Reviewerly/exHarmony huggingface dataset:
The 1990 Western Pacific Geophysics meeting

The 1990 Western Pacific Geophysics Meeting was held in Kanazawa, Japan from 15-21 Aug. 1990. This was the first meeting of a new series of meetings for the American Geophysical Union, and it proved to be very successful in terms of the scientific program and attendance, which included over 1,000 participants. The intent of this meeting was an effort on the part of the American Geophysical Union (AGU) and several Japanese geophysical societies to gather individual Earth and space scientists at a major scientific meeting to focus on geophysical problems being studied in the western Pacific rim. The meeting was organized along the lines of a typical AGU annual meeting with some invited talks, many contributed talks, poster sessions, and with emphasis on presentations and informal discussions. The program committee consisted of scientists from both the U.S. and Japan. This meeting provided ample opportunities for U.S. and Japanese scientists to get to know each other and their works on a one-to-one basis. It was also a valuable opportunity for students studying geophysics to get together and interact with each other and with scientists from both the U.S. and Japan. There were 939 abstracts submitted to the conference and a total of 102 sessions designed as a result of the abstracts received. The topics of interest are as follows: space geodetic and observatory measurements for earthquake and tectonic studies; gravity, sea level, and vertical motion; variations in earth rotation and earth dynamics; sedimentary magnetism; global processes and precipitation; subsurface contaminant transport; U.S. Western Pacific Rim initiatives in hydrology; shelf and coastal circulation; tectonics, magmatism, and hydrothermal processes; earthquake prediction and hazard assessment; seismic wave propagation in realistic media; and dynamics and structure of plate boundaries and of the Earth's deep interior.
Random Private Quantum States

The study of properties of randomly chosen quantum states has in recent years led to many insights into quantum entanglement. In this work, we study private quantum states from this point of view. Private quantum states are bipartite quantum states characterised by the property that carrying out simple local measurements yields a secret bit. This feature is shared by the maximally entangled pair of quantum bits, yet private quantum states are more general and can in their most extreme form be almost bound entangled. In this work, we study the entanglement properties of random private quantum states and show that they are hardly distinguishable from separable states and thus have low repeatable key, despite containing one bit of key. The technical tools we develop are centred around the concept of locally restricted measurements and include a new operator ordering, bounds on norms under tensoring with entangled states and a continuity bound for a relative entropy measure.
HTTP Authentication with SASL

Most application-level protocols standardise their authenticationexchanges under the SASL framework. HTTP has taken another course, andoften ends up replicating the work to allow individual mechanisms.This specification adopts full SASL authentication into HTTP.
6 Problems in group theory motivated by cryptography

This is a survey of algorithmic problems in group theory, old and new, motivated by applications to cryptography.
Some risk analysis problems in cyber insurance economics

espanolLas ciber amenazas afectan a todo tipo de organizaciones, causando frecuentes y costosos impactos globalmente. Recientemente, han surgido productos de ciberseguro con el potencial de reducir el impacto de los riesgos en el ciberespacio. Sin embargo, aun tienen que madurar. En este articulo presentamos varios modelos de analisis de riesgos que podrian facilitar la implantacion y adopcion de ciberseguros. Estos modelos, descritos como diagramas de influencia y diagramas de influencia bi-agente, aportan un marco para estimar el impacto economico de los ciber riesgos a los que se enfrentan aseguradores y asegurados, asi como tambien para calcular sus estrategias optimas de mitigacion y transferencia del riesgo. EnglishCyber threats affect all kinds of organisations with frequent and costly impacts worldwide. Cyber insurance products have recently emerged with the potential of lowering the impact of cyberspace risks. However, they have yet to mature. In this paper we present several risk analysis models that may facilitate the implementation and adoption of cyber insurance. These models, described in terms of influence diagrams and bi-agent influence diagrams, provide a framework for estimating the economic impact of cyber risks that may face insurers and insurees as well as calculating their optimal risk mitigation and transfer strategies.
Information Bottleneck and Representation Learning

A grand challenge in representation learning is the development of computational algorithms that learn the explanatory factors of variation behind high-dimensional data. Representation models (encoders) are often determined for optimizing performance on training data when the real objective is to generalize well to other (unseen) data. This chapter provides an overview of fundamental concepts in statistical learning theory and the information-bottleneck principle. This serves as a mathematical basis for the technical results, in which an upper bound to the generalization gap corresponding to the cross-entropy risk is given. When this penalty term times a suitable multiplier and the cross-entropy empirical risk are minimized jointly, the problem is equivalent to optimizing the information-bottleneck objective with respect to the empirical data distribution. This result provides an interesting connection between mutual information and generalization, and helps to explain why noise injection during the training phase can improve the generalization ability of encoder models and enforce invariances in the resulting representations.
Inadequacy of Modal Logic in Quantum Settings

