Reviewerly/exHarmony huggingface dataset:
A Gabor Odd Filter-Based Ratio Operator for SAR Image Matching

Synthetic aperture radar (SAR) image matching is a challenging issue in remote sensing as the images contain significant multiplicative speckle noise. Histogram of oriented gradients (HOG)-based descriptors have been used popularly for the matching of the SAR images. An appropriate gradient computation of the SAR images plays a significant role in the matching performance of these HOG-based descriptors. The ratio operators are preferred for computing the gradients in the SAR images as these are usually corrupted by the multiplicative noise. Recently, a multiscale Gabor odd filter-based ratio operator (GOFRO) has been proposed for the edge detection of the SAR images. In this letter, we utilize the GOFRO for SAR image matching. We propose a Gabor odd filter (GOF)-based descriptor, where the gradients are computed by the GOFRO. At first, scale-invariant feature transform (SIFT) features are extracted from the SAR images. A novel approach is presented to select the more repeatable SIFT features in SAR images. Then, the proposed GOF-based descriptor is formed for the SIFT features and, finally, feature matching is performed. Experiments on three sets of simulated and real SAR image pairs demonstrate the effectiveness of the proposed GOF-based descriptor for SAR image matching.
Stopping Memory Disclosures via Diversification and Replicated Execution

With the wide deployment of security mechanisms such as Address Space Layout Randomization (ASLR), memory disclosures have become a prerequisite for critical memory-corruption attacks (e.g., code-reuse attack)-adversaries are forced to exploit memory disclosures to circumvent ASLR as the first step. As a result, the security threats of memory disclosures are now significantly aggravated-they break not only data confidentiality but also the effectiveness of security mechanisms. In this paper, we propose a general detection methodology and develop a system to stop memory disclosures. We observe that memory disclosures are not root causes but rather consequences of a variety of hard-to-detect program errors such as memory corruption and uninitialized read. We thus propose a replicated execution-based methodology to generally detect memory disclosures, regardless of their causes. We realize this methodology with Buddy: By seamlessly maintaining two identical running instances of a target program and diversifying only its target data, Buddy can accurately detects memory disclosures of the data, as doing so will result in the two instances outputting different values. Extensive evaluation results show that Buddy is reliable and efficient while stopping real memory disclosures such as the Heartbleed leak.
Direct measurement of the two-dimensional spatial quantum wave function via strong measurements

Wavefunction is the foundation of quantum theory, which is assumed to give a complete description of a quantum system. For a long time, wavefunction is introduced as an abstract element of the theory and there lacks effective ways to measure it directly. The situation, however, is somewhat changed when Lundeen et al. reported the direct measurement of the quantum wavefunction via weak measurements, which gives the wavefunction a clearly operational definition [Nature 474, 188 (2011)]. The weak measurement method requires sequential measurements of conjugate observables position and momentum with the position measurement is weak enough. Surprisingly, the recent research by Vallone and Dequal shows that performing sequential strong measurements realizes the same target, in which case no approximation has to be made compared to the case of weak measurements[Phys. Rev. Lett. 116, 040502 (2016)]. Here we experimentally report the direct measurement of the two-dimensional transverse wavefunction of photons via strong measurements for the first time, which implies that an accurate and clear operational definition can be given to wavefunction. We have measured the Gaussian and Laguerre-Gaussian of l = 1 spatial wavefunctions of photons with R-square are 0.97 and 0.93 respectively. As a potentially important application, we show that the direct measurement of two-dimensional wavefunction provides an alternative way to realize digital holography of three-dimensional objects. The results presented here will not only deepen our understanding of abstract wavefunction but also have significant applications in quantum information processing and quantum imaging.
Identification of mechanical parameters of composites in tensile tests using mixed numerical-experimental method

This paper presents material parameters identification procedure, which is based on a mixed numerical-experimental method for an uniaxial tensile test. The method gives advantages in measuring heterogeneous strain fields and determining several parameters in single test compared to traditional methods. In this case four in-plane parameters of orthotropic composite material were determined using finite element updating method as a solution for the inverse problem, digital image correlation for full-field measurement and Gauss-Newton algorithm as an optimization procedure. Two geometries of uniaxially loaded specimens were tested: open-hole specimens and double U-notch specimens. The results showed robustness of the proposed implementation of Gauss-Newton algorithm and its fast computation time. It was found that both geometries were appropriate for the used procedure and the method can be used as an alternative tool for a material characterization.
Indexing the Event Calculus: Towards practical human-readable Personal Health Systems

