Reviewerly/exHarmony huggingface dataset:
Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation

Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the conventional GANs normally face an overfitting problem, consequently leads to an ill-conditioning problem in generating numeric and structured data. This paper studies the constrained network structures between generator G and discriminator D in WGAN, designs several structures including isomorphic, mirror and self-symmetric structures. We evaluates the performances of the constrained WGANs in data augmentations, taking the non-constrained GANs and WGANs as the baselines. Experiments prove the constrained structures have been improved in 17/20 groups of experiments. In twenty experiments on four UCI Machine Learning Repository datasets, Australian Credit Approval data, German Credit data, Pima Indians Diabetes data and SPECT heart data facing five conventional classifiers. Especially, Isomorphic WGAN is the best in 15/20 experiments. Finally, we theoretically proves that the effectiveness of constrained structures by the directed graphic model (DGM) analysis.
Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs.
Wigner distribution on a double-cylinder phase space for studying quantum error-correction protocols

We introduce a quasiprobability phase space distribution with two pairs of azimuthal-angular coordinates. This representation is well adapted to describe quantum systems with discrete symmetry. Quantum error correction of states encoded in continuous variables using translationally invariant states is studied as an example of application. We also propose an experimental scheme for measuring such distribution.
Prospecting of potential geothermal resources from the east to the Moroccan Sahara

Abstract Geothermal energy exploration is an old technique, updated in recent years and expected to develop strongly in the near future. Its potential is inexhaustible. Indeed, it is an energy drawn from the heat of the basement and is renewed permanently. Its uses are many and varied: individual heating, collective heating, networks of heat, production of electricity, storage of calories, exchanges of temperature.In this research, we will study, using GIS (Geographic Information System) and remote sensing, to detect potential geothermal reservoir areas. Three of satellite images types were used namely MODIS, ASTER and LANDSAT to detect and extract geothermal energy zones in the Sahara, the high Altas, the Middle Atlas and the Rif of Moroccan territory. Significant indices were calculated from satellite images to prospect for potential deposits, namely diurnal and nocturnal surface temperature, hydrothermal alteration indices and lineaments density layer. The present results include images of hydrothermal alteration indices, maps of lineaments and cracks for each zone studied and the surface temperature on the whole territory of Morocco. The good correlation between these three maps leads us to conclude that the Moroccan Sahara zone is considered as one of the most potential geothermal reservoir zone.
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm

While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear embeddings to previously unseen data is a rather overlooked subject. In this work, we first present a theoretical analysis of learning multi-modal nonlinear embeddings in a supervised setting. Our performance bounds indicate that for successful generalization in multi-modal classification and retrieval problems, the regularity of the interpolation functions extending the embedding to the whole data space is as important as the between-class separation and cross-modal alignment criteria. We then propose a multi-modal nonlinear representation learning algorithm that is motivated by these theoretical findings, where the embeddings of the training samples are optimized jointly with the Lipschitz regularity of the interpolators. Experimental comparison to recent multi-modal and single-modal learning algorithms suggests that the proposed method yields promising performance in multi-modal image classification and cross-modal image-text retrieval applications.
Fine Granularity Access in Interactive Compression of 360-Degree Images Based on Rate-adaptive Channel Codes

In this paper, we propose a new interactive compression scheme for omnidirectional images. This requires two characteristics: efficient compression of data, to lower the storage cost, and random access ability to extract part of the compressed stream requested by the user (for reducing the transmission rate). For efficient compression, data needs to be predicted by a series of references that have been pre-defined and compressed. This contrasts with the spirit of random accessibility. We propose a solution for this problem based on incremental codes implemented by rate-adaptive channel codes. This scheme encodes the image while adapting to any user request and leads to an efficient coding that is flexible in extracting data depending on the available information at the decoder. Therefore, only the information that is needed to be displayed at the user's side is transmitted during the user's request, as if the request was already known at the encoder. The experimental results demonstrate that our coder obtains a better transmission rate than the state-of-the-art tile-based methods at a small cost in storage. Moreover, the transmission rate grows gradually with the size of the request and avoids a staircase effect, which shows the perfect suitability of our coder for interactive transmission.
A Person Reidentification Algorithm Based on Improved Siamese Network and Hard Sample

