Reviewerly/exHarmony huggingface dataset:
Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data

Robust clustering from incomplete data is an important topic because, in many practical situations, real datasets are heavy-tailed, asymmetric, and/or have arbitrary patterns of missing observations. Flexible methods and algorithms for model-based clustering are presented via mixture of the generalized hyperbolic distributions and its limiting case, the mixture of multivariate skew-t distributions. An analytically feasible EM algorithm is formulated for parameter estimation and imputation of missing values for mixture models employing missing at random mechanisms. The proposed methodologies are investigated through a simulation study with varying proportions of synthetic missing values and illustrated using a real dataset. Comparisons are made with those obtained from the traditional mixture of generalized hyperbolic distribution counterparts by filling in the missing data using the mean imputation method.
Searching for variable-speed motions in long sequences of motion capture data

Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant level without any costly post-processing. Moreover, visiting adjacent levels makes possible subsequence searching of time-warped (i.e., faster or slower executed) queries. To efficiently search on a large scale, segment features can be binarized and segmentation levels independently indexed. We experimentally demonstrate effectiveness and efficiency of the proposed approach for subsequence searching on a real-life dataset.
Homogeneous functionals and Bayesian data fusion with unknown correlation

Information or data fusion concerns the aggregation, or combination, of probability measures. For example, in machine learning, statistics and signal processing, one may seek to ‘combine’ posterior distributions, [e.g. 1) Bayes classifiers or 2) posteriors over target states etc], arising from distinct but not necessarily independent sources. For example, sources might include partially disjoint trainers, or spatially distinct sensors correlated via state dependent measurements, etc. Data fusion is common in risk analysis where one is broadly interested in pooling expert opinions described by probability measures, and where it is often hard to assess and account for correlation among experts. The contribution of this work is the introduction of a broad class of data fusion rules that seek the combination of two (or more) probability distributions in the presence of non-zero, but unknown, correlation. We introduce rules that are improved in the sense that they are ‘closer’ to the true Bayesian result that would be computed if one could exploit knowledge of the correlation between the input distributions. We introduce these rules under the common algorithmic constraint of avoiding the so-called ‘double-counting’ of correlated information. The general framework proposed is based on homogeneous functionals. We examine the fusion performance and computational properties when using these functionals. We also consider distributed data fusion on (possibly) time-varying and incomplete network topologies and related convergence properties.
Quantum algorithms and lower bounds for convex optimization

While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization. We present a quantum algorithm that can optimize a convex function over an $n$-dimensional convex body using $\tilde{O}(n)$ queries to oracles that evaluate the objective function and determine membership in the convex body. This represents a quadratic improvement over the best-known classical algorithm. We also study limitations on the power of quantum computers for general convex optimization, showing that it requires $\tilde{\Omega}(\sqrt n)$ evaluation queries and $\Omega(\sqrt{n})$ membership queries.
Resource allocation and routing in parallel multi-server queues with abandonments for cloud profit maximization

This paper considers a Markov decision model for profit maximization of a cloud computing service provider catering to customers submitting jobs with firm real-time random deadlines. Customers are charged on a per-job basis, receiving a full refund if deadlines are missed. The service provider leases computing resources from an infrastructure provider in a two-tier scheme: long-term leasing of basic infrastructure, consisting of heterogeneous parallel service nodes, each modeled as a multi-server queue, and short-term leasing of external servers. Given the intractability of computing an optimal dynamic resource allocation and job routing policy, maximizing the long-run average profit rate, the paper addresses the design, implementation and testing of low-complexity heuristics. The policies considered are a static policy given by an optimal Bernoulli splitting, and three dynamic index policies based on different index definitions: individually optimal (IO), policy improvement (PI) and restless bandit (RB) indices. The paper shows how to implement efficiently each such policy, and presents a comprehensive empirical comparison, drawing qualitative insights on their strengths and weaknesses, and benchmarking their performance in an extensive study.
Toward a reconfigurable quantum network enabled by a broadband entangled source

We present a proof-of-principle experimental demonstration of a reconfigurable entanglement distribution scheme utilizing a poled fiber-based source of broadband polarization-entangled photon pairs and dense wavelength-division multiplexing. A large bandwidth (&gt;90 nm, centered at 1555 nm) and highly spectrally correlated nature of the entangled source can be exploited to allow for the generation of more than 25 frequency-conjugate entangled pairs when aligned to the standard 200 GHz ITU grid. In this work, three frequency-conjugate entangled pairs are used to demonstrate quantum key distribution, with the wavelength-selective switching done manually. The entangled pairs are delivered over 40 km of actual fiber, and an estimated secure key rate of up to 20 bits/s per bi-party is obtained.
Large-Scale Heterogeneous Feature Embedding

