Reviewerly/exHarmony huggingface dataset:
Tightening Mutual Information-Based Bounds on Generalization Error

An information-theoretic upper bound on the generalization error of supervised learning algorithms is derived. The bound is constructed in terms of the mutual information between each individual training sample and the output of the learning algorithm. The bound is derived under more general conditions on the loss function than in existing studies; nevertheless, it provides a tighter characterization of the generalization error. Examples of learning algorithms are provided to demonstrate the the tightness of the bound, and to show that it has a broad range of applicability. Application to noisy and iterative algorithms, e.g., stochastic gradient Langevin dynamics (SGLD), is also studied, where the constructed bound provides a tighter characterization of the generalization error than existing results. Finally, it is demonstrated that, unlike existing bounds, which are difficult to compute and evaluate empirically, the proposed bound can be estimated easily in practice.
Bayesian Neural Networks: An Introduction and Survey

Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason about uncertainty in their predictions. This article introduces Bayesian Neural Networks (BNNs) and the seminal research regarding their implementation. Different approximate inference methods are compared, and used to highlight where future research can improve on current methods.
Pay Attention to the Activations: A Modular Attention Mechanism for Fine-Grained Image Recognition

Fine-grained image recognition is central to many multimedia tasks such as search, retrieval, and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. This issue is mainly due to changes in deformation, pose, and the presence of clutter. In the literature, attention has been one of the most successful strategies to handle the aforementioned problems. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent, and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as wide residual networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%.
Nearly Optimal Measurement Scheduling for Partial Tomography of Quantum States

A new analysis provides bounds on the size of ``clique covers''---mutually commuting subsets of quantum state observables---and hence the time required for certain near-term quantum computing applications.
Parallel quantum simulation of large systems on small NISQ computers

Abstract Tensor networks permit computational and entanglement resources to be concentrated in interesting regions of Hilbert space. Implemented on NISQ machines they allow simulation of quantum systems that are much larger than the computational machine itself. This is achieved by parallelising the quantum simulation. Here, we demonstrate this in the simplest case; an infinite, translationally invariant quantum spin chain. We provide Cirq and Qiskit code that translates infinite, translationally invariant matrix product state (iMPS) algorithms to finite-depth quantum circuit machines, allowing the representation, optimisation and evolution of arbitrary one-dimensional systems. The illustrative simulated output of these codes for achievable circuit sizes is given.
Quantum Low-Density Parity-Check Codes

Quantum error correction is an indispensable ingredient for scalable quantum computing. In this Perspective we discuss a particular class of quantum codes called “quantum low-density parity-check (LDPC) codes.” The codes we discuss are alternatives to the surface code, which is currently the leading candidate to implement quantum fault tolerance. We introduce the zoo of quantum LDPC codes and discuss their potential for making quantum computers robust with regard to noise. In particular, we explain recent advances in the theory of quantum LDPC codes related to certain product constructions and discuss open problems in the field.4 MoreReceived 7 March 2021Revised 27 May 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040101Published 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 AreasQuantum error correctionQuantum Information
Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation

Depth-Image-Based-Rendering (DIBR) techniques are significant for three-dimensional (3D) video applications, e.g., 3D television and free viewpoint video (FVV). Unfortunately, the DIBR-synthesized image suffers from various distortions, which induce an annoying viewing experience for the entire FVV. Proposing a quality evaluator for DIBR-synthesized images is fundamental for the design of perceptual friendly FVV systems. Since the associated reference image is usually not accessible, full-reference (FR) methods cannot be directly applied for quality evaluation of the synthesized image. In addition, most traditional no-reference (NR) methods fail to effectively measure the specifically DIBR-related distortions. In this paper, we propose a novel NR quality evaluation method accounting for two categories of DIBR-related distortions, i.e., geometric distortions and sharpness. First, the disoccluded regions, as one of the most obvious geometric distortions, are captured by analyzing local similarity. Then, another typical geometric distortion (i.e., stretching) is detected and measured by calculating the similarity between it and its equal-size adjacent region. Second, considering the property of scale invariance, the global sharpness is measured as the distance between the distorted image and its downsampled version. Finally, the perceptual quality is estimated by linearly pooling the scores of two geometric distortions and sharpness together. Experimental results verify the superiority of the proposed method over the prevailing FR and NR metrics. More specifically, it is superior to all competing methods except APT in terms of effectiveness, but greatly outmatches APT in terms of implementation time.
Explicit Interaction Model towards Text Classification

Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multilabel and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.
Learning Robust Global Representations by Penalizing Local Predictive Power

Despite their renowned in-domain predictive power, convolutional neural networks are known to rely more on high-frequency patterns that humans deem superficial than on low-frequency patterns that agree better with intuitions about what constitutes category membership. This paper proposes a method for training robust convolutional networks by penalizing the predictive power of the local representations learned by earlier layers. Intuitively, our networks are forced to discard predictive signals such as color and texture that can be gleaned from local receptive fields and to rely instead on the global structures of the image. Across a battery of synthetic and benchmark domain adaptation tasks, our method confers improved generalization out of the domain. Additionally, to evaluate cross-domain transfer, we introduce ImageNet-Sketch, a new dataset consisting of sketch-like images that matches the ImageNet classification validation set in scale and dimension.
Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Harris' hawk optimization (HHO) is a recent addition to population-based metaheuristic paradigm, inspired from hunting behavior of Harris' hawks. It has demonstrated promising search behavior while employed on various optimization problems, however the diversity of search agents can be further enhanced. This paper represents a novel modified variant with a long-term memory concept, hence called long-term memory HHO (LMHHO), which provides information about multiple promising regions in problem landscape, for improvised search results. With this information, LMHHO maintains exploration up to a certain level even until search termination, thus produces better results than the original method. Moreover, the study proves that appropriate tools for in-depth performance analysis can help improve search efficiency of existing metaheuristic algorithms by making simple yet effective modification in search strategy. The diversity measurement and exploration-exploitation investigations prove that the proposed LMHHO maintains trade-off balance between exploration and exploitation. The proposed approach is investigated on high-dimensional numerical optimization problems, including classic benchmark and CEC'17 functions; also, on optimal power flow problem in power generation system. The experimental study suggests that LMHHO not only outperforms the original HHO but also various other established and recently introduced metaheuristic algorithms. Although, the research can be extended by implementing more efficient memory archive and retrieval approaches for enhanced results.
Fast Single Image Dehazing Using Saturation Based Transmission Map Estimation

Single image dehazing has been a challenging problem because of its ill-posed nature. For this reason, numerous efforts have been made in the field of haze removal. This paper proposes a simple, fast, and powerful algorithm for haze removal. The medium transmission is derived as a function of the saturation of the scene radiance only, and the saturation of scene radiance is estimated using a simple stretching method. A different medium transmission can be estimated for each pixel because this method does not assume that transmission is constant in a small patch. Furthermore, this paper presents a color veil removing algorithm, which is useful for an image with fine or yellow dust, using the white balance technique. The proposed algorithm requires no training, prior, and refinement process. The simulation results show that the proposed dehazing scheme outperforms state-of-theart dehazing approaches in terms of both computational complexity and dehazing efficiency.
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack

The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the $l_p$-norms for $p \in \{1,2,\infty\}$ aiming at finding the minimal perturbation necessary to change the class of a given input. It has an intuitive geometric meaning, yields quickly high quality results, minimizes the size of the perturbation (so that it returns the robust accuracy at every threshold with a single run). It performs better or similar to state-of-the-art attacks which are partially specialized to one $l_p$-norm, and is robust to the phenomenon of gradient masking.
Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019

The PhysioNet/Computing in Cardiology Challenge focused on the early detection of sepsis from clinical data.A total of 40,336 patient records from two distinct hospital systems were shared with participants while 22,761 patient records from three distinct hospital systems were sequestered as hidden test sets.Each patient record contained up to 40 measurements of vital sign, laboratory, and demographics data for over 2.5 million hourly time windows and over 15 million data points.We used the Sepsis-3 clinical criteria to define the onset time of sepsis.We challenged participants to design automated, opensource algorithms for predicting sepsis 6 hours before clinical recognition of sepsis.We developed a novel, clinical utility-based evaluation metric to assess each algorithm that rewards early sepsis predictions and penalizes late or missed predictions and false alarms.A total of 104 teams from academia and industry submitted a total of 853 entries during the official phase of the Challenge.We accepted 90 abstracts based on Challenge entries for presentations at Computing in Cardiology.We also compared entries to ensure that approaches from different teams remained independent.This article presents our analysis and discusses the implications of the Challenge for early sepsis predictions and related sequential prediction tasks.
Handling imbalanced medical image data: A deep-learning-based one-class classification approach