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. In general, a patient affected by a chronic disease can generate large amounts of events: for example, in Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. Just by itself, without considering other physiological parameters, it would be impossible for medical doctors to individually and accurately follow every patient, highlighting the need of simple approaches towards querying physiological time series. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. Anyhow, handling data streams efficiently is not enough. Domain experts' knowledge must be explicitly included into PHSs in a way that it can be easily readed and modified by medical staffs. Logic programming represents the perfect programming paradygm to accomplish this task. In this work, an Event Calculus-based reasoning framework to standardize and express domain-knowledge in the form of monitoring rules is suggested, and applied to three different use cases. However, if online monitoring has to be achieved, the reasoning performance must improve dramatically. For this reason, three promising mechanisms to index the Event Calculus Knowledge Base are proposed. All of them are based on different types of tree indexing structures: k-d trees, interval trees and red-black trees. The paper then compares and analyzes the performance of the three indexing techniques, by computing the time needed to check different type of rules (and eventually generating alerts), when the number of recorded events (e.g. values of physiological parameters) increases. The results show that customized jREC performs much better when the event average inter-arrival time is little compared to the checked rule time-window. Instead, where the events are more sparse, the use of k-d trees with standard EC is advisable. Finally, the Multi-Agent paradigm helps to wrap the various components of the system: the reasoning engines represent the agent minds, and the sensors are its body. The said agents have been developed in MAGPIE, a mobile event based Java agent platform.
Label-Aware Distributed Ensemble Learning: A Simplified Distributed Classifier Training Model for Big Data

Label-Aware Distributed Ensemble Learning (LADEL) is a programming model and an associated implementation for distributing any classifier training to handle Big Data. It only requires users to specify the training data source, the classification algorithm and the desired parallelization level. First, a distributed stratified sampling algorithm is proposed to generate stratified samples from large, pre-partitioned datasets in a shared-nothing architecture. It executes in a single pass over the data and minimizes inter-machine communication. Second, the specified classification algorithm training is parallelized and executed on any number of heterogeneous machines. Finally, the trained classifiers are aggregated to produce the final classifier. Data miners can use LADEL to run any classification algorithm on any distributed framework, without any experience in parallel and distributed systems. The proposed LADEL model can be implemented on any distributed framework (Drill, Spark, Hadoop, etc.) to speed up the development of its data mining capabilities. It is also generic and can be used to distribute the training of any classification algorithm of any sequential single-node data mining library (Weka, R, scikit-learn, etc.). Distributed frameworks can implement LADEL to distribute the execution of existing data mining libraries without rewriting the algorithms to run in parallel. As a proof-of-concept, the LADEL model is implemented on Apache Drill to distribute the training execution of Weka's classification algorithms. Our empirical studies show that LADEL classifiers have similar and sometimes even better accuracy to the single-node classifiers and they have a significantly faster training and scoring times.
Low-rank Bayesian tensor factorization for hyperspectral image denoising

In this paper, we present a low-rank Bayesian tensor factorization approach for hyperspectral image (HSI) denoising problem, where zero-mean white and homogeneous Gaussian additive noise is removed from a given HSI. The approach is based on two intrinsic properties underlying a HSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). We first adaptively construct the patch-based tensor representation for the HSI to extract the NSS knowledge while preserving the property of GCS. Then, we employ the low rank property in this representation to design a hierarchical probabilistic model based on Bayesian tensor factorization to capture the inherent spatial-spectral correlation of HSI, which can be effectively solved under the variational Bayesian framework. Furthermore, through incorporating these two procedures in an iterative manner, we build an effective HSI denoising model to recover HSI from its corruption. This leads to a state-of-the-art denoising performance, consistently surpassing recently published leading HSI denoising methods in terms of both comprehensive quantitative assessments and subjective visual quality.
Image Enhancement With PDEs and Nonconservative Advection Flow Fields

We propose a new method for the progressive enhancement of images degraded by noise and blur using shock filters. The originality of our approach lies in the iterative exploitation of nonconservative edge-based force fields normally designed for segmentation to determine both locations and amplitudes of the sharpening action. The proposed nonconservative shock filter (NCSF) produces images with strong discontinuities that can be used as an additional pre-processing step to facilitate higher level tasks such as edge detection or segmentation. NCSF is especially useful when coupled with an anisotropic diffusion equation in noisy configurations. Using the proposed method, edges that are not well defined prior to filtering can be iteratively recovered. We show processing results on various types of images, focusing on medical transrectal ultrasound and brain PET imaging.
A new outlier detection method based on OPTICS

