Reviewerly/exHarmony huggingface dataset:
Entropy Based Data Expansion Method for Blind Image Quality Assessment

Image quality assessment (IQA) is a fundamental technology for image applications that can help correct low-quality images during the capture process. The ability to expand distorted images and create human visual system (HVS)-aware labels for training is the key to performing IQA tasks using deep neural networks (DNNs), and image quality is highly sensitive to changes in entropy. Therefore, a new data expansion method based on entropy and guided by saliency and distortion is proposed in this paper. We introduce saliency into a large-scale expansion strategy for the first time. We regionally add distortion to a set of original images to obtain a distorted image database and label the distorted images using entropy. The careful design of the distorted images and the entropy-based labels fully reflects the influences of both saliency and distortion on quality. The expanded database plays an important role in the application of a DNN for IQA. Experimental results on IQA databases demonstrate the effectiveness of the expansion method, and the network’s prediction effect on the IQA databases is found to be improved compared with its predecessor algorithm. Therefore, we conclude that a data expansion approach that fully reflects HVS-aware quality factors is beneficial for IQA. This study presents a novel method for incorporating saliency into IQA, namely, representing it as regional distortion.
Supporting Research Data Management and Open Science in Academic Libraries: a Data Librarian’s View

The ‘data revolution’ has impacted researchers across the disciplines. As if the traditional work of teaching, competing for grants and promotion, doing research and publishing results was not challenging enough, researchers are required to make fundamental changes in the way they do all of these things. A similar shift can be seen for academic librarians. Librarians who were taught to meet the needs of their users based on information scarcity now need to retrain themselves to help users deal with information overload. Moreover, librarians increasingly find themselves ‘upstream’ in the research process, trying to assist their users in managing unwieldy amounts of data when their comfort zone is firmly ‘downstream’ in the post-publication stage. Unsettling as it may be, these are exciting developments for the library profession.
Blind information reconciliation with variable step sizes for quantum key distribution

Quantum key distribution (QKD) generates symmetric keys between two authenticated parties with the guarantee of information-theoretically security. A vital step in QKD to obtain fully-matched key between two parties is information reconciliation. The blind reconciliation protocol provides a useful tool that corrects the mismatch in a wide range of qubit error rate (QBER) but without a prior error estimation. However, there is a contradiction between the reconciliation efficiency and the processing time in this protocol. In this work, we propose a blind reconciliation protocol with variable step sizes to relieve this contradiction. The analysis and simulation results show that the improved protocol inherits all the advantages of the original blind reconciliation protocol and can obtain better reconciliation efficiency with less operation time. The improved blind reconciliation protocol enhances the final secret key rate and accelerates the processing speed of a QKD system.
Analytic Solutions of Two-photon Rabi Model Based on Bargmann Space

Abstract The quantum Rabi model (QRM) is identified as the simplest model to study the interaction between light and matter. In recent years, Braak proposed an alternative more efficient method to obtain the exact solvability and spectrum of the QRM in the Bargmann space, which is appropriate frame for extended squeezed coherent states. Based on this feasible method, some study for the two-photon QRM is concentrated. In this paper we diagonalized the Hamiltonian and obtain the Spectrum of the system of two-photon QRM in the special case that the level splitting of the atom was equal to 0, and compared with Travĕnec’s results without diagonalizing the Hamiltonian. Finally, we confirm the validity of diagonalization for the Hamiltonian in the two-photon QRM.
optimizing Motion-Planning Problem Setup via Bounded Evaluation with Application to Following Surgical Trajectories

A motion-planning problem's setup can drastically affect the quality of solutions returned by the planner. In this work we consider optimizing these setups, with a focus on doing so in a computationally-efficient fashion. Our approach interleaves optimization with motion planning, which allows us to consider the actual motions required of the robot. Similar prior work has treated the planner as a black box: our key insight is that opening this box in a simple-yet-effective manner enables a more efficient approach, by allowing us to bound the work done by the planner to optimizer-relevant computations. Finally, we apply our approach to a surgically-relevant motion-planning task, where our experiments validate our approach by more-efficiently optimizing the fixed insertion pose of a surgical robot.
Wavelet representation of light-front quantum field theory

