Reviewerly/exHarmony huggingface dataset:
Video Object Segmentation with Episodic Graph Memory Networks

How to make a segmentation model efficiently adapt to a specific video as well as online target appearance variations is a fundamental issue in the field of video object segmentation. In this work, a graph memory network is developed to address the novel idea of “learning to update the segmentation model”. Specifically, we exploit an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges. Further, learnable controllers are embedded to ease memory reading and writing, as well as maintain a fixed memory scale. The structured, external memory design enables our model to comprehensively mine and quickly store new knowledge, even with limited visual information, and the differentiable memory controllers slowly learn an abstract method for storing useful representations in the memory and how to later use these representations for prediction, via gradient descent. In addition, the proposed graph memory network yields a neat yet principled framework, which can generalize well to both one-shot and zero-shot video object segmentation tasks. Extensive experiments on four challenging benchmark datasets verify that our graph memory network is able to facilitate the adaptation of the segmentation network for case-by-case video object segmentation.
Deterministic creation of entangled atom–light Schrödinger-cat states

Quantum physics allows for entanglement between microscopic and macroscopic objects, described by discrete and continuous variables, respectively. As in Schr\"odinger's famous cat gedanken experiment, a box enclosing the objects can keep the entanglement alive. For applications in quantum information processing, however, it is essential to access the objects and manipulate them with suitable quantum tools. Here we reach this goal and deterministically generate entangled light-matter states by reflecting a coherent light pulse with up to four photons on average from an optical cavity containing one atom. The quantum light propagates freely and reaches a remote receiver for quantum state tomography. We produce a plethora of quantum states and observe negative-valued Wigner functions, a characteristic sign of non-classicality. As a first application, we demonstrate a quantum-logic gate between an atom and a light pulse, with the photonic qubit encoded in the phase of the light field.
Examining the effects of robotic service on brand experience: the moderating role of hotel segment

This research investigates the effects of robotic service on guest evaluations of hotel brand experience, and examines the moderating effects of hotel segment via a 2 (service delivery video: human or robot) x 3 (hotel segment: budget, midscale, or luxury) between-subjects experimental design. The findings suggest higher levels of sensory and intellectual experience from robotic service but lower levels of affective experience. For behavioral experience, robotic service influenced a higher rating for midscale and budget hotels, but not for a luxury hotel. Overall, robotic service may not necessarily enhance brand experience as influenced by the moderating role of hotel segment.
Ultrafast spin-lasers

Lasers have both ubiquitous applications and roles as model systems in which non-equilibrium and cooperative phenomena can be elucidated1. The introduction of novel concepts in laser operation thus has potential to lead to both new applications and fundamental insights2. Spintronics3, in which both the spin and the charge of the electron are used, has led to the development of spin-lasers, in which charge-carrier spin and photon spin are exploited. Here we show experimentally that the coupling between carrier spin and light polarization in common semiconductor lasers can enable room-temperature modulation frequencies above 200 gigahertz, exceeding by nearly an order of magnitude the best conventional semiconductor lasers. Surprisingly, this ultrafast operation of the resultant spin-laser relies on a short carrier spin relaxation time and a large anisotropy of the refractive index, both of which are commonly viewed as detrimental in spintronics3 and conventional lasers4. Our results overcome the key speed limitations of conventional directly modulated lasers and offer a prospect for the next generation of low-energy ultrafast optical communication. Room-temperature modulation frequencies exceeding 200 GHz are demonstrated in birefringent semiconductor spin-lasers by coupling the spin of the charge carriers to the light polarization.
UNITER: Learning UNiversal Image-TExt Representations

Joint image-text embedding is the bedrock for most Vision-and-Language (V+L)tasks, where multimodality inputs are jointly processed for visual and textualunderstanding. In this paper, we introduce UNITER, a UNiversal Image-TExtRepresentation, learned through large-scale pre-training over four image-textdatasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), whichcan power heterogeneous downstream V+L tasks with joint multimodal embeddings.We design three pre-training tasks: Masked Language Modeling (MLM), Image-TextMatching (ITM), and Masked Region Modeling (MRM, with three variants).Different from concurrent work on multimodal pre-training that apply jointrandom masking to both modalities, we use conditioned masking on pre-trainingtasks (i.e., masked language/region modeling is conditioned on full observationof image/text). Comprehensive analysis shows that conditioned masking yieldsbetter performance than unconditioned masking. We also conduct a thoroughablation study to find an optimal setting for the combination of pre-trainingtasks. Extensive experiments show that UNITER achieves new state of the artacross six V+L tasks (over nine datasets), including Visual Question Answering,Image-Text Retrieval, Referring Expression Comprehension, Visual CommonsenseReasoning, Visual Entailment, and NLVR2.
How 3D Virtual Reality Stores Can Shape Consumer Purchase Decisions: The Roles of Informativeness and Playfulness

