Reviewerly/exHarmony huggingface dataset:
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization

The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among different images and also the images themselves at the same time. To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains. To be specific, we formulate our framework with feature embedding using a multi-task learning paradigm. Besides conducting the common supervised recognition task, we seamlessly integrate a momentum metric learning task and a self-supervised auxiliary task to collectively utilize the extrinsic supervision and intrinsic supervision. Also, we develop an effective momentum metric learning scheme with K-hard negative mining to boost the network to capture image relationship for domain generalization. We demonstrate the effectiveness of our approach on two standard object recognition benchmarks VLCS and PACS, and show that our methods achieve state-of-the-art performance.
Applying Siracusa: A Call for a General Comment on Public Health Emergencies.

The COVID-19 pandemic is a public health emergency--as of Apr 23, 2020 there were over 2 7 million cases, with over 190,000 deaths globally Under the International Covenant on Civil and Political Rights, states may restrict certain rights during public emergencies that threaten the life of the nation to the extent that they are required by the exigencies of the situation  COVID-19 can be a fatal disease without known methods of prevention and treatment Given this situation, the question is not if, but how states may restrict rights to try to control the pandemic The Siracusa Principles are a foundation on which to build The principles outline the limits on rights restrictions that states may take during emergencies Under Siracusa, restrictions are only justified when they support a legitimate aim and are: provided for by law, strictly necessary, proportionate, of limited duration, and subject to review against abusive applications
Rates of Multipartite Entanglement Transformations

The theory of the asymptotic manipulation of pure bipartite quantum systems can be considered completely understood: the rates at which bipartite entangled states can be asymptotically transformed into each other are fully determined by a single number each, the respective entanglement entropy. In the multipartite setting, similar questions of the optimally achievable rates of transforming one pure state into another are notoriously open. This seems particularly unfortunate in the light of the revived interest in such questions due to the perspective of experimentally realizing multipartite quantum networks. In this Letter, we report substantial progress by deriving simple upper and lower bounds on the rates that can be achieved in asymptotic multipartite entanglement transformations. These bounds are based on ideas of entanglement combing and state merging. We identify cases where the bounds coincide and hence provide the exact rates. As an example, we bound rates at which resource states for the cryptographic scheme of quantum secret sharing can be distilled from arbitrary pure tripartite quantum states. This result provides further scope for quantum internet applications, supplying tools to study the implementation of multipartite protocols over quantum networks.
EVERYONE KNOWS THAT SOMEONE KNOWS: QUANTIFIERS OVER EPISTEMIC AGENTS

Abstract Modal logic S5 is commonly viewed as an epistemic logic that captures the most basic properties of knowledge. Kripke proved a completeness theorem for the first-order modal logic S5 with respect to a possible worlds semantics. A multiagent version of the propositional S5 as well as a version of the propositional S5 that describes properties of distributed knowledge in multiagent systems has also been previously studied. This article proposes a version of S5-like epistemic logic of distributed knowledge with quantifiers ranging over the set of agents, and proves its soundness and completeness with respect to a Kripke semantics.
Together or Alone: The Price of Privacy in Collaborative Learning

Abstract Machine learning algorithms have reached mainstream status and are widely deployed in many applications. The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model. For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones). Unfortunately, privacy concerns prevent them from straightforwardly doing so. While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration. In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset. We introduce the notion of Price of Privacy , a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework. Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player. Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.
A Blockchain-Based Scheme for Access Control in e-Health Scenarios

Access control is a crucial part of a system's security, restricting what actions users can perform on resources. Therefore, access control is a core component when dealing with e-Health data and resources, discriminating which is available for a certain party. We consider that current systems that attempt to assure the share of policies between facilities are prone to system's and network's faults and do not assure the integrity of policies lifecycle. By approaching this problem with the use of a distributed ledger, namely a consortium blockchain, where the operations are stored as transactions, we ensure that the different facilities have knowledge about all the parties that can act over the e-Health resources while maintaining integrity, auditability, authenticity, and scalability.
A tunable quantum dissipator for active resonator reset in circuit QED

