Reviewerly/exHarmony huggingface dataset:
Case‐specific three‐dimensional hologram with a mixed reality technique for tumor resection in otolaryngology

Abstract Objective We report our first experience of using a case‐specific three‐dimensional (3D) hologram for tumor resection in otolaryngology to show the proof of concept. In addition, a questionnaire was administered to assess the usefulness of the mixed reality technique in otolaryngology. Methods A case‐specific 3D hologram was developed from enhanced images of dynamic computed tomography, with reference to contrast‐enhanced magnetic resonance images, and used for preoperative planning and intraoperative image reference. To evaluate the usefulness of the 3D hologram with head mount displays (HMDs), 18 attendings and resident otolaryngologists completed a questionnaire with the Likert scale. Results The case‐specific 3D hologram on HMDs was successfully used by means of easy gesture‐handling without any monitors preoperatively and intraoperatively. The experience of picturing the tumor localization and evaluating the surgical approach was statistically better using the 3D hologram on HMDs than using the computer images ( P &lt; .01). Similarly, the holograms were observed to be better for intraoperative application and surgical education than computer images ( P &lt; .01). Conclusion We demonstrated the use of a case‐specific 3D hologram for tumor resection in otolaryngology. The technology may be useful for preoperative planning and intraoperative image reference, especially for challenging cases, and surgical education. Level of Evidence NA.
UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality

Tracking body and hand motions in 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as egocentric tracking based on embodied perception (e.g., egocentric cameras, handheld sensors). In this article, we propose a new data-driven framework for egocentric body tracking, targeting challenges of omnipresent occlusions in optimization-based methods (e.g., inverse kinematics solvers). We first collect a large-scale motion capture dataset with both body and finger motions using optical markers and inertial sensors. This dataset focuses on social scenarios and captures ground truth poses under self-occlusions and body-hand interactions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts. Our experiments show that our method is able to generate high-fidelity embodied poses by applying the proposed method to the task of real-time egocentric body tracking, finger motion synthesis, and 3-point inverse kinematics.
Fundamental Energy Requirement of Reversible Quantum Operations

Landauer's principle asserts that any computation has an unavoidable energy cost that grows proportionally to its degree of logical irreversibility. But even a logically reversible operation, when run on a physical processor that operates on different energy levels, requires energy. Here we quantify this energy requirement, providing upper and lower bounds that coincide up to a constant factor. We derive these bounds from a general quantum resource-theoretic argument, which implies that the initial resource requirement for implementing a unitary operation within an error~$\epsilon$ grows like $1/\sqrt \epsilon$ times the amount of resource generated by the operation. Applying these results to quantum circuits, we find that their energy requirement can, by an appropriate design, be made independent of their time complexity.
Blockchain in your grocery basket: trust and traceability as a strategy

Purpose The purpose of this paper is to gain insights and explicate how blockchain technology enables trust and traceability building from a real business use case. Design/methodology/approach The authors conducted a qualitative case study of a leading global French grocery retail firm that has started to integrate blockchain into their supply chain and products. Data was collected through semi-structured interviews and secondary data sources. Findings This paper developed a conceptual framework in unboxing the mechanism by which blockchain enables trust and explicating how information flows in a blockchain-based system compared to a traditional one in a real business application scenario through three main elements, namely, system architecture, data recovery and communication. Originality/value Given the upside potential of emerging technologies such as blockchain coupled with the current increasing demand for business use cases, the paper is timely in integrating the business and technological aspects of trust in formulating a firm-level blockchain strategy.
Improved CNN-Based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding

The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions. Unfortunately, these approaches typically bring significant increases in computational complexity and are difficult to interpret into explainable models, affecting their potential for implementation within practical video coding applications. This paper introduces a novel explainable neural network-based inter-prediction scheme, to improve the interpolation of reference samples needed for fractional precision motion compensation. The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived, as the network is easily interpretable due to its linear structure. A novel training framework enables each network branch to resemble a specific fractional shift. This practical solution makes it very efficient to use alongside conventional video coding schemes. When implemented in the context of the state-of-the-art Versatile Video Coding (VVC) test model, 0.77%, 1.27% and 2.25% BD-rate savings can be achieved on average for lower resolution sequences under the random access, low-delay B and low-delay P configurations, respectively, while the complexity of the learned interpolation schemes is significantly reduced compared to the interpolation with full CNNs.
Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach

Lung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography (CT) provides a wealth of information about cancer staging, but the free-text nature of the CT reports obstructs their computerization.We aimed to automatically extract the staging-related information from CT reports to support accurate clinical staging of lung cancer.In this study, we developed an information extraction (IE) system to extract the staging-related information from CT reports. The system consisted of the following three parts: named entity recognition (NER), relation classification (RC), and postprocessing (PP). We first summarized 22 questions about lung cancer staging based on the TNM staging guideline. Next, three state-of-the-art NER algorithms were implemented to recognize the entities of interest. Next, we designed a novel RC method using the relation sign constraint (RSC) to classify the relations between entities. Finally, a rule-based PP module was established to obtain the formatted answers using the results of NER and RC.We evaluated the developed IE system on a clinical data set containing 392 chest CT reports collected from the Department of Thoracic Surgery II in the Peking University Cancer Hospital. The experimental results showed that the bidirectional encoder representation from transformers (BERT) model outperformed the iterated dilated convolutional neural networks-conditional random field (ID-CNN-CRF) and bidirectional long short-term memory networks-conditional random field (Bi-LSTM-CRF) for NER tasks with macro-F1 scores of 80.97% and 90.06% under the exact and inexact matching schemes, respectively. For the RC task, the proposed RSC showed better performance than the baseline methods. Further, the BERT-RSC model achieved the best performance with a macro-F1 score of 97.13% and a micro-F1 score of 98.37%. Moreover, the rule-based PP module could correctly obtain the formatted results using the extractions of NER and RC, achieving a macro-F1 score of 94.57% and a micro-F1 score of 96.74% for all the 22 questions.We conclude that the developed IE system can effectively and accurately extract information about lung cancer staging from CT reports. Experimental results show that the extracted results have significant potential for further use in stage verification and prediction to facilitate accurate clinical staging.
Noisy Self-Knowledge Distillation for Text Summarization

In this paper we apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximumlikelihood training on single reference and noisy datasets.Instead of relying on one-hot annotation labels, our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training.Furthermore, to better model uncertainty during training, we introduce multiple noise signals for both teacher and student models.We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and nonpretrained summarizers achieving state-of-theart results. 1
JML and OpenJML for Java 16

As the Java language evolves, the Java Modeling Language (JML) and the OpenJML deductive verification tool must evolve with it. Changes in Java since Java 8 bring language and organizational changes which affect the semantics of JML and the implementation of OpenJML. They also raise questions about language definition, joint efforts, and community engagement, some enumerated in this paper, for the Java formal reasoning community to address.
Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach

Abstract Up to now, modern machine learning (ML) has been based on approximating big data sets with high-dimensional functions, taking advantage of huge computational resources. We show that biologically inspired neuron models such as the leaky-integrate-and-fire (LIF) neuron provide novel and efficient ways of information processing. They can be integrated in machine learning models and are a potential target to improve ML performance. Thus, we have derived simple update rules for LIF units to numerically integrate the differential equations. We apply a surrogate gradient approach to train the LIF units via backpropagation. We demonstrate that tuning the leak term of the LIF neurons can be used to run the neurons in different operating modes, such as simple signal integrators or coincidence detectors. Furthermore, we show that the constant surrogate gradient, in combination with tuning the leak term of the LIF units, can be used to achieve the learning dynamics of more complex surrogate gradients. To prove the validity of our method, we applied it to established image data sets (the Oxford 102 flower data set, MNIST), implemented various network architectures, used several input data encodings and demonstrated that the method is suitable to achieve state-of-the-art classification performance. We provide our method as well as further surrogate gradient methods to train spiking neural networks via backpropagation as an open-source KERAS package to make it available to the neuroscience and machine learning community. To increase the interpretability of the underlying effects and thus make a small step toward opening the black box of machine learning, we provide interactive illustrations, with the possibility of systematically monitoring the effects of parameter changes on the learning characteristics.
Unified framework for quantum classification

Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term quantum hardware in supervised learning. Motivated by these developments, we present an embedding-based framework for supervised learning with trainable quantum circuits. We introduce both explicit and implicit approaches. The aim of these approaches is to map data from different classes to separated locations in the Hilbert space via a parametrized quantum circuit. We will show that the implicit approach is a generalization of a recently introduced strategy, so-called quantum metric learning. Furthermore, we discuss an intrinsic connection between the explicit approach and those previously proposed quantum classification models. The implicit and explicit approaches, together, provide a unified framework for quantum classification. The utility of our framework is demonstrated by our noise-free and noisy numerical simulations. Moreover, we have conducted classification testing with both implicit and explicit approaches using several IBM Q devices.
One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI

This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human–Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed good potential for increasing the extensibility of sign vocabulary in sign language-based human–robot interactions. The expansibility and promising results of the one-shot Learning from Demonstration technique in this study are the main achievements of conducting such machine learning algorithms in social Human–Robot Interaction.
Analysis of diversification benefits for cryptocurrency portfolios before and during the COVID-19 pandemic

Purpose This study aims to investigate the diversification benefits attached to the crypto portfolios when combined with stocks, Forex instruments and commodity assets. Design/methodology/approach Markowitz diversification techniques have been used to analyze the risk-return tradeoffs of the individual portfolios. Daily prices on cryptocurrencies and the selected asset classes, cover the period before and during the pandemic COVID-19. The portfolio risk of the portfolios was calculated by identical techniques and analyzed with equal criteria. Findings The results with 270 trails indicate that stocks on average reduce the portfolio risk of crypto portfolios by 36% followed by fiat currency with 30.9% and commodities by 20.8%. Average daily returns stand in line with the standard portfolio theories where riskier portfolios offer higher returns and the other way around. Originality/value The authors contribute to the current literature by investigating the portfolio risk attached to the crypto portfolios when stocks, commodities and Forex instruments were added separately. To this end, results inform not only retail investors but also portfolio managers on the asset classes that generate better optimization for crypto portfolios.
End-to-End Supermask Pruning: Learning to Prune Image Captioning Models

With the advancement of deep models, research work on image captioning has led to a remarkable gain in raw performance over the last decade, along with increasing model complexity and computational cost. However, surprisingly works on compression of deep networks for image captioning task has received little to no attention. For the first time in image captioning research, we provide an extensive comparison of various unstructured weight pruning methods on three different popular image captioning architectures, namely Soft-Attention, Up-Down and Object Relation Transformer. Following this, we propose a novel end-to-end weight pruning method that performs gradual sparsification based on weight sensitivity to the training loss. The pruning schemes are then extended with encoder pruning, where we show that conducting both decoder pruning and training simultaneously prior to the encoder pruning provides good overall performance. Empirically, we show that an 80% to 95% sparse network (up to 75% reduction in model size) can either match or outperform its dense counterpart. The code and pre-trained models for Up-Down and Object Relation Transformer that are capable of achieving CIDEr scores >120 on the MS-COCO dataset but with only 8.7 MB and 14.5 MB in model size (size reduction of 96% and 94% respectively against dense versions) are publicly available at https://github.com/jiahuei/sparse-image-captioning.
Optical Aberrations Correction in Postprocessing Using Imaging Simulation

As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations.In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration. Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex (DSLR) camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras. Project Page: \url{https://github.com/TanGeeGo/ImagingSimulation}.
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module

The increasing integration of technology in our daily lives demands the development of more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations on the operating environment. Further, the deployment of such systems is often not performed in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper presents a novel hand gesture recognition system based on frequency-modulated continuous wave (FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems. First of all, the paper introduces a method to simplify radar preprocessing while preserving the main information of the performed gestures. Then, a deep neural classifier with the novel Depthwise Expansion Module based on the depthwise separable convolutions is presented. The introduced classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts eight different hand gestures performed by five users, offering a classification accuracy of 98.13% while operating in a low-power and resource-constrained environment.
An enhanced version of Harris Hawks Optimization by dimension learning-based hunting for Breast Cancer Detection

Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.
The Tale of Two Lexicons: Decomposing Complexity across a Distributed Lexicon

Abstract The notion of complexity is evasive and often left to intuition, yet it is often invoked when studying heritage language grammars. In this article, we propose a first pass at decomposing the notion of complexity into smaller components in a formal grammatical model. In particular, we argue that a distributed model of the lexicon (i.e., one that assumes that principles that generate both words and phrases are one and the same) allows us to identify three components: syntactic features, the hierarchical organization of features, and the mapping between syntactic features and their exponents. Based on grammatical gender in different language pairs, in particular the heritage language American Norwegian (AmNo), we illustrate how this distributed model can account for developments in heritage language grammars whereby the grammatical gender system is considered to have become less complex. More generally, the article demonstrates that a distributed architecture is better suited empirically and theoretically as a heuristic to understand complexity effects in heritage grammars and beyond.