Reviewerly/exHarmony huggingface dataset:
Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach

Bipartite graph embedding (BGE), as the fundamental task in bipartite network analysis, is to map each node to compact low-dimensional vectors that preserve intrinsic properties. The existing solutions towards BGE fall into two groups: metric-based methods and graph neural network-based (GNN-based) methods. The latter typically generates higher-quality embeddings than the former due to the strong representation ability of deep learning. Nevertheless, none of the existing GNN-based methods can handle billion-scale bipartite graphs due to the expensive message passing or complex modelling choices. Hence, existing solutions face a challenge in achieving both embedding quality and model scalability. Motivated by this, we propose a novel graph neural network named AnchorGNN based on global-local learning framework, which can generate high-quality BGE and scale to billion-scale bipartite graphs. Concretely, AnchorGNN leverages a novel anchor-based message passing schema for global learning, which enables global knowledge to be incorporated to generate node embeddings. Meanwhile, AnchorGNN offers an efficient one-hop local structure modelling using maximum likelihood estimation for bipartite graphs with rational analysis, avoiding large adjacency matrix construction. Both global information and local structure are integrated to generate distinguishable node embeddings. Extensive experiments demonstrate that AnchorGNN outperforms the best competitor by up to 36% in accuracy and achieves up to 28 times speed-up against the only metric-based baseline on billion-scale bipartite graphs.
Everest: GPU-Accelerated System for Mining Temporal Motifs

Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. Despite its utility in several critical application domains that demand high performance ( e.g. , detecting fraud in financial transaction graphs), the performance of existing software is limited on commercial hardware platforms, in that it runs for tens of hours. This paper presents Everest---a system that efficiently maps the workload of mining (supports both enumeration and counting) temporal motifs to the highly parallel GPU architecture. In particular, using an input temporal graph and a more expressive user-defined temporal motif query definition compared to prior works, Everest generates an execution plan and runtime primitives that optimize the workload execution by exploiting the high compute throughput of a GPU. Everest generates motif-specific mining code to reduce long-latency memory accesses and frequent thread divergence operations. Everest incorporates novel low-cost runtime mechanisms to enable load balancing to improve GPU hardware utilization. To support large graphs that do not fit on GPU memory, Everest also supports multi-GPU execution by intelligently partitioning the edge list that prevents inter-GPU communication. Everest hides the implementation complexity of presented optimizations away from the targeted system user for better usability. Our evaluation shows that, using proposed optimizations, Everest improves the performance of a baseline GPU implementation by 19X, on average.
Relational Query Synthesis ⋈ Decision Tree Learning

We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates. On the other hand, decision tree learning techniques are suited to synthesizing comparison predicates when the input database can be summarized as a single labelled relational table. In this paper, we adapt and interleave methods from the domains of relational query synthesis and decision tree learning, and present an end-to-end framework for synthesizing relational queries with categorical and numerical comparison predicates. Our technique guarantees the completeness of the synthesis procedure and strongly encourages minimality of the synthesized program. We present Libra, an implementation of this technique and evaluate it on a benchmark suite of 1,475 instances of queries over 159 databases with multiple tables. Libra solves 1,361 of these instances in an average of 59 seconds per instance. It outperforms state-of-the-art program synthesis tools Scythe and PatSQL in terms of both the running time and the quality of the synthesized programs.
Utility-Aware Payment Channel Network Rebalance

The payment channel network (PCN) is a promising solution to increase the throughput of blockchains. However, unidirectional transactions can deplete a user's deposits in a payment channel (PC), reducing the success ratio of transactions (SRoT). To address this depletion issue, rebalance protocols are used to shift tokens from well-deposited PCs to under-deposited PCs. To improve SRoT, it is beneficial to increase the balance of a PC with a lower balance and a higher weight (i.e., more transaction executions rely on the PC). In this paper, we define the utility of a transaction and the utility-aware rebalance (UAR) problem. The utility of a transaction is proportional to the weight of the PC and the amount of the transaction, and inversely proportional to the balance of the receiver. To maximize the effect of improving SRoT, UAR aims to find a set of transactions with maximized utilities, satisfying the budget and conservation constraints. The budget constraint limits the number of tokens shifted in a PC. The conservation constraint requires that the number of tokens each user sends equals the number of tokens received. We prove that UAR is NP-hard and cannot be approximately solved with a constant ratio. Thus, we propose two heuristic algorithms, namely Circuit Greedy and UAR_DC. Extensive experiments show that our approaches outperform the existing approach by at least 3.16 times in terms of utilities.
Roles of the Web in Commercial Energy Efficiency: IoT, Cloud Computing, and Opinion Mining

