Estimates of Power System Signals with Phase Steps

This paper analyzes how two Kalman Filter (KF) based frequency estimation algorithms react to phase steps. It is demonstrated that phase steps are interpreted as sharp changes in frequency. The paper studies whether the location of the phase step, within the sinusoidal waveform, has any effect on the frequency estimate. Because phase steps are not the product of a permanent change in the underlying frequency, the paper proposes an algorithm to correct frequency estimates deemed erroneous. The algorithm makes use of the residual of the KF to determine when an estimate is incorrect and to trigger a corrective action in which the frequency estimate is replaced by an average of the previous values that were considered accurate. Using synthesized and simulated data with distortions the paper shows the effectiveness of the correction algorithm in fixing frequency estimates.
Laminar chaos in experiments and nonlinear delayed Langevin equations: A time series analysis toolbox for the detection of laminar chaos

Recently, it was shown that certain systems with large time-varying delay exhibit different types of chaos, which are related to two types of time-varying delay: conservative and dissipative delays. The known high-dimensional turbulent chaos is characterized by strong fluctuations. In contrast, the recently discovered low-dimensional laminar chaos is characterized by nearly constant laminar phases with periodic durations and a chaotic variation of the intensity from phase to phase. In this paper we extend our results from our preceding publication [Hart, Roy, Müller-Bender, Otto, and Radons, Phys. Rev. Lett. 123, 154101 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.154101], where it is demonstrated that laminar chaos is a robust phenomenon, which can be observed in experimental systems. We provide a time series analysis toolbox for the detection of robust features of laminar chaos. We benchmark our toolbox by experimental time series and time series of a model system which is described by a nonlinear Langevin equation with time-varying delay. The benchmark is done for different noise strengths for both the experimental system and the model system, where laminar chaos can be detected, even if it is hard to distinguish from turbulent chaos by a visual analysis of the trajectory.
The number of rational points of certain quartic diagonal hypersurfaces over finite fields

Let $p$ be an odd prime and let $\mathbb{F}_q$ be a finite field of characteristic $p$ with order $q = p^s$. For $f(x_1, \cdots, x_n)\in\mathbb{F}_q[x_1, ..., x_n]$, we denote by $N(f(x_1, \cdots, x_n) = 0)$ the number of $\mathbb{F}_q$-rational points on the affine hypersurface $f(x_1, \cdots, x_n) = 0$. In 1981, Myerson gave a formula for $N(x_1^4+\cdots+x_n^4 = 0)$. Recently, Zhao and Zhao obtained an explicit formula for $N(x_1^4+x_2^4 = c)$ with $c\in\mathbb{F}_q^*: = \mathbb{F}_q\setminus \{0\}$. In this paper, by using the Gauss sum and Jacobi sum, we arrive at explicit formulas for $N(x_1^4+x_2^4+x_3^4 = c)$ and $N(x_1^4+x_2^4+x_3^4+x_4^4 = c)$ with $c\in\mathbb{F}_q^*$.
Modeling Institutional Reengineering for Inclusive Development (IRID) in Africa

Inclusive development is a newly emerging notion that is gaining substantial attention, especially in international civil societies. The aim of this study, therefore, is to discuss the drivers of inclusive development in Africa, paying special attention to the political economy and structural change variables. Ordinary Least Squares regression is run in STATA 14 to test if there are statistically significant correlations between the five-year average scores of inclusive development index (IDI) as an endogenous variable and (proxies of) the five-year trend in economic growth, technology, structural change, trade, and political economy as exogenous variables. The panel data are pooled from 21 African countries among which 9 countries are landlocked. The regression is run in two scenarios. As an alternative scenario, IDI is pooled from the World Economic Forum (WEF) 2017 report and used as a dependent variable. In the other scenario, IDI is computed by incorporating variables relevant to the African context then used as a dependent variable. In the IRID customized version, the five-year trends of GDP, health facility, the institutional and structural change variables viz. democracy and employment opportunity in the industry sector are statistically significant determinants of inclusive development. Accordingly, an inference is drawn claiming that inter alia a nation is as prosperous, inclusive and resilient as the quality of its governance institutions and enforcement capability. This is in line with the conventional thought in African studies which claim that a natural resource endowment per se is not the sole determinant of development. Finally, to anchor IDI with a pragmatic paradigm, a three-stage institutional reengineering model is proposed which could be applied in different development governance endeavors.
MLCD: A Unified Software Package for Cancer Diagnosis

PURPOSE Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. METHODS Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. RESULT The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. CONCLUSION Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.
Bone marrow cells recognition methods in the diagnosis of minimal residual disease

