Reviewerly/exHarmony huggingface dataset:
Unfolding the Alternating Optimization for Blind Super Resolution

Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel. This two-step solution involves two independently trained models, which may not be well compatible with each other. Small estimation error of the first step could cause severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from LR image, which makes it difficult to predict highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate blur kernel and restore SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores SR image based on predicted kernel, and \textit{Estimator} estimates blur kernel with the help of restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, \textit{Estimator} utilizes information from both LR and SR images, which makes the estimation of blur kernel easier. More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}. Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at much higher speed. The source code is available at https://github.com/greatlog/DAN.git.
The effects of the problem Hamiltonian parameters on the minimum spectral gap in adiabatic quantum optimization

We study the relation between the Ising problem Hamiltonian parameters and the minimum spectral gap (min-gap) of the system Hamiltonian in the Ising-based quantum annealer. The main argument we use in this paper to assess the performance of a QA algorithm is the presence or absence of an anti-crossing during quantum evolution. For this purpose, we introduce a new parametrization definition of the anti-crossing. Using the Maximum-weighted Independent Set (MIS) problem in which there are flexible parameters (energy penalties J between pairs of edges) in an Ising formulation as the model problem, we construct examples to show that by changing the value of J, we can change the quantum evolution from one that has an anti-crossing (that results in an exponential small min-gap) to one that does not have, or the other way around, and thus drastically change (increase or decrease) the min-gap. However, we also show that by changing the value of $J$ alone, one can not avoid the anti-crossing. We recall a polynomial reduction from an Ising problem to an MIS problem to show that the flexibility of changing parameters without changing the problem to be solved can be applied to any Ising problem. As an example, we show that by such a reduction alone, it is possible to remove the anti-crossing and thus increase the min-gap. Our anti-crossing definition is necessarily scaling invariant as scaling the problem Hamiltonian does not change the nature (i.e. presence or absence) of an anti-crossing. As a side note, we show exactly how the min-gap is scaled if we scale the problem Hamiltonian by a constant factor.
Quaternion-Based Self-Attentive Long Short-term User Preference Encoding for Recommendation

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting. Unfortunately, most of the current recommender systems rely on real-valued representations in Euclidean space to model either user's long-term or short-term interests. In this paper, we fully utilize Quaternion space to model both user's long-term and short-term preferences. We first propose a QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the user's long-term intents. Then, we propose a QUaternion-based self-Attentive Short term user Encoding (QUASE) to learn the user's short-term interests. To enhance our models' capability, we propose to fuse QUALE and QUASE into one model, namely QUALSE, by using a Quaternion-based gating mechanism. We further develop Quaternion-based Adversarial learning along with the Bayesian Personalized Ranking (QABPR) to improve our model's robustness. Extensive experiments on six real-world datasets show that our fused QUALSE model outperformed 11 state-of-the-art baselines, improving 8.43% at [email protected] and 10.27% at [email protected] on average compared with the best baseline.
Robustness of the quantum BKL scenario

Abstract The quantum Belinski–Khalatnikov–Lifshitz scenario presents an unitary evolution of the system. However, the affine coherent states quantization applied to the quantization of the underlying classical scenario depends on the choice of the group parametrization. Using the two simplest parameterizations of the affine group, we show that qualitative features of our quantum system do not depend on the choice. It means that the quantum bounce replacing a singular classical scenario is expected to be a generic feature of the considered system. This paper complements our recent article (Góźdź et al. in Eur Phys J C 79:45, 2019).
Unifying graded and parameterised monads

Monads are a useful tool for structuring effectful features of computation such as state, non-determinism, and continuations. In the last decade, several generalisations of monads have been suggested which provide a more fine-grained model of effects by replacing the single type constructor of a monad with an indexed family of constructors. Most notably, graded monads (indexed by a monoid) model effect systems and parameterised monads (indexed by pairs of pre- and post-conditions) model program logics. This paper studies the relationship between these two generalisations of monads via a third generalisation. This third generalisation, which we call category-graded monads, arises by generalising a view of monads as a particular special case of lax functors. A category-graded monad provides a family of functors T f indexed by morphisms f of some other category. This allows certain compositions of effects to be ruled out (in the style of a program logic) as well as an abstract description of effects (in the style of an effect system). Using this as a basis, we show how graded and parameterised monads can be unified, studying their similarities and differences along the way.
Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form often contain sensitive attributes and publishing such data will leak individuals' privacy. Especially, a privacy threat occurs when an attacker can link a record to a specific individual based on some known partial information. Therefore, maintaining privacy in the published data is a critical problem. To prevent record linkage, attribute linkage, and similarity attacks based on the background knowledge of trajectory data, we propose a data privacy preservation with enhanced l-diversity. First, we determine those critical spatial-temporal sequences which are more likely to cause privacy leakage. Then, we perturb these sequences by adding or deleting some spatial-temporal points while ensuring the published data satisfy our ( L,α,β )-privacy, an enhanced privacy model from l-diversity. Our experiments on both synthetic and real-life datasets suggest that our proposed scheme can achieve better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.
Probabilistic teleportation of a quantum dot spin qubit

