Reviewerly/exHarmony huggingface dataset:
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on controlling the contributions of current and previous information. However, the exploration of different importance levels of different elements within an input vector is always ignored. We propose a simple yet effective Element-wise-Attention Gate (EleAttG), which can be easily added to an RNN block (e.g. all RNN neurons in an RNN layer), to empower the RNN neurons to have attentiveness capability. For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i.e., attention, to each element/dimension of the input. We refer to an RNN block equipped with an EleAttG as an EleAtt-RNN block. Instead of modulating the input as a whole, the EleAttG modulates the input at fine granularity, i.e., element-wise, and the modulation is content adaptive. The proposed EleAttG, as an additional fundamental unit, is general and can be applied to any RNN structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed EleAtt-RNN by applying it to different tasks including the action recognition, from both skeleton-based data and RGB videos, gesture recognition, and sequential MNIST classification. Experiments show that adding attentiveness through EleAttGs to RNN blocks significantly improves the power of RNNs.
Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.
Quantum information processing with bosonic qubits in circuit QED

Abstract The unique features of quantum theory offer a powerful new paradigm for information processing. Translating these mathematical abstractions into useful algorithms and applications requires quantum systems with significant complexity and sufficiently low error rates. Such quantum systems must be made from robust hardware that can coherently store, process, and extract the encoded information, as well as possess effective quantum error correction (QEC) protocols to detect and correct errors. Circuit quantum electrodynamics (cQED) provides a promising hardware platform for implementing robust quantum devices. In particular, bosonic encodings in cQED that use multi-photon states of superconducting cavities to encode information have shown success in realizing hardware-efficient QEC. Here, we review recent developments in the theory and implementation of QEC with bosonic codes and report the progress made toward realizing fault-tolerant quantum information processing with cQED devices.
High-Fidelity Bell-State Preparation with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mmultiscripts><mml:mrow><mml:msup><mml:mrow><mml:mi>Ca</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mprescripts /><mml:none /><mml:mrow><mml:mn>40</mml:mn></mml:mrow></mml:mmultiscripts></mml:mrow></mml:math> Optical Qubits

Entanglement generation in trapped-ion systems has relied thus far on two distinct but related geometric phase gate techniques: M\o{}lmer-S\o{}rensen and light-shift gates. We recently proposed a variant of the light-shift scheme where the qubit levels are separated by an optical frequency [B. C. Sawyer and K. R. Brown, Phys. Rev. A 103, 022427 (2021)]. Here we report an experimental demonstration of this entangling gate using a pair of ${^{40}\mathrm{Ca}}^{+}$ ions in a cryogenic surface-electrode ion trap and a commercial, high-power, 532 nm Nd:YAG laser. Generating a Bell state in $35\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$, we directly measure an infidelity of $6(3)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$ without subtraction of experimental errors. The 532 nm gate laser wavelength suppresses intrinsic photon scattering error to $\ensuremath{\sim}1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$.
Cost Optimization for Dynamic Replication and Migration of Data in Cloud Data Centers

Cloud Storage Providers (CSPs) offer geographically data stores providing several storage classes with different prices. An important problem facing by cloud users is how to exploit these storage classes to serve an application with a time-varying workload on its objects at minimum cost. This cost consists of residential cost (i.e., storage, Put and Get costs) and potential migration cost (i.e., network cost). To address this problem, we first propose the optimal offline algorithm that leverages dynamic and linear programming techniques with the assumption of available exact knowledge of workload on objects. Due to the high time complexity of this algorithm and its requirement for a priori knowledge, we propose two online algorithms that make a trade-off between residential and migration costs and dynamically select storage classes across CSPs. The first online algorithm is deterministic with no need of any knowledge of workload and incurs no more than 2γ-1 times of the minimum cost obtained by the optimal offline algorithm, where γ is the ratio of the residential cost in the most expensive data store to the cheapest one in either network or storage cost. The second online algorithm is randomized that leverages “Receding Horizon Control” (RHC) technique with the exploitation of available future workload information for w time slots. This algorithm incurs at most 1 + γ/w times the optimal cost. The effectiveness of the proposed algorithms is demonstrated through simulations using a workload synthesized based on characteristics of the Facebook workload.
SOS: A multimedia recommender System for Online Social networks

