Reviewerly/exHarmony huggingface dataset:
Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation

Medical teleconsultation was among the initial use cases for early telepresence research projects since medical treatment often requires timely intervention by highly specialized experts. When remote medical experts support interventions, a holistic view of the surgical site can increase situation awareness and improve team communication. A possible solution is the concept of immersive telepresence, where remote users virtually join the operating theater that is transmitted based on a real-time reconstruction of the local site. Enabled by the availability of RGB-D sensors and sufficient computing capability, it becomes possible to capture such a site in real time using multiple stationary sensors. The 3D reconstruction and simplification of textured surface meshes from the point clouds of a dynamic scene in real time is challenging and becomes infeasible for increasing capture volumes. This work presents a tightly integrated, stateless 3D reconstruction pipeline for dynamic, room-scale environments that generates simplified surface meshes from multiple RGB-D sensors in real time. Our algorithm operates directly on the fused, voxelized point cloud instead of populating signed-distance volumes per frame and using a marching cube variant for surface reconstruction. We extend the formulation of the dual contouring algorithm to work for point cloud data stored in an octree and interleave a vertex-clustering-based simplification before extracting the surface geometry. Our 3D reconstruction pipeline can perform a live reconstruction of six incoming depth videos at their native frame rate of 30 frames per second, enabling the reconstruction of smooth movement. Arbitrarily complex scene changes are possible since we do not store persistent information between frames. In terms of mesh quality and hole filling, our method falls between the direct mesh reconstruction and expensive global fitting of implicit functions.
Joint Dispatching and Cooperative Trajectory Planning for Multiple Autonomous Forklifts in a Warehouse: A Search-and-Learning-Based Approach

Dispatching and cooperative trajectory planning for multiple autonomous forklifts in a warehouse is a widely applied research topic. The conventional methods in this domain regard dispatching and planning as isolated procedures, which render the overall motion quality of the forklift team imperfect. The dispatching and planning problems should be considered simultaneously to achieve optimal cooperative trajectories. However, this approach renders a large-scale nonconvex problem, which is extremely difficult to solve in real time. A joint dispatching and planning method is proposed to balance solution quality and speed. The proposed method is characterized by its fast runtime, light computational burden, and high solution quality. In particular, the candidate goals of each forklift are enumerated. Each candidate dispatch solution is measured after concrete trajectories are generated via an improved hybrid A* search algorithm, which is incorporated with an artificial neural network to improve the cost evaluation process. The proposed joint dispatching and planning method is computationally cheap, kinematically feasible, avoids collisions with obstacles/forklifts, and finds the global optimum quickly. The presented motion planning strategy demonstrates that the integration of a neural network with the dispatching approach leads to a warehouse filling/emptying mission completion time that is 2% shorter than the most efficient strategy lacking machine-learning integration. Notably, the mission completion times across these strategies vary by approximately 15%.
Towards Privacy Preserving in 6G Networks: Verifiable Searchable Symmetric Encryption Based on Blockchain

The blockchain-based searchable symmetric encryption (SSE) scheme allows the retrieval and verification of outsourced data on cloud servers in sixth generation (6G) networks while ensuring the privacy of data. However, existing schemes are challenging to comprehensively meet the requirements of 6G-based intelligent application systems for low latency, high security, and high reliability. To address these limitations, we present VSSE, a novel blockchain-based SSE scheme designed for 6G-based intelligent application systems. Our scheme constructs a state chain structure to resist file injection attacks, thereby ensuring forward privacy. Moreover, we execute the search and verification operations separately on the cloud server and blockchain, while introducing a bitmap index structure and message authentication code (MAC) technology to achieve efficient searching and dynamic verification. Notably, VSSE also includes access control functionality, permitting only authorized users to access relevant files. The combination of remarkable efficiency and strong security establishes our VSSE as an ideal solution suitable for implementation in G-based intelligent application systems.
Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI.
Operational Quantum Average-Case Distances