The study of methods and models for the analysis of images of bone marrow preparations in computer microscopy systems for the diagnosis of minimal residual disease is carried out. A sample of preparations for patients with acute lymphoblastic leukemia was formed for the study. Bone marrow cell images were obtained for this sample. As a result of experimental studies, the results of classification of bone marrow cells using different classifiers were obtained.
Remark on subcodes of linear complementary dual codes

We show that any ternary Euclidean (resp. quaternary Hermitian) linear complementary dual [n,k] code contains a Euclidean (resp. Hermitian) linear complementary dual [n,k−1] subcode for 2≤k≤n. As a consequence, we derive a bound on the largest minimum weights among ternary Euclidean linear complementary dual codes and quaternary Hermitian linear complementary dual codes.
Image Recognition Algorithm for Maintenance Data Digitization : CNN and FCN

Tabulated data has been widely used to facilitate systematic and intuitive management. In particular, tabular images that contain a few simple symbols are useful for maintaining mechanical systems. Several companies have accumulated tabular images as their property. Although these images are valuable as they can be used to solve difficult problems using data-based methods, such as deep learning, they still remain unavailable because it is expensive to digitize them. For these reasons, we propose a model comprised of a convolutional neural network (CNN) and fully convolutional network (FCN) to digitize tabular images. We used some ResNet components as they are well-suited to the characteristics of tabular image data. A training set for each model was constructed by writing symbols in blank tables and then augmenting them. As a result, the trained CNN and FCN models exhibited 99.2 % and 97.7 % accuracy in 4.75 s and 0.132 s of inference time, respectively.
Graph-Based Analysis of Visual Scanning Patterns: A Developmental Study on Green and Normal Images

The present study investigated the visual scanning pattern of children with typical development in three different age groups(4-6,6-8,8-10 years old). We used a data set from one related research, which included images with different low-level features: Green and Normal. This study analyzed age-associated inter-individual differences and was intended to show that graph profiling combined with a fixation time approach could help us to better understand the developmental visual pattern. Thus, degree centrality as one of the graph theory measures was implied to analyze gaze distribution. We explored the influence of bottom-up features, comparing the first 2 s (early phase) with the interval from 4 to 6 s (late phase) of scene exploration during age development. Our results indicated that degree centrality and fixation time increased with age. Furthermore, it was found that the effects of saliency are short-lived but significant. Moreover, we found that Green images during the early phase play an important role in visual anchoring, and the children's performance was significantly different between 4-6 y and 6-8y-group. This comparative study underscores the ability of degree centrality as a developing innovative measure to perform eye-tracking data analyses.
Boosting Recommender Systems with Advanced Embedding Models

Recommender systems are paramount in providing personalized content and intelligent content filtering on any social media platform, web portal, and online application. In line with the current trends in the field directed towards mapping problem and data encoding representations from other fields, this research investigates the feasibility of augmenting a graph-based recommender system for Amazon products with two state-of-the-art representation models. In particular, the potential benefits of using the language representation model BERT and GraphSage based representations of nodes and edges for improving the quality of the recommendations were investigated. Link prediction and link attribute inference were used to identify the products that the users will buy and predict the rating they will give to a product, respectively. The initial results of our exploratory study are encouraging and point to potential directions for future research.
On the Understanding of Experimentation Usage in Light of Lean Startup in Software Development Context

Experimentation can be used to validate software solutions, reducing waste on efforts and costs that would be spent had the solution not been validated from the beginning. Aware of experimentation benefits, a methodology named as Lean Startup have incorporated experiments and build-measure-learn loop as a manner to reduce waste from product development by focusing on efforts that will create value to customers, such as discovering the best solution. Aiming to characterize the use of experimentation in light of Lean Startup, we conducted a case study with two software development teams from a multinational information technology company. We characterize how experimentation works by teams perception, what are the benefits and challenges in the use of it. We identified in the study that experimentation was rooted in the workers' mindset and came up naturally should its need arise. Our findings indicate that the process of experimentation, albeit still a systematic effort, happens organically, and that it helps in development decisions and in reducing development efforts. Challenges are related to organizational culture and lack of belief in the approach by stakeholders.
A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

Purpose In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model. Design/methodology/approach The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination. Findings Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms. Originality/value The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.
Renormalization Analysis of Topic Models

In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of topic modeling demonstrates self-similar behavior under variation of the number of clusters. Such behavior allows using a renormalization technique. A combination of renormalization procedure with the Renyi entropy approach allows for quick searching of the optimal number of topics. In this paper, the renormalization procedure is developed for the probabilistic Latent Semantic Analysis (pLSA), and the Latent Dirichlet Allocation model with variational Expectation–Maximization algorithm (VLDA) and the Latent Dirichlet Allocation model with granulated Gibbs sampling procedure (GLDA). The experiments were conducted on two test datasets with a known number of topics in two different languages and on one unlabeled test dataset with an unknown number of topics. The paper shows that the renormalization procedure allows for finding an approximation of the optimal number of topics at least 30 times faster than the grid search without significant loss of quality.
Cheetah: Optimizations and Methods for PrivacyPreserving Inference via Homomorphic Encryption.

