Reviewerly/exHarmony huggingface dataset:
Rethinking the unpretentious U-net for medical ultrasound image segmentation

Breast tumor segmentation is one of the key steps that helps us characterize and localize tumor regions. However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors. Recently, many U-net variants have been proposed and widely used for breast tumors segmentation. However, these architectures suffer from two limitations: (1) Ignoring the characterize ability of the benchmark networks, and (2) Introducing extra complex operations increases the difficulty of understanding and reproducing the network. To alleviate these challenges, this paper proposes a simple yet powerful nested U-net (NU-net) for accurate segmentation of breast tumors. The key idea is to utilize U-Nets with different depths and shared weights to achieve robust characterization of breast tumors. NU-net mainly has the following advantages: (1) Improving network adaptability and robustness to breast tumors with different scales, (2) This method is easy to reproduce and execute, and (3) The extra operations increase network parameters without significantly increasing computational cost. Extensive experimental results with twelve state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that NU-net has more competitive segmentation performance on breast tumors. Furthermore, the robustness of NU-net is further illustrated on the segmentation of renal ultrasound images. The source code is publicly available on https://github.com/CGPzy/NU-net.
SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations

High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code will be available at: https://github.com/panxipeng/nuclear_segandcls.
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://github.com/ymy-k/DPText-DETR.
Postdigital

Since 2000, the concept of the postdigital has rapidly spread throughout diverse fields of human interest. Depending on the context, the postdigital has developed in various directions and carries different meanings. While there are good reasons for why the postdigital should not be firmly defined (see Jandrić and Ford https://doi.org/10.1007/s42438-020-00207-3 2020), there is a need to describe its many facets and their implications. This entry describes the main uses of the postdigital in the arts, humanities, and social sciences.
A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

Abstract The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
An Analysis of Concept Mapping Style in EFL Reading Comprehension from the Viewpoint of Paragraph Structure of Text

In language learning contexts, reading comprehension is an important learning activity. In English as a foreign language (EFL) reading comprehension learning, a frequent reading style is the sentence-by-sentence style, in which learners understand the text only as separate sentences, not as a whole structure. This study focuses on the structural understanding of the text and map-making the process from the viewpoint of the paragraph. The assumption in this study is that map-making in KB mapping does not follow sentence order but focuses on sets of meanings formed by paragraphs. This study investigates the relation between the map-making processes in KB and SB mapping and the paragraph structure of the text.
Applications of Latent Semantic Analysis

Latent Semantic Analysis (LSA) treats language learning and representation as a problem in mathematical induction. It casts the passages of a large and representative text corpus as a system of simultaneous linear equations in which passage meaning equals the sum of word meanings. LSA simulates human language understanding with surprising fidelity. Successes to date disprove the poverty of the stimulus argument for lexical meaning and recast the problem of syntax learning, but leave much room for improvement. Semantic atoms are not only single words; idioms need lexicalization. Syntax surely matters; LSA ignores word order. LSA’s knowledge resembles intuition; people also use language for logic. Relations to other input matter. LSA represents perceptual phenomena vicariously, e.g. color relations. Demonstrations that people think in other modes, or that LSA does not exhaust linguistic meaning do not question LS A’s validity, but call for more modeling, testing, and integration.
Information Ownership in the Cloud

Abstract This chapter discusses information ownership in the cloud. The law has struggled with ownership of digital information precisely because it is no longer recorded in permanent form on a physical object which can be owned. The law attempts to solve this problem by creating legal rights in some types of information, in the form of intellectual property rights. However, intellectual property rights are highly limited in scope in order to prevent the monopolisation of information. Thus, disputes over information ownership, and negotiations involving transfer of digital information, can be surprisingly difficult to resolve. The chapter then looks at copyright, database right, and the protection of confidential information. It shows that owning property rights in information, most likely copyright and database right, has little importance in terms of the cloud relationship. Cloud computing does, however, create some risks to confidential information because each player in the cloud is handing over some element of control to other players.
Parametric Trajectory Libraries for Online Motion Planning with Application to Soft Robots