The use of Online Social Networks has been rapidly increased over the last years. In particular, Social Media Networks allow people to communicate, share, comment and observe different types of multimedia content. This phenomenon produces a huge amount of data showing Big Data features, mainly due to their high change rate, large volume and intrinsic heterogeneity. In this perspective, in the last decade Recommender Systems have been introduced to support the browsing of such data collections, assisting users to find "what they really need" within this ocean of information. In this research work, we propose and describe a novel recommending system for big data applications able to provide recommendations on the base of the interactions among users and the generated multimedia contents in one or more social media networks. The proposed system relies on a "user-centered" approach. An experimental campaign, using data coming from many social media networks, has been performed in order to assess the proposed approach also showing how it can obtain very promising results.
Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices

We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the findings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; confirmation that some analysts see "finding something interesting" as a valid goal of data exploration while others explicitly disavow this goal; conflicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These findings provide guidelines for future tool development, as well as a better understanding of the meaning of the term "data exploration" based on the words of practitioners "in the wild."
Recommendations for Datasets for Source Code Summarization

Alexander LeClair, Collin McMillan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Strong Coupling of Two Individually Controlled Atoms via a Nanophotonic Cavity

We demonstrate photon-mediated interactions between two individually trapped atoms coupled to a nanophotonic cavity. Specifically, we observe superradiant line broadening when the atoms are resonant with the cavity, and level repulsion when the cavity is coupled to the atoms in the dispersive regime. Our approach makes use of individual control over the internal states of the atoms, their position with respect to the cavity mode, as well as the light shifts to tune atomic transitions individually, allowing us to directly observe the anti-crossing of the superradiant and subradiant two-atom states. These observations open the door for realizing quantum networks and studying quantum many-body physics based on atom arrays coupled to nanophotonic devices.
Long-Distance Distribution of Atom-Photon Entanglement at Telecom Wavelength

Entanglement between stationary quantum memories and photonic channels is the essential resource for future quantum networks. Together with entanglement distillation, it will enable efficient distribution of quantum states. We report on the generation and observation of entanglement between a ^{87}Rb atom and a photon at telecom wavelength transmitted through up to 20 km of optical fiber. For this purpose, we use polarization-preserving quantum frequency conversion to transform the wavelength of a photon entangled with the atomic spin state from 780 nm to the telecom S band at 1522 nm. We achieve an unprecedented external device conversion efficiency of 57% and observe an entanglement fidelity between the atom and telecom photon of ≥78.5±0.9% after transmission through 20 km of optical fiber, mainly limited by decoherence of the atomic state. This result is an important milestone on the road to distribute quantum information on a large scale.
What models can and cannot tell us about COVID-19

Structures from the Stone Age can provide unique insights into Late Glacial and Mesolithic cultures around the Baltic Sea. Such structures, however, usually did not survive within the densely populated Central European subcontinent. Here, we ...The Baltic Sea basins, some of which only submerged in the mid-Holocene, preserve Stone Age structures that did not survive on land. Yet, the discovery of these features is challenging and requires cross-disciplinary approaches between archeology and ...
Dynamic Framework for Criticality-Enhanced Quantum Sensing

Quantum criticality, as a fascinating quantum phenomenon, may provide significant advantages for quantum sensing. Here we propose a dynamic framework for quantum sensing with a family of Hamiltonians that undergo quantum phase transitions (QPTs). By giving the formalism of the quantum Fisher information (QFI) for quantum sensing based on critical quantum dynamics, we demonstrate its divergent feature when approaching the critical point. We illustrate the basic principle and the details of experimental implementation using quantum Rabi model. The framework is applicable to a variety of examples and does not rely on the stringent requirement for particular state preparation or adiabatic evolution. It is expected to provide a route towards the implementation of criticality-enhanced quantum sensing.
Asymmetric GAN for Unpaired Image-to-Image Translation

