Reviewerly/exHarmony huggingface dataset:
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization

Latent Dirichlet allocation (LDA) is useful in document analysis, image processing, and many information systems; however, its generalization performance has been left unknown because it is a singular learning machine to which regular statistical theory can not be applied. Stochastic matrix factorization (SMF) is a restricted matrix factorization in which matrix factors are stochastic; the column of the matrix is in a simplex. SMF is being applied to image recognition and text mining. We can understand SMF as a statistical model by which a stochastic matrix of given data is represented by a product of two stochastic matrices, whose generalization performance has also been left unknown because of non-regularity. In this paper, by using an algebraic and geometric method, we show the analytic equivalence of LDA and SMF, both of which have the same real log canonical threshold (RLCT), resulting in that they asymptotically have the same Bayesian generalization error and the same log marginal likelihood. Moreover, we derive the upper bound of the RLCT and prove that it is smaller than the dimension of the parameter divided by two, hence the Bayesian generalization errors of them are smaller than those of regular statistical models.
Efficient quantum tomography of two-mode Wigner functions

We introduce an efficient method to reconstruct the Wigner function of many-mode continuous variable systems. It is based on convex optimization with semidefinite programs, and also includes a version of the maximum entropy principle, in order to yield unbiased states. A key ingredient of the proposed approach is the representation of the state in a truncated Fock basis. As a bonus, the discrete finite representation allows to easily quantify the entanglement.
Quantum State Merging for Arbitrarily Small-Dimensional Systems

Recent advances in quantum technology facilitate the realization of information processing using quantum computers at least on the small and intermediate scales of up to several dozens of qubits. We investigate entanglement cost required for one-shot quantum state merging, aiming at quantum state transformation on these scales. In contrast to existing coding algorithms achieving nearly optimal approximate quantum state merging on a large scale, we construct algorithms for exact quantum state merging so that the algorithms are applicable to any given state of an arbitrarily small-dimensional system. In the algorithms, the entanglement cost can be reduced depending on a structure of the given state derived from the Koashi-Imoto decomposition. We also provide improved converse bounds for exact quantum state merging achievable for qubits but not necessarily achievable in general. As for approximate quantum state merging, we obtain algorithms and improved converse bounds by applying smoothing to those for exact state merging. Our results are applicable to distributed quantum information processing and multipartite entanglement transformation on small and intermediate scales.
Constructive Game Logic

Abstract Game Logic is an excellent setting to study proofs-about-programs via the interpretation of those proofs as programs, because constructive proofs for games correspond to effective winning strategies to follow in response to the opponent’s actions. We thus develop Constructive Game Logic , which extends Parikh’s Game Logic (GL) with constructivity and with first-order programs à la Pratt’s first-order dynamic logic (DL). Our major contributions include: 1. a novel realizability semantics capturing the adversarial dynamics of games, 2. a natural deduction calculus and operational semantics describing the computational meaning of strategies via proof-terms, and 3. theoretical results including soundness of the proof calculus w.r.t. realizability semantics, progress and preservation of the operational semantics of proofs, and Existential Properties on support of the extraction of computational artifacts from game proofs. Together, these results provide the most general account of a Curry-Howard interpretation for any program logic to date, and the first at all for Game Logic.
Auditing Differentially Private Machine Learning: How Private is Private SGD?

We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning attacks, which we show correspond to realistic privacy attacks. While previous work (Ma et al., arXiv 2019) proposed this connection between differential privacy and data poisoning as a defense against data poisoning, our use as a tool for understanding the privacy of a specific mechanism is new. More generally, our work takes a quantitative, empirical approach to understanding the privacy afforded by specific implementations of differentially private algorithms that we believe has the potential to complement and influence analytical work on differential privacy.
Global Distribution of Google Scholar Citations: A Size-independent Institution-based Analysis

