Reviewerly/exHarmony huggingface dataset:
Learning with Small Data: Subgraph Counting Queries

Abstract Deep Learning (DL) has been widely used in many applications, and its success is achieved with large training data. A key issue is how to provide a DL solution when there is no large training data to learn initially. In this paper, we explore a meta-learning approach for a specific problem, subgraph isomorphism counting, which is a fundamental problem in graph analysis to count the number of a given pattern graph, p , in a data graph, g , that matches p . There are various data graphs and pattern graphs. A subgraph isomorphism counting query is specified by a pair, ( g , p ). This problem is NP-hard and needs large training data to learn by DL in nature. We design a Gaussian Process (GP) model which combines Graph Neural Network with Bayesian nonparametric, and we train the GP by a meta-learning algorithm on a small set of training data. By meta-learning, we can obtain a generalized meta-model to better encode the information of data and pattern graphs and capture the prior of small tasks. With the meta-model learned, we handle a collection of pairs ( g , p ), as a task, where some pairs may be associated with the ground-truth, and some pairs are the queries to answer. There are two cases. One is there are some with ground-truth (few-shot), and one is there is none with ground-truth (zero-shot). We provide our solutions for both. In particular, for zero-shot, we propose a new data-driven approach to predict the count values. Note that zero-shot learning for our regression tasks is difficult, and there is no hands-on solution in the literature. We conducted extensive experimental studies to confirm that our approach is robust to model degeneration on small training data, and our meta-model can fast adapt to new queries by few-shot and zero-shot learning.
CPS: A Multipath Scheduling Algorithm for Low-Latency Applications in 5G Edge Networks

VR applications that require extremely low latency and high image quality are widely used in online games and other 5G scenarios, becoming a key research field in recent years. However, the limited bandwidth in 5G edge networks fails to meet the peak rate requirements for multiple VR flows. MPTCP is suitable for 5G edge networks, supporting the simultaneous use of multiple networks on mobile devices. Nevertheless, accurately scheduling VR data blocks to different sub flows to satisfy their low latency requirements is challenging due to their micro-burst characteristic. In this paper, we propose a novel MPTCP scheduler for cloud VR applications in 5G edge networks, called the Cross-Layer Information-based One-Way Delay Predictive Scheduler (CPS). CPS accurately predicts one-way delay by incorporating cross-layer information from both the application and edge wireless sides, and adaptively schedules VR data blocks to the optimal subflow. Experimental results show that CPS outperforms existing strategies, supporting 125% more users for VR applications in the typical scenario. Additionally, CPS maintains completion times for 99% of cloud VR packets below 7 ms. CPS successfully meets the quality of experience needs of more users, providing a promising solution for large-scale deployment of cloud VR services in 5G edge networks.
Influence of HW-SW-Co-Design on Quantum Computing Scalability

The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures–circuit depth and gate count–required to solve four cornerstone NP-complete problems vary with tailored hardware properties.Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches. Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains. The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software.
ImCT: A Feasible Scheme for Deploying Implicit Certificates with Certificate Transparency in IoT

Data security has become a major concern in end-to-end communication of Internet of Things (IoT) devices. To establish an encrypted and trusted channel, certificate-based authentication is broadly adopted in many resource-constrained IoT environments, especially Elliptic Curve Qu-Vanstone (ECQV) implicit certificates, optimized for bandwidth and processing time compared to X.509 certificates. However, certificates in Public Key Infrastructure (PKI) inherently face the security problem that Certificate Authorities (CAs) with absolute trust might issue fraudulent certificates maliciously/unintentionally. To mitigate this problem, Certificate Transparency (CT) has been widely deployed, but it brings more transmission and validation overhead, posing new challenges for IoT devices. To inherit the advantage of implicit certificates and make CT more friendly to resource-restricted IoT systems, this paper proposes ImCT, the first feasible and efficient scheme for deploying ECQV implicit certificates with CT in IoT. It (1) offers an ingenious and compact design of certificate extension for integrating CT into implicit certificates, ensuring sufficiently small memory use and low validation cost; (2) provides a generic Bloom Filter-based solution to efficiently deploy ImCT in interactive protocols with compatibility with existing CT validation rules; (3) allows for incremental deployment and deploys to TLS 1.3 easily with only a few self-defined TLS extensions. The security of ImCT is also analyzed, and the performance evaluation shows its practicality and high efficiency.
Quantum Assisted Scheduling Algorithm for Federated Learning in Distributed Networks