The overconsumption of energy in recent times has motivated many studies. Some of these explore the application of web technologies and machine learning models, aiming to increase energy efficiency and reduce the carbon footprint. This paper aims to review three areas that overlap between the web and energy usage in the commercial sector: IoT (Internet of Things), cloud computing and opinion mining. The paper elaborates on problems in terms of their causes, influences, and potential solutions, as found in multiple studies across these areas; and intends to identify potential gaps with the scope for further research. In the rapidly digitizing and automated world, these three areas can offer much contribution towards reducing energy consumption and making the commercial sector more energy efficient. IoT and smart manufacturing can assist much in effective production, and more efficient technologies as per energy usage. Cloud computing, with reference to its impact on green IT (information technology), is a major area that contributes towards the mitigation of carbon footprint and the reduction of costs on energy consumption. Opinion mining is significant as per the part it plays in understanding the feelings, requirements and demands of the consumers of energy as well as the related stakeholders, so as to help create more suitable policies and hence navigate towards more energy efficient strategies. This paper offers comprehensive analyses on the literature in the concerned areas to fathom the current status and explore future possibilities of research across these areas and the related multidisciplinary avenues.
Uninteresting Items: Concept and Its Application to Effective Collaborative Filtering in Recommender Systems

Recommender systems aim to predict user preferences by analyzing users' past behavior. Collaborative filtering (CF) is one of the key techniques employed in recommender systems that uses explicit ( e.g. , ratings) and implicit ( e.g. , browsing) feedback from users to predict unknown feedback, providing top- N recommendations. However, CF faces challenges when dealing with sparse data, which can decrease the accuracy of recommendations. To overcome these inherent challenges in recommender systems, this article introduces the concept of "uninteresting items" that have not been rated by a user, but are unlikely to be liked even when recommended. We then review our previous works that utilize both positive preferences from rated items and negative preferences from uninteresting items to improve recommendation accuracy. Specifically, we discuss a family of our eight CF methods that are assisted by the uninteresting items: Zero-injection (ZI), l -injection, Imputation, RAGAN, and Deep-ZI, which are designed for explicit feedback, as well as gOCCF, M-BPR, and CNS, which are designed for implicit feedback. Also, we report some evaluation results for showing their effectiveness.
High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes

This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos. The key observation is that while previous grid-based methods offer consistent rendering, they fall short in capturing appearance details of a complex dynamic scene, a domain where multi-view image-based rendering methods demonstrate the opposite properties. To combine the best of two worlds, we introduce Im4D, a hybrid scene representation that consists of a grid-based geometry representation and a multi-view image-based appearance representation. Specifically, the dynamic geometry is encoded as a 4D density function composed of spatiotemporal feature planes and a small MLP network, which globally models the scene structure and facilitates the rendering consistency. We represent the scene appearance by the original multi-view videos and a network that learns to predict the color of a 3D point from image features, instead of memorizing detailed appearance totally with networks, thereby naturally making the learning of networks easier. Our method is evaluated on five dynamic view synthesis datasets including DyNeRF, ZJU-MoCap, NHR, DNA-Rendering and ENeRF-Outdoor datasets. The results show that Im4D exhibits state-of-the-art performance in rendering quality and can be trained efficiently, while realizing real-time rendering with a speed of 79.8 FPS for 512x512 images, on a single RTX 3090 GPU. The code is available at https://zju3dv.github.io/im4d.
High-dimensional reinforcement learning for optimization and control of ultracold quantum gases

Abstract Machine-learning (ML) techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning (RL) offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply RL to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This RL agent determines an optimal set of 30 control parameters in a dynamically changing environment that is characterized by 30 sensed parameters. By comparing this method to that of training supervised-learning regression models, as well as to human-driven control schemes, we find that both ML approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the RL method achieves consistent outcomes, even in the presence of a dynamic environment.
Multi-purpose Image Sensors with Nonlinear Optical Neural Networks

We use a multilayer, nonlinear optical neural network (ONN) as a pre-processor for machine vision. On tasks including identification of real objects illuminated by broadband incoherent light, we show that nonlinear optical pre-processing outperforms linear pre-processing.
Entangling microwaves and telecom wavelength light

We entangled microwave and optical photons for the first time as verified by a measured two-mode vacuum squeezing of 0.7 dB. This electro-optic entanglement is the key resource needed to connect cryogenic quantum circuits.
Towards Trustworthy Digital Media In The Aigc Era: An Introduction To The Upcoming IsoJpegTrust Standard