We test the principles of classical modal logic in fully quantum settings. Modal logic models our reasoning in multi-agent problems, and allows us to solve puzzles like the muddy children paradox. The Frauchiger-Renner thought experiment highlighted fundamental problems in applying classical reasoning when quantum agents are involved; we take it as a guiding example to test the axioms of classical modal logic. In doing so, we find a problem in the original formulation of the Frauchiger-Renner theorem: a missing assumption about unitarity of evolution is necessary to derive a contradiction and prove the theorem. Adding this assumption clarifies how different interpretations of quantum theory fit in, i.e., which properties they violate. Finally, we show how most of the axioms of classical modal logic break down in quantum settings, and attempt to generalize them. Namely, we introduce constructions of trust and context, which highlight the importance of an exact structure of trust relations between agents. We propose a challenge to the community: to find conditions for the validity of trust relations, strong enough to exorcise the paradox and weak enough to still recover classical logic.
Causal Limit on Quantum Communication

The capacity of a channel is known to be equivalent to the highest rate at which it can generate entanglement. Analogous to entanglement, the notion of a causality measure characterizes the temporal aspect of quantum correlations. Despite holding an equally fundamental role in physics, temporal quantum correlations have yet to find their operational significance in quantum communication. Here we uncover a connection between quantum causality and channel capacity. We show the amount of temporal correlations between two ends of the noisy quantum channel, as quantified by a causality measure, implies a general upper bound on its channel capacity. The expression of this new bound is simpler to evaluate than most previously known bounds. We demonstrate the utility of this bound by applying it to a class of shifted depolarizing channels, which results in improvement over previously known bounds for this class of channels.
Another coin bites the dust: an analysis of dust in UTXO-based cryptocurrencies

Unspent Transaction Outputs (UTXOs) are the internal mechanism used in many cryptocurrencies to represent coins. Such representation has some clear benefits, but also entails some complexities that, if not properly handled, may leave the system in an inefficient state. Specifically, inefficiencies arise when wallets (the software responsible for transferring coins between parties) do not manage UTXOs properly when performing payments. In this paper, we study three cryptocurrencies: Bitcoin, Bitcoin Cash and Litecoin, by analysing the state of their UTXO sets, that is, the status of their sets of spendable coins. These three cryptocurrencies are the top-3 UTXO-based cryptocurrencies by market capitalization. Our analysis shows that the usage of each cryptocurrency presents some differences, and led to different results. Furthermore, it also points out that the management of the transactions has not always been performed efficiently and therefore, the current state of the UTXO sets is far from ideal.
Untangling entanglement and chaos

We present a method to calculate an upper bound on the generation of entanglement in any spin system using the Fannes-Audenaert inequality for the von Neumann entropy. Our method not only is useful for efficiently estimating entanglement, but also shows that entanglement generation depends on the distance of the quantum states of the system from corresponding minimum-uncertainty spin coherent states (SCSs). We illustrate our method using a quantum kicked top model, and show that our upper bound is a very good estimator for entanglement generated in both regular and chaotic regions. In a deep quantum regime, the upper bound on entanglement can be high in both regular and chaotic regions, while in the semiclassical regime, the bound is higher in chaotic regions where the quantum states diverge from the corresponding SCSs. Our analysis thus explains previous studies and clarifies the relationship between chaos and entanglement.
Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space

We present a direct manipulation technique that allows material scientists to interactively highlight relevant parameterized simulation instances located in dimensionally reduced spaces, enabling a user-defined understanding of a continuous parameter space. Our goals are two-fold: first, to build a user-directed intuition of dimensionally reduced data, and second, to provide a mechanism for creatively exploring parameter relationships in parameterized simulation sets, called ensembles. We start by visualizing ensemble data instances in dimensionally reduced scatter plots. To understand these abstract views, we employ user-defined virtual data instances that, through direct manipulation, search an ensemble for similar instances. Users can create multiple of these direct manipulation queries to visually annotate the spaces with sets of highlighted ensemble data instances. User-defined goals are therefore translated into custom illustrations that are projected onto the dimensionally reduced spaces. Combined forward and inverse searches of the parameter space follow naturally allowing for continuous parameter space prediction and visual query comparison in the context of an ensemble. The potential for this visualization technique is confirmed via expert user feedback for a shock physics application and synthetic model analysis.
3D Object Recognition System Based On Local Shape Descriptors and Depth Data Analysis

