Reviewerly/exHarmony huggingface dataset:
The Potential of Diffusion-Based Near-Infrared Image Colorization

Authors:
raw_author_name: Timm Haucke
raw_affiliation_string: Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA 02139, USA; Institute of Computer Science IV, University of Bonn, Friedrich-Hirzebruch-Allee 8, 53115 Bonn, Germany
raw_author_name: Volker Steinhage
raw_affiliation_string: Institute of Computer Science IV, University of Bonn, Friedrich-Hirzebruch-Allee 8, 53115 Bonn, Germany

Abstract: Camera traps, an invaluable tool for biodiversity monitoring, capture wildlife activities day and night. In low-light conditions, near-infrared (NIR) imaging is commonly employed to capture images without disturbing animals. However, the reflection properties of NIR light differ from those of visible light in terms of chrominance and luminance, creating a notable gap in human perception. Thus, the objective is to enrich near-infrared images with colors, thereby bridging this domain gap. Conventional colorization techniques are ineffective due to the difference between NIR and visible light. Moreover, regular supervised learning methods cannot be applied because paired training data are rare. Solutions to such unpaired image-to-image translation problems currently commonly involve generative adversarial networks (GANs), but recently, diffusion models gained attention for their superior performance in various tasks. In response to this, we present a novel framework utilizing diffusion models for the colorization of NIR images. This framework allows efficient implementation of various methods for colorizing NIR images. We show NIR colorization is primarily controlled by the translation of the near-infrared intensities to those of visible light. The experimental evaluation of three implementations with increasing complexity shows that even a simple implementation inspired by visible-near-infrared (VIS-NIR) fusion rivals GANs. Moreover, we show that the third implementation is capable of outperforming GANs. With our study, we introduce an intersection field joining the research areas of diffusion models, NIR colorization, and VIS-NIR fusion.
Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Authors:
raw_author_name: Steven E. Zhang
raw_affiliation_string: Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario, K1A 0E8, Canada
raw_author_name: Julie E. Bourdeau
raw_affiliation_string: Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario, K1A 0E8, Canada
raw_author_name: Glen T. Nwaila
raw_affiliation_string: Wits Mining Institute, University of the Witwatersrand,  1 Jan Smuts Ave, Johannesburg,  2000, South Africa
raw_author_name: Mohammad Parsa
raw_affiliation_string: Geological Survey of Canada, 601 Booth Street, Ottawa, Ontario, K1A 0E8, Canada
raw_author_name: Yousef Ghorbani
raw_affiliation_string: School of Chemistry, University of Lincoln, Joseph Banks Laboratories, Green Lane, Lincoln, Lincolnshire, LN6 7DL, UK

Abstract: Abstract Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.
Resource Scheduling in URLLC and eMBB Coexistence Based on Dynamic Selection Numerology

Authors:
raw_author_name: Lei Wang
raw_affiliation_string: Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
raw_author_name: Sijie Tao
raw_affiliation_string: School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
raw_author_name: Lindong Zhao
raw_affiliation_string: Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
raw_author_name: Zhe Liu
raw_affiliation_string: School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Abstract: This paper focuses on the resource allocation problem of multiplexing two different service scenarios, enhanced mobile broadband (eMBB) and ultrareliable low latency (URLLC) in 5G New Radio, based on dynamic numerology structure, mini-time slot scheduling, and puncturing to achieve optimal resource allocation. To obtain the optimal channel resource allocation under URLLC user constraints, this paper establishes a relevant channel model divided into two convex optimization problems: (a) eMBB resource allocation and (b) URLLC scheduling. We also determine the numerology values at the beginning of each time slot with the help of deep reinforcement learning to achieve flexible resource scheduling. The proposed algorithm is verified in simulation software, and the simulation results show that the dynamic selection of numerologies proposed in this paper can better improve the data transmission rate of eMBB users and reduce the latency of URLLC services compared with the fixed numerology scheme for the same URLLC packet arrival, while the reasonable resource allocation ensures the reliability of URLLC and eMBB communication.
Passive dynamical decoupling of trapped-ion qubits and qudits