In this paper we propose a method for online motion planning of constrained nonlinear systems. The method consists of three steps: the offline generation of a library of parametric trajectories via direct trajectory optimization, the online search in the library for the best candidate solution to the optimal control problem we aim to solve, and the online refinement of this trajectory. The last phase of this process takes advantage of a sensitivity-like analysis and guarantees to comply with the first-order approximation of the constraints even in case of active set changes. Efficiency of the trajectory generation process is discussed and a valid strategy to minimize online computations is proposed; together with this, an effective procedure for searching the candidate trajectory is also presented. As a case study, we examine optimal control of a planar soft manipulator performing a pick-and-place task: through simulations and experiments, we show how crucial online computation times are to achieve considerable energy savings in the presence of variability of the task to perform.
On cyclic codes over Galois rings

Let R be a Galois ring of characteristic pa, where p is a prime and a is a natural number. In this paper, the generators of cyclic codes of arbitrary length n over R in terms of minimal degree polynomials of certain subsets of codes have been obtained. Moreover, the explicit set of generators so obtained turns out to be a minimal strong Gröbner basis. Some results on torsion codes of a cyclic code over R have also been obtained. Using these results, the size of a cyclic code over R has been expressed in terms of the degrees of its generators.
Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance

In mathematical terms, an artificial neuron computes the inner product of a d -dimensional input vector x with its weight vector w , compares it with a bias value w0 and fires based on the result of this comparison. Therefore, its decision boundary is given by the equation wTx+w0=0 . In this paper, we propose replacing the linear hyperplane decision boundary of a neuron with a curved, paraboloid decision boundary. Thus, the decision boundary of the proposed paraboloid neuron is given by the equation (hTx+h0)2-||x-p||22=0 , where h and h0 denote the parameters of the directrix and p denotes the coordinates of the focus. Such paraboloid neural networks are proven to have superior recognition accuracy in a number of applications.
On Social Involvement in Mingling Scenarios: Detecting Associates of F-Formations in Still Images

In this paper, we carry out an extensive study of social involvement in free standing conversing groups (the so-called F-formations) from static images. By introducing a novel feature representation, we show that the standard features which have been used to represent full membership in an F-formation cannot be applied to the detection of so-called associates of F-formations due to their sparser nature. We also enrich state-of-the-art F-formation modelling by learning a frustum of attention that accounts for the spatial context. That is, F-formation configurations vary with respect to the arrangement of furniture and the non-uniform crowdedness in the space during mingling scenarios. Moroever, the majority of prior works have considered the labelling of conversing groups as an objective task, requiring only a single annotator. However, we show that by embracing the subjectivity of social involvement, we not only generate a richer model of the social interactions in a scene but can use the detected associates to improve initial estimates of the full members of an F-formation. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on two publicly available datasets; The Idiap Poster Data and SALSA data set. Moreover, we show that parameters learned from the Idiap Poster Data can be transferred to the SALSA data, showing the power of our proposed representation in generalising over new unseen data from a different environment.
An Attribute-Specific Ranking Method Based on Language Models for Keyword Search over Graphs

Many real-world networks such as Facebook, LinkedIn, and Wikipedia exhibit rich connectivity patterns along with worthwhile content nodes often labeled with meaningful attributes. Keyword search is an effective method to retrieve information from such useful networks. The aim of keyword search is to find a set of answers (subgraphs) covering all or part of the queried keywords. A challenge in keyword search systems is to rank answers according to their relevance to the query. This relevance lies in the textual content and structural compactness of the answers. In this paper, an attribute-specific ranking method is proposed based on language models to rank candidate answers according to their semantic information up to the attribute level. This method scores answers using a model enriched with attribute-specific preferences and integrating both the structure and content of answers. The proposed model is directly estimated on the sub-graphs (answers) and is defined such that it can preserve the local importance of keywords in nodes. Extensive experiments conducted on a standard evaluation framework with three real-world datasets illustrate the superior effectiveness of the proposed ranking method to that of the state-of-the-art methods.
Fast four-dimensional tensile test monitored via X-ray computed tomography: Elastoplastic identification from radiographs