Electron spin s in semiconductor quantum dot s have been intensively studied for implementing quantum computation and high fidelity single and two qubit operation s have recently been achieved . Quantum teleportation is a three qubit protocol exploiting quantum entanglement and it serv es as a n essential primitive for more sophisticated quantum algorithm s Here, we demonstrate a scheme for quantum teleportation based on direct Bell measurement for a single electron spin qubit in a triple quantum dot utilizing the Pauli exclusion principle to create and detect maximally entangled state s . T he single spin polarization is teleported from the input qubit to the output qubit with a fidelity of 0.9 1 We find this fidelity is primarily limited by singlet triplet mixing which can be improved by optimizing the device parameters Our results may be extended to quantum algorithms with a larger number of se miconductor spin qubit s
Quantifying tripartite entanglement with entropic correlations

The authors develop techniques to quantify multipartite and high-dimensional entanglement using information-based measures of correlation and uncertainty, and show the large amounts of multi-partite entanglement available in photon triplets generated in third-order Spontaneous Parametric Down-Conversion
Classical and Quantum Evaluation Codes at the Trace Roots

We introduce a new class of evaluation linear codes by evaluating polynomials at the roots of a suitable trace function. We give conditions for self-orthogonality of these codes and their subfield-subcodes with respect to the Hermitian inner product. They allow us to construct stabilizer quantum codes over several finite fields which substantially improve the codes in the literature and that are records at [http://www.codetables.de] for the binary case. Moreover, we obtain several classical linear codes over the field $\mathbb{F}_4$ which are records at [http://www.codetables.de].
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking

As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems.For practical usages, a major challenge to constructing a robust DST model is to process a conversation with multi-domain states.However, most existing approaches trained DST on a single domain independently, ignoring the information across domains.To tackle the multi-domain DST task, we first construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains.Then, we encode the graph information of dialogue states by graph convolutional networks and utilize a hard copy mechanism to directly copy historical states from the previous conversation.Experimental results show that our model improves the performances of the multi-domain DST baseline (TRADE) with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets, respectively.
On Romanization for Model Transfer Between Scripts in Neural Machine Translation

Transfer learning is a popular strategy to improve the quality of low-resource machine translation.For an optimal transfer of the embedding layer, the child and parent model should share a substantial part of the vocabulary.This is not the case when transferring to languages with a different script.We explore the benefit of romanization in this scenario.Our results show that romanization entails information loss and is thus not always superior to simpler vocabulary transfer methods, but can improve the transfer between related languages with different scripts.We compare two romanization tools and find that they exhibit different degrees of information loss, which affects translation quality.Finally, we extend romanization to the target side, showing that this can be a successful strategy when coupled with a simple deromanization model.
A Quantum-Based Database Query Scheme for Privacy Preservation in Cloud Environment

Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user’s ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server’s privacy, and in order to guarantee the clients’ privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme.
Improve topic modeling algorithms based on Twitter hashtags

Abstract Today with increase using social media, a lot of researchers have interested in topic extraction from Twitter. Twitter is an unstructured short text and messy that it is critical to find topics from tweets. While topic modeling algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are originally designed to derive topics from large documents such as articles, and books. They are often less efficient when applied to short text content like Twitter. Luckily, Twitter has many features that represent the interaction between users. Tweets have rich user-generated hashtags as keywords. In this paper, we exploit the hashtags feature to improve topics learned from Twitter content without modifying the basic topic model of LSA and LDA. Users who share the same hashtag at most discuss the same topic. We compare the performance of the two methods (LSA and LDA) using the topic coherence (with and without hashtags). The experiment result on the Twitter dataset showed that LSA has better coherence score with hashtags than that do not incorporate hashtags. In contrast, our experiments show that the LDA has a better coherence score without incorporating hashtags. Finally, LDA has a better coherence score than LSA and the best coherence result obtained from the LDA method was (0.6047) and the LSA method was (0.4744) but the number of topics in LDA was higher than LSA. Thus, LDA may cause the same tweets to discuss the same subject set into different clustering.
Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Abstract Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top- N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.
Documentation of Quality Requirements in Agile Software Development

Context: Quality requirements (QRs) have a significant role in the success of software projects. In agile software development (ASD), where working software is valued over comprehensive documentation, QRs are often under-specified or not documented. Consequently, they may be handled improperly and result in degraded software quality and increased maintenance costs. Investigating the documentation of QRs in ASD, would provide evidence on existing practices, tools and aspects considered in ASD that other practitioners might utilize to improve documentation and management of QRs in ASD. Although there are some studies examining documentation in ASD, those that specifically investigate the documentation of QRs in depth are lacking.
Training Behavior of Sparse Neural Network Topologies

