Reviewerly/exHarmony huggingface dataset:
Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism

Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm .
Stronger Heterogeneous Feature Learning for Visible-Infrared Person Re-Identification

Abstract Visible-Infrared person re-identification (VI-ReID) is of great importance in the field of intelligent surveillance. It enables re-identification of pedestrians between daytime and dark scenarios, which can help police find escaped criminals at night. Currently, existing methods suffer from inadequate utilisation of cross-modality information, missing modality-specific discriminative information and weaknesses in perceiving differences between different modalities. To solve the above problems, we innovatively propose a stronger heterogeneous feature learning (SHFL) method for VI-ReID. First, we innovatively propose a Cross-Modality Group-wise constraint to solve the problem of inadequate utilization of cross-modality information. Secondly, we innovatively propose a Second-Order Homogeneous Invariant Regularizer to address the problem that missing modality-specific discriminative information. Finally, we innovatively propose a Modality-Aware Batch Normalization to address the problem of weaknesses in perceiving differences between different modalities. Extensive experimental results on two generic VI-ReID datasets demonstrate that the proposed final method outperforms the state-of-the-art algorithms.
A comparative study of LSTM-ED architectures in forecasting day-ahead solar photovoltaic energy using Weather Data

Abstract Solar photovoltaic (PV) energy, with its clean, local, and renewable features, is an effective complement to traditional energy sources today. However, the photovoltaic power system is highly weather-dependent and therefore has unstable and intermittent characteristics. Despite the negative impact of these features on solar sources, the increase in worldwide installed PV capacity has made solar energy prediction an important research topic. This study compares three encoder-decoder (ED) networks for day-ahead solar PV energy prediction: Long Short-Term Memory ED (LSTM-ED), Convolutional LSTM ED (Conv-LSTM-ED), and Convolutional Neural Network and LSTM ED (CNN-LSTM-ED). The models are tested using 1741-day-long datasets from 26 PV panels in Istanbul, Turkey, considering both power and energy output of the panels and meteorological features. The results show that the Conv-LSTM-ED with 50 iterations is the most successful model, achieving an average prediction score of up to 0.88 over R-square (R 2 ). Evaluation of the iteration counts’ effect reveals that the Conv-LSTM-ED with 50 iterations also yields the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, confirming its success. In addition, the fitness and effectiveness of the models are evaluated, with the Conv-LSTM-ED achieving the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for each iteration. The findings of this work can help researchers build the best data-driven methods for forecasting PV solar energy based on PV features and meteorological features.
Gallatin: A General-Purpose GPU Memory Manager

Dynamic memory management is critical for efficiently porting modern data processing pipelines to GPUs. However, building a general-purpose dynamic memory manager on GPUs is challenging due to the massive parallelism and weak memory coherence. Existing state-of-the-art GPU memory managers, Ouroboros and Reg-Eff, employ traditional data structures such as arrays and linked lists to manage memory objects. They build specialized pipelines to achieve performance for a fixed set of allocation sizes and fall back to the CUDA allocator for allocating large sizes. In the process, they lose general-purpose usability and fail to support critical applications such as streaming graph processing.
POSTER: Pattern-Aware Sparse Communication for Scalable Recommendation Model Training

Recommendation models are an important category of deep learning models whose size is growing enormous. They consist of a sparse part with TBs of memory footprint and a dense part that demands PFLOPs of computing capability to train. Unfortunately, the high sparse communication cost to re-organize data for different parallel strategies of the two parts impedes the scalability in training.
FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters

Protein structure prediction helps to understand gene translation and protein function, which is of growing interest and importance in structural biology. The AlphaFold model, which used transformer architecture to achieve atomic-level accuracy in protein structure prediction, was a significant breakthrough. However, training and inference of AlphaFold model are challenging due to its high computation and memory cost. In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference. We propose Dynamic Axial Parallelism (DAP) as a novel model parallelism method. Additionally, we have implemented a series of low-level optimizations aimed at reducing communication, computation, and memory costs. These optimizations include Duality Async Operations, highly optimized kernels, and AutoChunk (an automated search algorithm finds the best chunk strategy to reduce memory peaks). Experimental results show that FastFold can efficiently scale to more GPUs using DAP and reduces overall training time from 11 days to 67 hours and achieves 7.5 ~ 9.5× speedup for long-sequence inference. Furthermore, AutoChunk can reduce memory cost by over 80% during inference by automatically partitioning the intermediate tensors during the computation.
Semantic-driven dimension reduction for wireless internet of things

