Reviewerly/exHarmony huggingface dataset:
Picosecond ion-qubit manipulation and spin-phonon entanglement with resonant laser pulses

Ultrafast spin-phonon entanglement based on spin-dependent momentum kicks (SDKs) provides an approach to realize fast entangling gates with intrinsic robustness and scalability for trapped ion quantum computing. Such SDKs so far have been implemented on a nanosecond timescale by off-resonant Raman transitions where each laser pulse is split into a sequence of perturbation pulses with carefully designed temporal patterns. Here we report an experimental realization of ultrafast qubit manipulation and spin-phonon entanglement in picoseconds using SDKs from single resonant laser pulses on the magnetic-field-insensitive hyperfine qubit states. This experiment demonstrates a convenient approach to ultrafast SDKs on noise-insensitive ion-spin qubits, with improvement in its speed by more than an order of magnitude. It removes the need to engineer the pattern of a sequence of perturbation pulses and is less vulnerable to noise, simplifying the approach to large-scale trapped-ion quantum computing based on fast quantum gates with SDKs.
Graph Structural Attack by Perturbing Spectral Distance

Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks. In this paper, an effective graph structural attack is investigated to disrupt graph spectral filters in the Fourier domain, which are the theoretical foundation of GCNs. We define the notion of spectral distance based on the eigenvalues of graph Laplacian to measure the disruption of spectral filters. We realize the attack by maximizing the spectral distance and propose an efficient approximation to reduce the time complexity brought by eigen-decomposition. The experiments demonstrate the remarkable effectiveness of the proposed attack in both black-box and white-box settings for both test-time evasion attacks and training-time poisoning attacks. Our qualitative analysis suggests the connection between the imposed spectral changes in the Fourier domain and the attack behavior in the spatial domain, which provides empirical evidence that maximizing spectral distance is an effective way to change the graph structural property and thus disturb the frequency components for graph filters to affect the learning of GCNs.
Communication-Efficient Robust Federated Learning with Noisy Labels

Federated learning (FL) is a promising privacy-preserving machine learning paradigm over distributed located data. In FL, the data is kept locally by each user. This protects the user privacy, but also makes the server difficult to verify data quality, especially if the data are correctly labeled. Training with corrupted labels is harmful to the federated learning task; however, little attention has been paid to FL in the case of label noise. In this paper, we focus on this problem and propose a learning-based reweighting approach to mitigate the effect of noisy labels in FL. More precisely, we tuned a weight for each training sample such that the learned model has optimal generalization performance over a validation set. More formally, the process can be formulated as a Federated Bilevel Optimization problem. Bilevel optimization problem is a type of optimization problem with two levels of entangled problems. The non-distributed bilevel problems have witnessed notable progress recently with new efficient algorithms. However, solving bilevel optimization problems under the Federated Learning setting is under-investigated. We identify that the high communication cost in hypergradient evaluation is the major bottleneck. So we propose \textit{Comm-FedBiO} to solve the general Federated Bilevel Optimization problems; more specifically, we propose two communication-efficient subroutines to estimate the hypergradient. Convergence analysis of the proposed algorithms is also provided. Finally, we apply the proposed algorithms to solve the noisy label problem. Our approach has shown superior performance on several real-world datasets compared to various baselines.
DDR: Dialogue Based Doctor Recommendation for Online Medical Service

Online medical consultation, which enables patients to remotely inquire doctors in the form of web chatting, has become an indispensable part of the social health care system. Intuitively, it is a crucial step to recommend suitable doctor candidates for patients, especially with suffering the severe cold-start challenge of patients due to the limited historical records and insufficient description of patient condition. Along this line, in this paper, we propose a novel Dialogue based Doctor Recommendation (DDR) model, which comprehensively integrates three types of information in modeling, including the profile and chief complaint from patients, the historical records of doctors and the patient-doctor dialogue. Accordingly, we propose 1) a patient encoder which represents the patient's condition and medical requirements; 2) a doctor encoder which distills the doctor's expertise and communication skills; 3) a dialogue encoder which extracts textual features from doctor-patient conversation. Specifically, since the patient-doctor dialogue is not available in the testing stage, we propose to simulate the dialogue embedding with patient embedding via a contrastive learning based module. Experimental results on a real-world data set show that the proposed DDR model can outperform state-of-the-art recommendation-based methods. Moreover, considering the accessibility variance of online medical consultation services between the youth and the elderly, we also conduct a fairness study on the proposed DDR model.
Enhancing Machine Learning Approaches for Graph Optimization Problems with Diversifying Graph Augmentation