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.
Implementation of XY entangling gates with a single calibrated pulse

Near-term applications of quantum information processors will rely on optimized circuit implementations to minimize gate depth and therefore mitigate the impact of gate errors in noisy intermediate-scale quantum (NISQ) computers. More expressive gate sets can significantly reduce the gate depth of generic circuits. Similarly, structured algorithms can benefit from a gate set that more directly matches the symmetries of the problem. The XY interaction generates a family of gates that provides expressiveness well tailored to quantum chemistry as well as to combinatorial optimization problems, while also offering reductions in circuit depth for more generic circuits. Here we implement the full family of XY entangling gates in a transmon-based superconducting qubit architecture. We use a composite pulse scheme that requires calibration of only a single gate pulse and maintains constant gate time for all members of the family. This allows us to maintain a high fidelity implementation of the gate across all entangling angles. The average fidelity of gates sampled from this family ranges from $95.67 \pm 0.60\%$ to $99.01 \pm 0.15\%$, with a median fidelity of $97.35 \pm 0.17\%$, which approaches the coherence-limited gate fidelity of the qubit pair. We furthermore demonstrate the utility of XY in a quantum approximation optimization algorithm in enabling circuit depth reductions as compared to the CZ only case.
Entangling logical qubits with lattice surgery

The development of quantum computing architectures from early designs and current noisy devices to fully fledged quantum computers hinges on achieving fault tolerance using quantum error correction1-4. However, these correction capabilities come with an overhead for performing the necessary fault-tolerant logical operations on logical qubits (qubits that are encoded in ensembles of physical qubits and protected by error-correction codes)5-8. One of the most resource-efficient ways to implement logical operations is lattice surgery9-11, where groups of physical qubits, arranged on lattices, can be merged and split to realize entangling gates and teleport logical information. Here we report the experimental realization of lattice surgery between two qubits protected via a topological error-correction code in a ten-qubit ion-trap quantum information processor. In this system, we can carry out the necessary quantum non-demolition measurements through a series of local and entangling gates, as well as measurements on auxiliary qubits. In particular, we demonstrate entanglement between two logical qubits and we implement logical state teleportation between them. The demonstration of these operations-fundamental building blocks for quantum computation-through lattice surgery represents a step towards the efficient realization of fault-tolerant quantum computation.
Toward a Smell-Aware Bug Prediction Model

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities between the set of buggy and smelly classes correctly classified by the two models. By evaluating the actual information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result, we devise and evaluate a smell-aware combined bug prediction model that included product, process, and smell-related features. We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13 percent higher than the existing state-of-the-art models.
AU R-CNN: Encoding expert prior knowledge into R-CNN for action unit detection

Detecting action units (AUs) on human faces is challenging because various AUs make subtle facial appearance change over various regions at different scales. Current works have attempted to recognize AUs by emphasizing important regions. However, the incorporation of expert prior knowledge into region definition remains under-exploited, and current AU detection approaches do not use regional convolutional neural networks (R-CNN) with expert prior knowledge to directly focus on AU-related regions adaptively. By incorporating expert prior knowledge, we propose a novel R-CNN based model named AU R-CNN. The proposed solution offers two main contributions: (1) AU R-CNN directly observes different facial regions, where various AUs are located. Expert prior knowledge is encoded in the region and the RoI-level label definition. This design produces considerably better detection performance than existing approaches. (2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models. Experiment results demonstrate that only static RGB image information and no optical flow-based AU R-CNN surpasses the one fused with dynamic models. AU R-CNN is also superior to traditional CNNs that use the same backbone on varying image resolutions. State-of-the-art recognition performance of AU detection is achieved. The complete network is end-to-end trainable. Experiments on BP4D and DISFA datasets show the effectiveness of our approach. Code will be made available.
Toward Intelligent Sensing: Intermediate Deep Feature Compression

The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features with high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layer features. We also present the results for evaluations of both lossless and lossy deep feature compression, which provide meaningful investigations and baselines for future research and standardization activities.