Reviewerly/exHarmony huggingface dataset:
A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere S∞ in L2, and the Fisher–Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the α-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback–Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Fréchet derivative operators motivated by the geometry of S∞, and examine its properties. Through simulations and real data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models. Supplementary materials for this article are available online.
Recovering Short Secret Keys of RLCE in Polynomial Time

We present a key recovery attack against Y. Wang’s Random Linear Code Encryption (RLCE) scheme recently submitted to the NIST call for post-quantum cryptography. The public key of this code based encryption scheme is a generator matrix of a generalised Reed Solomon code whose columns are mixed in a certain manner with purely random columns. In this paper, we show that it is possible to recover the underlying structure when there are not enough random columns. The attack reposes on a distinguisher on the dimension of the square code. This process allows to recover the secret key for all the short key parameters proposed by the author in $$O(n^5)$$ operations. Our analysis explains also why RLCE long keys stay out of reach of our attack.
Designing pretty good state transfer via isospectral reductions

We present an algorithm to design networks that feature pretty good state transfer (PGST), which is of interest for high-fidelity transfer of information in quantum computing. Realizations of PGST networks have so far mostly relied either on very special network geometries or imposed conditions such as transcendental on-site potentials. However, it was recently shown [Eisenberg et al., arXiv:1804.01645] that PGST generally arises when a network's eigenvectors and the factors $P_{\pm}$ of its characteristic polynomial $P$ fulfill certain conditions, where $P_{\pm}$ correspond to eigenvectors which have $\pm 1$ parity on the input and target sites. We combine this result with the so-called isospectral reduction of a network to obtain $P_{\pm}$ from a dimensionally reduced form of the Hamiltonian. Equipped with the knowledge of the factors $P_{\pm}$, we show how a variety of setups can be equipped with PGST by proper tuning of $P_{\pm}$. Having demonstrated a method of designing networks featuring pretty good state transfer of single site excitations, we further show how the obtained networks can be manipulated such that they allow for robust storage of qubits. We hereby rely on the concept of compact localized states, which are eigenstates of a Hamiltonian localized on a small subdomain, and whose amplitudes completely vanish outside of this domain. Such states are natural candidates for the storage of quantum information, and we show how certain Hamiltonians featuring pretty good state transfer of single site excitation can be equipped with compact localized states such that their transfer is made possible.
Deep Bayesian Mining, Learning and Understanding

This tutorial addresses the advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, long short-term memory, sequence-to-sequence model, variational auto-encoder, generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network, policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies are presented to tackle different issues in deep Bayesian mining, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.
Frequent Patterns Mining in DNA Sequence

As a common biological sequence, DNA sequences contain important information. The discovery of frequent patterns in DNA sequences can help to study the evolution, function and variation of genes. The findings are of great significance to genetic and mutation analysis, analysis of disease causes and treatment of diseases. Traditional methods of frequent pattern discovery need to scan DNA sequences multiple times. To overcome this shortcoming, this article proposes a new method to discover frequent patterns from DNA sequences. This method is based on a two-level nested hash table data structure and set operation. All frequent patterns and their positions in DNA sequences can be found by scanning DNA sequences only once. Experimental results show that this method can correctly recognize all the frequent patterns in DNA sequences and their locations. The method can also be applied to discover frequent patterns in RNA, protein or other biological sequences.
Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes

Deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out of distribution (OoD) data. This is a problem as autonomous agents will inevitably come across a wide range of objects, all of which cannot be included during training. We propose a novel method to distinguish any object (foreground) from empty building structure (background) in indoor environments. We use normalizing flow to estimate the probability distribution of high-dimensional background descriptors. Foreground objects are therefore detected as areas in an image for which the descriptors are unlikely given the background distribution. As our method does not explicitly learn the representation of individual objects, its performance generalizes well outside of the training examples. Our model results in an innovative solution to reliably segment foreground from background in indoor scenes, which opens the way to a safer deployment of robots in human environments.
RGCN: Recurrent Graph Convolutional Networks for Target-Dependent Sentiment Analysis

With the increasing numbers of user-generated content on the web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Existing deep learning methods ignore that the sentiment polarity of the target is related to the entire text structure, and prevalent approaches among them cannot effectively use the syntactic information. In this paper, we propose to use a novel framework named as recurrent graph convolutional network (RGCN) for target-dependent sentiment classification in which the given text is considered as a graph based on its syntactic structure and recurrent graph convolutional networks are used to encode the text and target. We conduct comprehensive experiments on publicly accessible datasets, and results demonstrate that our model outperforms the state-of-the-art baselines.
Adapted Rules for UML Modelling of Geospatial Information for Model-Driven Implementation as OWL Ontologies