OPTICS is a density-based clustering method that can address point sets with different densities; however, the outlier detection capability of OPTICS is limited by several factors, such as different parameter and different point set shapes. Therefore, an outlier detection method based on OPTICS was proposed, known as OD-OPTICS, which adds a pre-processing process and modifies the CD computing method. First, the Radius Filtration Strategy, which provides the key radii, is performed; and the crucial distances of the point set are reflected. Then, for the purpose of filtrating invalid radii and selecting the most appropriate one, the Covering Space Model was proposed. With the influence of the three rules that we established, the basic distance between two neighbouring points can be calculated by the covering space. Moreover, the CD calculation was optimized such that it could magnify the difference value between normal points and outliers. In the experiment part, the pre-processing details were demonstrated and the validity of OD-OPTICS on public point sets were tested; to verify the optimization and detectability of OD-OPTICS, the proposed method is compared to OPTICS and four other typical methods. The results demonstrated that the detection performance of OD-OPTICS is superior to that of OPTICS.
Eigenvalue measurement of topologically protected edge states in split-step quantum walks

We study topological phenomena of quantum walks by implementing a novel protocol that extends the range of accessible properties to the eigenvalues of the walk operator. To this end, we experimentally realise for the first time a split-step quantum walk with decoupling, which allows for investigating the effect of a bulk-boundary while realising only a single bulk configuration. We approximate the symmetry protected edge states with high similarities and read out the phase relative to a reference for all modes. In this way we observe eigenvalues which are distinguished by the presence or absence of sign flips between steps. Furthermore, the results show that investigating a bulk-boundary with a single bulk is experimentally feasible when decoupling the walk beforehand.
Mineralogical and textural controls on spectral induced polarization signatures of the Canadian Malartic gold deposit: Applications to mineral exploration

Applications of the spectral induced polarization (SIP) method to mineral exploration are limited by our knowledge of the relationships among rock texture, mineral composition, and electrical properties. Laboratory SIP responses were measured on rock samples from the Canadian Malartic gold deposit. Field SIP responses were also measured at the outcrop scale, along a profile that intersects a well-studied mineralized zone. The mineralogy and the texture of sedimentary rocks from this deposit were quantitatively determined with mineral liberation analysis. A systematic decrease (Pearson [Formula: see text]) in total chargeability with increasing fraction of the sulfide mineral interfaces associated with feldspar minerals (namely, K-feldspar and albite) was observed. On the other hand, total chargeability increased with the fraction of sulfide mineral interfaces associated with carbonates and micas (Pearson [Formula: see text]). At Canadian Malartic, proximal alteration in the mineralized zones is marked by rocks that lack a foliation plane and that were subjected to pervasive K-feldspar, albite, and pyrite alteration. In contrast, distal alteration in sedimentary rocks is marked by biotite, albite, carbonate, and pyrite that are oriented along the regional [Formula: see text] foliation. In the least-altered (LA) sedimentary rocks, quartz and biotite are associated with pyrrhotite and ilmenite as the main sulfide and oxide mineral phases, respectively. SIP measurements conducted at district and outcrop scales and along a drill core indicated that proximally altered sedimentary rocks were characterized by low total chargeability values ([Formula: see text] to [Formula: see text] in the laboratory and [Formula: see text] in the field). In contrast, the LA sedimentary rocks were characterized by total chargeability values up to [Formula: see text] in the laboratory and [Formula: see text] in the field. We conclude that mineralized zones associated with this type of ore deposit are characterized by low chargeability anomalies.
Hybrid Hierarchical Reinforcement Learning for online guidance and navigation with partial observability

