Reviewerly/exHarmony huggingface dataset:
DsUnet: A New Network Structure for Detection and Segmentation of Ultrasound Breast Lesions

Breast cancer is one of the leading causes of death among the women worldwide. The clinical medical system urgently needs an accurate and automatic breast segmentation method in order to detect the breast ultrasound lesions. Recently, some studies show that deep learning methods based on fully convolutional network, have demonstrated a competitive performance in breast ultrasound segmentation. However, some features are missed in the Unet in case of down-sampling that results in a low segmentation accuracy. Furthermore, there is a semantic gap between the feature maps of decoder and encoder in Unet, so the simple fusion of high and low level features is not conducive to the semantic classification of pixels. In addition, the poor quality of breast ultrasound also affects the accuracy of image segmentation. To solve these problems, we propose a new end-toend network model called Dense skip Unet (DsUnet), which consists of the Unet backbone, short skip connection and deep supervision. The proposed method can effectively avoid the missing of feature information caused by down-sampling and implement the fusion of multilevel semantic information. We used a new loss function to optimize the DsUnet, which is composed of a binary cross-entropy and dice coefficient. We employed the True Positive Fraction (TPF), False Positives per image (FPs) and F -measure as performance metrics for evaluating various methods. In this paper, we adopted the UDIAT 212 dataset and the experimental results validate that our new approach achieved better performance than other existing methods in detecting and segmenting the ultrasound breast lesions. When we used the DsUnet model and new loss function (binary cross-entropy + dice coefficient), the best performance indexes can be achieved, i.e., 0.87 in TPF, 0.13 in FPs/image and 0.86 in F -measure.
Efficient scheme for creating a W-type optical entangled coherent state

W -type optical entangled coherent states have important applications in quantum communication. Previous works require performing measurement in the preparation of such W states. We here propose an efficient scheme for creating a W -type optical entangled coherent state without measurement. This scheme employs a setup composed of three microwave cavities and a superconducting flux coupler qutrit. Because no measurement is required, the W state can be generated deterministically. In addition, the system complexity is greatly reduced because of using only one qutrit to couple the three cavities. Numerical analysis shows that within current experimental technology, the W state can be prepared with high fidelity. This scheme is universal and can be extended to create the W -type optical entangled coherent state, by using three microwave or optical cavities coupled via a three-level natural or artificial atom.
Web Scraping in Python Using Beautiful Soup Library

In this chapter, we’ll go through the basic building blocks of web pages such as HTML and CSS and demonstrate scraping structured information from them using popular Python libraries such as Beautiful Soup and lxml. Later, we’ll expand our knowledge and tackle issues that will make our scraper into a full-featured web crawler capable of fetching information from multiple web pages.
The optical image compression and encryption method based on Fresnel diffraction and discrete wavelet transform

Generally, the length of ciphertexts and encryption keys are usually longer than that of the plaintexts. In this study, an optical image compression method based on Fresnel diffraction and discrete wavelet transform is proposed. The Fresnel diffraction is used for acquiring more low-frequency information. The proposed method is a plaintext pre-processing scheme and can be adopted in any cryptosystem where the encryption key cannot be shortened effectively. In this method, we can obtain a compressed image whose length is shorter them the plaintext. By encrypting the compressed image obtained, the length of ciphertext and encryption keys can be reduced. In addition, some compression parameters naturally existed in the proposed Fresnel diffraction optical setup are also needed for decompression. Moreover, the proposed method can also further compress the plaintext if a slight increment of information loss is allowed. Besides, the realization of this scheme is simple, hence no substantial cost is added for a given optical cryptosystem. Besides the statistical analysis, extensive numerical simulations have been carried out to validate the feasibility, robustness, sensibility of the proposed method.
Off-Policy Imitation Learning from Observations

Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit through the reuse of incomplete resources. Compared to conventional imitation learning (IL), LfO is more challenging because of the lack of expert action guidance. In both conventional IL and LfO, distribution matching is at the heart of their foundation. Traditional distribution matching approaches are sample-costly which depend on on-policy transitions for policy learning. Towards sample-efficiency, some off-policy solutions have been proposed, which, however, either lack comprehensive theoretical justifications or depend on the guidance of expert actions. In this work, we propose a sample-efficient LfO approach that enables off-policy optimization in a principled manner. To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering. Extensive empirical results on challenging locomotion tasks indicate that our approach is comparable with state-of-the-art in terms of both sample-efficiency and asymptotic performance.
Evolving Normalization-Activation Layers