Most currently available schemes for performance-based ranking of Universities/ Research organizations, such as, Quacarelli Symonds (QS), Times Higher Education (THE), Shanghai University-based All Research of World Universities (ARWU) use a variety of criteria that include productivity, citations, awards, reputation, etc., while Leiden and Scimago use only bibliometric indicators. The research performance evaluation in the aforesaid cases is based on bibliometric data from Web of Science or Scopus, which are commercially available priced databases. The coverage includes peer-reviewed journals and conference proceedings. Google Scholar (GS) on the other hand, provides a free and open alternative to obtaining citations of papers available on the net, (though it is not clear exactly which journals are covered.) Citations are collected automatically from the net and also added to self-created individual author profiles under Google Scholar Citations (GSC). This data was used by Webometrics Lab, Spain to create a ranked list of 4000+ institutions in 2016, based on citations from only the top 10 individual GSC profiles in each organization. (GSC excludes the top paper for reason,s explained in the text; the simple selection procedure makes the ranked list size-independent as claimed by the Cybermetrics Lab). Using this data (Transparent Ranking TR, 2016), we find the regional and country-wise distribution of GS-TR Citations. The size-independent ranked list is subdivided into deciles of 400 institutions each and the number of institutions and citations of each country obtained for each decile. We test for correlation between institutional ranks between GS-TR and the other ranking schemes for the top 20 institutions. Finally, we discuss our results in the context of questions like (1) Is it necessary to have one more global ranking scheme? (2) What are the likely benefits of the GS size-independent formulation? (3) What are the likely sources of error? and (4) Whether a truncated sample as in GS can indeed give a representative ranking acceptable at the global level?
Structured Prediction for Conditional Meta-Learning

The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional meta-learning seeks task-specific initialization to better capture complex task distributions and improve performance. However, many existing conditional methods are difficult to generalize and lack theoretical guarantees. In this work, we propose a new perspective on conditional meta-learning via structured prediction. We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks. Our non-parametric approach is model-agnostic and can be combined with existing meta-learning methods to achieve conditioning. Empirically, we show that TASML improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets.
In Praise of Impredicativity: A Contribution to the Formalization of Meta-Programming

Abstract Processing programs as data is one of the successes of functional and logic programming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are called in logic programming, are widespread declarative programming techniques. In logic programming, there is a gap between the meta-programming practice and its theory: The formalizations of meta-programming do not explicitly address its impredicativity and are not fully adequate. This article aims at overcoming this unsatisfactory situation by discussing the relevance of impredicativity to meta-programming, by revisiting former formalizations of meta-programming, and by defining Reflective Predicate Logic, a conservative extension of first-order logic, which provides a simple formalization of meta-programming.
Learning to Learn Variational Semantic Memory

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. By doing so, it is able to accumulate long-term, general knowledge that enables it to learn new concepts of objects. We formulate memory recall as the variational inference of a latent memory variable from addressed contents, which offers a principled way to adapt the knowledge to individual tasks. Our variational semantic memory, as a new long-term memory module, confers principled recall and update mechanisms that enable semantic information to be efficiently accrued and adapted for few-shot learning. Experiments demonstrate that the probabilistic modelling of prototypes achieves a more informative representation of object classes compared to deterministic vectors. The consistent new state-of-the-art performance on four benchmarks shows the benefit of variational semantic memory in boosting few-shot recognition.
Additive-error fine-grained quantum supremacy

It is known that several sub-universal quantum computing models, such as the IQP model, the Boson sampling model, the one-clean qubit model, and the random circuit model, cannot be classically simulated in polynomial time under certain conjectures in classical complexity theory. Recently, these results have been improved to ``fine-grained" versions where even exponential-time classical simulations are excluded assuming certain classical fine-grained complexity conjectures. All these fine-grained results are, however, about the hardness of strong simulations or multiplicative-error sampling. It was open whether any fine-grained quantum supremacy result can be shown for a more realistic setup, namely, additive-error sampling. In this paper, we show the additive-error fine-grained quantum supremacy (under certain complexity assumptions). As examples, we consider the IQP model, a mixture of the IQP model and log-depth Boolean circuits, and Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> circuits. Similar results should hold for other sub-universal models.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding

We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier. The study of this problem is instructive and corresponds to many applications such as recommendations for newly formed groups with only a few users in online social networks. To cope with this problem, we propose a novel Meta Transformed Network Embedding framework (MetaTNE), which consists of three modules: (1) A \emph{structural module} provides each node a latent representation according to the graph structure. (2) A \emph{meta-learning module} captures the relationships between the graph structure and the node labels as prior knowledge in a meta-learning manner. Additionally, we introduce an \emph{embedding transformation function} that remedies the deficiency of the straightforward use of meta-learning. Inherently, the meta-learned prior knowledge can be used to facilitate the learning of few-shot novel labels. (3) An \emph{optimization module} employs a simple yet effective scheduling strategy to train the above two modules with a balance between graph structure learning and meta-learning. Experiments on four real-world datasets show that MetaTNE brings a huge improvement over the state-of-the-art methods.
On Target Segmentation for Direct Speech Translation.

Recent studies on direct speech translation show continuous improvements by means of data augmentation techniques and bigger deep learning models. While these methods are helping to close the gap between this new approach and the more traditional cascaded one, there are many incongruities among different studies that make it difficult to assess the state of the art. Surprisingly, one point of discussion is the segmentation of the target text. Character-level segmentation has been initially proposed to obtain an open vocabulary, but it results on long sequences and long training time. Then, subword-level segmentation became the state of the art in neural machine translation as it produces shorter sequences that reduce the training time, while being superior to word-level models. As such, recent works on speech translation started using target subwords despite the initial use of characters and some recent claims of better results at the character level. In this work, we perform an extensive comparison of the two methods on three benchmarks covering 8 language directions and multilingual training. Subword-level segmentation compares favorably in all settings, outperforming its character-level counterpart in a range of 1 to 3 BLEU points.
Valid attacks in argumentation frameworks with recursive attacks

The purpose of this work is to study a generalisation of Dung’s abstract argumentation frameworks that allows representing recursive attacks, that is, a class of attacks whose targets are other attacks. We do this by developing a theory of argumentation where the classic role of attacks in defeating arguments is replaced by a subset of them, which is “extension-dependent” and which, intuitively, represents a set of “valid attacks” with respect to the extension. The studied theory displays a conservative generalisation of Dung’s semantics (complete, preferred, stable and grounded) and also of its principles (conflict-freeness, acceptability and admissibility). Furthermore, despite its conceptual differences, we are also able to show that our theory agrees with the AFRA interpretation of recursive attacks for the complete, preferred, stable and grounded semantics and with a recent flattening method.
Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution

Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capable of evolving and optimizing CNNs with respect to the classification error and CNN complexity (expressed as the number of tunable CNN parameters), in which the inference phase can partly be executed using fixed point operations to further reduce power consumption. Experimental results are obtained with TinyDNN framework and presented using two common image classification benchmark problems -- MNIST and CIFAR-10.
Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximized. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, where an approximation of the negentropy for Gaussian mixtures is computed. Then, genetic algorithms are used to find the optimal, orthogonal projection basis by maximizing the former approximation. We show that this semiparametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualized. The performance of the proposed approach is shown on both artificial and real datasets. Supplementary materials for this article are available online.
Systematic errors in direct state measurements with quantum controlled measurements

Von Neumann measurement framework describes a dynamic interaction between a target system and a probe. In contrast, a quantum controlled measurement framework uses a qubit probe to control the actions of different operators on the target system, and convenient for establishing universal quantum computation. In this work, we use a quantum controlled measurement framework for measuring quantum states directly. We introduce two types of the quantum controlled measurement framework and investigate the systematic error (the bias between the true value and the estimated values) that caused by these types. We numerically investigate the systematic errors, evaluate the confidence region, and investigate the effect of experimental noise that arises from the imperfect detection. Our analysis has important applications in direct quantum state tomography.
Kernel-Based Ensemble Learning in Python