Authors:
raw_author_name: R. T. Sutherland
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas 78249, USA; Quantinuum, 303 S. Technology Ct, Broomfield, Colorado 80021, USA
raw_author_name: S. D. Erickson
raw_affiliation_string: Quantinuum, 303 S. Technology Ct, Broomfield, Colorado 80021, USA

Abstract: We propose a method to dynamically decouple every magnetically sensitive hyperfine sublevel of a trapped ion from magnetic field noise, simultaneously using integrated circuits to adiabatically rotate its local quantization field. These integrated circuits allow passive adjustment of the effective polarization of any external (control or noise) field. By rotating the ion's quantization direction relative to this field's polarization, we can perform ``passive'' dynamical decoupling (PDD), inverting the linear Zeeman sensitivity of every hyperfine sublevel. This dynamically decouples the entire ion, rather than just a qubit subspace. Fundamentally, PDD drives the transition ${m}_{F}\ensuremath{\rightarrow}\ensuremath{-}{m}_{F}$ for every magnetic quantum number ${m}_{F}$ in the system---with only one operation---indicating it applies to qudits with constant overhead in the dimensionality of the qudit. We show how to perform pulsed and continuous PDD, weighing each technique's insensitivity to external magnetic fields versus their sensitivity to diabaticity and control errors. Finally, we show that we can tune the sinusoidal oscillation of the quantization axis to a motional mode of the crystal in order to perform a laser-free two-qubit gate that is insensitive to magnetic field noise.
Robust gates with spin-locked superconducting qubits

Authors:
raw_author_name: Alexey V. Gorshkov
raw_affiliation_string: AWS Center for Quantum Computing, Pasadena, California 91125, USA
raw_author_name: Alex Retzker
raw_affiliation_string: AWS Center for Quantum Computing, Pasadena, California 91125, USA; Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 91904, Givat Ram, Israel

Abstract: Dynamical decoupling is effective in reducing gate errors in most quantum computation platforms and is therefore projected to play an essential role in future fault-tolerant constructions. In superconducting circuits, however, it has proven difficult to utilize the benefits of dynamical decoupling. In this work, we present a theoretical proposal that incorporates a continuous version of dynamical decoupling, namely, spin locking, with a coupler-based CZ gate for transmons and provide analytical and numerical results that demonstrate its effectiveness.
What Does 'Failure' Mean in Civic Tech? We Need Continued Conversations About Discontinuation

Authors:
raw_author_name: Andrea Hamm
raw_affiliation_string: Weizenbaum Institute for the Networked Society
raw_author_name: Yuya Shibuya
raw_affiliation_string: University of Tokyo
raw_author_name: Teresa Cerratto Pargman
raw_affiliation_string: Stockholm University
raw_author_name: Roy Bendor
raw_affiliation_string: Delft University of Technology
raw_author_name: Christoph Raetzsch
raw_affiliation_string: Aarhus University
raw_author_name: Mennatullah Hendawy
raw_affiliation_string: Ain Shams University
raw_author_name: Rainer Rehak
raw_affiliation_string: Weizenbaum Institute for the Networked Society
raw_author_name: Gwen Klerks
raw_affiliation_string: Eindhoven University of Technology
raw_author_name: Ben Schouten
raw_affiliation_string: Eindhoven University of Technology
raw_author_name: Nicolai Brodersen Hansen
raw_affiliation_string: Aalborg University