Background: A physical object, which is actually in 3D form, is captured by a sensor/ camera (in case of computer vision) and seen by a human eye (in case of a human vision). When someone is observing something, many other things are also involved there which make it more challenging to recognize. After capturing such a thing by a camera or sensor, a digital image is formed which is nothing other than a bunch of pixels. It is becoming important to know that how a computer understands images. Objective: This paper is for highlighting novel techniques on 3D object recognition system with local shape descriptors and depth data analysis. Methods: The proposed work is applied to RGBD and COIL-100 datasets and this is of four-fold as preprocessing, feature generation, dimensionality reduction, and classification. The first stage of preprocessing is smoothing by 2D median filtering on the depth (Z-value) and registration by orientation correction on 3D object data. The next stage is of feature generation and having two phases of shape map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is the third stage of this proposed work where linear discriminant analysis and principal component analysis are used. The final stage is fused on classification. Results: Here, calculation of the discriminative subspace for the training set, testing of object data and classification is done by comparing target and query data with different aspects for finding proper matching tasks. Conclusion: This concludes with new proposed approach of 3D Object Recognition. The local shape descriptors are used for 3D object recognition system to implement and test. This system is achieves 89.2% accuracy for Columbia object image library-100 images by using local shape descriptors.
Multilinear Side-Information based Discriminant Analysis for face and kinship verification in the wild

This paper presents a new approach for face and kinship verification under unconstrained environments. The proposed approach is based on high order tensor representation of face images. The face tensor is built based on local descriptors extracted at multiscales. Besides, we formulate a novel Multilinear Side-Information based Discriminant Analysis (MSIDA) to handle the weakly supervised multilinear subspace projection and classification. Using only the weak label information, MSIDA projects the input face tensor in a new subspace in which the discrimination is improved and the dimension of each tensor mode is reduced simultaneously. Experimental evaluation on four challenging face databases (LFW, Cornell KinFace, UB KinFace and TSKinface) demonstrates that the proposed approach significantly outperforms the current state of the art.
Out of time ordered quantum dissipation

We consider a quantum Brownian particle interacting with two harmonic baths, which is then perturbed by a cubic coupling linking the particle and the baths. This cubic coupling induces non-linear dissipation and noise terms in the influence functional/master equation of the particle. Its effect on the Out-of-Time-Ordered Correlators (OTOCs) of the particle cannot be captured by the conventional Feynman-Vernon formalism.We derive the generalised influence functional which correctly encodes the physics of OTO fluctuations, response, dissipation and decoherence. We examine an example where Markovian approximation is valid for the OTO dynamics. If the original cubic coupling has a definite time-reversal parity, the leading order OTO influence functional is completely determined by the couplings in the usual master equation via OTO generalisation of Onsager-Casimir relations. New OTO fluctuation-dissipation relations connect the non-Gaussianity of the thermal noise to the thermal jitter in the damping constant of the Brownian particle.
SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification

The availability of large-scale annotated data and uneven separability of different data categories become two major impediments of deep learning for image classification.In this paper, we present a Semi-Supervised Hierarchical Convolutional Neural Network (SS-HCNN) to address these two challenges.A large-scale unsupervised maximum margin clustering technique is designed, which splits images into a number of hierarchical clusters iteratively to learn cluster-level CNNs at parent nodes and category-level CNNs at leaf nodes.The splitting uses the similarity of CNN features to group visually similar images into the same cluster, which relieves the uneven data separability constraint.With the hierarchical cluster-level CNNs capturing certain high-level image category information, the category-level CNNs can be trained with a small amount of labelled images, and this relieves the data annotation constraint.A novel cluster splitting criterion is also designed which automatically terminates the image clustering in the tree hierarchy.The proposed SS-HCNN has been evaluated on the CIFAR-100 and ImageNet classification datasets.Experiments show that the SS-HCNN trained using a portion of labelled training images can achieve comparable performance with other fully trained CNNs using all labelled images.Additionally, the SS-HCNN trained using all labelled images clearly outperforms other fully trained CNNs.
Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data