The scheduling problem for federated learning (FL) with multiple models in a distributed network is challenging, as it involves NP-hard mixed-integer nonlinear programming. Moreover, it requires optimal participant selection and learning rate determination among multiple FL models to avoid high training costs and resource competition. To overcome those chal-lenges, in literature the Benders' decomposition algorithm (BD) can deal with mixed integer problems, however, it still suffers from limited scalability. To address this issue, in this paper, we present the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm, which combines the power of quantum and classical computing to solve the joint participant selection and learning scheduling problem in multi-model FL. HQCBD decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables. This collaboration maximizes the potential of both quantum and classical computing, and optimizes the complex joint optimization problem. Simulation on the commercial D-Wave quantum annealing machine demonstrates the effectiveness and robustness of the proposed method, with up to 18% improvement of iterations and 81% improvement of computation time over BD algorithm on classical CPUs even at small scales.
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks

Variational quantum algorithms (VQAs) have recently received much attention due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we proposed a Look Around Warm-Start (LAWS) quantum natural gradient (QNG) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.
Effects of Imperfections on Quantum Algorithms: A Software Engineering Perspective

Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems.In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach.Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.
A Systematic Approach for Temporal Traffic Selection Across Various Applications

The paper presents a framework that analyzes temporal traffic in applications, with a focus on statistical analysis and traffic classification. The framework utilizes time-based sampling and traffic flow selection to identify the characteristics of idle time, continuous traffic and burst threshold. It also includes time-based feature selection to improve the accuracy and efficiency of predictive models by removing irrelevant or redundant features. Our study involves exploratory data analysis and machine learning-based classification, and we found that our method improves application analysis in both statistical analysis and the precision of encrypted application traffic. We compared our approach to various state-of-the-art methods and consistently outperformed them in terms of performance. By focusing on traffic classification, our framework can benefit various domains such as Quality of Service (QoS) and security. For example, it can help network administrators identify and analyze various application characteristics, which can lead to better security measures. Overall, our approach offers a promising solution for improving temporal traffic analysis.
Detection of Fake Generated Scientific Abstracts

The widespread adoption of Large Language Models, such as the publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people’s everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated, while researchers already work on developing effective systems to identify machine-generated text. In this study, we examine the performance of different methodological approaches for this task. To achieve this, we utilize the GPT-3 model to generate scientific paper abstracts of real research papers. By conducting this research, we shed light on the capabilities and limitations of current Machine Learning models for discriminating machine-generated text. Simultaneously, we enhance our understanding of the operation of the Large Language Models.
Underprovisioned GPUs: On Sufficient Capacity for Real-Time Mission-Critical Perception

Recent work suggests that computing resources, such as GPUs in real-time edge-based perception systems, need not have sufficient capacity to keep up with the input frame rates of all input devices (e.g., cameras) at their full-frame resolution. Rather, they can be under-provisioned because only parts of any given frame need to be inspected (i.e., paid attention to). This paper derives an attention allocation policy, called canvas-based attention scheduling that decides which parts of each frame of each device to inspect, and a corresponding schedulability condition that relates the spatiotemporal properties of surrounding objects to the ability of the edge-based perception subsystem to keep up with the state of the environment in real-time. It provides a quantitative estimate of adequate computing capacity for the expected perception workload. We implement a canvas-based attention scheduler for an object detection application and perform an empirical comparative study based on actual GPU hardware and surveillance videos. Results show that canvas-based attention scheduling keeps up with the environment while using a much smaller GPU capacity, compared with prior approaches.
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Images of Hepatocellular Carcinoma

In digital histopathology, entire neoplasm segmentation on Whole Slide Image (WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as a preprocessing filter to automatically exclude healthy tissue, in histological molecular correlations mining and other downstream histopathological tasks. The segmentation task remains challenging due to HCC’s inherent high-heterogeneity and the lack of dependency learning in large field of view. In this article, we propose a novel deep learning architecture with a hierarchical Transformer encoder, HiTrans, to learn the global dependencies within expanded 4096×4096 WSI patches. Hi-Trans is designed to encode and decode the patches with larger reception fields and the learned global dependencies, compared to the state-of-the-art Fully Convolutional Neural networks (FCNN). Empirical evaluations verified that Hi-Trans leads to better segmentation performance by taking into account regional and global dependency information.
Robust Multi-Contrast Mri Denoising Using Trainable Bilateral Filters Without Noise-Free Targets

Magnetic resonance imaging (MRI) acquisitions inherently suffer from noise which can be reduced using image denoising algorithms. Most MRI denoising methods are based on either non-linear filters or convolutional neural networks (CNNs). In this work we propose to combine the best of both worlds by employing a set of trainable bilateral filter (BF) layers without ground-truth data using an adapted version of Stein’s unbiased risk estimator (SURE) to enable model-based supervision. Our quantitative and qualitative experiments suggest that a neural network consisting of trainable BF layers outperforms a standard CNN when tested on unseen MR image contrasts by 14.7% in terms of PSNR. Thus, the proposed method provides not only a parameter efficient but also a considerably more robust alternative to deep neural networks, making it particularly valuable for applications in clinical practice.
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images

