Reviewerly/exHarmony huggingface dataset:
Augmented reality: a view to future visual supports for people with disability

Background: Augmented reality (AR) technologies may provide immersive visual supports that foster active user engagement in activities. However, there is little research examining the use of AR as a visual support to guide its use in research or therapy settings.Aims: To investigate the development and use of AR for delivering visual supports in an immersive environment, using the Microsoft® HoloLens2® and Microsoft® Dynamics 365 Guides® software.Method: In a duo-ethnography, two speech-language pathologists who were novice users of the HoloLens2®, examined the affordances of the device for potential use in future research with people with neurodevelopmental disability. In a proof-of-concept study, an AR application was designed by the first author and used by two researchers in a duo-ethnography. The first and second author tested the AR guide and reflected on opportunities and barriers to further use of AR technology, specifically the HoloLens2®, to support people with disability to participate and be included in meaningful activities.Results: The guide created provided situated visual instructions, video models, and holographic symbols to direct the second author in making of a cup of tea. While a moderate level of technological literacy was needed to establish and install a guide, effective use could be established with minimal training.Discussion & conclusions: AR guides offer a situated and integrated means of providing visual support to people with disabilities. This proof-of-concept study justifies further testing and evaluation of AR as an assistive technology for people with neurodevelopmental disability.IMPLICATIONS FOR REHABILITATIONEmerging immersive Augmented Reality technology provides new opportunities to create integrated visual supports that function within the user's environment to enable active participation in activities and interactions.Visual supports integrated with the user's environment may better support people with disability to actively engage and attend to objects and to their communication partners.While new and emerging technologies like Augmented Reality are largely untested for disability support, they offer opportunities to enable participation in independent activities.
Single-node attacks for fooling graph neural networks

Graph neural networks (GNNs) have shown broad applicability in a variety of domains. These domains, e.g., social networks and product recommendations, are fertile ground for malicious users and behavior. In this paper, we show that GNNs are vulnerable to the extremely limited (and thus quite realistic) scenarios of a single-node adversarial attack, where the perturbed node cannot be chosen by the attacker. That is, an attacker can force the GNN to classify any target node to a chosen label, by only slightly perturbing the features or the neighbors list of another single arbitrary node in the graph, even when not being able to select that specific attacker node. When the adversary is allowed to select the attacker node, these attacks are even more effective. We demonstrate empirically that our attack is effective across various common GNN types (e.g., GCN, GraphSAGE, GAT, GIN) and robustly optimized GNNs (e.g., Robust GCN, SM GCN, GAL, LAT-GCN), outperforming previous attacks across different real-world datasets both in a targeted and non-targeted attacks. Our code is available anonymously at https://github.com/gnnattack/SINGLE.
MR-DSS – Smaller MinRank-Based (Ring-)Signatures

In the light of NIST’s announced reopening of the call for digital signature proposals in 2023 due to lacking diversity, there is a strong need for constructions based on other established hardness assumptions. In this work we construct a new post-quantum secure digital signature scheme based on the MinRank problem, a problem with a long history of applications in cryptanalysis that led to a strong belief in its hardness. Initially following a design by Courtois (Asiacrypt ’01) based on the Fiat–Shamir transform, we make use of several recent developments in the design of sigma protocols to reduce signature size and improve efficiency. This includes the recently introduced sigma protocol with helper paradigm (Eurocrypt ’19) and combinations with cut-and-choose techniques (CCS ’18). Moreover, we introduce several improvements to the core of the scheme to further reduce its signature size. As a second contribution, we formalize the natural extension of our construction to a ring signature scheme and show that it achieves desired anonymity and unforgeability guarantees. Our ring signature is characterized by a sublinear scaling of the signature size in the number of users. Moreover, we achieve competitive practical signature sizes for moderate amount of users in comparison to recent ring signature proposals.
Ability of strong-pulse illumination to hack self-differencing avalanche photodiode detectors in a high-speed quantum-key-distribution system

Implementation of high-speed quantum key distribution (QKD) has become one of the major focuses in the field, which produces a high key-generation rate for applications. To achieve high-speed QKD, tailored techniques are developed and employed to quickly generate and detect quantum states. However, these techniques may introduce unique loopholes to compromise the security of QKD systems. In this paper, we investigate the loopholes of the self-differencing (SD) avalanche photodiode (APD) detector, typically used for high-speed detection in a QKD system, and demonstrate experimental testing of the SD APD detector under strong-pulse-illumination attack. This attack presents blinding stability and helps an eavesdropper to learn the secret key without introducing extra quantum bit rate error. Based on this testing, we propose a set of criteria for protecting SD APD detectors from the strong-pulse-illumination attack.
Experimental demonstration of coherent photonic neural computing based on a Fabry–Perot laser with a saturable absorber

