Reviewerly/exHarmony huggingface dataset:
Accelerated Optimization in the PDE Framework Formulations for the Active Contour Case

Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, A Variational Perspective on Accelerated Methods in Optimization, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODEs. We show how their formulation may be further extended to infinite dimensional manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The coevolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.
A New Nonlocal Nonlinear Diffusion Equation for Data Analysis

In this paper we introduce and study a new feature-preserving nonlinear nonlocal diffusion equation for denoising signals. The proposed partial differential equation is based on a novel diffusivity coefficient that uses a nonlocal automatically detected parameter related to the local bounded variation and the local oscillating pattern of the noisy input signal. We provide a mathematical analysis of the existence of the solution in the two dimensional case, but easily extensible to the one-dimensional model. Finally, we show some numerical experiments, which demonstrate the effectiveness of the new approach.
One-Particle Approximation as a Simple Playground for Irreversible Quantum Evolution

One-particle approximation as a simple playground for irreversible quantum evolution 1A.
Asymptotic security analysis of teleportation-based quantum cryptography

We prove that the teleportation-based quantum cryptography protocol presented in Gordon and Rigolin (Opt Commun 283:184, 2010), which is built using only orthogonal states encoding the classical bits that are teleported from Alice to Bob, is asymptotically secure against all types of individual and collective attacks. We then investigate modifications to that protocol leading to greater secret-key rates and to security against coherent attacks. In other words, we show an unconditional secure quantum key distribution protocol that does not need non-orthogonal quantum states to encode the bits of the secret key sent from Alice to Bob. We also revisit the security proof of the BB84 protocol by exploring the non-uniqueness of the Schmidt decomposition of its entanglement-based representation. This allows us to arrive at a secure transmission of the key for a slightly greater quantum bit error rate (quantum communication channel’s noise) when compared to its standard security analysis.
Algebraic quantum codes: linking quantum mechanics and discrete mathematics

We present a general framework of quantum error-correcting codes (QECCs) as a subspace of a complex Hilbert space and the corresponding error models. Then we illustrate how QECCs can be constructed using techniques from algebraic coding theory. Additionally, we discuss secondary constructions for QECCs, leading to propagation rules for the parameters of QECCs.
Quantum Thermalization and Vanishing Thermal Entanglement in the Open Jaynes–Cummings Model

Abstract The quantum thermalization of the Jaynes–Cummings (JC) model in both equilibrium and non‐equilibrium open‐system cases is studied, in which the two subsystems, a two‐level system and a single‐mode bosonic field, are in contact with either two individual heat baths or a common heat bath. It is found that in the individual heat‐bath case, the JC model can only be thermalized when either the two heat baths have the same temperature or the coupling of the JC system to one of the two baths is turned off. In the common heat‐bath case, the JC system can be thermalized irrespective of the bath temperature and the system–bath coupling strengths. The thermal entanglement in this system is also studied. A counterintuitive phenomenon of vanishing thermal entanglement in the JC system is found and proved.
A Dependently Typed Library for Static Information-Flow Control in Idris

Safely integrating third-party code in applications while protecting the confidentiality of information is a long-standing problem. Pure functional programming languages, like Haskell, make it possible to enforce lightweight information-flow control through libraries like MAC by Russo. This work presents DepSec, a MAC inspired, dependently typed library for static information-flow control in Idris. We showcase how adding dependent types increases the expressiveness of state-of-the-art static information-flow control libraries and how DepSec matches a special-purpose dependent information-flow type system on a key example. Finally, we show novel and powerful means of specifying statically enforced declassification policies using dependent types.
Gaussian kernel in quantum learning

The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel is approximated to [Formula: see text] and even [Formula: see text] when [Formula: see text] and the error [Formula: see text] are small enough to be ignored, where [Formula: see text] is the dimension of the training instances, [Formula: see text] is the accuracy, [Formula: see text] is the dot product of the two quantum states, and [Formula: see text] is the Taylor remainder error term. Therefore, the run time complexity of the quantum version of the Gaussian kernel seems to be significantly faster when compared with its classical version.
New Fusion Algorithm Provides an Alternative Approach to Robotic Path Planning

For rapid growth in technology and automation, human tasks are being taken over by robots as robots have proven to be better with both speed and precision. One of the major and widespread usages of these robots is in the industrial businesses, where they are employed to carry massive loads in and around work areas. As these working environments might not be completely localized and could be dynamically changing, new approaches must be evaluated to guarantee a crash-free way of performing duties. This paper presents a new and efficient fusion algorithm for solving the path planning problem in a custom 2D environment. This fusion algorithm integrates an improved and optimized version of both, A* algorithm and the Artificial potential field method. Firstly, an initial or preliminary path is planned in the environmental model by adopting the A* algorithm. The heuristic function of this A* algorithm is optimized and improved according to the environmental model. This is followed by selecting and saving the key nodes in the initial path. Lastly, on the basis of these saved key nodes, path smoothing is done by artificial potential field method. Our simulation results carried out using Python viz. libraries indicate that the new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.
Observable-Geometric Phases and Quantum Computation