In recent years, mobile communication and artificial intelligence technologies have been widely used in the construction of wireless networks, bringing about a dramatic increase in data size. Existing wireless networks usually consist of a large number of nodes, with the potential risk of the curse of dimensionality. High dimensionality plays a negative role in learning effectiveness and efficiency, which should have been studied in depth but is neglected in existing wireless network research. In order to generate effective semantic-driven efficiency, this paper focuses on semantic-driven dimensionality reduction for wireless Internet of Things. Specifically, this paper introduces a series of feature selection techniques centered on Mahalanobis distance for dimensionality reduction, which helps to select discriminative features by measuring the effectiveness of semantic preferences and semantic-driven efficiency through Mahalanobis distance. Experiments on a set of wireless sensor data and various high-dimensional microarray data validate the superior performance of the proposed method.
Event-based data authenticity analytics for IoT and blockchain-enabled ESG disclosure

Environment, social, and governance (ESG) disclosure has raised significant interest in academia and industry for sustainable development and investing. However, the data authenticity of the ESG disclosure is still a serious matter of concern. This study proposes a novel solution to solve the above problem. First, an ESG information disclosure system (IBESG) enabled by IoT and blockchain is originally designed. The IBESG integrates IoT and blockchain technologies to facilitate the collection and transmission of data during ESG disclosure, and to ensures the data authenticity, consistency, and transparency. Second, the authors design a Local and Global Authenticity Verification Flow (LGA) consisting of edge computing and cloud computing to sufficiently verify the authenticity through the data flow. In addition, data authenticity analytics algorithms are developed in this study, which contains event-based spatial–temporal analytics and authenticity index computation. Finally, an experimental simulation is carried out to illustrate the implementation of the IBESG and the performance of the verification solution, and the sensitivity analysis of the above solution is conducted. Moreover, the relevant suggestions on deployment are given according to the findings during the experiment, and future work on improving the algorithm and conducting experiments in field manufacturing factories is illustrated. This study is expected to help academia and industry apply the solution in similar scenarios and inspire new ideas.
GPT classifications, with application to credit lending

Generative Pre-trained Transformers (GPT) and Large language models (LLMs) have made significant advancements in natural language processing in recent years. The practical applications of LLMs are undeniable, rendering moot any debate about their impending influence. The power of LLMs has made them similar to machine learning models for decision-making problems. In this paper, we focus on binary classification which is a common use of ML models, particularly in credit lending applications. We show how a GPT model can perform almost as accurately as a classical logistic machine learning model but with a much lower number of sample observations. In particular, we show how, in the context of credit lending, LLMs can be improved and reach performances similar to classical logistic regression models using only a small set of examples.
Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

As landmines and other unexploded ordnances (UXOs) present a great risk to civilians and infrastructure, humanitarian demining is an essential component of any post-conflict reconstruction. This paper introduces the Minefield Observatory, a novel web-based datastore service that semantically integrates diverse data in humanitarian demining to comprehensively and formally describe suspected minefields. Because of the high heterogeneity and isolation of the available minefield datasets, extracting relevant information to determine the optimal course of demining efforts is time-consuming, labor-intensive and requires highly specialized knowledge. Data consolidation and artificial intelligence techniques are used to convert unstructured data sources and store them in an ontology-based knowledge database that can be efficiently accessed through a Semantic Web application serving as the Minefield Observatory user interface. The MINEONT+ ontology was developed to integrate diverse mine scene information obtained through non-technical surveys and remote sensing, such as aerial and hyperspectral satellite imagery, indicators of mine presence and absence, contextual data, terrain analysis information, and battlefield reports. The Minefield Observatory uses the Microdata API to embed this dataset into dynamic HTML5 content, allowing seamless usage in a user-centric web tool. A use-case example was provided demonstrating the viability of the proposed approach.
Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.
Solvent Distribution Effects on Quantum Chemical Calculations with Quantum Computers

We present a combination of three-dimensional reference interaction site model self-consistent field (3D-RISM-SCF) theory and the variational quantum eigensolver (VQE) to consider the solvent distribution effects within the framework of quantum-classical hybrid computing.The present method, 3D-RISM-VQE, does not include any statistical errors from the solvent configuration sampling owing to the analytical treatment of the statistical solvent distribution.We apply 3D-RISM-VQE to compute the spatial distribution functions of solvent water around a water molecule, the potential and Helmholtz energy curves of NaCl, and to conduct Helmholtz energy component analysis of H2O and NH + 4 .Moreover, we utilize 3D-RISM-VQE to analyze the extent to which solvent effects alter the efficiency of quantum calculations compared with calculations in the gas phase using the L 1norms of molecular electronic Hamiltonians.Our results demonstrate that the efficiency of quantum chemical calculations on a quantum computer in solution is virtually the same as in the gas phase.I.
Fashion intelligence in the Metaverse: promise and future prospects