A formally exact discrete multiresolution representation of quantum field theory on a light front is presented. The formulation uses an orthonormal basis of compactly supported wavelets to expand the fields restricted to a light front. The representation has a number of useful properties. First, light-front preserving Poincar\'e transformations can be computed by transforming the arguments of the basis functions. The discrete field operators, which are defined by integrating the product of the field and a basis function over the light front, represent localized degrees of freedom on the light-front hyperplane. These discrete fields are irreducible and the vacuum is formally trivial. The light-front Hamiltonian and all of the Poincar\'e generators are linear combinations of normal ordered products of the discrete field operators with analytically computable constant coefficients. The representation is discrete and has natural resolution and volume truncations like lattice formulations. Because it is formally exact, it is possible to systematically compute corrections for eliminated degrees of freedom.
Divide and conquer: high-accuracy and real-time 3D reconstruction of static objects using multiple-phase-shifted structured light illumination

Multiple-phase-shifted structured light illumination achieves high-accuracy 3D reconstructions of static objects, while typically it can’t achieve real-time phase computation. In this paper, we propose to compute modulations and phases of multiple scans in real time by using divide-and-conquer solutions. First, we categorize total N = KM images into M groups and each group contains K phase equally shifted images; second, we compute the phase of each group; and finally, we obtain the final phase by averaging all the separately computed phases. When K = 3, 4 or 6, we can use integer-valued intensities of images as inputs and build one or M look-up tables storing real-valued phases computed by using arctangent function. Thus, with addition and/or subtraction operations computing indices of the tables, we can directly access the pre-computed phases and avoid time-consuming arctangent computation. Compared with K -step phase measuring profilometry repeated for M times, the proposed is robust to nonlinear distortion of structured light systems. Experiments show that, first, the proposed is of the same accuracy level as the traditional algorithm, and secondly, with employing one core of a central processing unit, compared with the classical 12-step phase measuring profilometry algorithm, for K = 4 and M = 3, the proposed improves phase computation by a factor of 6 ×.
ANALYSIS OF LOWER BOUNDS FOR QUANTUM CONTROL TIMES AND THEIR RELATION TO THE QUANTUM SPEED LIMIT

Limitations to the speed of evolution of quantum systems, typically referred to as quantum speed limits (QSLs), have important consequences for quantum control problems. However, in its standard formulation, is not straightforward to obtain meaningful QSL bounds for time-dependent Hamiltonians with unknown control parameters. In this paper we present a short introductory overview of quantum speed limit for unitary dynamics and its connection to quantum control. We then analyze potential methods for obtaining new bounds on control times inspired by the QSL. We finally extend the work in [Poggi, Lombardo and Wisniacki EPL 104 40005 (2013)] by studying the properties and limitations of these new bounds in the context of a driven two-level quantum system
Ensemble Feature Selection Method Based on Recently Developed Nature-Inspired Algorithms