In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods.
SQISign: Compact Post-quantum Signatures from Quaternions and Isogenies

We introduce a new signature scheme, SQISign, (for Short Quaternion and Isogeny Signature) from isogeny graphs of supersingular elliptic curves. The signature scheme is derived from a new one-round, high soundness, interactive identification protocol. Targeting the post-quantum NIST-1 level of security, our implementation results in signatures of 204 bytes, secret keys of 16 bytes and public keys of 64 bytes. In particular, the signature and public key sizes combined are an order of magnitude smaller than all other post-quantum signature schemes. On a modern workstation, our implementation in C takes 0.6 s for key generation, 2.5 s for signing, and 50 ms for verification. While the soundness of the identification protocol follows from classical assumptions, the zero-knowledge property relies on the second main contribution of this paper. We introduce a new algorithm to find an isogeny path connecting two given supersingular elliptic curves of known endomorphism rings. A previous algorithm to solve this problem, due to Kohel, Lauter, Petit and Tignol, systematically reveals paths from the input curves to a ‘special’ curve. This leakage would break the zero-knowledge property of the protocol. Our algorithm does not directly reveal such a path, and subject to a new computational assumption, we prove that the resulting identification protocol is zero-knowledge.
Satellite-based links for quantum key distribution: beam effects and weather dependence

Abstract The establishment of a world-wide quantum communication network relies on the synergistic integration of satellite-based links and fiber-based networks. The first are helpful for long-distance communication, as the photon losses introduced by the optical fibers are too detrimental for lengths greater than about 200 km. This work aims at giving, on the one hand, a comprehensive and fundamental model for the losses suffered by the quantum signals during the propagation along an atmospheric free-space link. On the other hand, a performance analysis of different quantum key distribution (QKD) implementations is performed, including finite-key effects, focusing on different interesting practical scenarios. The specific approach that we chose allows to precisely model the contribution due to different weather conditions, paving the way towards more accurate feasibility studies of satellite-based QKD missions.
Content Adaptive and Error Propagation Aware Deep Video Compression

Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a content adaptive and error propagation aware video compression system. Specifically, our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Based on the learned long-term temporal information, our approach effectively alleviates error propagation in reconstructed frames. More importantly, instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system. The proposed approach updates the parameters for encoder according to the rate-distortion criterion but keeps the decoder unchanged in the inference stage. Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets. Our method is simple yet effective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed.
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks

(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images’ center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.
Hierarchical Contextual Refinement Networks for Human Pose Estimation

Predicting human pose in the wild is a challenging problem due to high flexibility of joints and possible occlusion. Existing approaches generally tackle the difficulties either by holistic prediction or multi-stage processing, which suffer from poor performance for locating challenging joints or high computational cost. In this paper, we propose a new Hierarchical Contextual Refinement Network (HCRN) to robustly predict human poses in an efficient manner, where human body joints of different complexities are processed at different layers in a context hierarchy. Different from existing approaches, our proposed model predicts positions of joints from easy to difficult in a single stage through effectively exploiting informative contexts provided in the previous layer. Such approach offers two appealing advantages over state-of-the-arts: (1) more accurate than predicting all the joints together and (2) more efficient than multi-stage processing methods. We design a Contextual Refinement Unit (CRU) to implement the proposed model, which enables auto-diffusion of joint detection results to effectively transfer informative context from easy joints to difficult ones. In this way, difficult joints can be reliably detected even in presence of occlusion or severe distracting factors. Multiple CRUs are organized into a tree-structured hierarchy which is end-to-end trainable and does not require processing joints for multiple iterations. Comprehensive experiments evaluate the efficacy and efficiency of the proposed HCRN model to improve well-established baselines and achieve new state-of-the-art on multiple human pose estimation benchmarks.