This paper presents an alternative approach to geometric phases from the observable point of view. Precisely, we introduce the notion of observable-geometric phases, which is defined as a sequence of phases associated with a complete set of eigenstates of the observable. The observable-geometric phases are shown to be connected with the quantum geometry of the observable space evolving according to the Heisenberg equation. They are indeed distinct from Berry’s phase (Berry Proc. R. Soc. London Series A 392:45–57, 1984; Simon Phys. Rev. Lett. 51:2167–2170, 1983) as the system evolves adiabatically. It is shown that the observable-geometric phases can be used to realize a universal set of quantum gates in quantum computation. This scheme leads to the same gates as the Abelian geometric gates of Zhu and Wang (Phys. Rev. Lett. 89: 097902: 1–4, 2002, Phys. Rev. A 67: 022319: 1–9, 2003), but based on the quantum geometry of the observable space beyond the state space.
Information-Theoretically Secure Data Origin Authentication with Quantum and Classical Resources

In conventional cryptography, information-theoretically secure message authentication can be achieved by means of universal hash functions, and requires that the two legitimate users share a random secret key, which is at least twice as long as the tag. We address the question of whether quantum resources can offer any advantage over classical unconditionally secure message authentication codes. It is shown that a broad class of symmetric prepare-and-measure quantum message-authentication schemes cannot do better than their classical counterparts.
A 360-degree Panoramic Image Inpainting Network Using a Cube Map

Inpainting has been continuously studied in the field of computer vision.As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance.Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video.However, deep learning-based inpainting technology for panoramic images has not been actively studied.We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs).The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network.Since the cube map format is used, the correlation of the six sides of the cube map should be considered.Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image.The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.
Towards a Geometry Automated Provers Competition

The geometry automated theorem proving area distinguishes itself by a large number of specific methods and implementations, different approaches (synthetic, algebraic, semi-synthetic) and different goals and applications (from research in the area of artificial intelligence to applications in education).Apart from the usual measures of efficiency (e.g.CPU time), the possibility of visual and/or readable proofs is also an expected output against which the geometry automated theorem provers (GATP) should be measured.The implementation of a competition between GATP would allow to create a test bench for GATP developers to improve the existing ones and to propose new ones.It would also allow to establish a ranking for GATP that could be used by "clients" (e.g.developers of educational e-learning systems) to choose the best implementation for a given intended use.
Symmetry Constrained Machine Learning

Symmetry, a central concept in understanding the laws of nature, has been used for centuries in physics, mathematics, and chemistry, to help make mathematical models tractable. Yet, despite its power, symmetry has not been used extensively in machine learning, until rather recently. In this article we show a general way to incorporate symmetries into machine learning models. We demonstrate this with a detailed analysis on a rather simple real world machine learning system - a neural network for classifying handwritten digits, lacking bias terms for every neuron. We demonstrate that ignoring symmetries can have dire over-fitting consequences, and that incorporating symmetry into the model reduces over-fitting, while at the same time reducing complexity, ultimately requiring less training data, and taking less time and resources to train.
Continuous variable quantum perceptron

We present a model of Continuous Variable Quantum Perceptron (CVQP) whose architecture implements a classical perceptron. The necessary non-linearity is obtained via measuring the output qubit and using the measurement outcome as input to an activation function. The latter is chosen to be the so-called ReLu activation function by virtue of its practical feasibility and the advantages it provides in learning tasks. The encoding of classical data into realistic finitely squeezed states and the use of superposed (entangled) input states for specific binary problems are discussed.
OAK: Ontology-Based Knowledge Map Model for Digital Agriculture

Nowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining processes, the scalability still remains an open issue. In this study, we propose a knowledge representation model, called an ontology-based knowledge map, which can collect knowledge from different sources, store it, and exploit either directly by stakeholders or as an input to the knowledge discovery process (Data Mining). The proposed model consists of two stages, 1) build an ontology as a knowledge base for a specific domain and data mining concepts, and 2) build the ontology-based knowledge map model for representing and storing the knowledge mined on the crop datasets. A framework of the proposed model has been implemented in agriculture domain. It is an efficient and scalable model, and it can be used as knowledge repository a digital agriculture.
A Self-Relevant CNN-SVM Model for Problem Classification in K-12 Question-Driven Learning

With the development and progress of science and technology, the learning patterns also evolve. In Question-Driven learning, students clarify and validate what they learn by answering questions. Such a large number of questions needs good management. A well-performed management can avoid the situation that learning materials with the same knowledge set are defined into different sections due to ambiguous expressions. In this work, we propose a hybrid classification model using the CNN-SVM that focuses on K-12 learning materials. We combine the Word2Vec feature and the hidden layer feature of CNN. In response to a current question that contains text and image, we also introduce a multi-modal preprocessing approach. The experiment results validate that the preprocessing method and the hybrid model can outperform the the state-of-the-art method and baseline methods.
Formation of Regression Model for Analysis of Complex Systems Using Methodology of Genetic Algorithms

