Reviewerly/exHarmony huggingface dataset:
Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis

We investigate the use of recent advances in deep learning and propose an end-to-end trainable multi-instance convolutional neural network within a mixture-of-experts formulation that combines information from two types of data-images and clinical attributes-for the diagnosis of lymphocytosis. The convolutional network learns to extract meaningful features from images of blood cells using an embedding level approach and aggregates them. Moreover, the mixture-of-experts model combines information from these images as well as clinical attributes to form an end-to-end trainable pipeline for diagnosis of lymphocytosis. Our results demonstrate that even the convolutional network by itself is able to discover meaningful associations between the images and the diagnosis, indicating the presence of important unexploited information in the images. The mixture-of-experts formulation is shown to be more robust while maintaining performance via. a repeatability study to assess the effect of variability in data acquisition on the predictions. The proposed methods are compared with different methods from literature based both on conventional handcrafted features and machine learning, and on recent deep learning models based on attention mechanisms. Our method reports a balanced accuracy of 85.41% and outperfroms the handcrafted feature-based and attention-based approaches as well that of biologists which scored 79.44%, 82.89% and 77.07% respectively. These results give insights on the potentials of the applicability of the proposed method in clinical practice. Our code and datasets can be found at https://github.com/msahasrabudhe/lymphoMIL.
Cyclic CNN: Image Classification With Multiscale and Multilocation Contexts

Improving the capability of models at limited computational cost is an urgent demand in many vision-based Internet-of-Things applications. Recent progress on deep convolutional neural network (CNN) has largely accelerated the development of image classification. Although the hierarchical structure of CNN naturally helps to extract image features in different scales and locations progressively, conventional convolution can only handle contexts of one scale and on a limited area of a single location in a specific layer, limiting the utilization of multiscale and multilocation information. In this work, we present a cyclic CNN framework, which enables sufficient utilization of multiscale and multilocation contexts in a single layer of convolution. The cyclic CNN is an extremely simple but effective improvement upon conventional convolution, which occupies no additional parameter and negligible computation (even less than 0.1%). Moreover, cyclic CNN can be easily plugged into many existing CNN pipelines, e.g., the ResNet family, obtaining extremely low-cost performance gain upon them. Extensive experiments on both small-scale (CIFAR10 and CIFAR100) and large-scale (ILSVRC2012) image classification benchmarks demonstrate that a consistent performance promotion is obtained with the help of cyclic CNN.
X-CAL: Explicit Calibration for Survival Analysis

Survival analysis models the distribution of time until an event of interest, such as discharge from the hospital or admission to the ICU. When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated. A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals. Classically, calibration is addressed in post-training analysis. We develop explicit calibration (X-CAL), which turns D-CALIBRATION into a differentiable objective that can be used in survival modeling alongside maximum likelihood estimation and other objectives. X-CAL allows practitioners to directly optimize calibration and strike a desired balance between predictive power and calibration. In our experiments, we fit a variety of shallow and deep models on simulated data, a survival dataset based on MNIST, on length-of-stay prediction using MIMIC-III data, and on brain cancer data from The Cancer Genome Atlas. We show that the models we study can be miscalibrated. We give experimental evidence on these datasets that X-CAL improves D-CALIBRATION without a large decrease in concordance or likelihood.
A simulational model for witnessing quantum effects of gravity using IBM quantum computer

Witnessing quantum effects in the gravitational field is found to be exceptionally difficult in practice due to the lack of empirical evidence. Hence, a debate is going on among physicists whether gravity has a quantum domain or not. To date, no convincing experimental proposal was put forward until two recent independent works by Bose et al. (Phys Rev Lett 119:240401, 2017) and Marletto and Vedral (Phys Rev Lett 119:240402, 2017). The authors have proposed schemes to test the quantumness of gravity in two small test masses by entangling two spatially separated objects using gravitational interactions. They have provided a method to witness the entanglement using spin correlation measurements, which could imply evidence for gravity being a quantum coherent mediator. Here we have proposed a simulational model by providing a new quantum circuit for verifying the above schemes. We have simulated the schemes for the first time in IBM’s 5-qubit quantum chip ‘ibmqx4’ by developing a quantum system which shows effects analogous to quantum gravity and calculates the degree of entanglement of the spin correlation. The entanglement witness over a range is obtained for different experimental parameters.
Quantum search with a continuous-time quantum walk in momentum space