Person reidentification is aimed at solving the problem of matching and identifying people under the scene of cross cameras. However, due to the complicated changes of different surveillance scenes, the error rate of person reidentification exists greatly. In order to solve this problem and improve the accuracy of person reidentification, a new method is proposed, which is integrated by attention mechanism, hard sample acceleration, and similarity optimization. First, the bilinear channel fusion attention mechanism is introduced to improve the bottleneck of ResNet50 and fine-grained information in the way of multireceptive field feature channel fusion is fully learnt, which enhances the robustness of pedestrian features. Meanwhile, a hard sample selection mechanism is designed on the basis of the P2G optimization model, which can simplify and accelerate picking out hard samples. The hard samples are used as the objects of similarity optimization to realize the compression of the model and the enhancement of the generalization ability. Finally, a local and global feature similarity fusion module is designed, in which the weights of each part are learned through the training process, and the importance of key parts is automatically perceived. Experimental results on Market-1501 and CUHK03 datasets show that, compared with existing methods, the algorithm in this paper can effectively improve the accuracy of person reidentification.
Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images

We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the trained model. Experimental results showed a 24% point increase in correct localization (CorLoc) measure, which is the ratio of correctly localized and classified images, by assigning the properly controlled weight. Performing active learning after a model is trained showed an additional increase in CorLoc. We tested the proposed method on the Stanford Dog datasets to assure that it can be applied to general cases, where strong annotations are insufficient to obtain resembling results. The presented method showed that higher performance is achievable with lesser annotation effort.
Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors*

Artificial neural networks can achieve impressive performances, and even outperform humans in some specific tasks. Nevertheless, unlike biological brains, the artificial neural networks suffer from tiny perturbations in sensory input, under various kinds of adversarial attacks. It is therefore necessary to study the origin of the adversarial vulnerability. Here, we establish a fundamental relationship between geometry of hidden representations (manifold perspective) and the generalization capability of the deep networks. For this purpose, we choose a deep neural network trained by local errors, and then analyze emergent properties of the trained networks through the manifold dimensionality, manifold smoothness, and the generalization capability. To explore effects of adversarial examples, we consider independent Gaussian noise attacks and fast-gradient-sign-method (FGSM) attacks. Our study reveals that a high generalization accuracy requires a relatively fast power-law decay of the eigen-spectrum of hidden representations. Under Gaussian attacks, the relationship between generalization accuracy and power-law exponent is monotonic, while a non-monotonic behavior is observed for FGSM attacks. Our empirical study provides a route towards a final mechanistic interpretation of adversarial vulnerability under adversarial attacks.
Nonclassical Effects Based on Husimi Distributions in Two Open Cavities Linked by an Optical Waveguide

Nonclassical effects are investigated in a system formed by two quantum wells, each of which is inside an open cavity. The cavities are spatially separated, linked by a fiber, and filled with a linear optical medium. Based on Husimi distributions (HDs) and Wehrl entropy, we explore the effects of the physical parameters on the generation and the robustness of the mixedness and HD information in the phase space. The generated quantum coherence and the HD information depend crucially on the cavity-exciton and fiber cavity couplings as well as on the optical medium density. The HD information and purity are lost due to the dissipation. This loss may be inhibited by increasing the optical susceptibility as well as the couplings of the exciton-cavity and the fiber-cavity. These parameters control the regularity, amplitudes, and frequencies of the generated mixedness.
Realizing $$\omega $$-regular Hyperproperties