Feature embedding aims to learn a low-dimensional vector representation for each instance to preserve the information in its features. These representations can benefit various offthe-shelf learning algorithms. While embedding models for a single type of features have been well-studied, real-world instances often contain multiple types of correlated features or even information within a different modality such as networks. Existing studies such as multiview learning show that it is promising to learn unified vector representations from all sources. However, high computational costs of incorporating heterogeneous information limit the applications of existing algorithms. The number of instances and dimensions of features in practice are often large. To bridge the gap, we propose a scalable framework FeatWalk, which can model and incorporate instance similarities in terms of different types of features into a unified embedding representation. To enable the scalability, FeatWalk does not directly calculate any similarity measure, but provides an alternative way to simulate the similarity-based random walks among instances to extract the local instance proximity and preserve it in a set of instance index sequences. These sequences are homogeneous with each other. A scalable word embedding algorithm is applied to them to learn a joint embedding representation of instances. Experiments on four real-world datasets demonstrate the efficiency and effectiveness of FeatWalk.
Collaborative, Creative, Participative: Trends in Public Library Innovation

Innovation in public libraries has been concentrated in the areas of participation, making & creating, learning, new outreach and partnerships. These trends represent the emerging areas of strategic focus for public libraries and are strongly influencing the development of library services towards a model that is collaborative, creative and participative, and that places the library at the centre of its user’s daily lives. This article provides an overview of these trends, with examples of how they are manifesting in library services, programming and strategy.
Aperiodic space-inhomogeneous quantum walks: Localization properties, energy spectra, and enhancement of entanglement

We study the localization properties, energy spectra and coin-position entanglement of the aperiodic discrete-time quantum walks. The aperiodicity is described by spatially dependent quantum coins distributed on the lattice, whose distribution is neither periodic (Bloch-like) nor random (Anderson-like). Within transport properties we identified delocalized/localized quantum walks mediated by a proper adjusting of aperiodic parameter. Both scenarios are studied by exploring typical quantities (inverse participation ratio and survival probability), as well as the energy spectra of an associated effective Hamiltonian. By using the energy spectra analysis, we show that the early stage the inhomogeneity leads to vanishing gap between two main bands, which justifies the the delocalized character observed for $\nu<0.5$. With increase of $\nu$ arises gaps and flat-bands on the energy spectra, which corroborates the suppresion of transport detected for $\nu>0.5$. For $\nu$ high enough we observe an energy spectra which resembles that described by the 1d Anderson model. Within coin-position entanglement, we show many settings in which an enhancement in the ability to entangle is observed. This behavior brings new informations about the role played by aperiodicity on the coin-poisition entanglement for static inhomogeneous systems, reported before as almost always reducing the entanglement when comparing with the homogeneous case. We extend the analysis in order to show that systems with static inhomogeneity are able to exhibit asymptotic limit of entanglement.
Tensorized Embedding Layers for Efficient Model Compression

The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.
Unbounded Dynamic Predicate Compositions in Attribute-Based Encryption

We present several transformations that combine a set of attribute-based encryption (ABE) schemes for simpler predicates into a new ABE scheme for more expressive composed predicates. Previous proposals for predicate compositions of this kind, the most recent one being that of Ambrona et al. at Crypto’17, can be considered static (or partially dynamic), meaning that the policy (or its structure) that specifies a composition must be fixed at the setup. Contrastingly, our transformations are dynamic and unbounded: they allow a user to specify an arbitrary and unbounded-size composition policy right into his/her own key or ciphertext. We propose transformations for three classes of composition policies, namely, the classes of any monotone span programs, any branching programs, and any deterministic finite automata. These generalized policies are defined over arbitrary predicates, hence admitting modular compositions. One application from modularity is a new kind of ABE for which policies can be “nested” over ciphertext and key policies. As another application, we achieve the first fully secure completely unbounded key-policy ABE for non-monotone span programs, in a modular and clean manner, under the q-ratio assumption. Our transformations work inside a generic framework for ABE called symbolic pair encoding, proposed by Agrawal and Chase at Eurocrypt’17. At the core of our transformations, we observe and exploit an unbounded nature of the symbolic property so as to achieve unbounded-size policy compositions.
Information backflow as a resource for entanglement

The issue of whether non-Markovianity (NM) could be considered as a resource in quantum information has been a subject of intense debate in recent years. Recently, a simple mechanism was proposed in which one of the main features of NM, the backflow of information from the environment to the system, represents a fundamental and quantifiable resource for generating entanglement within an open quantum system coupled to a finite and small environment [N. Mirkin, P. Poggi, and D. Wisniacki, Phys. Rev. A 99, 020301(R) (2019)]. In this work, we extend the validity of this resource mechanism by studying a completely different and more general scheme where the system is coupled to an infinite structured reservoir. Under both setups, we show that the degree of NM univocally determines the optimal degree of entanglement reachable by controlling the open system. This result strongly suggests the universality of a quantitative relation between entanglement and NM by using quantum optimal control.
An Updated Duet Model for Passage Re-ranking