We propose a method for fast, deterministic resonator reset based on tunable dissipative modes. The dissipator is based on a Josephson junction with relatively low quality factor. When the dissipator is tuned into resonance with a high quality microwave resonator, resonator photons are absorbed by the dissipator at a rate orders of magnitude faster than the resonator relaxation rate. We determine the optimal parameters for realization of the tunable dissipator, and examine application of the dissipator to removing spurious photon population in the qubit readout resonator in circuit quantum electrodynamics. We show that even in the nonlinear large photon occupation regime, this enhanced resonator decay rate can be attained by appropriate modulation of the dissipator frequency.
Matching Larger Image Areas for Unconstrained Face Identification

Many approaches to unconstrained face identification exploit small patches which are unaffected by distortions outside their locality. A larger area usually contains more discriminative information, but may be unidentifiable due to local appearance changes across its area, given limited training data. We propose a novel block-based approach, as a complement to existing patch-based approaches, to exploit the greater discriminative information in larger areas, while maintaining robustness to limited training data. A testing block contains several neighboring patches, each of a small size. We identify the matching training block by jointly estimating all of the matching patches, as a means of reducing the uncertainty of each small matching patch with the addition of the neighboring patch information, without assuming additional training data. We further propose a multiscale extension in which we carry out block-based matching at several block sizes, to combine complementary information across scales for further robustness. We have conducted face identification experiments using three datasets, the constrained Georgia Tech dataset to validate the new approach, and two unconstrained datasets, LFW and UFI, to evaluate its potential for improving robustness. The results show that the new approach is able to significantly improve over existing patch-based face identification approaches, in the presence of uncontrolled pose, expression, and lighting variations, using small training datasets. It is also shown that the new block-based scheme can be combined with existing approaches to further improve performance.
Universal Logical Gates on Topologically Encoded Qubits via Constant-Depth Unitary Circuits

A fundamental question in the theory of quantum computation is to understand the ultimate space-time resource costs for performing a universal set of logical quantum gates to arbitrary precision. Here we demonstrate that non-Abelian anyons in Turaev-Viro quantum error correcting codes can be moved over a distance of order of the code distance, and thus braided, by a constant depth local unitary quantum circuit followed by a permutation of qubits. Our gates are protected in the sense that the lengths of error strings do not grow by more than a constant factor. When applied to the Fibonacci code, our results demonstrate that a universal logical gate set can be implemented on encoded qubits through a constant depth unitary quantum circuit, and without increasing the asymptotic scaling of the space overhead. These results also apply directly to braiding of topological defects in surface codes. Our results reformulate the notion of braiding in general as an effectively instantaneous process, rather than as an adiabatic, slow process.
Fast detection and segmentation of partial image blur based on discrete Walsh–Hadamard transform

Image signals can be blurred due to defocus or motion. Blur may be undesirable for image sensing, but may also contain useful information. Therefore, detecting the blurriness of each pixel and segmenting the partial blur regions in natural images are important and yet challenging in the field of machine vision. A concise no-reference method based on discrete Walsh–Hadamard transform is proposed to detect and segment partial blur in this paper. First, a re-blurring strategy is performed over multiple overlapping image patches extracted from the test image. Then, for both test image and re-blurred image, discrete Walsh–Hadamard transforms are utilized in each image patches to obtain the blur map. This blur map can characterize the blurriness of each pixel in test image. Based on it, combined with K-Means clustering and region-growing, the test image can be segmented into blurry/non-blurry regions. The experiments, performed on a public dataset, demonstrate the capability of the proposed metric in the detection and segmentation of the blur region. Comparative results with the state-of-the-art show the superiority of the proposed approach in image segmentation for both defocus and motion blur images. The proposed approach is compendious without data training and possesses a high time efficiency because of the fast sequency transform.
A scientometric look at mathematics education from Scopus database

The present work offers a characterization of the development of mathematics education, based on scientometric indicators.The study analyzes 5633 documents registered in the Scopus database which are all related to research in mathematics education.The main results show an exponential increase on the scientific productivity, a tendency to increase collaborative work over individual work, a greater impact per index of the cited references during the last two decades, a high level of international collaboration, a certain concentration of the publications on a reduced group of investigators in some international impact journals, and the existence of 17 invisible colleagues that represent scientific communities with common research interests.
Enhancing quantum transduction via long-range waveguide-mediated interactions between quantum emitters