As Moore’s law has reached its limits, it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power. Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck. However, while photonic neural networks are good at linear computing, it is difficult to achieve nonlinear computing. Here, we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators (MZMs) as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber (FP-SA) as the photonic spiking neuron. Both linear computation and nonlinear computation are realized in the experiment. In such a coherent architecture, two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier. The nonlinear neuron-like dynamics including temporal integration, threshold, and refractory period are successfully demonstrated. Besides, the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored, and the frequency detuning condition is revealed. The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.
Adaptive Online Mutual Learning Bi-Decoders for Video Object Segmentation

One of the major challenges facing video object segmentation (VOS) is the gap between the training and test datasets due to unseen category in test set, as well as object appearance change over time in the video sequence. To overcome such challenges, an adaptive online framework for VOS is developed with bi-decoders mutual learning. We learn object representation per pixel with bi-level attention features in addition to CNN features, and then feed them into mutual learning bi-decoders whose outputs are further fused to obtain the final segmentation result. We design an adaptive online learning mechanism via a deviation correcting trigger such that bi-decoders online mutual learning will be activated when the previous frame is segmented well meanwhile the current frame is segmented relatively worse. Knowledge distillation from the well segmented previous frames, along with mutual learning between bi-decoders, improves generalization ability and robustness of VOS model. Thus, the proposed model adapts to the challenging scenarios including unseen categories, object deformation, and appearance variation during inference. We extensively evaluate our model on widely-used VOS benchmarks including DAVIS-2016, DAVIS-2017, YouTubeVOS-2018, YouTubeVOS-2019, and UVO. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods.
Metaverse and education: the pioneering case of Minecraft in immersive digital learning

The metaverse is presented as a possible new technological iteration for the Internet. The generation of virtual universes in which the physical and the digital converge raises the question of how education will be addressed in these new systems. We find, however, pioneering exercises such as Minecraft: Education Edition. This platform is a version based on the popular sandbox video game, which was originally created by a community of teachers. The aim of this research is to analyze and describe the idiosyncratic characteristics of Minecraft as an educational platform, framing it as one of the pioneering exercises in the metaverse. To this end, we have employed a methodology that combines Multimodal Discourse Analysis with Grounded Theory and the Constant Comparative Method. As conclusions, we observe how the Minecraft Education platform reinforces from its approach pre-existing aspects from the physical world, resizing them to adapt them to its connected digital environment. These are key elements such as the identity of the participants, their ability to act within the system, creativity through lessons as a guide to the educational objectives and the community as the backbone of the process. At the same time, it presents differential components, such as the use of avatars, the transition from textual literacy to multimodal literacy, game mechanics that boost creativity or transhuman capabilities that defy physical space-time. All in all, the platform is designed for teachers, parents and managers, to whom it offers a series of benefits. Therefore, the pedagogical action will depend on their judgment and execution, especially through the elaboration of lessons and worlds, the management of the sessions and their interaction in community environments. It is their responsibility to ensure that the educational experience is truly empowering or, on the contrary, that it ends up being governed by reproductive criteria linked to symbolic violence.
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data

Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization is modeled using normal distributions. To find better probabilistic models, algorithms such as RankMat, ZeroMat and DotMat have been invented in recent years. In this paper, we model the user rating behavior in recommender system as a Poisson process, and design an algorithm that relies on no input data to solve the recommendation problem and the cold start issue at the same time. We prove the superiority of our algorithm in comparison with matrix factorization, random placement, Zipf placement, ZeroMat, DotMat, etc.
Computational capability for physical reservoir computing using a spin-torque oscillator with two free layers

A numerical analysis on the computational capability of physical reservoir computing utilizing a spin-torque oscillator with two free layers is reported. Conventional spintronics devices usually consist of two ferromagnets, where the direction of magnetization in one layer, called the free layer, can move while that of the other, the reference layer, is fixed. Recently, however, devices with two free layers, where the reference layer is replaced by another free layer, have been developed for various practical applications. Adding another free layer drastically changes the dynamical response of the device through the couplings via the spin-transfer effect and the dipole magnetic field. A numerical simulation of the Landau-Lifshitz-Gilbert equation and a statistical analyses of the Lyapunov exponent and the synchronization index reveal the appearance of an amplitude-modulated oscillation and chaos in the oscillators with two free layers. Such complex dynamics qualitatively change the computational capability of physical reservoir computing because the computational resource is dynamics of the physical system. An evaluation of the short-term memory capacity clarifies that oscillators with two free layers have a larger capacity than those of conventional oscillators. An enhancement in capacity near the edge of echo state property, i.e., the boundary between zero and finite synchronization index, is also found.
Remote Preparation of Optical Cat States Based on Gaussian Entanglement