The aim of using atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. We therefore base the atypicality criterion on codelength. In a prior paper we developed the methodology for discrete-valued data, and the current paper extends this to real-valued data. This is done by using minimum description length (MDL). We develop the information-theoretic methodology for a number of “universal” signal processing models, and finally apply them to recorded hydrophone data and heart rate variability (HRV) signal.
Variational Textured Dirichlet Process Mixture Model With Pairwise Constraint for Unsupervised Classification of Polarimetric SAR Images

This paper proposes an unsupervised classification method for multilook polarimetric synthetic aperture radar (Pol-SAR) data. The proposed method simultaneously deals with the heterogeneity and incorporates the local correlation in PolSAR images. Specifically, within the probabilistic framework of the Dirichlet process mixture model (DPMM), an observed PolSAR data point is described by the multiplication of a Wishartdistributed component and a class-dependent random variable (i.e., the textual variable). This modeling scheme leads to the proposed textured DPMM (tDPMM), which possesses more flexibility in characterizing PolSAR data in heterogeneous areas and from high-resolution images due to the introduction of the classdependent texture variable. The proposed tDPMM is learned by solving an optimization problem to achieve its Bayesian inference. With the knowledge of this optimization-based learning, the local correlation is incorporated through the pairwise constraint, which integrates an appropriate penalty term into the objective function so as to encourage the neighboring pixels to fall into the same category and to alleviate the "salt-and-pepper" classification appearance.We develop the learning algorithm with all the closed-form updates. The performance of the proposed method is evaluated with both low-resolution and high-resolution PolSAR images, which involve homogeneous, heterogeneous, and extremely heterogeneous areas. The experimental results reveal that the class-dependent texture variable is beneficial to PolSAR image classification and the pairwise constraint can effectively incorporate the local correlation in PolSAR images.
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems

Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A testbed for a smart building

This paper addresses the design and implementation of a smart building prototype. The implementation utilizes Internet of Things (IoT) solutions to collect, analyze, and manage data from building systems in a smart city environment. The developed smart building prototype is capable of realtime interactions with the residents. The main objective is to adapt the building settings to the residents' needs and provide the maximum comfort level with minimum operational costs. For this purpose, building parameters are collected via a set of sensors and transferred to a database in real-time, which can be accessed, analyzed and visualized. Environment properties such as temperature, light, humidity, audio, video, surveillance, and access status are managed through a model-based controller. The developed testbed and control scheme are generic and modular. The prototype can also be utilized for testing cyber-physical systems' monitoring and management technologies.
Benchmarking Approximate Inference Methods for Neural Structured Prediction

Lifu Tu, Kevin Gimpel. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Extending comb-based spectral estimation to multiaxis quantum noise

We show how to achieve full spectral characterization of general multiaxis additive noise on a single qubit, including arbitrary cross-axis noise correlations. Our pulsed spectral estimation technique is based on sequence repetition and frequency-comb sampling and is applicable in principle even to models where a large qubit energy splitting is present, as long as the noise is stationary and a second-order (Gaussian) approximation to the controlled reduced dynamics is viable. A key innovation in our approach is a spherical representation of the noise in terms of operators that couple directly to raising and lowering qubit operators, which is instrumental to show that only three suitably defined spectra effectively contribute in the large-splitting regime. Our result is crucial to extend the applicability of comb-based spectral estimation, which has been so far employed under the assumption of dephasing-dominated dynamics, to experimental platforms where both ${T}_{1}$ and ${T}_{2}$ processes may occur on comparable timescales or be otherwise significant, such as superconducting qubits.
Hydrological Time Series Anomaly Pattern Detection based on Isolation Forest

Anomaly detection is one of the basic problems in the field of data mining. It has been concerned and widely used by industry and academia. With the continuous increase of hydrological data, the current anomaly detection algorithm is too low in time efficiency. Besides, there are too many anomaly points excavated. In the face of so many anomaly points, the analysis decision-makers have no way to start. For this problem, this paper uses the isolation forest algorithm for the hydrological data of the pattern representation, and a hydrological time series anomaly pattern detection algorithm based on isolation forest is proposed. At the same time, it is difficult to determine the partition threshold in isolation forest and can not output top-k. K-means clustering respectively algorithm and nearest neighbor algorithm are used to improve the isolation forest algorithm, which can effectively overcome the subjectivity of artificially setting threshold and improve the stability of results expression. It is applied to the measured data of the Chuhe River Basin, and compared with other improved algorithms in accuracy and time complexity. The effectiveness of the improved isolation forest algorithm is verified by experiments.