Abstract: research-article Open Access Share on What Does 'Failure' Mean in Civic Tech? We Need Continued Conversations About Discontinuation Authors: Andrea Hamm Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Yuya Shibuya University of Tokyo University of TokyoSearch about this author , Teresa Cerratto Pargman Stockholm University Stockholm UniversitySearch about this author , Roy Bendor Delft University of Technology Delft University of TechnologySearch about this author , Christoph Raetzsch Aarhus University Aarhus UniversitySearch about this author , Mennatullah Hendawy Ain Shams University Ain Shams UniversitySearch about this author , Rainer Rehak Weizenbaum Institute for the Networked Society Weizenbaum Institute for the Networked SocietySearch about this author , Gwen Klerks Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Ben Schouten Eindhoven University of Technology Eindhoven University of TechnologySearch about this author , Nicolai Brodersen Hansen Aalborg University Aalborg UniversitySearch about this author Authors Info & Claims InteractionsVolume 31Issue 2March - April 2024pp 34–38https://doi.org/10.1145/3641815Published:28 February 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Nonlinear dimensionality reduction with q-Gaussian distribution

Authors:
raw_author_name: Motoshi Abe
raw_affiliation_string: Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8527, Japan
raw_author_name: Yuichiro Nomura
raw_affiliation_string: Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8527, Japan
raw_author_name: Takio Kurita
raw_affiliation_string: Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8527, Japan

Abstract: Abstract In recent years, the dimensionality reduction has become more important as the number of dimensions of data used in various tasks such as regression and classification has increased. As popular nonlinear dimensionality reduction methods, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have been proposed. However, the former outputs only one low-dimensional space determined by the t-distribution and the latter is difficult to control the distribution of distance between each pair of samples in low-dimensional space. To tackle these issues, we propose novel t-SNE and UMAP extended by q-Gaussian distribution, called q-Gaussian-distributed stochastic neighbor embedding (q-SNE) and q-Gaussian-distributed uniform manifold approximation and projection (q-UMAP). The q-Gaussian distribution is a probability distribution derived by maximizing the tsallis entropy by escort distribution with mean and variance, and a generalized version of Gaussian distribution with a hyperparameter q. Since the shape of the q-Gaussian distribution can be tuned smoothly by the hyperparameter q, q-SNE and q-UMAP can in- tuitively derive different embedding spaces. To show the quality of the proposed method, we compared the visualization of the low-dimensional embedding space and the classification accuracy by k-NN in the low-dimensional space. Empirical results on MNIST, COIL-20, OliverttiFaces and FashionMNIST demonstrate that the q-SNE and q-UMAP can derive better embedding spaces than t-SNE and UMAP.
Photonic Which-Path Entangler Based on Longitudinal Cavity-Qubit Coupling

Authors:
raw_author_name: W. A. Coish
raw_affiliation_string: Department of Physics, McGill University, 3600 rue University, Montreal, Québec H3A 2T8, Canada

Abstract: We show that a modulated longitudinal cavity-qubit coupling can be used to control the path taken by a multiphoton coherent-state wave packet conditioned on the state of a qubit, resulting in a qubit--which-path (QWP) entangled state. QWP states can generate long-range multipartite entanglement using strategies for interfacing discrete- and continuous-variable degrees of freedom. Using the approach presented here, entanglement can be distributed in a quantum network without the need for single-photon sources or detectors.
GrassCaré: Visualizing the Grassmannian on the Poincaré Disk

Authors:
raw_author_name: Daniel Pimentel-Alarcón
raw_affiliation_string: Department of Biostatistics, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, USA

Abstract: Abstract This paper introduces a novel method for visualizing high-dimensional Grassmannians through 2D embeddings on the Poincaré disk. The proposed approach involves the construction of affinity matrices on each manifold, followed by the minimization of KL-divergence between the geodesics affinity. This process enables the identification of an optimal projection that effectively preserves the geometry of the original high-dimensional Grassmannian. Our main theoretical contribution lies in bounding the embedding loss with respect to two factors: the logarithm of the number of subspaces and a term dependent on the distribution of subspaces within the Grassmannian. Notably, this term is smaller when subspaces exhibit well-defined clusters, and larger in the absence of any discernible structure. We complement our theoretical analysis with comprehensive experiments on both synthetic and real datasets. The experimental results showcase the superiority of our embedding in accurately visualizing Grassmannians compared to existing representations.
Tensions between the political, institutional, and project levels when developing professional digital competence in teacher education. A cultural historical activity theory analysis of inhibiting and facilitating factors