A projection-based digital volume correlation method (presented in a companion paper) is extended to an integrated approach for the calibration of an elastoplastic law based on a single radiograph per loading step. Instead of following a two-step sequential procedure (i.e. first, measurement of the displacement field; second, identification), the integrated method aims at identifying few model parameters directly from the gray-level projections. The analysis of an in situ tensile test composed of 127 loading steps performed in 6 min is presented. An isotropic elastoplastic constitutive law with free-form hardening behavior (i.e. controlled by only eight parameters) is identified and shows a ductile behavior (up to 6.3% strain before failure). A large improvement on the residual quality is shown and validates the proposed model and procedure. The obtained displacement fields are similar to those measured with no mechanical integration. A different parameterization of the constitutive law provides a very close result, thereby assessing the robustness of the procedure.
Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines

Visual tracking has been an active research area in computer vision for decades. However, the performance of existing techniques is still challenged by various factors, such as occlusion and change in appearance of the target. In this paper, we propose a novel framework based on correlation filtering and probabilistic finite state machines (FSMs) to handle occlusion. In our tracking framework, the target is partitioned into several parts whose occlusion states are automatically detected. A set of states for the target is defined in terms of the combination of the parts' occlusion states. The probabilistic FSMs are then used to model the target's state transitions so as to reduce the effect of noise in the output response maps of correlation filters. Our target model's update strategy is adaptable online depending on the estimated state of the target. Extensive experiments have been performed on several public benchmarks and the proposed algorithm achieves competitive results against state-of-the-art techniques.
Managing Diversity in Distributed Software Development Education—A Longitudinal Case Study

Teaching Distributed Software Development with real distributed settings is a challenging and rewarding task. Distributed courses are idiosyncratically more challenging than standard local courses. We have experienced this during our distributed course, which has been run for 14 consecutive years. In this article, we present and analyze the emerging diversities specific to distributed project-based courses. We base our arguments on our experience, and we exploit a three-layered distributed course model, which we use to analyze several course elements throughout the 14-years lifetime of our distributed project-based course. In particular, we focus on the changes that the course underwent throughout the years, combining findings obtained from the analyzed data with our own teaching perceptions. Additionally, we propose insights on how to manage the various diversity aspects.
Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network

Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can take full advantage of global intermediate features. Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information memory mechanism. Experiments on the benchmark tests show that the proposed method DFFNet achieves favorable performance against the state-of-art methods.
Double-grating with multiple diffractions enabled small angle measurement

We demonstrate a small angle measurement setup enabled by the double-grating configuration, where the multiple diffractions are used to magnify the deflection angle of the optical beam. Such small angle measurement setup has characteristics of high sensitivity and compact size. The use of two unparallel blazed gratings with a special included angle can realize multiple diffractions for the incident light, leading to the realization of deflection angle amplification. Experimental results verify that small angle can be accurately characterized and the angular resolution of measurement can be improved more than 40 times by inserting the double-grating configuration into the conventional auto-collimation angle measurement setup. Therefore, the micro-radian angle can be accurately measured with our proposed compact characterization setup.
Measuring the importance of context when modeling language comprehension

It is widely accepted that language requires context in order to function as communication between speakers and listeners. As listeners, we make use of background knowledge - about the speaker, about entities and concepts, about previous utterances - in order to infer the speaker's intended meaning. But even if there is consensus that these sources of information are a necessary component of linguistic communication, it is another matter entirely to provide a thorough, quantitative accounting for context's interaction with language. When does context matter? What kinds of context matter in which kinds of domains? The empirical investigation of these questions is inhibited by a number of factors: the challenge of quantifying language, the boundless combinations of domains and types of context to be measured, and the challenge of selecting and applying a given construct to natural language data. In response to these factors, we introduce and demonstrate a methodological framework for testing the importance of contextual information in inferring speaker intentions from text. We apply Long Short-term Memory (LSTM) networks, a standard for representing language in its natural, sequential state, and conduct a set of experiments for predicting the persuasive intentions of speakers in political debates using different combinations of text and background information about the speaker. We show, in our modeling and discussion, that the proposed framework is suitable for empirically evaluating the manner and magnitude of context's relevance for any number of domains and constructs.