This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.
Deterministic assembly of a charged-quantum-dot–micropillar cavity device

Quantum-dot-based spin-photon interfaces are highly sought systems to implement deterministic photon-photon gates as well as to generate photonic cluster states. This requires mastering the technological challenge of fully controlling the coupling of a charged quantum dot to a cavity mode. Here, we report on a set of technological and experimental developments that allows doing so. The first ingredient consists in combining the in situ lithography technique, which allows a deterministic spatial and spectral coupling of the emitter to a pillar cavity mode, with a preidentification of the quantum dot charge states, using spectral fingerprints. The second ingredient relies on the design of an asymmetric tunneling barrier to inject the carrier in the quantum dot and an optical control of the charge occupation probability. We show that we can ensure both a high occupation probability of the charge in the quantum dot, reaching $91\ifmmode\pm\else\textpm\fi{}1\phantom{\rule{0.16em}{0ex}}%$, and an optimal coupling to the cavity mode, resulting in a single-photon source combining a highly polarized brightness of $33\ifmmode\pm\else\textpm\fi{}5\phantom{\rule{0.16em}{0ex}}%$ with both a high purity $({g}^{(2)}(0)=1.6\ifmmode\pm\else\textpm\fi{}0.4\phantom{\rule{0.16em}{0ex}}%)$ and indistinguishability ($97\ifmmode\pm\else\textpm\fi{}0.4\phantom{\rule{0.16em}{0ex}}%$) of the single photons.
An approach on the implementation of full batch, online and mini-batch learning on a Mamdani based neuro-fuzzy system with center-of-sets defuzzification: Analysis and evaluation about its functionality, performance, and behavior

Due to the rapid technological evolution and communications accessibility, data generated from different sources of information show an exponential growth behavior. That is, volume of data samples that need to be analyzed are getting larger, so the methods for its processing have to adapt to this condition, focusing mainly on ensuring the computation is efficient, especially when the analysis tools are based on computational intelligence techniques. As we know, if you do not have a good control of the handling of the volume of the data, some techniques that are based on learning iterative processes could represent an excessive load of computation and could take a prohibitive time in trying to find a solution that could not come close to desired. There are learning methods known as full batch, online and mini-batch, and they represent a good strategy to this problem since they are oriented to the processing of data according to the size or volume of available data samples that require analysis. In this first approach, synthetic datasets with a small and medium volume were used, since the main objective is to define its implementation and in experimentation phase through regression analysis obtain information that allows us to assess the performance and behavior of different learning methods under distinct conditions. To carry out this study, a Mamdani based neuro-fuzzy system with center-of-sets defuzzification with support of multiple inputs and outputs was designed and implemented that had the flexibility to use any of the three learning methods, which were implemented within the training process. Finally, results show that the learning method with best performances was Mini-Batch when compared to full batch and online learning methods. The results obtained by mini-batch learning method are as follows; mean correlation coefficient [Formula: see text] with 0.8268 and coefficient of determination [Formula: see text] with 0.7444, and is also the method with better control of the dispersion between the results obtained from the 30 experiments executed per each dataset processed.
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search

Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically searches for optimal network architectures using evolutionary algorithms, has recently become very popular. Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i.e., network architectures) and is therefore memory expensive. In this work, we propose a Regularized Evolutionary Algorithm with low memory footprint to evolve a dynamic image classifier. In details, we introduce novel custom operators that regularize the evolutionary process of a micro-population of 10 individuals. We conduct experiments on three different digits datasets (MNIST, USPS, SVHN) and show that our evolutionary method obtains competitive results with the current state-of-the-art.
Privacy-Preserving Variational Information Feature Extraction for Domestic Activity Monitoring versus Speaker Identification

In this paper we highlight the privacy risks entailed in deep neural network feature extraction for domestic activity monitoring. We employ the baseline system proposed in the Task 5 of the DCASE 2018 challenge and simulate a feature interception attack by an eavesdropper who wants to perform speaker identification. We then propose to reduce the aforementioned privacy risks by introducing a variational information feature extraction scheme that allows for good activity monitoring performance while at the same time minimizing the information of the feature representation, thus restricting speaker identification attempts. We analyze the resulting model’s composite loss function and the budget scaling factor used to control the balance between the performance of the trusted and attacker tasks. It is empirically demonstrated that the proposed method reduces speaker identification privacy risks without significantly deprecating the performance of domestic activity monitoring tasks.
Qubit-photon corner states in all dimensions