With the rapid development of Artificial Intelligence Generated Content comes the need for more advanced technologies to distinguish fake medias from authentic ones. Current standards for authenticating media assets are developed by the Coalition for Content Provenance and Authenticity and the International Press Telecommunications Council. The former focuses on providing complete provenance for media assets to allow users to backtrack the history of creation and modification, while the latter establishes a standard method for asserting and storing information related to media assets. JPEG Trust, on the other hand, is an evolving standard that aims to provide a way of proving the trustworthiness of media assets, thus directly addressing the aforementioned issue. Through the generation of a trust report, JPEG Trust allows users to directly view the evaluation results of the trustworthiness of media assets and make decisions accordingly. JPEG Trust is expected to bring application values for countries, platforms, and individuals and commercial values targeting companies and the wider society. However, there still exists potential challenges for JPEG Trust that demand a solution. With the possibilities JPEG Trust brings, it is expected to leave considerable impacts on the trustworthiness of the future after addressing challenges along the way.
A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Wavelet neural networks have been widely applied to dynamical system identification fields. The most difficult issue lies in selecting the optimal control parameters (the wavelet base type and corresponding resolution level) of the network structure. This paper utilizes the advantages of Legendre multiwavelet (LW) bases to construct a Legendre multiwavelet neural network (LWNN), whose simple structure consists of an input layer, hidden layer, and output layer. It is noted that the activation functions in the hidden layer are adopted as LW bases. This selection if based on the its rich properties of LW bases, such as piecewise polynomials, orthogonality, various regularities, and more. These properties contribute to making LWNNs more effective in approximating the complex characteristics exhibited by uncertainties, step, nonlinear, and ramp in the dynamical systems compared to traditional wavelet neural networks. Then, the number of selection LW bases and the corresponding resolution level are effectively optimized by the simple Genetic Algorithm, and the improved gradient descent algorithm is implemented to learn the weight coefficients of LWNN. Finally, four nonlinear dynamical system identification problems are applied to validate the efficiency and feasibility of the proposed LWNN-GA method. The experiment results indicate that the LWNN-GA method achieves better identification accuracy with a simpler network structure than other existing methods.
AI-Native Communications

The emergence of artificial intelligence (AI)-based methods evolving from 5G to 6G is accelerating. Therefore, to optimize the communication system in the 6G era, it is essential to adapt several AI-based optimization methods according to each environment. In this chapter, we introduce two general AI-based optimization methods, named AI-aided and AI-native. In addition, we describe the pros and cons of each method. Finally, by illustrating previous studies on AI in communication, we aim to speed up the development of both AI-based and AI-native optimization methods in line with the upcoming 6G era.
Emotion differentiation through features of eye-tracking and pupil diameter for monitoring well-being

Emotions are an important contributor to human self-expression and well-being. However, many populations express their emotions differently from what is considered "typical". Previous literature has indicated a possible relationship between emotion and eye-movement. The objective of this paper is to further explore this proposed relationship by identifying specific features of eye-movement that relate to six emotion categories: joy, surprise, indifference, disgust, sadness, and fear. Features of eye-movement are extracted from measurements of pupil diameter, saccades, and fixations. These measurements are collected as participants view images from the International Affective Picture System, a validated image deck used to evoke known levels of pleasure, arousal, and dominance. Example features of eye-movement measurements such as pupil diameter include maximum or minimum values, means, and standard deviations. Statistical analyses indicate that the extracted features of eye-tracking in this paper can identify fear and sadness with relative accuracy, while more work is needed to differentiate among joy, indifference, disgust, and surprise. Future work aims to understand differences between typically developing populations such as the individuals included in this analysis, and clinical populations such as individuals with cerebral palsy.Clinical Relevance- This pilot study suggests a link between emotion and features of eye-movement. The information will later be used to develop assistive communication devices that better meet the self-expression needs of individuals with motor and communication challenges.
Prediction of Near-Term Breast Cancer Occurrence using Subtraction of Temporally Sequential Digital Mammograms