The recent rise of consumer virtual reality (VR) hardware raises important questions in the field of online marketing: what makes 3D VR more informative and playful than conventional 2D media such as a still image and a video, and how it affects the online purchase decision-making process. In this study, we mainly focus on three interface features—interactivity, visual–spatial cues, and graphics quality. We explore how each of these three interface features enhances playfulness and informativeness of shopping interface and further influences subsequent product evaluation and purchase intention. The results of the study provide two meaningful insights. First, interactivity and visual–spatial cues significantly enhance perceived informativeness and playfulness; however, the role of graphics quality was found to be more critical for 2D displays than for 3D VR environment. Second, informativeness and playfulness influence the purchase decision-making process in distinct ways. More specifically, a playful interface may enhance consumers’ preference for hedonic product benefits (e.g., a stylish and attractive design), whereas informativeness is a more important explanatory variable for subsequent purchase intentions. We discuss the theoretical contribution and managerial insights the research provides for online retailers and designers.
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes

Some optimization problems are characterized by an objective that is very expensive, that lacks an analytical expression, and whose evaluations can be contaminated by noise. Bayesian Optimization (BO) methods can be used to solve these problems efficiently. BO relies on a probabilistic model of the objective, which is typically a Gaussian process (GP). This model is used to compute an acquisition function that estimates the expected utility (for solving the optimization problem) of evaluating the objective at each potential new point. A problem with GPs is, however, that they assume real-valued input variables and cannot easily deal with categorical or integer-valued values. Common methods to account for these variables, before evaluating the objective, include assuming they are real and then using a one-hot encoding, for categorical variables, or rounding to the closest integer, for integer-valued variables. We show that this leads to suboptimal results and introduce a novel approach to tackle categorical or integer-valued input variables within the context of BO with GPs. Several synthetic and real-world experiments support our hypotheses and show that our approach outperforms the results of standard BO using GPs on problems with categorical or integer-valued input variables.
Going From RGB to RGBD Saliency: A Depth-Guided Transformation Model

Depth information has been demonstrated to be useful for saliency detection. However, the existing methods for RGBD saliency detection mainly focus on designing straightforward and comprehensive models, while ignoring the transferable ability of the existing RGB saliency detection models. In this article, we propose a novel depth-guided transformation model (DTM) going from RGB saliency to RGBD saliency. The proposed model includes three components, that is: 1) multilevel RGBD saliency initialization; 2) depth-guided saliency refinement; and 3) saliency optimization with depth constraints. The explicit depth feature is first utilized in the multilevel RGBD saliency model to initialize the RGBD saliency by combining the global compactness saliency cue and local geodesic saliency cue. The depth-guided saliency refinement is used to further highlight the salient objects and suppress the background regions by introducing the prior depth domain knowledge and prior refined depth shape. Benefiting from the consistency of the entire object in the depth map, we formulate an optimization model to attain more consistent and accurate saliency results via an energy function, which integrates the unary data term, color smooth term, and depth consistency term. Experiments on three public RGBD saliency detection benchmarks demonstrate the effectiveness and performance improvement of the proposed DTM from RGB to RGBD saliency.
Variational fast forwarding for quantum simulation beyond the coherence time

Trotterization-based, iterative approaches to quantum simulation are restricted to simulation times less than the coherence time of the quantum computer, which limits their utility in the near term. Here, we present a hybrid quantum-classical algorithm, called Variational Fast Forwarding (VFF), for decreasing the quantum circuit depth of quantum simulations. VFF seeks an approximate diagonalization of a short-time simulation to enable longer-time simulations using a constant number of gates. Our error analysis provides two results: (1) the simulation error of VFF scales at worst linearly in the fast-forwarded simulation time, and (2) our cost function's operational meaning as an upper bound on average-case simulation error provides a natural termination condition for VFF. We implement VFF for the Hubbard, Ising, and Heisenberg models on a simulator. Additionally, we implement VFF on Rigetti's quantum computer to demonstrate simulation beyond the coherence time. Finally, we show how to estimate energy eigenvalues using VFF.
BPE-Dropout: Simple and Effective Subword Regularization

Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 2.3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization.
Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&amp;E stains