Recently, many machine learning-based approaches that effectively solve graph optimization problems have been proposed. These approaches are usually trained on graphs randomly generated with graph generators or sampled from existing datasets. However, we observe that such training graphs lead to poor testing performance if the testing graphs are not generated analogously, i.e., the generalibility of the models trained on those randomly generated training graphs are very limited. To address this critical issue, in this paper, we propose a new framework, named Learning with Iterative Graph Diversification (LIGD), and formulate a new research problem, named Diverse Graph Modification Problem (DGMP), that iteratively generate diversified training graphs and train the models that solve graph optimization problems to significantly improve their performance. We propose three approaches to solve DGMP by considering both the performance of the machine-learning approaches and the structural properties of the training graphs. Experimental results on well-known problems show that our proposed approaches significantly boost the performance of both supervised and reinforcement learning approaches and produce near-optimal results, significantly outperforming the baseline approaches, such as graph augmentation and deep learning-based graph generation approaches.
A novel rotated sigmoid weight function for higher performance in heterogeneous deformation measurement with digital image correlation

• A new rotated sigmoid weight (RSW) function based DIC (RSW-DIC) is proposed to tackle the problem that the results of the weight function based DIC are influenced by the initial subset size (here ‘initial’ means ‘original’, the initial subset size keepsconstant over iterations, but the equivalent subset size is varying according to the weight distribution). The RSW-DIC has much better performance than other weight function based methods for unknown heterogeneous displacement. • The spatial resoltion of the proposed method is much better than other DIC methods using the same subset and shape functionorder. • The proposed RSW-DIC has better noise resistance than other participated methods. The Measurement resolution can be reduced by using a big initial subset size without increasing spatial resolution much. Conventional digital image correlation (C-DIC) combined with a rotated Gaussian weight (RGW) function for subset has demonstrated attractive ability in resolving heterogeneous deformation parameters. To further improve the performance, the selection of an optimum weight function becomes the key issue. In this paper, a novel rotated sigmoid weight (RSW) function is proposed. RSW function aims to get a more uniform weight distribution near the subset center, and to realize the continuous change of the equivalent subset size as well. The performance of the RSW function is compared with the Gaussian weight (GW) function, RGW function, the rotated inverse distance weight (RIDW) function and the inverse distance square weight (RIDSW) function through Star 5 image set from the DIC challenge 2.0 and the simulated image set. A total of six methods with different weight functions and rotated weights are systematically compared. The experiment results clearly show that DIC combined with RSW function (i.e. RSW-DIC) has the best spatial resolution without sacrificing much measurement resolution. The spatial resolution of RSW-DIC is only about half of the other methods for both first- and second-order shape functions when a big initial subset size is adopted.
DuIVA: An Intelligent Voice Assistant for Hands-free and Eyes-free Voice Interaction with the Baidu Maps App

Mobile map apps such as the Baidu Maps app have become a ubiquitous and essential tool for users to find optimal routes and get turn-by-turn navigation services while driving. However, interacting with such apps while driving through visual-manual interaction modality inevitably causes driver distraction, due to the highly conspicuous nature of the time-sharing, multi-tasking behavior of the driver. In this paper, we present our efforts and findings of a 4-year longitudinal study on designing and implementing DuIVA, which is an intelligent voice assistant (IVA) embedded in the Baidu Maps app for hands-free, eyes-free human-to-app interaction in a fully voice-controlled manner. Specifically, DuIVA is designed to enable users to control the functionalities of Baidu Maps (e.g., navigation and location search) through voice interaction, rather than visual-manual interaction, which minimizes driver distraction and promotes safe driving by allowing the driver to keep "eyes on the road and hands on the wheel'' while interacting with the Baidu Maps app. DuIVA has already been deployed in production at Baidu Maps since November 2017, which facilitates a better interaction modality with the Baidu Maps app and improves the accessibility and usability of the app by providing users with in-app voice activation, natural language queries, and multi-round dialogue. As of December 31, 2021, over 530 million users have used DuIVA, which demonstrates that DuIVA is an industrial-grade and production-proven solution for in-app intelligent voice assistants.
Semantic Retrieval at Walmart