Remote state preparation enables one to prepare and manipulate quantum state non-locally. As an essential quantum resource, optical cat state is usually prepared locally by subtracting photons from a squeezed vacuum state. For remote quantum information processing, it is essential to prepare and manipulate optical cat states remotely based on Gaussian entanglement, which remains a challenge. Here, we present experimental preparation of optical cat states based on a remotely distributed two-mode Gaussian entangled state in a lossy channel. By performing photon subtraction and homodyne projective measurement at Alice's station, an optical cat state is prepared remotely at Bob's station. Furthermore, the prepared cat state is rotated by changing Alice's measurement basis of homodyne detection, which demonstrates the remote manipulation of it. By distributing two modes of the two-mode Gaussian entangled state in lossy channels, we demonstrate that the remotely prepared cat state can tolerate much more loss in Alice's channel than that in Bob's channel. We also show that cat states with amplitudes larger than 2 can be prepared by increasing the squeezing level and subtracting photon numbers. Our results make a crucial step toward remote hybrid quantum information processing involving discrete- and continuous-variable techniques.
Gene Therapy for Hemophilia A: A Mixed Methods Study of Patient Preferences and Shared Decision-Making

Purpose: As gene therapies for hemophilia are incorporated into clinical practice, shared decision-making (SDM) is recommended for implementation.SDM tools may facilitate informed decision-making for gene therapy and other novel therapies.Objective: To inform the development of SDM tools for hemophilia gene therapy.Patients and Methods: Men with severe hemophilia were recruited from the National Hemophilia Foundation (NHF) Community Voices in Research (CVR).Semi-structured interviews were completed, and the interviews were transcribed verbatim for quantitative and qualitative analysis.Results: Twenty-five men with severe hemophilia A participated.All participants reported treatment with prophylaxis, nine (36%) on continuous prophylaxis with clotting factor, one (4%) on intermittent prophylaxis with clotting factor, and 15 (60%) on continuous prophylaxis with emicizumab.Ten (40%) indicated that they are excited about gene therapy, 12 (48%) indicated that they are hopeful about gene therapy, one (4%) indicated that they are worried or scared about gene therapy, and one (4%) indicated that they do not have strong feelings about gene therapy.Participants reported engaging Hemophilia Treatment Center, family, and the hemophilia community in their decision-making process.The most reported information needs are efficacy, safety, cost/insurance, mechanism of action, and followup.In addition, key information themes that emerged were patient testimonials, hard data and statistics, and comparison to other products.Twenty-two (88%) indicated that a SDM tool would be useful when discussing gene therapy with their hemophilia team.Two indicated that they do their own research, and the tool would not add anything.One needed more information to provide an answer.Conclusion: These data highlight the utility of a SDM tool for hemophilia gene therapy and key information needs.Data including comparison to other treatments should be provided along with patient testimonials in a transparent format.Patients will engage the Hemophilia Treatment Center, family, and community members in the decision-making process.
Unifying temporal phase unwrapping framework using deep learning

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.
MNGNAS: Distilling Adaptive Combination of Multiple Searched Networks for One-Shot Neural Architecture Search

Recently neural architecture (NAS) search has attracted great interest in academia and industry. It remains a challenging problem due to the huge search space and computational costs. Recent studies in NAS mainly focused on the usage of weight sharing to train a SuperNet once. However, the corresponding branch of each subnetwork is not guaranteed to be fully trained. It may not only incur huge computation costs but also affect the architecture ranking in the retraining procedure. We propose a multi-teacher-guided NAS, which proposes to use the adaptive ensemble and perturbation-aware knowledge distillation algorithm in the one-shot-based NAS algorithm. The optimization method aiming to find the optimal descent directions is used to obtain adaptive coefficients for the feature maps of the combined teacher model. Besides, we propose a specific knowledge distillation process for optimal architectures and perturbed ones in each searching process to learn better feature maps for later distillation procedures. Comprehensive experiments verify our approach is flexible and effective. We show improvement in precision and search efficiency in the standard recognition dataset. We also show improvement in correlation between the accuracy of the search algorithm and true accuracy by NAS benchmark datasets.
Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?

Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis.During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI).The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score.The average reported ACC from the collected studies was 95.8 ±3.8%.Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign.Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks.However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
Hamiltonian Extrema of an Arbitrary Flux-Biased Josephson Circuit

Flux-biased loops including one or more Josephson junctions are ubiquitous elements in quantum information experiments based on superconducting circuits. These quantum circuits can be tuned to implement a variety of Hamiltonians, with applications ranging from decoherence-protected qubits to quantum limited converters and amplifiers. The extrema of the Hamiltonian of these circuits are of special interest because they govern their low-energy dynamics. However, the theory of superconducting quantum circuits so far lacks a systematic method to find these extrema and compute the series expansion of the Hamiltonian in their vicinity for an arbitrary nonlinear superconducting circuit. We present such a method, which can aid the synthesis of new functionalities in quantum devices.Received 22 February 2023Accepted 20 July 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030324Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCircuit quantum electrodynamicsPhysical SystemsJosephson junctionsSuperconducting devicesSuperconducting qubitsCondensed Matter, Materials & Applied PhysicsQuantum Information
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Abstract With the increase in large multimodal cohorts and high‐throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. Highlights Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real‐world validation, and interdisciplinary collaboration are required.
Communicative strategies and socio-cultural identities in talk shows

Preview this article: Communicative strategies and socio-cultural identities in talk shows, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/prag.5.3.01cal-1.gif