For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining-which highlights cellular morphology-is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm-trained strictly with WSI-level annotations-is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians' capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye.
Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures

In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language. Hand gesture recognition has always been an interesting topic in the areas of computer vision and multimedia. These gestures can be represented as sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, have been chosen because the majority of human gestures produce joint movements that generate truly characteristic corners. A challenging subset composed by a large number of gestures defined by the American Sign Language (ASL) is used to test the proposed solution and the effectiveness of the selected angles. Moreover, the proposed method has been compared to other state of the art works on the SHREC dataset, thus demonstrating its superiority in hand gesture recognition accuracy.
Reverse Attention-Based Residual Network for Salient Object Detection

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel reverse attention block to guide side-output residual learning in a top-down manner. Specifically, the current predicted salient regions are erased from each side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in high resolution and accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art methods, and with advantages in terms of simplicity, efficiency and model size.
Exponential suppression of bit-flips in a qubit encoded in an oscillator

A quantum system interacts with its environment—if ever so slightly—no matter how much care is put into isolating it1. Therefore, quantum bits undergo errors, putting dauntingly difficult constraints on the hardware suitable for quantum computation2. New strategies are emerging to circumvent this problem by encoding a quantum bit non-locally across the phase space of a physical system. Because most sources of decoherence result from local fluctuations, the foundational promise is to exponentially suppress errors by increasing a measure of this non-locality3,4. Prominent examples are topological quantum bits, which delocalize information over real space and where spatial extent measures non-locality. Here, we encode a quantum bit in the field quadrature space of a superconducting resonator endowed with a special mechanism that dissipates photons in pairs5,6. This process pins down two computational states to separate locations in phase space. By increasing this separation, we measure an exponential decrease of the bit-flip rate while only linearly increasing the phase-flip rate7. Because bit-flips are autonomously corrected, only phase-flips remain to be corrected via a one-dimensional quantum error correction code. This exponential scaling demonstrates that resonators with nonlinear dissipation are promising building blocks for quantum computation with drastically reduced hardware overhead8. The choice of the physical system that represents a qubit can help reduce errors. Encoding them in the quadrature space of a superconducting resonator leads to exponentially reduced bit-flip rates, while phase-flip errors increase only linearly.
Photonic tensor cores for machine learning

With an ongoing trend in computing hardware toward increased heterogeneity, domain-specific coprocessors are emerging as alternatives to centralized paradigms. The tensor core unit has been shown to outperform graphic processing units by almost 3 orders of magnitude, enabled by a stronger signal and greater energy efficiency. In this context, photons bear several synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non-von Neumann architectures. While several photonic neural network designs have been explored, a photonic tensor core to perform tensor operations is yet to be implemented. In this manuscript, we introduce an integrated photonics-based tensor core unit by strategically utilizing (i) photonic parallelism via wavelength division multiplexing, (ii) high 2 peta-operations-per-second throughputs enabled by tens of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and (iii) near-zero static power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show, supported by numerical simulations, that the performance of this 4-bit photonic tensor core unit can be 1 order of magnitude higher for electrical data. The full potential of this photonic tensor processor is delivered for optical data being processed, where we find a 2–3 orders higher performance (operations per joule), as compared to an electrical tensor core unit, while featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.
Unsupervised deep learning for super-resolution reconstruction of turbulence

Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows have used supervised learning, which requires paired data for training. This limitation hinders more practical applications of super-resolution reconstruction. Therefore, we present an unsupervised learning model that adopts a cycle-consistent generative adversarial network (CycleGAN) that can be trained with unpaired turbulence data for super-resolution reconstruction. Our model is validated using three examples: (i) recovering the original flow field from filtered data using direct numerical simulation (DNS) of homogeneous isotropic turbulence; (ii) reconstructing full-resolution fields using partially measured data from the DNS of turbulent channel flows; and (iii) generating a DNS-resolution flow field from large-eddy simulation (LES) data for turbulent channel flows. In examples (i) and (ii), for which paired data are available for supervised learning, our unsupervised model demonstrates qualitatively and quantitatively similar performance as that of the best supervised learning model. More importantly, in example (iii), where supervised learning is impossible, our model successfully reconstructs the high-resolution flow field of statistical DNS quality from the LES data. Furthermore, we find that the present model has almost universal applicability to all values of Reynolds numbers within the tested range. This demonstrates that unsupervised learning of turbulence data is indeed possible, opening a new door for the wide application of super-resolution reconstruction of turbulent fields.
Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.