Efficient transduction of electromagnetic signals between different frequency scales is an essential ingredient for modern communication technologies as well as for the emergent field of quantum information processing. Recent advances in waveguide photonics have enabled a breakthrough in light-matter coupling, where individual two-level emitters are strongly coupled to individual photons. Here we propose a scheme which exploits this coupling to boost the performance of transducers between low-frequency signals and optical fields operating at the level of individual photons. Specifically, we demonstrate how to engineer the interaction between quantum dots in waveguides to enable efficient transduction of electric fields coupled to quantum dots. Owing to the scalability and integrability of the solid-state platform, our transducer can potentially become a key building block of a quantum internet node. To demonstrate this, we show how it can be used as a coherent quantum interface between optical photons and a two-level system like a superconducting qubit.
Adaptive tone mapping for display enhancement under ambient light using constrained optimization

We propose adaptive tone mapping for display enhancement under ambient light using constrained optimization. To deal with the visibility reduction caused by ambient light in displays, we perform different operations for display enhancement according to the intensity of ambient light. Since weak ambient light has little effect on displayed images, we only perform contrast enhancement for them. However, strong ambient light makes displayed images dark for human eyes, causing severe visibility reduction in luminance and contrast. To enhance the visibility of displayed images under strong ambient light, we formulate a constraint optimization problem which consists of luminance enhancement, contrast enhancement, and distortion minimization terms, and find an optimal trade-off among them by solving it. Finally, we conduct color scaling to reproduce vivid colors in displayed images. Experimental results demonstrate that the proposed method significantly enhances the brightness, contrast, details, and colors of displayed images and outperforms other state-of-the-art methods under ambient light.
Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology

To implement a system for unsupervised extraction of tumor stage and prognostic data in patients with genitourinary cancers using clinicopathological and radiology text. A corpus of 1054 electronic notes (clinician notes, radiology reports and pathology reports) was annotated for tumor stage, prostate specific antigen (PSA) and Gleason grade. Annotations from five clinicians were reconciled to form a gold standard dataset. A training dataset of 386 documents was sequestered. The Medtex algorithm was adapted using the training dataset. Adapted Medtex equaled or exceeded human performance in most annotations, except for implicit M stage (F-measure of 0.69 vs 0.84) and PSA (0.92 vs 0.96). Overall Medtex performed with an F-measure of 0.86 compared to human annotations of 0.92. There was significant inter-observer variability when comparing human annotators to the gold standard. The Medtex algorithm performed similarly to human annotators for extracting stage and prognostic data from varied clinical texts.
Higher-Order DCA against Standard Side-Channel Countermeasures

At CHES 2016, Bos et al. introduced differential computational analysis (DCA) as an attack on white-box software implementations of block ciphers. This attack builds on the same principles as DPA in the classical side-channel context, but uses computational traces consisting of plain values computed by the implementation during execution. It was shown to be able to recover the key of many existing AES white-box implementations. The DCA adversary is passive, and so does not exploit the full power of the white-box setting, implying that many white-box schemes are insecure even in a weaker setting than the one they were designed for. It is therefore important to develop implementations which are resistant to this attack. We investigate the approach of applying standard side-channel countermeasures such as masking and shuffling. Under some necessary conditions on the underlying randomness generation, we show that these countermeasures provide resistance to standard (first-order) DCA. Furthermore, we introduce higher-order DCA, along with an enhanced multivariate version, and analyze the security of the countermeasures against these attacks. We derive analytic expressions for the complexity of the attacks – backed up through extensive attack experiments – enabling a designer to quantify the security level of a masked and shuffled implementation in the (higher-order) DCA setting.
Quantum jump metrology