As the application of deep learning continues to grow, so does the amount of data used to make predictions. While traditionally, big-data deep learning was constrained by computing performance and off-chip memory bandwidth, a new constraint has emerged: privacy. One solution is homomorphic encryption (HE). Applying HE to the client-cloud model allows cloud services to perform inference directly on the client's encrypted data. While HE can meet privacy constraints, it introduces enormous computational challenges and remains impractically slow in current systems. This paper introduces Cheetah, a set of algorithmic and hardware optimizations for HE DNN inference to achieve plaintext DNN inference speeds. Cheetah proposes HE-parameter tuning optimization and operator scheduling optimizations, which together deliver 79x speedup over the state-of-the-art. However, this still falls short of plaintext inference speeds by almost four orders of magnitude. To bridge the remaining performance gap, Cheetah further proposes an accelerator architecture that, when combined with the algorithmic optimizations, approaches plaintext DNN inference speeds. We evaluate several common neural network models (e.g., ResNet50, VGG16, and AlexNet) and show that plaintext-level HE inference for each is feasible with a custom accelerator consuming 30W and 545mm^2.
Efficient Simulation of Random States and Random Unitaries

We consider the problem of efficiently simulating random quantum states and random unitary operators, in a manner which is convincing to unbounded adversaries with black-box oracle access. This problem has previously only been considered for restricted adversaries. Against adversaries with an a priori bound on the number of queries, it is well-known that t-designs suffice. Against polynomial-time adversaries, one can use pseudorandom states (PRS) and pseudorandom unitaries (PRU), as defined in a recent work of Ji, Liu, and Song; unfortunately, no provably secure construction is known for PRUs. In our setting, we are concerned with unbounded adversaries. Nonetheless, we are able to give stateful quantum algorithms which simulate the ideal object in both settings of interest. In the case of Haar-random states, our simulator is polynomial-time, has negligible error, and can also simulate verification and reflection through the simulated state. This yields an immediate application to quantum money: a money scheme which is information-theoretically unforgeable and untraceable. In the case of Haar-random unitaries, our simulator takes polynomial space, but simulates both forward and inverse access with zero error. These results can be seen as the first significant steps in developing a theory of lazy sampling for random quantum objects.
Experimental demonstration of the advantage of using coherent measurements for phase estimation in the presence of depolarizing noise

We report an experimental investigation of the role of measurement in quantum metrology when the states of the probes are mixed. In particular, we investigate optimized local measurements and general global projective measurements, involving entangling operations, of noisy Werner states of polarization entangled photons. We demonstrate experimentally that global measurement presents an advantage in parameter estimation with respect to the optimized local strategy. Moreover, the global strategy provides unambiguous information about the parameter of interest even when the amount of noise is not well characterized. This shows that the coherence in quantum operations, such as the Bell-state projection device used in our protocol, can be used to further boost the quantum advantage in metrology and play a fundamental role in the design of future quantum measurement devices.
Binocular Camera Calibration for Underwater Stereo Matching

Abstract This paper presents an efficient stereo matching method applied to underwater with a binocular camera. Since underwater calibration is an inconvenient task and the traditional imaging model in the air and the parameters of the camera are no longer applicable to the underwater because of the distortion caused by light refraction. To solve this problem, we proposed a way to calibrate the binocular camera in the air for underwater stereo matching. We studied the imaging principle of the camera, and derived the relationship of the camera between in the air and underwater. Then, we performed underwater stereo matching experiments using the parameters of the camera calibrated in the air and found it to be effective.
INTERPRETABILITY LOGICS AND GENERALISED VELTMAN SEMANTICS

Abstract We obtain modal completeness of the interpretability logics IL $\!\!\textsf {P}_{\textsf {0}}$ and IL R w.r.t. generalised Veltman semantics. Our proofs are based on the notion of full labels [2]. We also give shorter proofs of completeness w.r.t. the generalised semantics for many classical interpretability logics. We obtain decidability and finite model property w.r.t. the generalised semantics for IL $\textsf {P}_{\textsf {0}}$ and IL R . Finally, we develop a construction that might be useful for proofs of completeness of extensions of IL W w.r.t. the generalised semantics in the future, and demonstrate its usage with $\textbf {IL}\textsf {W}^\ast = \textbf {IL}\textsf {WM}_{\textsf {0}}$ .