Nature-inspired algorithmsArora, Jatin focus on many real-lifeAgrawal, Utkarsh applications, majority of whichTiwari, Prayag address differentGupta, Deepak typesKhanna, Ashish of optimisation problems at a basic level. Feature selection, a type of optimisation problem, is an extremely important aspect of machine learning. This paper proposes an ensemble algorithm for feature selection using four recently developed evolutionary algorithms which are OCSA, OCFA, OBBA and MGWO. A ensemble set is created by combining the reduced feature set(s) obtained from the above-mentioned algorithms. The ensemble set so obtained represents a subset of features that are more robust and stable in nature. Ensemble creates a better composite global model by integrating various “differently biased” classifiers and thus reduces variance error by ensuring diverse “biasing”. The performance of this approach is validated using four classifiers, Decision Tree, Logistic Regression, K-nearest neighbours (KNN) and Random Forest. The application of the proposed method has been demonstrated using ten publicly available datasets. The suggested method shows promising results by either reducing the number of features with not much loss of accuracy or by including more relevant features and thereby increasing accuracy of predictions. Theoretical and empirical results presented in this paper validate the hypothesis that this method can help in finding a better feature subset.
Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Automatic extraction of region objects from high-resolution satellite imagery presents a great challenge, because there may be very large variations of the objects in terms of their size, texture, shape, and contextual complexity in the image. To handle these issues, we present a novel, deep-learning-based approach to interactively extract non-artificial region objects, such as water bodies, woodland, farmland, etc., from high-resolution satellite imagery. First, our algorithm transforms user-provided positive and negative clicks or scribbles into guidance maps, which consist of a relevance map modified from Euclidean distance maps, two geodesic distance maps (for positive and negative, respectively), and a sampling map. Then, feature maps are extracted by applying a VGG convolutional neural network pre-trained on the ImageNet dataset to the image X, and they are then upsampled to the resolution of X. Image X, guidance maps, and feature maps are integrated as the input tensor. We feed the proposed attention-guided, multi-scale segmentation neural network (AGMSSeg-Net) with the input tensor above to obtain the mask that assigns a binary label to each pixel. After a post-processing operation based on a fully connected Conditional Random Field (CRF), we extract the selected object boundary from the segmentation result. Experiments were conducted on two typical datasets with diverse region object types from complex scenes. The results demonstrate the effectiveness of the proposed method, and our approach outperforms existing methods for interactive image segmentation.
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels. In this work, we apply MIL with a deep convolutional neural network to identify whether clinical CT head image volumes possess one or more large hemorrhages (&gt;; 20cm<sup>3</sup> ), resulting in a learned 2D model without the need for 2D slice annotations. Individual image volumes are considered separate bags, and the slices in each volume are instances. Such a framework sets the stage for incorporating information obtained in clinical reports to help train a 2D segmentation approach. Within this context, we evaluate the data requirements to enable generalization of MIL by varying the amount of training data. Our results show that a training size of at least 400 patient image volumes was needed to achieve accurate per-slice hemorrhage detection. Over a five-fold cross-validation, the leading model, which made use of the maximum number of training volumes, had an average true positive rate of 98.10%, an average true negative rate of 99.36%, and an average precision of 0.9698. The models have been made available along with source code<sup>1</sup> to enabled continued exploration and adaption of MIL in CT neuroimaging.
Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial-temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements.
Rethinking Batch Normalization in Transformers

The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (BN), which is widely-adopted in Computer Vision. The preferred use of LN in NLP is principally due to the empirical observation that a (naive/vanilla) use of BN leads to significant performance degradation for NLP tasks; however, a thorough understanding of the underlying reasons for this is not always evident. In this paper, we perform a systematic study of NLP transformer models to understand why BN has a poor performance, as compared to LN. We find that the statistics of NLP data across the batch dimension exhibit large fluctuations throughout training. This results in instability, if BN is naively implemented. To address this, we propose Power Normalization (PN), a novel normalization scheme that resolves this issue by (i) relaxing zero-mean normalization in BN, (ii) incorporating a running quadratic mean instead of per batch statistics to stabilize fluctuations, and (iii) using an approximate backpropagation for incorporating the running statistics in the forward pass. We show theoretically, under mild assumptions, that PN leads to a smaller Lipschitz constant for the loss, compared with BN. Furthermore, we prove that the approximate backpropagation scheme leads to bounded gradients. We extensively test PN for transformers on a range of NLP tasks, and we show that it significantly outperforms both LN and BN. In particular, PN outperforms LN by 0.4/0.6 BLEU on IWSLT14/WMT14 and 5.6/3.0 PPL on PTB/WikiText-103. We make our code publicly available at \url{this https URL}.
The Role of Non-Positional Arithmetic on Efficient Emerging Cryptographic Algorithms