We study the expressiveness and reactive synthesis problem of HyperQPTL, a logic that specifies $$\omega $$ -regular hyperproperties. HyperQPTL is an extension of linear-time temporal logic (LTL) with explicit trace and propositional quantification and therefore truly combines trace relations and $$\omega $$ -regularity. As such, HyperQPTL can express promptness, which states that there is a common bound on the number of steps up to which an event must have happened. We demonstrate how the HyperQPTL formulation of promptness differs from the type of promptness expressible in the logic Prompt-LTL. Furthermore, we study the realizability problem of HyperQPTL by identifying decidable fragments, where one decidable fragment contains formulas for promptness. We show that, in contrast to the satisfiability problem of HyperQPTL, propositional quantification has an immediate impact on the decidability of the realizability problem. We present a reduction to the realizability problem of HyperLTL, which immediately yields a bounded synthesis procedure. We implemented the synthesis procedure for HyperQPTL in the bounded synthesis tool BoSy. Our experimental results show that a range of arbiter satisfying promptness can be synthesized.
Numerical and analytical results for geometric measure of coherence and geometric measure of entanglement

Abstract Quantifying coherence and entanglement is extremely important in quantum information processing. Here, we present numerical and analytical results for the geometric measure of coherence, and also present numerical results for the geometric measure of entanglement. On the one hand, we first provide a semidefinite algorithm to numerically calculate geometric measure of coherence for arbitrary finite-dimensional mixed states. Based on this semidefinite algorithm, we test randomly generated single-qubit states, single-qutrit states, and a special kind of d -dimensional mixed states. Moreover, we also obtain an analytical solution of geometric measure of coherence for a special kind of mixed states. On the other hand, another algorithm is proposed to calculate the geometric measure of entanglement for arbitrary two-qubit and qubit-qutrit states, and some special kinds of higher dimensional mixed states. For other states, the algorithm can get a lower bound of the geometric measure of entanglement. Randomly generated two-qubit states, the isotropic states and the Werner states are tested. Furthermore, we compare our numerical results with some analytical results, which coincide with each other.
Fast and Unbiased Estimation of Volume Under Ordered Three-Class ROC Surface (VUS) With Continuous or Discrete Measurements

Receiver Operating Characteristic (ROC) surfaces have been studied in the literature essentially during the last decade and are considered as a natural generalization of ROC curves in three-class problems. The volume under the surface (VUS) is useful for evaluating the performance of a trichotomous diagnostic system or a three-class classifier’s overall accuracy when the possible disease condition or sample belongs to one of three ordered categories. In the areas of medical studies and machine learning, the VUS of a new statistical model is typically estimated through a sample of ordinal and continuous measurements obtained by some suitable specimens. However, discrete scales of the prediction are also frequently encountered in practice. To deal with such scenario, in this paper, we proposed a unified and efficient algorithm of linearithmic order, based on dynamic programming, for unbiased estimation of the mean and variance of VUS with unidimensional samples drawn from continuous or non-continuous distributions. Monte Carlo simulations verify our theoretical findings and developed algorithms.
Value of Virtual Reality Technology in Image Inspection and 3D Geometric Modeling

Aiming at the poor expressive ability of image statistical information during the reconstruction process of traditional 3D image reconstruction method based on virtual reality technology, resulting in low accuracy of 3D image after reconstruction, a new image detection and 3D image reconstruction based on virtual reality technology are studied method. This paper first proposed a new two-level cascade convolutional neural network structure. The first level of the network predicts target positioning based on the image-level labels of the training image, generates a bounding box of the target in the original image, and generates a cropped image. The cropped image is input to the second-level network. The cropped image may contain areas where the target is stuck in the original image. Level 2 networks only use the adhesion area as training data. Secondly, the visualization software development platform and virtual reality 3D image processing software are selected as the platform for 3D image reconstruction. After the original image is imported into the computer through data input and file analysis steps, the original image is detected. The detected image is in the virtual in the real software, the bounding box method is first used to construct the three-dimensional data field of image reconstruction, and the three-dimensional direct volume of the image is drawn according to the three-dimensional data field of image reconstruction. Preferably, the three-dimensional image reconstruction output formula is obtained through the three-dimensional image direct volume to realize the three-dimensional image reconstruction based on the virtual reality technology. The simulation results show that the method proposed in this paper can effectively detect images. The average traversal coverage of 3D image reconstruction is up to 0.979, and the reconstruction accuracy is higher than 0.97.
Fast Bayesian inversion for high dimensional inverse problems