We propose several small modifications to Duet---a deep neural ranking model---and evaluate the updated model on the MS MARCO passage ranking task. We report significant improvements from the proposed changes based on an ablation study.
Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning

Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has resulted in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, we propose using fully convolutional neural networks (FCNNs) to classify older adults at low or high risk of falling using inertial sensor data collected from a smartphone. Due to the limited nature of older adult inertial gait datasets, we first pre-train the FCNN models using a publicly available dataset for pedestrian activity recognition. Then via transfer learning, we train the network for falls risk classification. We show that via transfer learning, our falls risk classifier obtains an area under the receiver operating characteristic curve of 93.3%, which is 10.6% higher than the equivalent model trained without the use of transfer learning. Additionally, we show that our method outperforms other standard machine learning classifiers trained on features developed in prior research.
The Code That Never Ran: Modeling Attacks on Speculative Evaluation

This paper studies information flow caused by speculation mechanisms in hardware and software. The Spectre attack shows that there are practical information flow attacks which use an interaction of dynamic security checks, speculative evaluation and cache timing. Previous formal models of program execution are designed to capture computer architecture, rather than micro-architecture, and so do not capture attacks such as Spectre. In this paper, we propose a model based on pomsets which is designed to model speculative evaluation. The model is abstract with respect to specific micro-architectural features, such as caches and pipelines, yet is powerful enough to express known attacks such as Spectre and Prime+Abort, and verify their countermeasures. The model also allows for the prediction of new information flow attacks. We derive two such attacks, which exploit compiler optimizations, and validate these experimentally against gcc and clang.
"But why?" Understanding explainable artificial intelligence

Opaque algorithms get to score and choose in many areas using their own inscrutable logic. To whom are said algorithms held accountable? And what is being done to ensure explainability of these algorithms?
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years. However, existing perturbation methods for images cannotbe directly applied to texts as text data is discrete. In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs. We collect, select, summarize, discuss and analyze these works in a comprehensive way andcover all the related information to make the article self-contained. Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic.
Unikernels

Unikernels have demonstrated enormous advantages over Linux in many important domains, causing some to propose that the days of Linux's dominance may be coming to an end. On the contrary, we believe that unikernels' advantages represent the next natural evolution for Linux, as it can adopt the best ideas from the unikernel approach and, along with its battle-tested codebase and large open source community, continue to dominate. In this paper, we posit that an upstreamable unikernel target is achievable from the Linux kernel, and, through an early Linux unikernel prototype, demonstrate that some simple changes can bring dramatic performance advantages.
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.
The efficiency threshold for the offspring population size of the ( <i>µ, λ</i> ) EA

Understanding when evolutionary algorithms are efficient or not, and how they efficiently solve problems, is one of the central research tasks in evolutionary computation. In this work, we make progress in understanding the interplay between parent and offspring population size of the (µ, λ) EA. Previous works, roughly speaking, indicate that for λ ≥ (1 + ε)eµ, this EA easily optimizes the OneMax function, whereas an offspring population size λ ≤ (1 - ε)eµ leads to an exponential runtime.
Information-Theoretic Considerations in Batch Reinforcement Learning

Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucially rely on two types of assumptions: (1) mild distribution shift, and (2) representation conditions that are stronger than realizability. However, the necessity ("why do we need them?") and the naturalness ("when do they hold?") of such assumptions have largely eluded the literature. In this paper, we revisit these assumptions and provide theoretical results towards answering the above questions, and make steps towards a deeper understanding of value-function approximation.
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network

Hitesh Golchha, Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya. 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.
Emergence of exploratory look-around behaviors through active observation completion

Standard computer vision systems assume access to intelligently captured inputs (e.g., photos from a human photographer), yet autonomously capturing good observations is a major challenge in itself. We address the problem of learning to look around: how can an agent learn to acquire informative visual observations? We propose a reinforcement learning solution, where the agent is rewarded for reducing its uncertainty about the unobserved portions of its environment. Specifically, the agent is trained to select a short sequence of glimpses after which it must infer the appearance of its full environment. To address the challenge of sparse rewards, we further introduce sidekick policy learning, which exploits the asymmetry in observability between training and test time. The proposed methods learn observation policies that not only perform the completion task for which they are trained, but also generalize to exhibit useful "look-around" behavior for a range of active perception tasks.