The atom-optics kicked rotor can be used to prepare specific momentum distributions on a discrete basis set. We implement a continuous-time quantum walk and a quantum search protocol in this momentum basis. In particular we propose ways to identify a specific marked state from the final momentum distribution after the walker's evolution. Our protocol is guided by current experimental possibilities making it accessible to experimentally implemented quantum walks with Bose-Einstein condensates.
Accelerating quantum optics experiments with statistical learning

Quantum optics experiments, involving the measurement of low-probability photon events, are known to be extremely time-consuming. We present a new methodology for accelerating such experiments using physically-motivated ansatzes together with simple statistical learning techniques such as Bayesian maximum a posteriori estimation based on few-shot data. We show that it is possible to reconstruct time-dependent data using a small number of detected photons, allowing for fast estimates in under a minute and providing a one-to-two order of magnitude speed up in data acquisition time. We test our approach using real experimental data to retrieve the second order intensity correlation function, $G^{(2)}(\tau)$, as a function of time delay $\tau$ between detector counts, for thermal light as well as anti-bunched light emitted by a quantum dot driven by periodic laser pulses. The proposed methodology has a wide range of applicability and has the potential to impact the scientific discovery process across a multitude of domains.
Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization

Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., < 0.5%).
Implicative algebras: a new foundation for realizability and forcing

We introduce the notion of implicative algebra, a simple algebraic structure intended to factorize the model constructions underlying forcing and realizability (both in intuitionistic and classical logic). The salient feature of this structure is that its elements can be seen both as truth values and as (generalized) realizers, thus blurring the frontier between proofs and types. We show that each implicative algebra induces a (Set-based) tripos, using a construction that is reminiscent from the construction of a realizability tripos from a partial combinatory algebra. Relating this construction with the corresponding constructions in forcing and realizability, we conclude that the class of implicative triposes encompass all forcing triposes (both intuitionistic and classical), all classical realizability triposes (in the sense of Krivine) and all intuitionistic realizability triposes built from partial combinatory algebras.
Hardware and software video encoding comparison

Long time of data encoding with software encoding mechanisms might become a significant problem when transferring digital data from cameras of mobile robots. At the same time, processor manufacturers claim that an encoding process is significantly accelerated by using a hardware encoding. This work is dedicated to a hardware and a software video encoding comparison of two state-of-the-art codecs, which were selected due to their high popularity in computer vision and robotics fields - a hardware encoding with h264 vaapi and a software encoding with FFmpeg API libx265 codec. We encoded six video sequences of different resolutions and sizes with the two codecs and evaluated obtained video quality using the Structural Similarity Index, the Peak Signal-to-Noise ratio, and the Video Multimethod Assessment Fusion metrics. Software and hardware encoding processes were also compared by CPU and memory usage, and time that was taken by the encoding process. Our results demonstrated that the hardware encoding with h264 vaapi was 5 times more memory efficient and 6 times more time-efficient relatively to the software encoding with libx265, with an insignificant difference of an output video quality.
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets

Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets (RelSnip) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github.
Implementing quantum stochastic differential equations on a quantum computer

We study how to implement quantum stochastic differential equations (QSDEs) on a quantum computer. This is illustrated by an implementation of the QSDE that couples a laser driven two-level atom to the electromagnetic field in the vacuum state on the IBMqx4 Tenerife computer. We compare the resulting master equation and quantum filtering equations to existing theory. In this way we characterize the performance of the computer.
Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion

A case-based reasoning (CBR) system solves a new problem by retrieving 'cases' that are similar to the given problem.If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability.In this paper, we demonstrate that such a system is achievable for reasoning in knowledgebases (KBs).Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB.Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity.The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization.Our model is non-parametric, growing dynamically as new entities and relations are added to the KB.On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches.Furthermore, we demonstrate the effectiveness of our model in an "open-world" setting where new entities arrive in an online fashion, significantly outperforming state-ofthe-art approaches and nearly matching the best offline method.
Detection-device-independent verification of nonclassical light

The authors present an approach for the detection-device independent verification of nonclassical light. This method applies to any multiplexing system with on-off detectors, and works for both unequal splitting ratios and uncharacterized detectors. The derived nonclassicality conditions yield highly significant verifications of the quantum nature of light from clusters of single-photon emitters, which is detected through time-bin multiplexing.
Heralded amplification of nonlocality via entanglement swapping

Abstract To observe the loophole-free violation of the Clauser–Horne–Shimony–Holt (CHSH) inequality between distant two parties, i.e. the CHSH value <?CDATA $S\gt 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>S</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>2</mml:mn> </mml:math> , the main limitation of distance stems from the loss in the transmission channel. The entanglement swapping relay (ESR) is a simple way to amplify the signal and enables us to evade the impact of the transmission loss. Here, we experimentally test the heralded nonlocality amplifier protocol based on the ESR. We observe that the obtained probability distribution is in excellent agreement with those expected by the numerical simulation with experimental parameters which are precisely characterized in a separate measurement. Moreover, we experimentally estimate the nonlocality of the heralded state after the transmission of 10 dB loss just before final detection. The estimated CHSH value is <?CDATA $S=2.113\gt 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>S</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2.113</mml:mn> <mml:mo>&gt;</mml:mo> <mml:mn>2</mml:mn> </mml:math> , which indicates that our final state possesses nonlocality even with transmission loss and various experimental imperfections. Our result clarifies an important benchmark of the ESR protocol, and paves the way towards the long-distance realization of the loophole-free CHSH-violation as well as device-independent quantum key distribution.
Animating Through Warping: An Efficient Method for High-Quality Facial Expression Animation

Advances in deep neural networks have considerably improved the art of animating a still image without operating in 3D domain. Whereas, prior arts can only animate small images (typically no larger than 512x512) due to memory limitations, difficulty of training and lack of high-resolution (HD) training datasets, which significantly reduce their potential for applications in movie production and interactive systems. Motivated by the idea that HD images can be generated by adding high-frequency residuals to low-resolution results produced by a neural network, we propose a novel framework known as Animating Through Warping (ATW) to enable efficient animation of HD images.
Dissipative adiabatic measurements: Beating the quantum Cramér-Rao bound

It is challenged only recently that the precision attainable in any measurement of a physical parameter is fundamentally limited by the quantum Cramér-Rao Bound (QCRB). Here, targeting at measuring parameters in strongly dissipative systems, we propose an innovative measurement scheme called dissipative adiabatic measurement and theoretically show that it can beat the QCRB. Unlike projective measurements, our measurement scheme, though consuming more time, does not collapse the measured state and, more importantly, yields the expectation value of an observable as its measurement outcome, which is directly connected to the parameter of interest. Such a direct connection allows us to extract the value of the parameter from the measurement outcomes in a straightforward manner, with no fundamental limitation on precision, in principle. Our findings not only provide a marked insight into quantum metrology but also are highly useful in dissipative quantum information processing.4 MoreReceived 4 February 2020Accepted 11 June 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.023418Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systems & decoherenceQuantum information with solid state qubitsQuantum measurementsQuantum metrologyTechniquesAdiabatic approximationQuantum Information
Sparse Graphical Memory for Robust Planning

