Reviewerly/exHarmony huggingface dataset:
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"

Visual reasoning tasks such as visual question answering (VQA) require an interplay of visual perception with reasoning about the question semantics grounded in perception. Various benchmarks for reasoning across language and vision like VQA, VCR and more recently GQA for compositional question answering facilitate scientific progress from perception models to visual reasoning. However, recent advances are still primarily driven by perception improvements (e.g. scene graph generation) rather than reasoning. Neuro-symbolic models such as Neural Module Networks bring the benefits of compositional reasoning to VQA, but they are still entangled with visual representation learning, and thus neural reasoning is hard to improve and assess on its own. To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception. To this end, we introduce a differentiable first-order logic formalism for VQA that explicitly decouples question answering from visual perception. On the challenging GQA dataset, this framework is used to perform in-depth, disentangled comparisons between well-known VQA models leading to informative insights regarding the participating models as well as the task.
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation

This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs).It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings.Extending Transformer and conditional VAEs, our proposed latent variable model measurably prevents posterior collapse by (1) using a modified evidence lower bound (ELBO) objective which promotes mutual information between the latent variable and the target, and (2) guiding the latent variable with an auxiliary bag-of-words prediction task.As a result, the proposed model yields improved translation quality compared to existing variational NMT models on WMT Ro↔En and De↔En.With latent variables being effectively utilized, our model demonstrates improved robustness over non-latent Transformer in handling uncertainty: exploiting noisy source-side monolingual data (up to +3.2 BLEU), and training with weakly aligned web-mined parallel data (up to +4.7 BLEU).
Multi-Client Inner-Product Functional Encryption in the Random-Oracle Model

Multi-client functional encryption (MCFE) is an extension of functional encryption (FE) in which the decryption procedure involves ciphertexts from multiple parties. It is particularly useful in the context of data outsourcing and cloud computing where the data may come from different sources and where some data centers or servers may need to perform different types of computation on this data. In order to protect the privacy of the encrypted data, the server, in possession of a functional decryption key, should only be able to compute the final result in the clear, but no other information regarding the encrypted data. In this paper, we consider MCFE schemes supporting encryption labels, which allow the encryptor to limit the amount of possible mix-and-match that can take place during the decryption. This is achieved by only allowing the decryption of ciphertexts that were generated with respect to the same label. This flexible form of FE was already investigated by Chotard et al. at Asiacrypt 2018 and Abdalla et al. at Asiacrypt 2019. The former provided a general construction based on different standard assumptions, but its ciphertext size grows quadratically with the number of clients. The latter gave a MCFE based on Decisional Diffie-Hellman (DDH) assumption which requires a small inner-product space. In this work, we overcome the deficiency of these works by presenting three constructions with linear-sized ciphertexts based on the Matrix-DDH (MDDH), Decisional Composite Residuosity (DCR) and Learning with Errors (LWE) assumption in the random-oracle model. We also implement our constructions to evaluate their concrete efficiency.
Quantum Computational Advantage with String Order Parameters of One-Dimensional Symmetry-Protected Topological Order

Nonlocal games with advantageous quantum strategies give arguably the most fundamental demonstration of the power of quantum resources over their classical counterparts. Recently, certain multiplayer generalizations of nonlocal games have been used to prove unconditional separations between limited computational complexity classes of shallow-depth circuits. Here, we show advantageous strategies for these nonlocal games for generic ground states of one-dimensional symmetry-protected topological orders (SPTOs), when a discrete invariant of a SPTO known as a twist phase is nontrivial and $\ensuremath{-}1$. Our construction demonstrates that sufficiently large string order parameters of such SPTOs are indicative of globally constrained correlations useful for the unconditional computational separation.
Social license for the use of big data in the COVID-19 era

Strategies to enable the reopening of businesses and schools in countries emerging from social-distancing measures revolve around knowledge of who has COVID-19 or is displaying recognized symptoms, the people with whom they have had physical contact, and which groups are most likely to experience adverse outcomes. Efforts to clarify these issues are drawing on the collection and use of large datasets about peoples' movements and their health. In this Comment, we outline the importance of earning social license for public approval of big data initiatives, and specify principles of data law and data governance practices that can promote social license. We provide illustrative examples from the United States, Canada, and the United Kingdom.
Innovative and Assistive eHealth Technologies for Smart Therapeutic and Rehabilitation Outdoor Spaces for the Elderly Demographic