Normalization layers and activation functions are fundamental components in deep networks and typically co-locate with each other. Here we propose to design them using an automated approach. Instead of designing them separately, we unify them into a single tensor-to-tensor computation graph, and evolve its structure starting from basic mathematical functions. Examples of such mathematical functions are addition, multiplication and statistical moments. The use of low-level mathematical functions, in contrast to the use of high-level modules in mainstream NAS, leads to a highly sparse and large search space which can be challenging for search methods. To address the challenge, we develop efficient rejection protocols to quickly filter out candidate layers that do not work well. We also use multi-objective evolution to optimize each layer's performance across many architectures to prevent overfitting. Our method leads to the discovery of EvoNorms, a set of new normalization-activation layers with novel, and sometimes surprising structures that go beyond existing design patterns. For example, some EvoNorms do not assume that normalization and activation functions must be applied sequentially, nor need to center the feature maps, nor require explicit activation functions. Our experiments show that EvoNorms work well on image classification models including ResNets, MobileNets and EfficientNets but also transfer well to Mask R-CNN with FPN/SpineNet for instance segmentation and to BigGAN for image synthesis, outperforming BatchNorm and GroupNorm based layers in many cases.
Deep Transformers with Latent Depth

The Transformer model has achieved state-of-the-art performance in many sequence modeling tasks. However, how to leverage model capacity with large or variable depths is still an open challenge. We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. The proposed method alleviates the vanishing gradient issue and enables stable training of deep Transformers (e.g. 100 layers). We evaluate on WMT English-German machine translation and masked language modeling tasks, where our method outperforms existing approaches for training deeper Transformers. Experiments on multilingual machine translation demonstrate that this approach can effectively leverage increased model capacity and bring universal improvement for both many-to-one and one-to-many translation with diverse language pairs.
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling

With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial.However, the lack of training data in the scientific domain limits the performance of fact verification models.This paper proposes an in-domain language modeling method for fact extraction and verification systems.We come up with SciKGAT to combine the advantages of open-domain literature search, state-of-the-art fact verification systems and in-domain medical knowledge through language modeling.Our experiments on SCIFACT, a dataset of expert-written scientific fact verification, show that SciKGAT achieves 30% absolute improvement on precision.Our analyses show that such improvement thrives from our in-domain language model by picking up more related evidence pieces and accurate fact verification.Our codes and data are released via Github 1 .
Context Analysis for Pre-trained Masked Language Models

Pre-trained language models that learn contextualized word representations from a large un-annotated corpus have become a standard component for many state-of-the-art NLP systems. Despite their successful applications in various downstream NLP tasks, the extent of contextual impact on the word representation has not been explored. In this paper, we present a detailed analysis of contextual impact in Transformer- and BiLSTM-based masked language models. We follow two different approaches to evaluate the impact of context: a masking based approach that is architecture agnostic, and a gradient based approach that requires back-propagation through networks. The findings suggest significant differences on the contextual impact between the two model architectures. Through further breakdown of analysis by syntactic categories, we find the contextual impact in Transformer-based MLM aligns well with linguistic intuition. We further explore the Transformer attention pruning based on our findings in contextual analysis.
Sparse and Continuous Attention Mechanisms

Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation). Distributions in each of these families have fixed support. In contrast, for finite domains, there has been recent work on sparse alternatives to softmax (e.g. sparsemax and alpha-entmax), which have varying support, being able to assign zero probability to irrelevant categories. This paper expands that work in two directions: first, we extend alpha-entmax to continuous domains, revealing a link with Tsallis statistics and deformed exponential families. Second, we introduce continuous-domain attention mechanisms, deriving efficient gradient backpropagation algorithms for alpha in {1,2}. Experiments on attention-based text classification, machine translation, and visual question answering illustrate the use of continuous attention in 1D and 2D, showing that it allows attending to time intervals and compact regions.
A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments

In this paper, we focus on the problem of optimizing deadline violations for executing tasks in various heterogeneous computational environments. To address the problem, we formulated it as a binary nonlinear programming (BNP) model, which maximize the number of completed tasks and optimize the resource utilization of servers. To solve the BNP model in a polynomial complexity, we propose a heuristic task scheduling method, which iteratively schedules a task to the first core such that the accumulated slack time of all scheduled tasks is minimum, until the core cannot finish any task, and executes tasks with the earliest deadline first in each core to execute as many task as possible in a core. Experiment results based on a real world trace show that our method has upto 100% less task violations, and has the best performance in resource efficiency optimization in overall, compared with eight classical and state-of-the-art heuristic methods.
A counterexample to Fuglede's conjecture in $(\mathbb{Z}/p\mathbb{Z})^4$ for all odd primes

We construct a spectral, non-tiling set of size $2p$ in $\mathbb Z/p\mathbb Z{4}$, $p$ odd prime. This example complements a previous counterexample in [C. Aten et al., \textit{Tiling sets and spectral sets over finite fields}, arXiv:1509.01090], which existed only for $p \equiv 3 \pmod{4}$. On the contrary we show that the conjecture does hold in $(\mathbb Z/2\mathbb Z)^4$.
Project-Based Learning in a Machine Learning Course with Differentiated Industrial Projects for Various Computer Science Master Programs

Graduating computer science students with skills sufficient for industrial needs is a priority in higher education teaching. Project-based approaches are promising to develop practical and social skills, needed to address real-world problems in teams. However, rapid technological transition makes an initial training of contemporary methods challenging. This affects the currently much-discussed machine learning domain as well. The study at hand describes a re-framed teaching approach for a machine learning course, offered to various computer science master programs. Project-based learning is introduced with differentiated projects provided by industrial partners that address the diverse study programs. Course attendees are supported with manuals, tools, and tutoring, passing through the Cross Industry Standard Process for Data Mining (CRISP-DM). Observations made during two iterations are reported, accompanied by a first empiric evaluation of student experiences.
Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability.

In Bansal et al. (2019), a novel visual navigation framework that combines learning-based and model-based approaches has been proposed. Specifically, a Convolutional Neural Network (CNN) predicts a waypoint that is used by the dynamics model for planning and tracking a trajectory to the waypoint. However, the CNN inevitably makes prediction errors which often lead to collisions in cluttered and tight spaces. In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction in an unseen environment. By modeling CNN prediction error as disturbances in robot's dynamics, our generated waypoints are robust to these disturbances, and consequently to the prediction errors. Moreover, using globally optimal HJ reachability analysis leads to predicting waypoints that are time-efficient and avoid greedy behavior. Through simulations and hardware experiments, we demonstrate the advantages of the proposed approach on navigating through cluttered, narrow indoor environments.
An Image Encryption Algorithm Based on Combination of Chaos and DNA Encoding

Aiming at the problem of low complexity and security of digital image encryption algorithm, a new cryptographic method based on chaotic system and DNA coding is proposed. DNA coding and fundamental operations are performed on the random matrix and plain-text images. The random matrix is produced by the improved logistic map. The methods of coding and operation are dynamically given by the random sequence produced by the hyper-chaotic system. Meanwhile, to get better diffusion effect, cipher-text feedback mechanism is proposed. In addition, the algorithm's key is determined by the plain-text, enabling one-time encryption. The experimental analysis shows that the method has enormous key space, intense key sensitivity, high security, and can resist multiple attacks as well.
Survey on user studies on the effectiveness of treemaps

Treemaps are a commonly used tool for the visual display and communication of tree-structured, multi-variate data. In order to confidently know when and how treemaps can best be applied, the research community uses usability studies and controlled experiments to "understand the potential and limitations of our tools" (Plaisant, 2004). To support the communities' understanding and usage of treemaps, this survey provides a comprehensive review and detailed overview of 69 user studies related to treemaps. However, due to pitfalls and shortcomings in design, conduct, and reporting of the user studies, there is little that can be reliably derived or accepted as a generalized statement. Fundamental open questions include configuration, compatible tasks, use cases, and perceptional characteristics of treemaps. The reliability of findings and statements is discussed and common pitfalls of treemap user studies are identified.
NODIS: Neural Ordinary Differential Scene Understanding