A single quantum emitter coupled to a one-dimensional photon field can perfectly trap a photon when placed close to a mirror. This occurs when the interference between the emitted and reflected light is completely destructive, leading to photon confinement between the emitter and the mirror. In higher dimensions, the spread of the light field in all directions hinders interference and, consequently, photon trapping by a single emitter is considered to be impossible. In this work, we show that is not the case by proving that a single emitter can indeed trap light in any dimension. We provide a constructive recipe based on judiciously coupling an emitter to a photonic crystal-like bath with properly designed open boundary conditions. The directional propagation of the photons in such baths enables perfect destructive interference, forming what we denote as \emph{qubit-photon corner states}. We characterize these states in all dimensions, showing that they are robust under fluctuations of the emitter's properties, and persist also in the ultrastrong coupling regime.
Complete state tomography of a quantum dot spin qubit

Semiconductor quantum dots are the preferred choice for interfacing anchored matter spin qubits and flying photonic qubits. They are particularly attractive for generating highly entangled hybrid spin-multiphoton entangled cluster and graph states. These highly entangled multiqubit states are required for measurement-based quantum communication and computing. Here, the authors develop a novel all-optical method for conducting full tomography of the confined electronic spin qubits. This method is essential for characterization and benchmarking these hybrid entangled spin-multiphoton cluster and graph states.
Analysis of Statistical Forward Planning Methods in Pommerman

Pommerman is a complex multi-player and partially observable game where agents try to be the last standing to win. This game poses very interesting challenges to AI, such as collaboration, learning and planning. In this paper, we compare two Statistical Forward Planning algorithms, Monte Carlo Tree Search (MCTS) and Rolling Horizon Evolutionary Algorithm (RHEA) in Pommerman. We provide insights on how the agents actually play the game, inspecting their behaviours to explain their performance. Results show that MCTS outperforms RHEA in several game settings, but leaving room for multiple avenues of future work: tuning these methods, improving opponent modelling, identifying trap moves and introducing of assumptions for partial observability settings.
Completeness of Cyclic Proofs for Symbolic Heaps with Inductive Definitions

Separation logic is successful for software verification in both theory and practice. Decision procedure for symbolic heaps is one of the key issues. This paper proposes a cyclic proof system for symbolic heaps with general form of inductive definitions called cone inductive definitions, and shows its soundness and completeness. Cone inductive definitions are obtained from bounded-treewidth inductive definitions by imposing some restrictions for existentials, but they still include a wide class of recursive data structures. The completeness is proved by using a proof search algorithm and it also gives us a decision procedure for entailments of symbolic heaps with cone inductive definitions. The time complexity of the algorithm is nondeterministic double exponential. A prototype system for the algorithm has been implemented and experimental results are also presented.
Exponential families for Bayesian quantum process tomography

A Bayesian approach to quantum process tomography has yet to be fully developed due to the lack of appropriate probability distributions on the space of quantum channels. Here, by associating the Choi matrix form of a completely positive, trace preserving (CPTP) map with a particular space of matrices with orthonormal columns, called a Stiefel manifold, we present two parametric probability distributions on the space of CPTP maps that enable Bayesian analysis of process tomography. The first is a probability distribution that has an average Choi matrix as a sufficient statistic. The second is a distribution resulting from binomial likelihood data that enables a simple connection to data gathered through process tomography experiments. These are examples of continuous, nonunitary random CPTP maps that capture meaningful prior information for use in Bayesian estimation. We show how these distributions can be used for point estimation using either maximum a posteriori estimates or expected a posteriori estimates, as well as full Bayesian tomography resulting in posterior credibility intervals. This approach will enable the full power of Bayesian analysis in all forms of quantum characterization, verification, and validation.
From self-organization in relativistic electron bunches to coherent synchrotron light: observation using a photonic time-stretch digitizer

Abstract In recent and future synchrotron radiation facilities, relativistic electron bunches with increasingly high charge density are needed for producing brilliant light at various wavelengths, from X-rays to terahertz. In such conditions, interaction of electron bunches with their own emitted electromagnetic fields leads to instabilities and spontaneous formation of complex spatial structures. Understanding these instabilities is therefore key in most electron accelerators. However, investigations suffer from the lack of non-destructive recording tools for electron bunch shapes. In storage rings, most studies thus focus on the resulting emitted radiation. Here, we present measurements of the electric field in the immediate vicinity of the electron bunch in a storage ring, over many turns. For recording the ultrafast electric field, we designed a photonic time-stretch analog-to-digital converter with terasamples/second acquisition rate. We could thus observe the predicted link between spontaneous pattern formation and giant bursts of coherent synchrotron radiation in a storage ring.