Breast cancer remains one of the leading cancers for women worldwide. Fortunately, with the introduction of mammography, the mortality rate has significantly decreased. However, earlier breast cancer prediction could effectively increase the survival rates, improve patient outcomes, and avoid unnecessary biopsies. For that purpose, prediction of breast cancer, using subtraction of temporally sequential digital mammograms and machine learning, is proposed. A new dataset was collected with 192 images from 32 patients (three screening rounds, with two views of each breast). This dataset included precise annotation of each individual malignant mass, present in the most recent mammogram, with the two priors being radiologically evaluated as normal. The most recent mammogram was considered as the "future" screening round and provided the location of the mass as the ground truth for the training. The two previous mammograms, the "current" and the "prior", were processed and a new, difference image was formed for the prediction. Ninety-six features were extracted and five feature selection algorithms were combined to identify the most important features. Ten classifiers were tested in leave-one-patient-out and k-fold-patient cross-validation (k = 4 and 8). Ensemble Voting achieved the highest performance in the prediction of the development of breast mass in the next screening round, with 85.7% sensitivity, 83.7% specificity, 83.7% accuracy and 0.85 AUC. The proposed methodology could lead to a new mammography-based model that could predict the short-term risk for developing a malignancy, thus providing an earlier diagnosis.
Classification of fall risk across the lifespan using gait derived features from a wearable device

Falls are one of the leading factors of injury and fatality in older adults. Given the importance of early detection of adults at higher risk of falls, we evaluated the ability of machine learning to classify fall risk in adults across the lifespan using wearable sensors embedded in a smartshirt. We evaluated the classification performance of binary and multiclass fall risk classifier models using SciKit Digital Health in adults across the lifespan. Using a k-fold and group k-fold cross-validation strategy, we demonstrate the feasibility of fall risk classification using accelerometer data from 10 second epochs of treadmill walking data from adults across the lifespan. We achieved an 88% accuracy in a binary clasifier of fallers vs. non-fallers, and an 86% accuracy in a multiclass classifier comparing non-fallers, fallers, and recurrent fallers using retrospective fall histories. Comparing group k-fold vs. k-fold cross-validation strategies, we find a 22-27% drop-off in accuracy performance. Furthering the evaluation framework presented in this study would be valuable to the development of more robust and clinically relevant models used in the prediction of fall risk. These models could one day be applied in clinical settings to help better diagnose and monitor fall risk among older adults, improving the care of at-risk individuals and reducing the injury and associated cost of falls.
Meta-EHR: A meta-learning approach for electronic health records with a high imbalanced ratio and missing rate

Data imbalance is a practical and crucial issue in deep learning. Moreover, real-world datasets, such as electronic health records (EHR), often suffer from high missing rates. Both issues can be understood as noises in data that may lead to bad generalization results for standard deep-learning algorithms. This paper introduces a novel meta-learning approach to deal with these noise issues in an EHR dataset for a binary classification task. This meta-learning approach leverages the information from a selected subset of balanced, low-missing rate data to automatically assign proper weight to each sample. Such weights would enhance the informative samples and suppress the opposites during training. Furthermore, the meta-learning approach is model-agnostic for deep learning-based architectures that simultaneously handle the high imbalanced ratio and high missing rate problems. Through experiments, we demonstrate that this meta-learning approach is better in extreme cases. In the most extreme one, with an imbalance ratio of 172 and a 74.6% missing rate, our method outperforms the original model without meta-learning by as much as 10.3% of the area under the receiver-operating characteristic curve (AUROC) and 3.2% of the area under the precision-recall curve (AUPRC). Our results mark the first step towards training a robust model for extremely noisy EHR datasets.
A virtual reality platform to evaluate the effects of supernumerary limbs’ appearance

Supernumerary robot limbs (SL) can expand the ability of users by increasing the number of degrees of freedom that they control. While several SLs have been designed and tested on human participants, the effect of the limb’s appearance on the user’s acceptance, embodiment and device usage is not yet understood. We developed a virtual reality platform with a three-arm avatar that enabled us to systematically investigate the effect of the supernumerary limb’s appearance on their perception and motion control performance. A pilot study with 14 participants exhibited similar performance, workload and preference in human-like or robot-like appearance with a trend of preference for the robotic appearance.
Digital Electronic and Analog Photonic Acceleration for Point Cloud Classifiers

We propose a method for training photonic neuromorphic accelerators using only software, and demonstrate its potential in autonomous driving. The method is modular and drop-in compatible with existing PyTorch training pipelines.
Cutting Edge Quality Assessment of Surgical Tools Using Knife-edge Interferometric NDT Technique

This work introduces a noncontact measurement technique, based on knife-edge interferometry, for monitoring corrosive wear propagation on cutting edges. The corrosion progression can be identified by comparing diffraction patterns of fresh and corroded surgical blades.