Quantum metrology exploits quantum correlations in specially prepared entangled or other non-classical states to perform measurements that exceed the standard quantum limit. Typically though, such states are hard to engineer, particularly when larger numbers of resources are desired. As an alternative, this paper aims to establish quantum jump metrology which is based on generalised sequential measurements as a general design principle for quantum metrology and discusses how to exploit open quantum systems to obtain a quantum enhancement. By analysing a simple toy model, we illustrate that parameter-dependent quantum feedback can indeed be used to exceed the standard quantum limit without the need for complex state preparation.
Evolving behaviors for bounded-flow tracking control of second-order dynamical systems

A two-stage methodology for the development of nonlinear analytical controllers for tracking control in second-order dynamical systems subject to flow variable constraints is proposed. It extends the concepts of behavior-based control to describe the system as the summation of its unforced, forced, and learned behaviors. While the unforced behavior is characterized by its analytical dynamical model, the forced and learned behaviors are introduced in the system by means of a Control-Theory-based controller and an evolutionary learning process based in the Genetic Programming paradigm. The integration of both approaches in a unified framework allows the system to exhibit a good tracking performance while keeping the flow variable bounded to a desired value, parametrized as a boundary interval. A set of 180993 learned behaviors, which preserves asymptotic convergence to the desired behavior while achieving a bounded flow variable, were discovered by the evolutionary process. Simulation results show the effectiveness of the found nonlinear tracking controllers with the highest fitness value, as well as the one with the lower structural complexity. A performance comparison between numerical simulations and real-time experiments for a mechatronic prototype is also provided to illustrate the feasibility of the proposed method in real-world applications.
Hypersonic Target Tracking With High Dynamic Biases

The chirp signal, which is of large time bandwidth product, is widely used in the current radar systems. However, high dynamic biases are brought into the radar ranging when the signal is used in the hypersonic target tracking, due to the distance-Doppler coupling. In view of the situation, this letter proposes an unbiased tracking algorithm which can eliminate the influence of high dynamic biases at the same time of target tracking.
Semisupervised Fuzzy Clustering With Partition Information of Subsets

Pairwise constraint is a type of side information that is widely considered in existing semisupervised clustering approaches. In this paper, we explore a new form of supervision for clustering. We consider the partition results of a number of subsets as additional information to assist clustering. Compared to the pairwise constraint, which only involves the “must-link” or “cannot-link” relationship of two objects, the partition of a subset of objects provides information about the group structure of more objects and hence can possibly serve as a more effective form of supervision for clustering. In this paper, we instantiate the idea of clustering with subset partitions under the fuzzy clustering framework for document categorization. The proposed fuzzy clustering approach is formulated to learn from the partition of subsets and has the ability to handle high-dimensional document data. Specifically, the partition results of subsets are collectively transformed into pairwise relationships, based on which a penalty term is constructed and incorporated into a cosine-distance-based fuzzy c-means approach. The experimental results on benchmark data sets demonstrate the effectiveness of the proposed approach for a semisupervised document clustering.
Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction

In this paper, a novel, real-time, error-free, reversible data hiding method for encrypted images has been proposed. Based on the (7, 4) Hamming code, we designed an efficient encoding scheme to embed secret data into the least significant bits (LSBs) of the encrypted image. For reversibility, we designed a most significant bit (MSB) prediction scheme that can recover a portion of the modified MSBs after the image is decrypted. These MSBs can be modified to accommodate the additional information that is used to recover the LSBs. After embedding the data, the original image can be recovered with no error and the secret data can be extracted from both the encrypted image and the decrypted image. The experimental results proved that compared with existing methods, the proposed method can achieve higher embedding rate, better quality of the marked image and less execution time of data embedding. Therefore, the proposed method is suitable for real-time applications in the cloud.
Accelerating nonconvex learning via replica exchange Langevin diffusion

Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature parameter controlling the noise level gives rise to a tradeoff between ``global exploration'' and ``local exploitation'', which correspond to high and low temperatures. To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures. We theoretically analyze the acceleration effect of replica exchange from two perspectives: (i) the convergence in \chi^2-divergence, and (ii) the large deviation principle. Such an acceleration effect allows us to faster approach the global minima. Furthermore, by discretizing the replica exchange Langevin diffusion, we obtain a discrete-time algorithm. For such an algorithm, we quantify its discretization error in theory and demonstrate its acceleration effect in practice.