In product search, the retrieval of candidate products before re-ranking is more mission critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines traditional inverted index and embedding-based neural retrieval to better answer user tail queries. Our system significantly improved the relevance of the search engine, measured by both offline and online evaluations. The improvements were achieved through a combination of different approaches. We present a new technique to train the neural model at scale. and describe how the system was deployed in production with little impact on response time. We highlight multiple learnings and practical tricks that were used in the deployment of this system.
Friend Recommendations with Self-Rescaling Graph Neural Networks

Friend recommendation service plays an important role in shaping and facilitating the growth of online social networks. Graph embedding models, which can learn low-dimensional embeddings for nodes in the social graph to effectively represent the proximity between nodes, have been widely adopted for friend recommendations. Recently, Graph Neural Networks (GNNs) have demonstrated superiority over shallow graph embedding methods, thanks to their ability to explicitly encode neighborhood context. This is also verified in our Xbox friend recommendation scenario, where some simplified GNNs, such as LightGCN and PPRGo, achieve the best performance. However, we observe that many GNN variants, including LightGCN and PPRGo, use a static and pre-defined normalizer in neighborhood aggregation, which is decoupled with the representation learning process and can cause the scale distortion issue. As a consequence, the true power of GNNs has not yet been fully demonstrated in friend recommendations.
PinnerFormer

Sequential models have become increasingly popular in powering personalized recommendation systems over the past several years. These approaches traditionally model a user's actions on a website as a sequence to predict the user's next action. While theoretically simplistic, these models are quite challenging to deploy in production, commonly requiring streaming infrastructure to reflect the latest user activity and potentially managing mutable data for encoding a user's hidden state. Here we introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement using a sequential model of a user's recent actions. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss, modeling long-term future actions instead of next action prediction. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action. We describe our design decisions via extensive offline experimentation and ablations and validate the efficacy of our approach in A/B experiments showing substantial improvements in Pinterest's user retention and engagement when comparing PinnerFormer against our previous user representation. PinnerFormer is deployed in production as of Fall 2021.
UD-GNN

Recent studies on Graph Neural Networks (GNNs) point out that most GNNs depend on the homophily assumption but fail to generalize to graphs with heterophily where dissimilar nodes connect. The concept of homophily or heterophily defined previously is a global measurement of the whole graph and cannot describe the local connectivity of a node. From the node-level perspective, we find that real-world graph structures exhibit a mixture of homophily and heterophily, which refers to the co-existence of both homophilous and heterophilous nodes. Under such a mixture, we reveal that GNNs are severely biased towards homophilous nodes, suffering a sharp performance drop on heterophilous nodes. To mitigate the bias issue, we explore an Uncertainty-aware Debiasing (UD) framework, which retains the knowledge of the biased model on certain nodes and compensates for the nodes with high uncertainty. In particular, UD estimates the uncertainty of the GNN output to recognize heterophilous nodes. UD then trains a debiased GNN by pruning the biased parameters with certain nodes and retraining the pruned parameters on nodes with high uncertainty. We apply UD on both homophilous GNNs (GCN and GAT) and heterophilous GNNs (Mixhop and GPR-GNN) and conduct extensive experiments on synthetic and benchmark datasets, where the debiased model consistently performs better and narrows the performance gap between homophilous and heterophilous nodes.
Connecting the Hosts: Street-Level IP Geolocation with Graph Neural Networks

Pinpointing the geographic location of an IP address is important for a range of location-aware applications spanning from targeted advertising to fraud prevention. The majority of traditional measurement-based and recent learning-based methods either focus on the efficient employment of topology or utilize data mining to find clues of the target IP in publicly available sources. Motivated by the limitations in existing works, we propose a novel framework named GraphGeo, which provides a complete processing methodology for street-level IP geolocation with the application of graph neural networks. It incorporates IP hosts knowledge and kinds of neighborhood relationships into the graph to infer spatial topology for high-quality geolocation prediction. We explicitly consider and alleviate the negative impact of uncertainty caused by network jitter and congestion, which are pervasive in complicated network environments. Extensive evaluations across three large-scale real-world datasets demonstrate that GraphGeo significantly reduces the geolocation errors compared to the state-of-the-art methods. Moreover, the proposed framework has been deployed on the web platform as an online service for 6 months.
Domain Adaptation in Physical Systems via Graph Kernel

