Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Technologies for Sign Language

AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.
Local, expressive, quantum-number-preserving VQE ansätze for fermionic systems

Abstract We propose VQE circuit fabrics with advantageous properties for the simulation of strongly correlated ground and excited states of molecules and materials under the Jordan–Wigner mapping that can be implemented linearly locally and preserve all relevant quantum numbers: the number of spin up ( α ) and down ( β ) electrons and the total spin squared. We demonstrate that our entangler circuits are expressive already at low depth and parameter count, appear to become universal, and may be trainable without having to cross regions of vanishing gradient, when the number of parameters becomes sufficiently large and when these parameters are suitably initialized. One particularly appealing construction achieves this with just orbital rotations and pair exchange gates. We derive optimal four-term parameter shift rules for and provide explicit decompositions of our quantum number preserving gates and perform numerical demonstrations on highly correlated molecules on up to 20 qubits.
A Survival Guide for the Rapid Transition to a Fully Digital Workflow: The “Caltagirone Example”

Digital pathology for the routine assessment of cases for primary diagnosis has been implemented by few laboratories worldwide. The Gravina Hospital in Caltagirone (Sicily, Italy), which collects cases from 7 different hospitals distributed in the Catania area, converted the entire workflow to digital starting from 2019. Before the transition, the Caltagirone pathology laboratory was characterized by a non-tracked workflow, based on paper requests, hand-written blocks and slides, as well as manual assembling and delivering of the cases and glass slides to the pathologists. Moreover, the arrangement of the spaces and offices in the department was illogical and under-productive for the linearity of the workflow. For these reasons, an adequate 2D barcode system for tracking purposes, the redistribution of the spaces inside the laboratory and the implementation of the whole-slide imaging (WSI) technology based on a laboratory information system (LIS)-centric approach were adopted as a needed prerequisite to switch to a digital workflow. The adoption of a dedicated connection for transfer of clinical and administrative data between different software and interfaces using an internationally recognised standard (Health Level 7, HL7) in the pathology department further facilitated the transition, helping in the integration of the LIS with WSI scanners. As per previous reports, the components and devices chosen for the pathologists’ workstations did not significantly impact on the WSI-based reporting phase in primary histological diagnosis. An analysis of all the steps of this transition has been made retrospectively to provide a useful “handy” guide to lead the digital transition of “analog”, non-tracked pathology laboratories following the experience of the Caltagirone pathology department. Following the step-by-step instructions, the implementation of a paperless routine with more standardized and safe processes, the possibility to manage the priority of the cases and to implement artificial intelligence (AI) tools are no more an utopia for every “analog” pathology department.
BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis

Background: Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists and physicians are constantly striving to find new methods of diagnosis, treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of organizing and retrieving the massive and growing amount of biomedical knowledge. Objective: We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes using the Bidirectional Encoder Representations from Transformers (BERT) model and Conditional Random Field (CRF) layer. Methods: The approach is based on knowledge graphs, which can effectively process abstract biomedical concepts such as relationships and interactions between medical entities. Besides offering an intuitive way to visualize these concepts, KGs can solve more complex knowledge retrieval problems by simplifying them into simpler representations or by transforming the problems into representations from different perspectives. We created a biomedical Knowledge Graph using using Natural Language Processing models for named entity recognition and relation extraction. The generated biomedical knowledge graphs (KGs) are then used for question answering. Results: The proposed framework can successfully extract relevant structured information with high accuracy (90.7% for Named-entity recognition (NER), 88% for relation extraction (RE)), according to experimental findings based on real-world 505 patient biomedical unstructured clinical notes. Conclusions:In this paper, we propose a novel end-to-end system for the construction of a biomedical knowledge graph from clinical textual using a variation of BERT models.
Smooth Minimization of Nonsmooth Functions with Parallel Coordinate Descent Methods