To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. Current deep reinforcement and imitation learning methods can learn directly from high-dimensional inputs but do not scale well to long-horizon tasks. In contrast, classical graphical methods like A* search are able to solve long-horizon tasks, but assume that the state space is abstracted away from raw sensory input. Recent works have attempted to combine the strengths of deep learning and classical planning; however, dominant methods in this domain are still quite brittle and scale poorly with the size of the environment. We introduce Sparse Graphical Memory (SGM), a new data structure that stores states and feasible transitions in a sparse memory. SGM aggregates states according to a novel two-way consistency objective, adapting classic state aggregation criteria to goal-conditioned RL: two states are redundant when they are interchangeable both as goals and as starting states. Theoretically, we prove that merging nodes according to two-way consistency leads to an increase in shortest path lengths that scales only linearly with the merging threshold. Experimentally, we show that SGM significantly outperforms current state of the art methods on long horizon, sparse-reward visual navigation tasks. Project video and code are available at this https URL
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies

Many recent datasets contain a variety of different data modalities, for instance, image, question, and answer data in visual question answering (VQA). When training deep net classifiers on those multi-modal datasets, the modalities get exploited at different scales, i.e., some modalities can more easily contribute to the classification results than others. This is suboptimal because the classifier is inherently biased towards a subset of the modalities. To alleviate this shortcoming, we propose a novel regularization term based on the functional entropy. Intuitively, this term encourages to balance the contribution of each modality to the classification result. However, regularization with the functional entropy is challenging. To address this, we develop a method based on the log-Sobolev inequality, which bounds the functional entropy with the functional-Fisher-information. Intuitively, this maximizes the amount of information that the modalities contribute. On the two challenging multi-modal datasets VQA-CPv2 and SocialIQ, we obtain state-of-the-art results while more uniformly exploiting the modalities. In addition, we demonstrate the efficacy of our method on Colored MNIST.
Component-Based Attention for Large-Scale Trademark Retrieval

The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accuracy of current approaches using either hand-crafted or pre-trained deep convolution neural network (DCNN) features is inadequate for large-scale deployments. We show in this paper that the ranking accuracy of TR systems can be significantly improved by incorporating hard and soft attention mechanisms, which direct attention to critical information such as figurative elements and reduce attention given to distracting and uninformative elements such as text and background. Our proposed approach achieves state-of-the-art results on a challenging large-scale trademark dataset.
Experimental protection of quantum coherence by using a phase-tunable image drive

The protection of quantum coherence is essential for building a practical quantum computer able to manipulate, store and read quantum information with a high degree of fidelity. Recently, it has been proposed to increase the operation time of a qubit by means of strong pulses to achieve a dynamical decoupling of the qubit from its environment. We propose and demonstrate a simple and highly efficient alternative route based on Floquet modes, which increases the Rabi decay time ([Formula: see text]) in a number of materials with different spin Hamiltonians and environments. We demonstrate the regime [Formula: see text] with [Formula: see text] the relaxation time, thus providing a route for spin qubits and spin ensembles to be used in quantum information processing and storage.
IMPROVING STUDENTS’ SPEAKING PERFORMANCE THROUGH VLOG

The shifting of English language learning from conventional way to technology-based learning is popular nowadays. It is necessary to identify the effect of using technology towards the students’ performance. One of technology that currently well-known is Vlog. This study aims at identifying the improvement of the students’ performance after learning English using Vlog. The participants of this study is 40 students majoring English Education Department of Universitas Teknokrat Indonesia. They enrolled in Transactional Conversation subject where practicing English through vlog was carried out. The design of this research is quantitative study in which the researcher employed pre-test and post-test. The result of pre-test and post-test was analyzed by using statistics descriptive and statistics inferential (Mann-Whitney U). The result of analysis is there is significant improvement towards students’ performance after learning English by using Vlog.
Correction to: “SPOCU”: scaled polynomial constant unit activation function

Addendum on SPOCU fitting and Erratum.