We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. \texttt{KernelCobra} builds on the COBRA algorithm introduced by \citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and \texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, \texttt{KernelCobra} deals with classification and regression. \texttt{KernelCobra} is included as part of the open source Python package \texttt{Pycobra} (0.2.4 and onward), introduced by \citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of \texttt{KernelCobra} on real-life and synthetic datasets.
Ethereum's Recursive Length Prefix in ACL2

Recursive Length Prefix (RLP) is used to encode a wide variety of data in Ethereum, including transactions.The work described in this paper provides a formal specification of RLP encoding and a verified implementation of RLP decoding, developed in the ACL2 theorem prover.This work has led to improvements to the Ethereum documentation and additions to the Ethereum test suite. Problem and ContributionErrors in cryptocurrency code may lead to particularly direct financial losses.This applies not only to smart contracts, but also to the underlying execution engines, to wallets, and to other critical components.In Ethereum [20], Recursive Length Prefix (RLP) [21, Page 'RLP'] [23, Appendix B] is used to encode a wide variety of data, including transactions.It is thus important for this fundamental building block to be specified precisely and implemented correctly.The work described in this paper contributes to this goal by providing a formal specification of RLP encoding and a verified implementation of RLP decoding, developed in the ACL2 theorem prover [11].The development is available [18, Path books/kestrel/ethereum/rlp] and thoroughly documented [17, Topic rlp].Some excerpts of the development shown in this paper are slightly simplified for brevity.This work has led to improvements to the Ethereum Yellow Paper [23] and to the Ethereum Wiki [21], which are major components of the Ethereum documentation.It has also led to additions to the Ethereum test suite [19], which contains tests for all Ethereum implementations.See Section 5 for details.This work is part of an ongoing effort to develop, in ACL2, a formal specification and a verified implementation of a complete Ethereum client [12].This formal specification will also be useful for formally verifying existing client implementations, smart contracts at the level of the EVM (Ethereum Virtual Machine), and the compilation of higher-level programming languages to EVM code.
Receiver Selective Opening CCA Secure Public Key Encryption from Various Assumptions

Receiver selective opening (RSO) attacks for public key encryption (PKE) capture a situation where one sender sends messages to multiple receivers, and an adversary can corrupt a set of receivers and get their messages and secret keys. Security against RSO attack for a PKE scheme ensures confidentiality of other uncorrupted receivers’ ciphertexts. Among all of the RSO security notions, simulation-based RSO security against chosen ciphertext attack (SIM-RSO-CCA security) is the strongest notion. In this paper, we explore constructions of SIM-RSO-CCA secure PKE from various computational assumptions. Toward this goal, we show that a SIM-RSO-CCA secure PKE scheme can be constructed based on an IND-CPA secure PKE scheme and a designated-verifier non-interactive zero-knowledge (DV-NIZK) argument satisfying one-time simulation soundness. Moreover, we give the first construction of DV-NIZK argument satisfying one-time simulation soundness. Consequently, through our generic construction, we obtain the first SIM-RSO-CCA secure PKE scheme under the computational Diffie-Hellman (CDH) or learning parity with noise (LPN) assumption.
A multiplayer MR application based on adaptive synchronization algorithm

With the popularity of virtual reality technology, it’s application in the field of engineering educational and training has gradually increased. This article introduces a multiplayer mixed reality (MR) application that supports remote participants to operate engineering equipment and learn operation process in the shared virtual environment, and it explores the impact of network factors on user experience. According to the characteristics of the application, it proposes a corresponding adaptive strategy based on an existing synchronization algorithm, meantime, a comprehensive evaluation method is proposed.