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as (Mixed-)Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. The connection between (Mixed-)Integer Linear Program and ODEs in combination with the end-to-end training amounts to learning how to solve assignment problems with image-specific objective functions. Intuitive, visual explanations are provided for the role of the single free variable of the ODE modules which are associated with time in many natural processes. The proposed model achieves results equal to or above state-of-the-art on all three benchmark tasks: scene graph generation (SGGEN), classification (SGCLS) and visual relationship detection (PREDCLS) on Visual Genome benchmark. The strong results on scene graph classification support the claim that assignment problems can indeed be solved by neural ODEs.
DiscFace: Minimum Discrepancy Learning for Deep Face Recognition

Softmax-based learning methods have shown state-of-the-art performances on large-scale face recognition tasks. In this paper, we discover an important issue of softmax-based approaches: the sample features around the corresponding class weight are similarly penalized in the training phase even though their directions are different from each other. This directional discrepancy, i.e., process discrepancy leads to performance degradation at the evaluation phase. To mitigate the issue, we propose a novel training scheme, called minimum discrepancy learning that enforces directions of intra-class sample features to be aligned toward an optimal direction by using a single learnable basis. Furthermore, the single learnable basis facilitates disentangling the so-called class-invariant vectors from sample features, such that they are effective to train under class-imbalanced datasets.
A Consensus-Based Global Optimization Method with Adaptive Momentum Estimation

Objective functions in large-scale machine-learning and artificial intelligence applications often live in high dimensions with strong non-convexity and massive local minima. First-order methods, such as the stochastic gradient method and Adam, are often used to find global minima. Recently, the consensus-based optimization (CBO) method has been introduced as one of the gradient-free optimization methods and its convergence is proven with dimension-dependent parameters, which may suffer from the curse of dimensionality. By replacing the isotropic geometric Brownian motion with the component-wise one, the latest improvement of the CBO method is guaranteed to converge to the global minimizer with dimension-independent parameters, although the initial data need to be well-chosen. In this paper, based on the CBO method and Adam, we propose a consensus-based global optimization method with adaptive momentum estimation (Adam-CBO). Advantages of the Adam-CBO method include: (1) capable of finding global minima of non-convex objective functions with high success rates and low costs; (2) can handle non-differentiable activation functions and thus approximate low-regularity functions with better accuracy. The former is verified by approximating the $1000$ dimensional Rastrigin function with $100\%$ success rate at a cost only growing linearly with respect to the dimensionality. The latter is confirmed by solving a machine learning task for partial differential equations with low-regularity solutions where the Adam-CBO method provides better results than the state-of-the-art method Adam. A linear stability analysis is provided to understand the asymptotic behavior of the Adam-CBO method.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements

Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep learning models or training models on large datasets), vanilla Bayesian optimization (BO) is typically computationally infeasible. To alleviate this issue, Hyperband (HB) utilizes the early stopping mechanism to speed up configuration evaluations by terminating those badly-performing configurations in advance. This leads to two kinds of quality measurements: (1) many low-fidelity measurements for configurations that get early-stopped, and (2) few high-fidelity measurements for configurations that are evaluated without being early stopped. The state-of-the-art HB-style method, BOHB, aims to combine the benefits of both BO and HB. Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only. However, the scarcity of high-fidelity measurements greatly hampers the efficiency of BO to guide the configuration search. In this paper, we present MFES-HB, an efficient Hyperband method that is capable of utilizing both the high-fidelity and low-fidelity measurements to accelerate the convergence of HPO tasks. Designing MFES-HB is not trivial as the low-fidelity measurements can be biased yet informative to guide the configuration search. Thus we propose to build a Multi-Fidelity Ensemble Surrogate (MFES) based on the generalized Product of Experts framework, which can integrate useful information from multi-fidelity measurements effectively. The empirical studies on the real-world AutoML tasks demonstrate that MFES-HB can achieve 3.3-8.9x speedups over the state-of-the-art approach --- BOHB.