This study presents the approach to analyze evolution of an arbitrary complex system whose behavior is characterized by a set of different time-dependent factors. The key requirement for these factors is that they must contain an information about the system only; it does not matter at all what the nature (physical, biological, social, economic, etc.) of a complex system is. Within the framework of the presented theoretical approach, the problem of searching for non-linear regression models that express the relationship between these factors for a complex system under study is solved. It will be shown that this problem can be solved using the methodology of genetic (evolutionary) algorithms. The resulting regression models make it possible to predict the most probable evolution of the considered system, as well as to determine the significance of some factors and, thereby, to formulate some recommendations to drive by this system. It will be shown that the presented theoretical approach can be used to analyze data (information) characterizing the educational process in the discipline"Physic" in the secondary school, and to develop the strategies for improving academic performance in this discipline.
Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks

We utilize a connection between compositional kernels and branching processes via Mehler's formula to study deep neural networks. This new probabilistic insight provides us a novel perspective on the mathematical role of activation functions in compositional neural networks. We study the unscaled and rescaled limits of the compositional kernels and explore the different phases of the limiting behavior, as the compositional depth increases. We investigate the memorization capacity of the compositional kernels and neural networks by characterizing the interplay among compositional depth, sample size, dimensionality, and non-linearity of the activation. Explicit formulas on the eigenvalues of the compositional kernel are provided, which quantify the complexity of the corresponding reproducing kernel Hilbert space. On the methodological front, we propose a new random features algorithm, which compresses the compositional layers by devising a new activation function.
Feature space approximation for kernel-based supervised learning

We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves the generalizability of learned target functions. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.
Set It and Forget It! Turnkey ECC for Instant Integration

Historically, Elliptic Curve Cryptography (ECC) is an active field of applied cryptography where recent focus is on high speed, constant time, and formally verified implementations. While there are a handful of outliers where all these concepts join and land in real-world deployments, these are generally on a case-by-case basis: e.g. a library may feature such X25519 or P-256 code, but not for all curves. In this work, we propose and implement a methodology that fully automates the implementation, testing, and integration of ECC stacks with the above properties. We demonstrate the flexibility and applicability of our methodology by seamlessly integrating into three real-world projects: OpenSSL, Mozilla’s NSS, and the GOST OpenSSL Engine, achieving roughly 9.5x, 4.5x, 13.3x, and 3.7x speedup on any given curve for key generation, key agreement, signing, and verifying, respectively. Furthermore, we showcase the efficacy of our testing methodology by uncovering flaws and vulnerabilities in OpenSSL, and a specification-level vulnerability in a Russian standard. Our work bridges the gap between significant applied cryptography research results and deployed software, fully automating the process.
Improving Zero-Shot Learning Baselines with Commonsense Knowledge

Zero-shot learning — the problem of training and testing on a completely disjoint set of classes — relies greatly on its ability to transfer knowledge from train classes to test classes. Traditionally semantic embeddings consisting of human-defined attributes or distributed word embeddings are used to facilitate this transfer by improving the association between visual and semantic embeddings. In this paper, we take advantage of explicit relations between nodes defined in ConceptNet, a commonsense knowledge graph, to generate commonsense embeddings of the class labels by using a graph convolution network-based autoencoder. Our experiments performed on three standard benchmark datasets surpass the strong baselines when we fuse our commonsense embeddings with existing semantic embeddings, i.e., human-defined attributes and distributed word embeddings. This work paves the path to more brain-inspired approaches to zero-short learning.
Improved biomedical word embeddings in the transformer era

Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g., skip-gram, GLoVE) using free text corpora is often used to embed both words and concepts. Pre-trained embeddings are typically leveraged in downstream tasks using various neural architectures that are designed to optimize task-specific objectives that might further tune such embeddings. Despite advances in contextualized language model based embeddings, static word embeddings still form an essential starting point in BioNLP research and applications. They are useful in low resource settings and in lexical semantics studies. Our main goal is to build improved biomedical word embeddings and make them publicly available for downstream applications. We jointly learn word and concept embeddings by first using the skip-gram method and further fine-tuning them with correlational information manifesting in co-occurring Medical Subject Heading (MeSH) concepts in biomedical citations. This fine-tuning is accomplished with the transformer-based BERT architecture in the two-sentence input mode with a classification objective that captures MeSH pair co-occurrence. We conduct evaluations of these tuned static embeddings using multiple datasets for word relatedness developed by previous efforts. Both in qualitative and quantitative evaluations we demonstrate that our methods produce improved biomedical embeddings in comparison with other static embedding efforts. Without selectively culling concepts and terms (as was pursued by previous efforts), we believe we offer the most exhaustive evaluation of biomedical embeddings to date with clear performance improvements across the board. We repurposed a transformer architecture (typically used to generate dynamic embeddings) to improve static biomedical word embeddings using concept correlations. We provide our code and embeddings for public use for downstream applications and research endeavors: https://github.com/bionlproc/BERT-CRel-Embeddings.