We introduce distance measures between quantum states, measurements, and channels based on their statistical distinguishability in generic experiments. Specifically, we analyze the average Total Variation Distance (TVD) between output statistics of protocols in which quantum objects are intertwined with random circuits and measured in standard basis. We show that for circuits forming approximate 4-designs, the average TVDs can be approximated by simple explicit functions of the underlying objects – the average-case distances (ACDs). We apply them to analyze the effects of noise in quantum advantage experiments and for efficient discrimination of high-dimensional states and channels without quantum memory. We argue that ACDs are better suited for assessing the quality of NISQ devices than common distance measures such as trace distance or the diamond norm.
Blockchain Traceability for Sustainability Communication in Food Supply Chains: An Architectural Framework, Design Pathway and Considerations

The increasing demand for sustainable and ethically sourced food products has highlighted the importance of effective sustainability communication within the food supply chain. Existing sustainability communication approaches encounter limitations such as a lack of standardised frameworks, information overload, greenwashing, and an absence of transparent reporting. These challenges hinder their effectiveness and reliability in communicating sustainability efforts and commitments to businesses and consumers in a food chain. Blockchain technology, with its transparent, traceable, verifiable, and immutable features, offers a promising solution to address these limitations and facilitate effective sustainability communication. This paper explores the benefits of applying blockchain traceability to enhance sustainability communication in food supply chains. Using the system architecture approach, this paper proposes a high-level architectural framework, which can navigate the design and development of a blockchain-enabled solution for food sustainability communication. To assist with the translation of the architectural framework into a tailored solution, this paper further presents an action design pathway and discusses the design considerations around organisation, technology, governance, cost, and the user interface. The discussions and insights offered by this study can guide system developers and business analysts in the design and development of industry-oriented solutions, helping them make informed decisions before and during the design process. This paper contributes to advancing and expanding blockchain applications with a particular focus on sustainability communication in food supply chains.
OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience

Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent that can complete user tasks and support information seeking, it is important to develop a system that can handle both TOD and QA with access to various external knowledge sources. In this work, we propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA and expands the external knowledge sources to include both explicit sources (e.g., the web) and implicit sources (e.g., pre-trained language models). We create a new dataset OB-MultiWOZ, where we enrich TOD sessions with QA-like information-seeking experience grounded on external knowledge. We propose a unified model OPERA ( Op en-book E nd-to-end Task-o r iented Di a log) which can appropriately access explicit and implicit external knowledge to tackle the OB-TOD task. Experimental results show that OPERA outperforms closed-book baselines, highlighting the value of both types of knowledge.
Dynamic Weights and Prior Reward in Policy Fusion for Compound Agent Learning

In Deep Reinforcement Learning (DRL) domain, a compound learning task is often decomposed into several sub-tasks in a divide-and-conquer manner, each trained separately and then fused concurrently to achieve the original task, referred to as policy fusion. However, the state-of-the-art (SOTA) policy fusion methods treat the importance of sub-tasks equally throughout the task process, eliminating the possibility of the agent relying on different sub-tasks at various stages. To address this limitation, we propose a generic policy fusion approach, referred to as Policy Fusion Learning with Dynamic Weights and Prior Reward (PFLDWPR), to automate the time-varying selection of sub-tasks. Specifically, PFLDWPR produces a time-varying one-hot vector for sub-tasks to dynamically select a suitable sub-task and mask the rest throughout the entire task process, enabling the fused strategy to optimally guide the agent in executing the compound task. The sub-tasks with the dynamic one-hot vector are then aggregated to obtain the action policy for the original task. Moreover, we collect sub-tasks’s rewards at the pre-training stage as a prior reward, which, along with the current reward, is used to train the policy fusion network. Thus, this approach reduces fusion bias by leveraging prior experience. Experimental results under three popular learning tasks demonstrate that the proposed method significantly improves three SOTA policy fusion methods in terms of task duration, episode reward, and score difference.
TA-DARTS: Temperature Annealing of Discrete Operator Distribution for Effective Differential Architecture Search