Number representation systems establish ways in which numbers are mapped to computer architectures, and how operations over the numbers are translated into computer instructions.The efficiency of public-key cryptography is strongly affected by the used number representations, as these systems are constructed from mathematically inspired problems to ensure security, and thus rely on operations over large integers.In this paper, unconventional representations systems, including the Residue Number System (RNS) and stochastic number representations, are systematically reviewed.Homomorphic representations, which allow for parties to operate on data without having access to their plaintext representation, are also considered.The main goal of this survey is to introduce the reader to key aspects of non-traditional number representations that may be exploited for public-key cryptography, without delving too much into the details.Examples of the methods and algorithms herein surveyed include subquadratic modular multiplication for isogeny-based cryptography, the acceleration of Goldreich-Goldwasser-Halevi (GGH) decryption by an order of magnitude, the improvement of the Direct Anonymous Attestation (DAA) protocol both in terms of storage requirements and the time taken to execute it, and efficient algorithm-hiding Fully Homomorphic Encryption (FHE).The implementation of this type of systems in both sequential and parallel platforms is analysed, and their performance is compared with traditional approaches.We hope this work sows the seed of further research on the application of non-positional number arithmetic to other cryptographic use-cases.
Practically Lossless Affine Image Transformation

In this contribution we introduce an almost lossless affine 2D image transformation method. To this end we extend the theory of the well-known Chirp-z transform to allow for fully affine transformation of general n-dimensional images. In addition we give a practical spatial and spectral zero-padding approach dramatically reducing losses of our transform, where usual transforms introduce blurring artifacts due to sub-optimal interpolation. The proposed method improves the mean squared error by approx. a factor of 1800 compared to the commonly used linear interpolation, and by a factor of 250 to the best competitor. We derive the transform from basic principles with special attention to implementation details and supplement this paper with python code for 2D images. In demonstration experiments we show the superior image quality compared to usual approaches, when using our method. However runtimes are considerably larger than when using toolbox algorithms.
Smart Platform for Film Shooting Management

Producing a movie involves difficult and time-consuming phases, specially, pre-production and production. It's a challenging task to find out suitable locations for each scene and building a schedule without any clashes. We have proposed and implemented a platform for film shooting management with following modules: (1) identify required background for each scene, (2) classify available film shooting locations, (3) compare the required background and available film shooting locations and (4) schedule the shooting of each scene. We have used natural language processing, image processing, string matching algorithms and optimization techniques to implement the above-mentioned modules. Our results show that, using our proposed modules, the film shooting management related services can be automated efficiently and effectively.
Breast Cancer Classification with Ultrasound Images Based on SLIC

Ultrasound image diagnosis of breast tumors has been widely used in recent years. However, there are some problems of it, for instance, poor quality, intense noise and uneven echo distribution, which has created a huge obstacle to diagnosis. To overcome these problems, we propose a novel method, a breast cancer classification with ultrasound images based on SLIC (BCCUI). We first utilize the Region of Interest (ROI) extraction based on Simple Linear Iterative Clustering (SLIC) algorithm and region growing algorithm to extract the ROI at the super-pixel level. Next, the features of ROI are extracted. Furthermore, the Support Vector Machine (SVM) classifier is applied. The calculation states that the accuracy of this segment algorithm is up to 88.00% and the sensitivity of the algorithm is up to 92.05%, which proves that the classifier presents in this paper has certain research meaning and applied worthiness.
Improved SSD-Based Multi-scale Pedestrian Detection Algorithm

This paper proposes an improved pedestrian detection algorithm based on SSD (Single Shot MultiBox Detector) model. The algorithm mainly solves the problem that the detection effect on small-scale pedestrians is not ideal when the SSD pedestrian detection algorithm performs the pedestrian detection task. The algorithm improves the original SSD model by introducing a shallower feature map. The pedestrian detection is carried out by using the characteristics of different output layers in the model, and the multi-layer detection results are combined to improve the detection effect of small-scale pedestrian. The Squeeze-and-Excitation Module is introduced in the additional feature layer of the SSD model. The improved model can automatically acquire the importance of each channel by self-learning, according to which useful features can be enhanced and features that are not useful for current tasks can be suppressed, thereby further improving the detection ability of the algorithm for small-scale pedestrians. Experiments show that the accuracy of the proposed algorithm in the INRIA dataset reaches 93.17%, and the missed detection rate is as low as 8.72%.