Authors:
raw_author_name: A-T. Arstorp
raw_affiliation_string: Department of Educational Science, University of South-Eastern Norway, Drammen, Norway

Abstract: This study is a Cultural Historical Activity Theory analysis of the tensions between object and motive at the political, institutional and project management levels for five digitalisation projects aimed at integrating professional digital competence (PDC) in Norwegian teacher education (TE). The study is inspired by ethnography and builds on a variety of qualitative data (including qualitative interviews (N = 14)) gathered through the author’s participation in different roles (2017–2021). Identifying objects and motives across levels allows for the identification of factors inhibiting and facilitating factors the development of PDC in TE. An analytical model was developed to identify the objects and motives at each level, showing a political object of preparing for the future, an institutional object of creating high quality TE, and a project management object of delivering a lasting impact on TE. These differing objects and motives guide the activities at each level and reveal tension, including between the political push for change, project management’s attempts to deliver impact, and a culture built on academic freedom which seems counterproductive by slowing things down. The study also reveals facilitating factors, such as funding and relevance in collaboration with schools, and inhibiting factors like academic freedom and the expected speed of change.
Activity scenarios simulation by discovering knowledge through activities of daily living datasets

Authors:
raw_author_name: Swe Nwe Nwe Htun
raw_affiliation_string: Data-knowledge Integration Research Team, Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
raw_author_name: Shusaku Egami
raw_affiliation_string: Data-knowledge Integration Research Team, Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
raw_author_name: Ken Fukuda
raw_affiliation_string: Data-knowledge Integration Research Team, Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

Abstract: Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequences of actions and interactions with objects, considering real-life scenarios and resource constraints. This study investigates the potential of generating synthetic training data for ADLs recognition using the VirtualHome2KG framework. Furthermore, we investigate the transformative potential of simulating activities in virtual spaces, as evidenced by our survey of real-world activity datasets and exploration of synthetic datasets in virtual environments. Our work explicitly simulates activities in the 3D Unity platform, affording seamless transitions between environments and camera perspectives. Furthermore, we meticulously construct scenarios not only for regular daily activities but also for abnormal activities to detect risky situations for independent living, ensuring the incorporation of critical criteria. We incorporate one contemporary method for abnormal activity detection to demonstrate the efficacy of our simulated activity data. Our findings suggest that our activity scenario preparation accomplishes the intended research objective while paving the way for an interesting research avenue.
Intelligent 3D garment system of the human body based on deep spiking neural network

Authors:
raw_author_name: Minghua Jiang
raw_affiliation_string: Engineering Research Center of Hubei Province for Clothing Information, Wuhan 430200,China; School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
raw_author_name: Chenyu Yu
raw_affiliation_string: School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
raw_author_name: Yankang Shi
raw_affiliation_string: School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
raw_author_name: Tao Peng
raw_affiliation_string: School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
raw_author_name: Xinrong Hu
raw_affiliation_string: School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
raw_author_name: Feng Yu
raw_affiliation_string: Engineering Research Center of Hubei Province for Clothing Information, Wuhan 430200,China; School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China

Abstract: Intelligent garments, a burgeoning class of wearable devices, have extensive applications in domains such as sports training and medical rehabilitation. Nonetheless, existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity, often skipping crucial aspects related to user experience and interaction. To address this gap, this study introduces a novel real-time 3D interactive system based on intelligent garments. The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements, thereby achieving real-time interaction between users and sensors. Additionally, the system in- corporates 3D human visualization functionality, which visualizes sensor data and recognizes human actions as 3D models in realtime, providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion. This system has significant potential for applications in motion detection, medical monitoring, virtual reality, and other fields. The accurate classification of human actions con- tributes to the development of personalized training plans and injury prevention strategies. This study has substantial implications in the domains of intelligent garments, human motion monitoring, and digital twin visualization. The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.