In the realm of machine learning, the optimization of hyperparameters and the design of neural architectures entail laborious and time-intensive endeavors. To address these challenges, considerable research effort has been directed towards Automated Machine Learning (AutoML), with a focus on enhancing these inherent inefficiencies. A pivotal facet of this pursuit is Neural Architecture Search (NAS), a domain dedicated to the automated formulation of neural network architectures. Given the pronounced impact of network architecture on neural network performance, NAS techniques strive to identify architectures that can manifest optimal performance outcomes. A prominent algorithm in this area is Differentiable Architecture Search (DARTS), which transforms discrete search spaces into continuous counterparts using gradient-based methodologies, thereby surpassing prior NAS methodologies. Notwithstanding DARTS’ achievements, a discrepancy between discrete and continuously encoded architectures persists. To ameliorate this disparity, we propose TA-DARTS in this study—a temperature annealing technique applied to the Softmax function, utilized for encoding the continuous search space. By leveraging temperature values, architectural weights are judiciously adjusted to alleviate biases in the search process or to align resulting architectures more closely with discrete values. Our findings exhibit advancements over the original DARTS methodology, evidenced by a 0.07%p enhancement in validation accuracy and a 0.16%p improvement in test accuracy on the CIFAR-100 dataset. Through systematic experimentation on benchmark datasets, we establish the superiority of TA-DARTS over the original mixed operator, thereby underscoring its efficacy in automating neural architecture design.
Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks.
Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.
Denoising of Nifti (MRI) Images with a Regularized Neighborhood Pixel Similarity Wavelet Algorithm

The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.
Quantum Control of Rydberg Atoms for Mesoscopic Quantum State and Circuit Preparation

Individually trapped Rydberg atoms show significant promise as a platform for scalable quantum simulation and for the development of programmable quantum computers. In particular, the Rydberg-blockade effect can be used to facilitate both fast qubit-qubit interactions and long coherence times via low-lying electronic states encoding the physical qubits. To bring existing Rydberg-atom-based platforms a step closer to fault-tolerant quantum computation, we demonstrate high-fidelity state and circuit preparation in a system of five atoms. We specifically show that quantum control can be used to reliably generate fully connected cluster states and to simulate the error-correction encoding circuit based on the ``Perfect Quantum Error Correcting Code'' by Laflamme et al. [Phys. Rev. Lett. 77, 198 (1996)]. Our results make these ideas and their implementation directly accessible to experiments and demonstrate a promising level of noise tolerance with respect to experimental errors. With this approach, we motivate the application of quantum control in small subsystems in combination with the standard gate-based quantum circuits for direct and high-fidelity implementation of few-qubit modules.
Multiplicative noise removal using complementary total generalized variation and nonlocal low rank regularization

The problem of multiplicative image denoising is a typical ill-posed inverse problem which has been extensively studied in the past few decades and is still an open problem. Exploiting appropriate image priors is critical to solve ill-posed inverse image restoration problem. The existing total generalized variation (TGV) based approaches have achieved significant performance in removing the multiplicative noise. However, these TGV regularized approaches only consider the local smoothness prior of the potential clean image, which limits their ability for image restoration. Other than the local smoothness property, the nonlocal self-similarity prior of images can also be exploited to offer helpful remedy for better image reconstruction. In this paper, to simultaneously make use of both the local smoothness prior and nonlocal self-similarity prior, we integrate the TGV regularizer with the nonlocal low rank regularizer to propose a novel variational model for multiplicative image denoising. Specifically, in the objective function, the nonlocal low rank regularization is expressed by the celebrated weighted nuclear norm minimization (WNNM) which has recently been applied to image denoising and presents state-of-art result. Then, we apply the split Bregman algorithm to efficiently solve the resulting model. Numerical experiments demonstrate that the proposed method achieve superior performance over the competing methods, including the state-of-the-art SAR-BM3D method.
Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR)

Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However, the MMM likelihood being multi-modal, the challenge remains in its maximization. Although Expectation-Maximization (EM) algorithm remains one of the most popular ways to estimate the MMM parameters, however, convergence of EM algorithm is not always guaranteed. Given the computational challenges in maximizing the mixture likelihood on the constrained parameter space, we develop a pattern search-based global optimization technique which can optimize any objective function on a collection of simplexes, which is eventually used to maximize MMM likelihood. This is shown to outperform other related global optimization techniques. In simulation experiments, the proposed method is shown to outperform the expectation-maximization (EM) algorithm in the context of MMM estimation performance. The proposed method is applied to cluster Multiple sclerosis (MS) patients based on their treatment sequences of disease-modifying therapies (DMTs). We also propose a novel method to cluster people with MS based on DMT prescriptions and associated clinical features (covariates) using MMM with covariates. Based on the analysis, we divided MS patients into three clusters. Further cluster-specific summaries of relevant covariates indicate patient differences among the clusters. Supplementary materials for this article are available online.