We study the performance of a family of randomized parallel coordinate descent methods for minimizing a nonsmooth nonseparable convex function. The problem class includes as a special case L1-regularized L1 regression and the minimization of the exponential loss (“AdaBoost problem”). We assume that the input data defining the loss function is contained in a sparse $$m\times n$$ matrix A with at most $$\omega $$ nonzeros in each row and that the objective function has a “max structure”, allowing us to smooth it. Our main contribution consists in identifying parameters with a closed-form expression that guarantees a parallelization speedup that depends on basic quantities of the problem (like its size and the number of processors). The theory relies on a fine study of the Lipschitz constant of the smoothed objective restricted to low dimensional subspaces and shows an increased acceleration for sparser problems.
VOD-ADAC: Anonymous Distributed Fine-Grained Access Control Protocol with Verifiable Outsourced Decryption in Public Cloud

Remote data access control is of crucial importance in public cloud. Based on its own inclinations, the data owner predefines the access policy. When the user satisfies the data owner's access policy, it has the right to access the data owner's remote data. In order to improve flexibility and efficiency of remote data access control, attribute-based encryption (for short, ABE) is used to realize the remote data fine-grained access control. For the low-capacity terminals, verifiable outsourced decryption is a very attractive technique. In the real application scenarios, the user's attributes are usually managed by many authorities. When some authorized users access some sensitive remote data, they hope to preserve their identity privacy. From the two points, we propose an anonymous distributed fine-grained access control protocol with verifiable outsourced decryption in public cloud (for short, VOD-ADAC). VOD-ADAC is a novel concept which is proposed for the first time in the paper. By adopting the pseudonym technique, the user's high anonymity can be achieved by frequently changing the independent pseudonyms at some highly social spots. This paper formalizes the system model and security model of VOD-ADAC protocol. Then, by using hybrid encryption technique of distributed ABE and symmetric encryption, a concrete VOD-ADAC protocol is designed from the bilinear pairings. Through security analysis and performance analysis, our proposed VOD-ADAC protocol is provably secure and efficient.
Design Exposition with Literate Visualization

We propose a new approach to the visualization design and communication process, literate visualization, based upon and extending, Donald Knuth's idea of literate programming. It integrates the process of writing data visualization code with description of the design choices that led to the implementation (design exposition). We develop a model of design exposition characterised by four visualization designer architypes: the evaluator, the autonomist, the didacticist and the rationalist. The model is used to justify the key characteristics of literate visualization: ‘notebook’ documents that integrate live coding input, rendered output and textual narrative; low cost of authoring textual narrative; guidelines to encourage structured visualization design and its documentation. We propose narrative schemas for structuring and validating a wide range of visualization design approaches and models, and branching narratives for capturing alternative designs and design views. We describe a new open source literate visualization environment, litvis, based on a declarative interface to Vega and Vega-Lite through the functional programming language Elm combined with markdown for formatted narrative. We informally assess the approach, its implementation and potential by considering three examples spanning a range of design abstractions: new visualization idioms; validation though visualization algebra; and feminist data visualization. We argue that the rich documentation of the design process provided by literate visualization offers the potential to improve the validity of visualization design and so benefit both academic visualization and visualization practice.
Temperature Control in Dissipative Cavities by Entangled Dimers

We show that the temperature of a cavity field can be drastically varied by its interaction with suitably entangled atom pairs (dimers) traversing the cavity under realistic atomic decoherence. To this end we resort to the hitherto untapped resource of naturally entangled dimers whose state can be simply controlled via molecular dissociation, collisions forming the dimer, or unstable dimers such as positronium. Depending on the chosen state of the dimer, the cavity-field mode can be driven to a steady-state temperature that is either much lower or much higher than the ambient temperature, despite adverse effects of cavity loss and atomic decoherence. Entangled dimers enable much broader range of cavity temperature control than single “phaseonium” atoms with coherently superposed levels. Such dimers are shown to constitute highly caloric fuel that can ensure high efficiency or power in photonic thermal engines. Alternatively, they can serve as controllable thermal baths for quantum simulation of energy exchange in photosynthesis or quantum annealing.
A holographic mobile-based application for practicing pronunciation of basic English vocabulary for Spanish speaking children