We investigate the use of learning approaches to handle Bayesian inverse problems in a computationally efficient way when the signals to be inverted present a moderately high number of dimensions and are in large number. We propose a tractable inverse regression approach which has the advantage to produce full probability distributions as approximations of the target posterior distributions. In addition to provide confidence indices on the predictions, these distributions allow a better exploration of inverse problems when multiple equivalent solutions exist. We then show how these distributions can be used for further refined predictions using importance sampling, while also providing a way to carry out uncertainty level estimation if necessary. The relevance of the proposed approach is illustrated both on simulated and real data in the context of a physical model inversion in planetary remote sensing. The approach shows interesting capabilities both in terms of computational efficiency and multimodal inference.
BLENDED LEARNING IN BADMINTON TRAINING FOR PROFESSIONALS: STUDENTS’ PERCEPTIONS AND PERFORMANCE IMPACTS

As with other subject areas, badminton instruction for practitioners is experiencing a lot of changes under the impact of technology. Recently there has been the possibility of moving badminton training classes to the online platform but there is no consensus on its efficacy. This study is conducted to study the effects of blended learning activities on the perceptions and performance of students in physical education. Forty students in physical education are selected and divided into two groups: an experimental group, and a control group. All groups in face-to-face learning sessions have the same curriculum, course-book, equipment and teaching method. The questionnaire and interview data show that students in blended class sessions had positive perceptions of learning activities. Article visualizations:
Trakhtenbrot’s Theorem in Coq

We study finite first-order satisfiability (FSAT) in the constructive setting of dependent type theory. Employing synthetic accounts of enumerability and decidability, we give a full classification of FSAT depending on the first-order signature of non-logical symbols. On the one hand, our development focuses on Trakhtenbrot’s theorem, stating that FSAT is undecidable as soon as the signature contains an at least binary relation symbol. Our proof proceeds by a many-one reduction chain starting from the Post correspondence problem. On the other hand, we establish the decidability of FSAT for monadic first-order logic, i.e. where the signature only contains at most unary function and relation symbols, as well as the enumerability of FSAT for arbitrary enumerable signatures. All our results are mechanised in the framework of a growing Coq library of synthetic undecidability proofs.
A generalized worst-case complexity analysis for non-monotone line searches

We study the worst-case complexity of a non-monotone line search framework that covers a wide variety of known techniques published in the literature. In this framework, the non-monotonicity is controlled by a sequence of nonnegative parameters. We obtain complexity bounds to achieve approximate first-order optimality even when this sequence is not summable.
Gabor Feature-Based LogDemons With Inertial Constraint for Nonrigid Image Registration

Nonrigid image registration plays an important role in the field of computer vision and medical application. The methods based on Demons algorithm for image registration usually use intensity difference as similarity criteria. However, intensity based methods can not preserve image texture details well and are limited by local minima. In order to solve these problems, we propose a Gabor feature based LogDemons registration method in this paper, called GFDemons. We extract Gabor features of the registered images to construct feature similarity metric since Gabor filters are suitable to extract image texture information. Furthermore, because of the weak gradients in some image regions, the update fields are too small to transform the moving image to the fixed image correctly. In order to compensate this deficiency, we propose an inertial constraint strategy based on GFDemons, named IGFDemons, using the previous update fields to provide guided information for the current update field. The inertial constraint strategy can further improve the performance of the proposed method in terms of accuracy and convergence. We conduct experiments on three different types of images and the results demonstrate that the proposed methods achieve better performance than some popular methods.