How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire dataset of WSIs as a kernel matrix for WSI level clustering, weakly-supervised prediction of TP53 mutation status in breast cancer patients from their routine WSIs as well as survival analysis with state of the art prediction performance. We believe that this work will open up further avenues for application of WSI-level kernels for predictive and prognostic tasks in computational pathology.
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a small number of labeled samples from the target domain. In this paper, we develop a semi-supervised domain adaptation method, which has robustness to class-imbalanced situations, which are common in medical image classification tasks. For robustness, we propose a weakly-supervised clustering pipeline to obtain high-purity clusters and utilize the clusters in representation learning for domain adaptation. The proposed method showed state-of-the-art performance in the experiment using severely class-imbalanced pathological image patches.
Self-Supervised Arbitrary Scale Super-Resolution Framework for Anisotropic MRI

In this paper, we propose an efficient self-supervised arbitrary-scale super-resolution (SR) framework to reconstruct isotropic magnetic resonance (MR) images from anisotropic MRI inputs without involving external training data. The proposed framework builds a training dataset using "in-the-wild" anisotropic MR volumes with arbitrary image resolution. We then formulate the 3D volume SR task as a SR problem for 2D image slices. The anisotropic volume’s high-resolution (HR) plane is used to build the HR-LR image pairs for model training. We further adapt the implicit neural representation (INR) network to implement the 2D arbitrary-scale image SR model. Finally, we leverage the well-trained proposed model to up-sample the 2D LR plane extracted from the anisotropic MR volumes to their HR views. The isotropic MR volumes thus can be reconstructed by stacking and averaging the generated HR slices. Our proposed framework has two major advantages: (1) It only involves the arbitrary-resolution anisotropic MR volumes, which greatly improves the model practicality in real MR imaging scenarios (e.g., clinical brain image acquisition); (2) The INR-based SR model enables arbitrary-scale image SR from the arbitrary-resolution input image, which significantly improves model training efficiency. We perform experiments on a simulated public adult brain dataset and a real collected 7T brain dataset. The results indicate that our current framework greatly outperforms two well-known self-supervised models for anisotropic MR image SR tasks.
GLFA-NET: A Hybrid Network for Mr-To-Ct Synthesis Via Global and Local Feature Aggregation

Synthesis of Computed Tomography (CT) images from Magnetic Resonance (MR) images is clinical significance for MR-only treatment planning to eliminate the co-registration errors between MR and CT images. Existing convolutional neural network-based methods, suffering from the inherent local inductive biases, struggle to distinguish bone and air which show low signals in conventional MR images. ViT-based methods can learn long-range contextual information by a global self-attention mechanism but are limited by the quadratic complexity and generating local detailed structures. Combining the merits of these two architectures, we propose a hybrid network for MR-to-CT synthesis via global and local feature aggregation from Transformer and CNN, named GLFA-NET. Specifically, we add a global patch embedding branch to supplement the patch-based global representative features directly from the image and design a residual dilated swin transformer block aggregating the local detailed features and global features to improve the synthesis performance of bone and air and reduce computational overhead. Furthermore, we adopt a wavelet PatchGAN discriminator to enhance the high-frequency detailed information of the synthetic CT. Our GLFA-NET was implemented on a dataset with 154 pairs of 3D MR-CT head and neck images. Experiments show that our GLFA-NET achieves impressive performance with MAE of 71.12 ± 10.87, SSIM of 0.771 ± 0.028, and PSNR of 28.91 ± 1.33. The visual synthetic CT results also show that the proposed GLFA-NET method achieves better discrimination of bone and air and higher structural similarity than other state-of-the-art methods.
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-Based Video Classification Frameworks

To address the problem of medical image recognition, computer vision techniques like convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models dominate the field of magnetic resonance image (MRI) analytics. Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification? Our work suggests that advanced video techniques benefit MRI classification. In this paper, four datasets of Alzheimer’s and Parkinson’s disease recognition are utilized in experiments, together with three alternative video recognition models and data augmentation techniques that are frequently applied to video tasks. In terms of efficiency, the results reveal that the video framework performs better than 3D-CNN models by 5% ~ 11% with 50% ~ 66% less trainable parameters. This report pushes forward the potential fusion of 3D medical imaging and video understanding research.
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL-based analytical pipelines for WSI-level analysis, the WSIs are divided into patches, and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs’ limited RAM. Because of this bottleneck, incorporating patch-level data augmentations during training adds an extra computational burden. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.