This paper describes a holographic mobile-based application designed to help Spanish-speaking children to practice the pronunciation of basic English vocabulary words. The mastery of vocabulary is a fundamental step when learning a language but is often perceived as boring. Producing the correct pronunciation is frequently regarded as the most difficult and complex skill for new learners of English. In order to address these problems this research takes advantage of the power of multi-channel stimuli (sound, image and interaction) in a mobile-based hologram application in order to motivate students and improve their experience of practicing. We adapted the prize-winning HolograFX game and developed a new mobile application to help practice English pronunciation. A 3D holographic robot that acts as a virtual teacher interacts via voice with the children. To test the tool we carried out an experiment with 70 Spanish pre-school children divided into three classes, the control group using traditional methods such as images in books and on the blackboard, and two experimental groups using our drills and practice software. One experimental group used the mobile application without the holographic game and the other experimental group used the application with the holographic game. We performed pre-test and post-test performance assessments, a satisfaction survey and emotion analysis. The results are very promising. They show that the use of the holographic mobile-based application had a significant impact on the children's motivation. It also improved their performance compared to traditional methods used in the classroom.
Focal Boundary Guided Salient Object Detection

The performance of salient object segmentation has been significantly advanced by using deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations, and might hinder downstream applications that require precise object shapes. To address this issue, we propose a novel deep model-Focal Boundary Guided (Focal- BG) network. Our model is designed to jointly learn to segment salient object masks and detect salient object boundaries. Our key idea is that additional knowledge about object boundaries can help to precisely identify the shape of the object. Moreover, our model incorporates a refinement pathway to refine the mask prediction, and makes use of the focal loss to facilitate the learning of the hard boundary pixels. To evaluate our model, we conduct extensive experiments. Our Focal-BG network consistently outperforms state-of-the-art methods on five major benchmarks. We provide a detailed analysis of these results and demonstrate that our joint modeling of salient object boundary and mask helps to better capture shape details, especially in the vicinity of object boundaries.
White Blood Cell Classification Using Convolutional Neural Network

The densitySharma, Mayank of white blood cells in bloodstream provides a glimpse into the state of the immune system and any potential risks such as heart disease or infection. A dramaticBhave, Aishwarya change in the white blood cell count relative to your baseline is generally a sign that your body is currently being affected by an antigenJanghel, Rekh Ram. A variation in a specific type of white blood cell generally correlates with a specific type of antigen. Currently, a manual approach is followed for white blood cell classification; however, some semi-automated approaches have been proposed which involves manual feature extraction and selection and an automated classification using microscopic blood smear images. In this work, we propose deep learning methodology to automate the entire process using convolutional neural networks for a binary class with an accuracy of 96% as well as multiclass classification with an accuracy of 87%.
A contrast of meta and metafor packages for meta‐analyses in R

Abstract There is extensive choice in R to support meta‐analyses. Two packages in this ecosystem include meta and metafor and provide an excellent opportunity to apply a structured checklist previously developed for contrasts between R packages relevant to challenges in ecology and evolution. Meta is a direct, intuitive choice for rapid implementation of general meta‐analytical statistics. Metafor is a comprehensive package best suited for relatively more complex models. Both packages provide estimates of heterogeneity, excellent visualization tools, and functions to explore publication bias. The package metafor has a steeper learning curve but greater rewards. Reference to the learning curve and capacities of the statistical software Stata provided a benchmark outside the R ecosystem and confirmed the consistency in statistics. The usefulness of meta‐analyses is not just in the synthesis of the research but in the process of doing the scientific synthesis. Reporting of contrasts and checks for robust statistics is an important contribution to more transparent and reproducible scientific syntheses.
Feature Pyramid Reconfiguration With Consistent Loss for Object Detection

Taking the feature pyramids into account has become a crucial way to boost the object detection performance. While various pyramid representations have been developed, previous works are still inefficient to integrate the semantical information over different scales. Moreover, recent object detectors are suffering from accurate object location applications, mainly due to the coarse definition of the "positive" examples at training and predicting phases. In this paper, we begin by analyzing current pyramid solutions, and then propose a novel architecture by reconfiguring the feature hierarchy in a flexible yet effective way. In particular, our architecture consists of two lightweight and trainable processes: global attention and local reconfiguration. The global attention is to emphasize the global information of each feature scale, while the local reconfiguration is to capture the local correlations across different scales. Both the global attention and local reconfiguration are non-linear and thus exhibit more expressive ability. Then, we discover that the loss function for object detectors during training is the central cause of the inaccurate location problem. We propose to address this issue by reshaping the standard cross entropy loss such that it focuses more on accurate predictions. Both the feature reconfiguration and the consistent loss could be utilized in popular one-stage (SSD, RetinaNet) and two-stage (Faster R-CNN) detection frameworks. Extensive experimental evaluations on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets demonstrate that, our models achieve consistent and significant boosts compared with other state-of-the-art methods.
Modular Bosonic Subsystem Codes