Improvements in the performance of deep neural networks have often come through the design of larger and more complex networks. As a result, fast memory is a significant limiting factor in our ability to improve network performance. One approach to overcoming this limit is the design of sparse neural networks, which can be both very large and efficiently trained. In this paper we experiment training on sparse neural network topologies. We test pruning-based topologies, which are derived from an initially dense network whose connections are pruned, as well as RadiX-Nets, a class of network topologies with proven connectivity and sparsity properties. Results show that sparse networks obtain accuracies comparable to dense networks, but extreme levels of sparsity cause instability in training, which merits further study.
Countermeasure against bright-light attack on superconducting nanowire single-photon detector in quantum key distribution

We present an active anti-latching system for superconducting nanowire single-photon detectors. We experimentally test it against a bright-light attack, previously used to compromise security of quantum key distribution. Although our system detects continuous blinding, the detector is shown to be partially blindable and controllable by specially tailored sequences of bright pulses. Improvements to the countermeasure are suggested.
Squeezing‐Enhanced Atom–Cavity Interaction in Coupled Cavities with High Dissipation Rates

The realization of the strong coupling regime is requisite for implementing quantum information tasks. Here, a method for enhancing the atom-field coupling in highly dissipative coupled cavities is proposed. By introducing parametric squeezing into the primary cavity which is only virtually excited under specific parametric conditions, coupling enhancement between atom and auxiliary cavity is realized for proper squeezing parameters. This enables the system to be robust against large cavity decay and atomic spontaneous emission. The observation of vacuum Rabi oscillations show that the originally weak-coupled system can be enhanced into effective strong coupling regime.
Tensor network compressed sensing with unsupervised machine learning

We propose tensor-network compressed sensing (TNCS) by combining the ideas of compressed sensing, tensor network (TN), and machine learning, which permits novel and efficient quantum communications of realistic data. The strategy is to use the unsupervised TN machine learning algorithm to obtain the entangled state $|\Psi \rangle$ that describes the probability distribution of a huge amount of classical information considered to be communicated. To transfer a specific piece of information with $|\Psi \rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi \rangle$ that is obtained by partially measuring on $|\Psi \rangle$ in a designed way. To this end, a measuring protocol analogous to the compressed sensing with neural-network machine learning is suggested, where the measurements are designed to minimize uncertainty of information from the probability distribution given by $|\Phi \rangle$. In this way, those who have $|\Phi \rangle$ can reliably access the information by simply measuring on $|\Phi \rangle$. We propose q-sparsity to characterize the sparsity of quantum states and the efficiency of the quantum communications by TNCS. The high q-sparsity is essentially due to the fact that the TN states describing nicely the probability distribution obey the area law of entanglement entropy. Testing on realistic datasets (hand-written digits and fashion images), TNCS is shown to possess high efficiency and accuracy, where the security of communications is guaranteed by the fundamental quantum principles.
SoK: Tools for Game Theoretic Models of Security for Cryptocurrencies

Cryptocurrencies have garnered much attention in recent years, both from the academic community and industry.One interesting aspect of cryptocurrencies is their explicit consideration of incentives at the protocol level, which has motivated a large body of work-yet many open problems still exist, and current systems rarely deal with incentive related problems well.This issue arises due to the gap between cryptography and distributed systems security, which deals with traditional security problems that ignore the explicit consideration of incentives, and game theory, which deals best with situations involving incentives.With this work, we offer a systematization of the work that relates to this problem, considering papers that blend game theory with cryptography or distributed systems.This gives an overview of the available tools, and we look at their (potential) use in practice in the context of existing blockchain-based systems that have been proposed or implemented.
A tight runtime analysis for the cGA on jump functions

We prove that the compact genetic algorithm (cGA) with hypothetical population size [MATH HERE] poly(n) with high probability finds the optimum of any n-dimensional jump function with jump size [MATH HERE] ln n in [MATH HERE] iterations. Since it is known that the cGA with high probability needs at least [MATH HERE] iterations to optimize the unimodal OneMax function, our result shows that the cGA in contrast to most classic evolutionary algorithms here is able to cross mo derate-sized valleys of low fitness at no extra cost. Our runtime guarantee improves over the recent upper bound O(µn1.5 log n) valid for µ = Ω(n3.5+e) of Hasenohrl and Sutton (GECCO 2018). For the best choice of the hypothetical population size, this result gives a runtime guarantee of O(n5+e), whereas ours gives O(n log n). We also provide a simple general method based on parallel runs that, under mild conditions, (i) overcomes the need to specify a suitable population size, but gives a performance close to the one stemming from the best-possible population size, and (ii) transforms EDAs with high-probability performance guarantees into EDAs with similar bounds on the expected runtime.