The use of technology for social connectivity and achieving engagement goals is increasingly essential to the overall well-being of our rapidly ageing population. While much of the extant literature has focused on home automation and indoor remote health monitoring; there is a growing literature that finds personal health and overall well-being improves when physical activities are conducted outdoors. This study presents a review of possible innovative and assistive eHealth technologies suitable for smart therapeutic and rehabilitation outdoor spaces for older persons. The article also presents key performance metrics required of eHealth technologies to ensure robust, timely and reliable biometric data transfer between patients in a therapeutic landscape environment and respective medical centres. A literature review of relevant publications with a primary focus of integrating sensors and eHealth technologies in outdoor spaces to collect and transfer data from the elderly demographic who engage such built landscapes to appropriate stakeholders was conducted. A content analysis was carried out to synthesize outcomes of the literature review. The study finds that research in assistive eHealth technologies and interfaces for outdoor therapeutic spaces is in its nascent stages and has limited generalisability. The level of technology uptake and readiness for smart outdoor spaces is still developing and is currently being outpaced by the growth of elderly fitness zones in public spaces. Further research is needed to explore those eHealth technologies with interactive feedback mechanisms that are suitable for outdoor therapeutic environments.
Compound conditionals, Fréchet-Hoeffding bounds, and Frank t-norms

In this paper we consider compound conditionals, Fréchet-Hoeffding bounds and the probabilistic interpretation of Frank t-norms. By studying the solvability of suitable linear systems, we show under logical independence the sharpness of the Fréchet-Hoeffding bounds for the prevision of conjunctions and disjunctions of n conditional events. In addition, we illustrate some details in the case of three conditional events. We study the set of all coherent prevision assessments on a family containing n conditional events and their conjunction, by verifying that it is convex. We discuss the case where the prevision of conjunctions is assessed by Lukasiewicz t-norms and we give explicit solutions for the linear systems; then, we analyze a selected example. We obtain a probabilistic interpretation of Frank t-norms and t-conorms as prevision of conjunctions and disjunctions of conditional events, respectively. Then, we characterize the sets of coherent prevision assessments on a family containing n conditional events and their conjunction, or their disjunction, by using Frank t-norms, or Frank t-conorms. By assuming logical independence, we show that any Frank t-norm (resp., t-conorm) of two conditional events A|H and B|K, Tλ(A|H,B|K) (resp., Sλ(A|H,B|K)), is a conjunction (A|H)∧(B|K) (resp., a disjunction (A|H)∨(B|K)). Then, we analyze the case of logical dependence where A=B and we obtain the set of coherent assessments on A|H,A|K,(A|H)∧(A|K); moreover we represent it in terms of the class of Frank t-norms Tλ, with λ∈[0,1]. By considering a family F containing three conditional events, their conjunction, and all pairwise conjunctions, we give some results on Frank t-norms and coherence of the prevision assessments on F. By assuming logical independence, we show that it is coherent to assess the previsions of all the conjunctions by means of Minimum and Product t-norms. In this case all the conjunctions coincide with the t-norms of the corresponding conditional events. We verify by a counterexample that, when the previsions of conjunctions are assessed by the Lukasiewicz t-norm, coherence is not assured. Then, the Lukasiewicz t-norm of conditional events may not be interpreted as their conjunction. Finally, we give two sufficient conditions for coherence and incoherence when using the Lukasiewicz t-norm.
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes

Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit sememes in specific tasks and few efforts are made to utilize sememes more fundamentally. In this paper, we propose to incorporate sememes into recurrent neural networks (RNNs) to improve their sequence modeling ability, which is beneficial to all kinds of downstream tasks. We design three different sememe incorporation methods and employ them in typical RNNs including LSTM, GRU and their bidirectional variants. In evaluation, we use several benchmark datasets involving PTB and WikiText-2 for language modeling, SNLI for natural language inference and another two datasets for sentiment analysis and paraphrase detection. Experimental results show evident and consistent improvement of our sememe-incorporated models compared with vanilla RNNs, which proves the effectiveness of our sememe incorporation methods. Moreover, we find the sememe-incorporated models have higher robustness and outperform adversarial training in defending adversarial attack. All the code and data of this work can be obtained at https://github.com/thunlp/SememeRNN.
Double-Oracle Sampling Method for Stackelberg Equilibrium Approximation in General-Sum Extensive-Form Games