We introduce a framework to decompose a bosonic mode into two virtual subsystems---a logical qubit and a gauge mode. This framework allows the entire toolkit of qubit-based quantum information to be applied in the continuous-variable setting. We give a detailed example based on a modular decomposition of the position basis and apply it in two situations. First, we decompose Gottesman-Kitaev-Preskill grid states and find that the encoded logical state can be damaged due to entanglement with the gauge mode. Second, we identify and disentangle qubit cluster states hidden inside of Gaussian continuous-variable cluster states.
Electric-circuit simulation of the Schrödinger equation and non-Hermitian quantum walks

Recent progress has witnessed that various topological physics can be simulated by electric circuits under alternating current. However, it is still a nontrivial problem if it is possible to simulate the dynamics subject to the Schr\"odinger equation based on electric circuits. In this work, we reformulate the Kirchhoff law in one dimension in the form of the Schr\"odinger equation. As a typical example, we investigate quantum walks in $LC$ circuits. We also investigate how quantum walks are different in topological and trivial phases by simulating the Su-Schrieffer-Heeger model in electric circuits. We then generalize them to include dissipation and nonreciprocity by introducing resistors, which produce non-Hermitian effects. We point out that the time evolution of one-dimensional quantum walks is exactly solvable with the use of the generating function made of the Bessel functions.
Graph States as a Resource for Quantum Metrology

By using highly entangled states, quantum metrology guarantees precision impossible with classical measurements. Unfortunately such states can be very susceptible to noise, and it is a great challenge of the field to maintain quantum advantage in realistic conditions. In this study we investigate the practicality of graph states for quantum metrology. Graph states are a natural resource for much of quantum information, and here we characterize their quantum Fisher information (QFI) for an arbitrary graph state. We then construct families of graph states which approximately achieves the Heisenberg limit, we call these states bundled graph states. We demonstrate that bundled graph states maintain a quantum advantage after being subjected to iid dephasing or finite erasures. This shows that these graph states are good resources for robust quantum metrology. We also quantify the number of n qubit stabilizer states that are useful as a resource for quantum metrology.
Quantum Bits with Josephson Junctions

Already in the first edition of this book [1], a great number of interesting and important applications for Josephson junctions were discussed. In the decades that have passed since then, several new applications have emerged. This chapter treats one such new class of applications: quantum optics and quantum information processing (QIP) based on superconducting circuits with Josephson junctions. At the time of writing, the most recent and comprehensive reviews of this field, which has grown rapidly in the past two decades, are [2, 3]. We also recommend the reviews in [4–12] for additional perspectives on the field. In this chapter, we aim to explain the basics of superconducting quantum circuits with Josephson junctions and demonstrate how these systems open up new prospects, both for QIP and for the study of quantum optics and atomic physics.
Hierarchical Recurrent Deep Fusion Using Adaptive Clip Summarization for Sign Language Translation

Vision-based sign language translation (SLT) is a challenging task due to the complicated variations of facial expressions, gestures, and articulated poses involved in sign linguistics. As a weakly supervised sequence-to-sequence learning problem, in SLT there are usually no exact temporal boundaries of actions. To adequately explore temporal hints in videos, we propose a novel framework named Hierarchical deep Recurrent Fusion (HRF). Aiming at modeling discriminative action patterns, in HRF we design an adaptive temporal encoder to capture crucial RGB visemes and skeleton signees. Specifically, RGB visemes and skeleton signees are learned by the same scheme named Adaptive Clip Summarization (ACS), respectively. ACS consists of three key modules, i.e., variable-length clip mining, adaptive temporal pooling, and attention-aware weighting. Besides, based on unaligned action patterns (RGB visemes and skeleton signees), a query-adaptive decoding fusion is proposed to translate the target sentence. Extensive experiments demonstrate the effectiveness of the proposed HRF framework.