Abstract With the development of artificial intelligence (AI) and the constraints on offline activities imposed due to the sudden outbreak of the COVID epidemic, the Metaverse has recently attracted significant research attention from both academia and industrial practitioners. Fashion, as an expression of a consumer’s aesthetics and personality, has enormous economic potential in both the real world and the Metaverse. In this research, we provide a comprehensive survey of two of the most important components of fashion in the Metaverse: virtual digital humans, and tasks related to fashion items. We survey state-of-the-art articles from 2007 to the present and provide a new taxonomy of extant research topics based on these articles. We also highlight the applications of these topics in the Metaverse from the perspectives of designers and consumers. Finally, we describe possible scenes involving fashion in the Metaverse. The current challenges and open issues related to the fashion industry in the Metaverse are also discussed in order to provide guidance for fashion practitioners, and to shed some light on the future development of fashion AI in the Metaverse.
Evaluating Design Guidelines for Intuitive, Therefore Sustainable, Virtual Reality Authoring Tools

Virtual reality software might be challenging to utilize for beginners and unskilled professionals who do not have a programming or 3D modeling background. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. These tools are frequently insufficient due to a lack of support and standard operating procedures. This study evaluates the validity of fourteen design guidelines for the development of intuitive virtual reality authoring tools. Adopting the Design Science Research approach, a previous study completed the first steps of the protocol by identifying problems, defining solution objectives, and developing and demonstrating the design guidelines. This work evaluates their application in a experiment. A group of engineering students with no prior experience in creating virtual worlds were tasked with examining the design guidelines while using the NVIDIA Omniverse Enterprise as an exemplary use case by following the tutorials in the platform. Previously, the students answered a Likert-scale questionnaire and a focus group interview, with eighteen questions about how they perceived these guidelines. An average score value for the tool was estimated through the questionnaire answers and a correlation analysis and, using the Pearson Correlation Coefficient (PCC), it was confirmed that most guidelines scores behaved as expected and were ranked according to the use-case functionality. The participants understood the guidelines’ definition and could decide if they agreed or disagreed with their presence during the experiment. We evaluated that, in accordance with the Design Science Research, the proposed artifact is useful, i.e., the design guidelines for virtual reality authoring tools perform what they are designed to do and are operationally reliable in accomplishing their goals.
Design and development of prime herder optimization based BiLSTM congestion predictor model in live video streaming

High-quality content for the user in video streaming services depends critically on the ability to predict the continuous user’s quality of experience (QoE). However, continuous QoE prediction has proven challenging due to the complexity imposed by the temporal dependencies in QoE data and the non-linear correlations among QoE impact elements. In this research congestion prediction model is developed using the prime herder optimization-based BiLSTM (PHO-based BiLSTM). The input database is first gathered from the NIMS and darpa99 week 1 database and, the data collection is analyzed and the packet information is extracted after that the extracted features are then fed into the optimized BiLSTM classifier to train the classifier. The classifier’s hyperparameters are successfully tuned by the recommended prime herder optimization, which is made by fusing the herding characteristics of a prime sheepdog and herder optimization. Based on the traffic congestion prediction achievements, at training percentage (TP) 90, the accuracy is 94.81%, specificity is 94.90%, and mean square error (MSE) is 4.91 respectively for D1, similarly based on D2 the accuracy is 95.62%, specificity is 95.96%, and MSE is 0.38 respectively.
Sound field reconstruction using neural processes with dynamic kernels

Abstract Accurately representing the sound field with high spatial resolution is crucial for immersive and interactive sound field reproduction technology. In recent studies, there has been a notable emphasis on efficiently estimating sound fields from a limited number of discrete observations. In particular, kernel-based methods using Gaussian processes (GPs) with a covariance function to model spatial correlations have been proposed. However, the current methods rely on pre-defined kernels for modeling, requiring the manual identification of optimal kernels and their parameters for different sound fields. In this work, we propose a novel approach that parameterizes GPs using a deep neural network based on neural processes (NPs) to reconstruct the magnitude of the sound field. This method has the advantage of dynamically learning kernels from data using an attention mechanism, allowing for greater flexibility and adaptability to the acoustic properties of the sound field. Numerical experiments demonstrate that our proposed approach outperforms current methods in reconstructing accuracy, providing a promising alternative for sound field reconstruction.
Better-than-classical Grover search via quantum error detection and suppression

Abstract We report better-than-classical success probabilities for a complete Grover quantum search algorithm on the largest scale demonstrated to date, of up to five qubits, using two different IBM platforms. This is enabled by error suppression via robust dynamical decoupling. Further improvements arise after the use of measurement error mitigation, but the latter is insufficient by itself for achieving better-than-classical performance. For two qubits, we demonstrate a 99.5% success probability via the use of the [[4, 2, 2]] quantum error-detection (QED) code. This constitutes a demonstration of quantum algorithmic breakeven via QED. Along the way, we introduce algorithmic error tomography (AET), a method that provides a holistic view of the errors accumulated throughout an entire quantum algorithm, filtered via the errors detected by the QED code used to encode the circuit. We demonstrate that AET provides a stringent test of an error model based on a combination of amplitude damping, dephasing, and depolarization.