The paper presents a new method for approximating Strong Stackelberg Equilibrium in general-sum sequential games with imperfect information and perfect recall. The proposed approach is generic as it does not rely on any specific properties of a particular game model. The method is based on iterative interleaving of the two following phases: (1) guided Monte Carlo Tree Search sampling of the Follower's strategy space and (2) building the Leader's behavior strategy tree for which the sampled Follower's strategy is an optimal response. The above solution scheme is evaluated with respect to expected Leader's utility and time requirements on three sets of interception games with variable characteristics, played on graphs. A comparison with three state-of-the-art MILP/LP-based methods shows that in vast majority of test cases proposed simulation-based approach leads to optimal Leader's strategies, while excelling the competitive methods in terms of better time scalability and lower memory requirements.
Nanowire single-photon detectors made of atomic layer-deposited niobium nitride

We demonstrate and characterize first superconducting nanowire single-photon detectors (SNSPDs) made from atomic layer-deposited (ALD) NbN layers. To assess the suitability of these films as a detector material, transport properties of bare films and bridges of different dimensions and thicknesses are investigated. Similar ratios of the measured critical current to the depairing current are obtained for micro-bridges made from ALD and sputtered NbN films. Furthermore, we characterized the single-photon response for 5 and 10 nm-thick nanowire detectors. A 100 nm-wide straight nanowire with a length of 5 $\mu$m exhibits saturated count-rate dependencies on bias current and a cut-off wavelength in the near-infrared range. The ALD technique could open up the possibility to fabricate NbN-based detectors on the wafer scale and to conformally cover also non-planar surfaces for novel device concepts.
Generation and Distribution of Quantum Oblivious Keys for Secure Multiparty Computation

The oblivious transfer primitive is sufficient to implement secure multiparty computation. However, secure multiparty computation based on public-key cryptography is limited by the security and efficiency of the oblivious transfer implementation. We present a method to generate and distribute oblivious keys by exchanging qubits and by performing commitments using classical hash functions. With the presented hybrid approach of quantum and classical, we obtain a practical and high-speed oblivious transfer protocol. We analyse the security and efficiency features of the technique and conclude that it presents advantages in both areas when compared to public-key based techniques.
Cloud Benchmarking for Maximising Performance of Scientific Applications

How can applications be deployed on the cloud to achieve maximum performance? This question is challenging to address with the availability of a wide variety of cloud Virtual Machines (VMs) with different performance capabilities. The research reported in this paper addresses the above question by proposing a six step benchmarking methodology in which a user provides a set of weights that indicate how important memory, local communication, computation and storage related operations are to an application. The user can either provide a set of four abstract weights or eight fine grain weights based on the knowledge of the application. The weights along with benchmarking data collected from the cloud are used to generate a set of two rankings-one based only on the performance of the VMs and the other takes both performance and costs into account. The rankings are validated on three case study applications using two validation techniques. The case studies on a set of experimental VMs highlight that maximum performance can be achieved by the three top ranked VMs and maximum performance in a cost-effective manner is achieved by at least one of the top three ranked VMs produced by the methodology.
Enhanced connectivity of quantum hardware with digital-analog control

Quantum computers based on superconducting circuits are experiencing rapid development, with the aim to outperform classical computers in certain useful tasks in the near future. However, the currently available chip fabrication technologies limit the capability of gathering a large number of high-quality qubits in a single superconducting chip, a requirement for implementing quantum error correction. Furthermore, achieving high connectivity in a chip poses a formidable technological challenge. Here, we propose a hybrid digital-analog quantum algorithm that enhances the physical connectivity among qubits coupled by an arbitrary inhomogeneous nearest-neighbor Ising Hamiltonian and generates an arbitrary all-to-all Ising Hamiltonian only by employing single-qubit rotations. Additionally, we optimize the proposed algorithm in the number of analog blocks and in the time required for the simulation. These results take advantage of the natural evolution of the system by combining the flexibility of digital steps with the robustness of analog quantum computing, allowing us to improve the connectivity of the hardware and the efficiency of quantum algorithms.Received 23 December 2019Accepted 23 June 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033103Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum information architectures & platformsQuantum simulationQuantum Information
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation

We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space.Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log probability of the target sentence, using only the latent variable as input.This allows us to use gradient-based optimization to find the target sentence at inference time that approximately maximizes its marginal probability.As each refinement step only involves computation in the latent space of low dimensionality (we use 8 in our experiments), we avoid computational overhead incurred by existing non-autoregressive inference procedures that often refine in token space.We compare our approach to a recently proposed EM-like inference procedure (Shu et al., 2020) that optimizes in a hybrid space, consisting of both discrete and continuous variables.We evaluate our approach on WMT'14 En→De, WMT'16 Ro→En and IWSLT'16 De→En, and observe two advantages over the EM-like inference:(1) it is computationally efficient, i.e. each refinement step is twice as fast, and (2) it is more effective, resulting in higher marginal probabilities and BLEU scores with the same number of refinement steps.On WMT'14 En→De, for instance, our approach is able to decode 6.2 times faster than the autoregressive model with minimal degradation to translation quality (0.9 BLEU).
Quantum Simulation Logic, Oracles, and the Quantum Advantage

Query complexity is a common tool for comparing quantum and classical computation, and it has produced many examples of how quantum algorithms differ from classical ones. Here we investigate in detail the role that oracles play for the advantage of quantum algorithms. We do so by using a simulation framework, Quantum Simulation Logic (QSL), to construct oracles and algorithms that solve some problems with the same success probability and number of queries as the quantum algorithms. The framework can be simulated using only classical resources at a constant overhead as compared to the quantum resources used in quantum computation. Our results clarify the assumptions made and the conditions needed when using quantum oracles. Using the same assumptions on oracles within the simulation framework we show that for some specific algorithms, like the Deutsch-Jozsa and Simon's algorithms, there simply is no advantage in terms of query complexity. This does not detract from the fact that quantum query complexity provides examples of how a quantum computer can be expected to behave, which in turn has proved useful for finding new quantum algorithms outside of the oracle paradigm, where the most prominent example is Shor's algorithm for integer factorization.
Data Diversification: A Simple Strategy For Neural Machine Translation

Xuan-Phi is supported by the A*STAR Computing and Information Science (ACIS) scholarship, provided by the Agency for Science, Technology and Research Singapore (A*STAR). Shafiq Joty would like to thank thefunding support from NRF(NRF2016IDM-TRANS001-062), Singapore
Modeling Content Importance for Summarization with Pre-trained Language Models

Modeling content importance is an essential yet challenging task for summarization.Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance.It is thus hard for these methods to generalize to semantic units of longer text spans.In this work, we apply information theory on top of pretrained language models and define the concept of importance from the perspective of information amount.It considers both the semantics and context when evaluating the importance of each semantic unit.With the help of pre-trained language models, it can easily generalize to different kinds of semantic units (n-grams or sentences).Experiments on CNN/Daily Mail and New York Times datasets demonstrate that our method can better model the importance of content than prior work based on F1 and ROUGE scores.
Java decompiler diversity and its application to meta-decompilation

During compilation from Java source code to bytecode, some information is irreversibly lost. In other words, compilation and decompilation of Java code is not symmetric. Consequently, decompilation, which aims at producing source code from bytecode, relies on strategies to reconstruct the information that has been lost. Different Java decompilers use distinct strategies to achieve proper decompilation. In this work, we hypothesize that the diverse ways in which bytecode can be decompiled has a direct impact on the quality of the source code produced by decompilers. In this paper, we assess the strategies of eight Java decompilers with respect to three quality indicators: syntactic correctness, syntactic distortion and semantic equivalence modulo inputs. Our results show that no single modern decompiler is able to correctly handle the variety of bytecode structures coming from real-world programs. The highest ranking decompiler in this study produces syntactically correct, and semantically equivalent code output for 84%, respectively 78%, of the classes in our dataset. Our results demonstrate that each decompiler correctly handles a different set of bytecode classes. We propose a new decompiler called Arlecchino that leverages the diversity of existing decompilers. To do so, we merge partial decompilation into a new one based on compilation errors. Arlecchino handles 37.6% of bytecode classes that were previously handled by no decompiler. We publish the sources of this new bytecode decompiler.