Physical systems are extending their monitoring capacities to edge areas with low-cost, low-power sensors and advanced data mining and machine learning techniques. However, new systems often have limited data for training the model, calling for effective knowledge transfer from other relevant grids. Specifically, Domain Adaptation (DA) seeks domain-invariant features to boost the model performance in the target domain. Nonetheless, existing DA techniques face significant challenges due to the unique characteristics of physical datasets: (1) complex spatial-temporal correlations, (2) diverse data sources including node/edge measurements and labels, and (3) large-scale data sizes. In this paper, we propose a novel cross-graph DA based on two core designs of graph kernels and graph coarsening. The former design handles spatial-temporal correlations and can incorporate networked measurements and labels conveniently. The spatial structures, temporal trends, measurement similarity, and label information together determine the similarity of two graphs, guiding the DA to find domain-invariant features. Mathematically, we construct a Graph kerNel-based distribution Adaptation (GNA) with a specifically-designed graph kernel. Then, we prove the proposed kernel is positive definite and universal, which strictly guarantees the feasibility of the used DA measure. However, the computation cost of the kernel is prohibitive for large systems. In response, we propose a novel coarsening process to obtain much smaller graphs for GNA. Finally, we report the superiority of GNA in diversified systems, including power systems, mass-damper systems, and human-activity sensing systems.
Multi-source pseudo-label learning of semantic segmentation for the scene recognition of agricultural mobile robots

This paper describes a novel method of training a semantic segmentation model for scene recognition of agricultural mobile robots exploiting publicly available datasets of outdoor scenes that are different from the target greenhouse environments. Semantic segmentation models require abundant labels given by tedious manual annotation. A method to work around it is unsupervised domain adaptation (UDA) that transfers knowledge from labeled source datasets to unlabeled target datasets. However, the effectiveness of existing methods is not well studied in adaptation between heterogeneous environments, such as urban scenes and greenhouses. In this paper, we propose a method to train a semantic segmentation model for greenhouse images without manually labeled datasets of greenhouse images. The core of our idea is to use multiple rich image datasets of different environments with segmentation labels to generate pseudo-labels for the target images to effectively transfer the knowledge from multiple sources and realize a precise training of semantic segmentation. Along with the pseudo-label generation, we introduce state-of-the-art methods to deal with noise in the pseudo-labels to further improve the performance. We demonstrate in experiments with multiple greenhouse datasets that our proposed method improves the performance compared to the single-source baselines and an existing approach.
Memory‐augmented neural networks based dynamic complex image segmentation in digital twins for self‐driving vehicle

With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.
Fully automated breast segmentation on spiral breast computed tomography images

The quantification of the amount of the glandular tissue and breast density is important to assess breast cancer risk. Novel photon-counting breast computed tomography (CT) technology has the potential to quantify them. For accurate analysis, a dedicated method to segment the breast components-the adipose and glandular tissue, skin, pectoralis muscle, skinfold section, rib, and implant-is required. We propose a fully automated breast segmentation method for breast CT images.The framework consists of four parts: (1) investigate, (2) segment the components excluding adipose and glandular tissue, (3) assess the breast density, and (4) iteratively segment the glandular tissue according to the estimated density. For the method, adapted seeded watershed and region growing algorithm were dedicatedly developed for the breast CT images and optimized on 68 breast images. The segmentation performance was qualitatively (five-point Likert scale) and quantitatively (Dice similarity coefficient [DSC] and difference coefficient [DC]) demonstrated according to human reading by experienced radiologists.The performance evaluation on each component and overall segmentation for 17 breast CT images resulted in DSCs ranging 0.90-0.97 and in DCs 0.01-0.08. The readers rated 4.5-4.8 (5 highest score) with an excellent inter-reader agreement. The breast density varied by 3.7%-7.1% when including mis-segmented muscle or skin.The automatic segmentation results coincided with the human expert's reading. The accurate segmentation is important to avoid the significant bias in breast density analysis. Our method enables accurate quantification of the breast density and amount of the glandular tissue that is directly related to breast cancer risk.