Autonomous guidance and navigation problems often have high-dimensional spaces, multiple objectives, and consequently a large number of states and actions, which is known as the 'curse of dimensionality'. Furthermore, systems often have partial observability instead of a perfect perception of their environment. Recent research has sought to deal with these problems by using Hierarchical Reinforcement Learning, which often uses same or similar reinforcement learning methods within one application so that multiple objectives can be combined. However, there is not a single learning method that can benefit all targets. To acquire optimal decision-making most efficiently, this paper proposes a hybrid Hierarchical Reinforcement Learning method consisting of several levels, where each level uses various methods to optimize the learning with different types of information and objectives. An algorithm is provided using the proposed method and applied to an online guidance and navigation task. The navigation environments are complex, partially observable, and a priori unknown. Simulation results indicate that the proposed hybrid Hierarchical Reinforcement Learning method, compared to flat or non-hybrid methods, can help to accelerate learning, to alleviate the 'curse of dimensionality' in complex decision-making tasks. In addition, the mixture of relative micro states and absolute macro states can help to reduce the uncertainty or ambiguity at high levels, to transfer the learned results within and across tasks efficiently, and to apply to non-stationary environments. This proposed method can yield a hierarchical optimal policy for autonomous guidance and navigation without a priori knowledge of the system or the environment.
A novel mixture model using the multivariate normal mean–variance mixture of Birnbaum–Saunders distributions and its application to extrasolar planets

This paper presents a new finite mixture model based on the multivariate normal mean–variance mixture of Birnbaum–Saunders (NMVBS) distribution. We develop a computationally analytical EM algorithm for model fitting. Due to the dependence of this algorithm on initial values and the number of mixing components, a learning-based EM algorithm and an extended variant are proposed. Numerical simulations show that the proposed algorithms allow for better clustering performance and classification accuracy than some competing approaches. The effectiveness and prominence of the proposed methodology are also shown through an application to an extrasolar planet dataset.
Machine Learning Fundamentals

This chapter aims to be a smooth introduction to the basic concepts of machine learning, and, building on them, explain some to the latest advanced techniques. After a brief historical perspective, we overview the two currently most popular machine learning frameworks—deep learning and probabilistic graphical models. We conclude the chapter with practical advices about machine learning experiments which are necessary to know for a beginner. A good understanding of these fundamentals opens up a wide portfolio of opportunities for predictive models in transportation, and is hopefully a good basis for the remainder of this book.
Lower Bounds for Differentially Private RAMs

In this work, we study privacy-preserving storage primitives that are suitable for use in data analysis on outsourced databases within the differential privacy framework. The goal in differentially private data analysis is to disclose global properties of a group without compromising any individual's privacy. Typically, differentially private adversaries only ever learn global properties. For the case of outsourced databases, the adversary also views the patterns of access to data. Oblivious RAM (ORAM) can be used to hide access patterns but ORAM might be excessive as in some settings it could be sufficient to be compatible with differential privacy and only protect the privacy of individual accesses. We consider $$(\epsilon ,\delta )$$ -Differentially Private RAM, a weakening of ORAM that only protects individual operations and seems better suited for use in data analysis on outsourced databases. As differentially private RAM has weaker security than ORAM, there is hope that we can bypass the $$\varOmega (\log (nb/c))$$ bandwidth lower bounds for ORAM by Larsen and Nielsen [CRYPTO '18] for storing an array of n b-bit entries and a client with c bits of memory. We answer in the negative and present an $$\varOmega (\log (nb/c))$$ bandwidth lower bound for privacy budgets of $$\epsilon = O(1)$$ and $$\delta \le 1/3$$ . The information transfer technique used for ORAM lower bounds does not seem adaptable for use with the weaker security guarantees of differential privacy. Instead, we prove our lower bounds by adapting the chronogram technique to our setting. To our knowledge, this is the first work that uses the chronogram technique for lower bounds on privacy-preserving storage primitives.
Emergent neural turing machine and its visual navigation

Traditional Turing Machines (TMs) are symbolic whose hand-crafted representations are static and limited. Developmental Network 1 (DN-1) uses emergent representation to perform Turing Computation. But DN-1 lacks hierarchy in its internal representations, and is hard to handle the complex visual navigation tasks. In this paper, we improve DN-1 with several new mechanisms and presenta new emergent neural Turing Machine - Developmental Network 2 (DN-2). By neural, we mean that the control of the TM has neurons as basic computing elements. The major novelty of DN-2 over DN-1 is that the representational hierarchy inside DN-2 is emergent and fluid. DN-2 grows complex hierarchies by dynamically allowing initialization of neurons with different ranges of connection. We present a complex task - vision guided navigation in simulated and natural worlds using DN-2. A major function that has not been demonstrated before is that the hierarchy enables attention that disregards distracting features based on the navigation context. In simulated navigation experiments, DN-2 can perform with no errors, and in real-world navigation experiments, the error rate is only 0.78%. These experimental results showed that DN-2 successfully learned rules of navigation with image inputs.