Reviewerly/exHarmony huggingface dataset:
FBCU-Net: A fine-grained context modeling network using boundary semantic features for medical image segmentation

The performance of deep learning-based medical image segmentation methods largely depends on the segmentation accuracy of tissue boundaries. However, since the boundary region is at the junction of areas of different categories, the pixels located at the boundary inevitably carry features belonging to other classes and difficult to distinguish. This paper proposes a fine-grained contextual modeling network for medical image segmentation based on boundary semantic features, FBCU-Net, which uses the semantic features of boundary regions to reduce the influence of irrelevant features on boundary pixels. First, based on the discovery that indistinguishable pixels are usually boundary pixels in medical images, we introduce new supervision information to find and classify boundary pixels. Second, based on the existing relational context modeling schemes, we generate the boundary region representations representing the semantic features of boundary regions. Last, we use boundary region representations to reduce the influence of irrelevant features on boundary pixels and generate highly discriminative pixel representations. Furthermore, to enhance the attention of the network to the boundary region, we also propose the boundary enhancement strategy. We evaluate the proposed model on five datasets, TUI (Thyroid Tumor), ISIC-2018 (Dermoscopy), 2018 Data Science Bowl (Cell Nuclei), Glas (Colon Cancer), and BUSI (Breast Cancer). The results show that FBCU-Net has better boundary segmentation performance and overall performance for different medical images than other state-of-the-art (SOTA) methods, and has great potential for clinical application.
Cycleiqa: Blind Image Quality Assessment Via Cycle-Consistent Adversarial Networks

Existing blind image quality assessment (BIQA) methods aim to extract distortion features by training deep learning models to predict quality scores. However, images suffer from various distortions. Training a single model is typically hard to handle distortion variation problems. To solve the problem, we propose a novel BIQA method to model the quality degradation process caused by image distortions. It consists of a generative adversarial network (GAN)-based quality perception network and a quality regression network. The GAN-based quality perception network is designed to simulate the process of distortion information introduced to images in both forward and reverse directions. The quality regression network extracts the learned hierarchical restoration features from the quality perception network to learn the relationship between features and quality scores. Experimental results on four representation IQA datasets show that the proposed method achieves comparable quality prediction performance with other state-of-the-art methods.
Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm

An encouraging development is the quick expansion of renewable energy extraction. Harnessing renewable energy is economically feasible at the current rate of technological advancement. Traditional energy sources, such as coal, petroleum, and hydrocarbons, which have negative effects on the environment, are coming under more social and financial pressure. Companies need more solar and wind power because this calls for a well-balanced mix of renewable resources and a higher proportion of alternative energy sources. Sustainable energy can be captured using a variety of techniques. Massive scale and small-sized are the two most prevalent techniques. No renewable energy source possesses an inherent property that restricts how it may be managed or how it can be planned to produce electricity. A number of factors have contributed to a growth in the use of alternative sources, one of which is to mitigate the effects of rising temperatures. To improve the ability to estimate renewable energy, various modeling approaches have been created. This region might use an HRES to give many sources with the inclusion of different energy sources. The inventiveness of solar and wind power and the brilliant ability of neural networks to handle complex time-series data signals have both aided in the prediction of sustainable energy. Therefore, this research will examine the numerous information models in order to determine which proposed models can provide accurate projections of renewable energy output, such as sunlight, wind, or pumped storage. In the fields of sustainable energy predictions, a number of machine learning methods, such as multilayer perceptions MLP, RNN CNN, and LSTM designs, are frequently utilized. This form of modeling uses historical data to predict potential values and can predict short-term patterns in solar and wind generation.
Semisupervised white matter hyperintensities segmentation on MRI

Abstract This study proposed a semisupervised loss function named level‐set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid‐attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V‐Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU‐SVD, n = 360) and the multiple sclerosis cohort (HKU‐MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer‐assisted Intervention (MICCAI) WMH challenge database (MICCAI‐WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI‐CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU‐SVD testing set ( n = 20), DSC = 0.77 on the HKU‐MS testing set ( n = 5), and DSC = 0.78 on MICCAI‐WMH testing set ( n = 30). The segmentation results obtained by our semisupervised V‐Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.
MEGC2022

Facial micro-expressions (MEs) are involuntary movements of the face that occur spontaneously when a person experiences an emotion but attempts to suppress or repress the facial expression, typically found in a high-stakes environment. Unfortunately, the small sample problem severely limits the automation of ME analysis. Furthermore, due to the brief and subtle nature of ME, ME spotting is a challenging task, and the performance is still not satisfactory yet. This challenge focuses on two tasks, i.e., the micro- and macro-expression spotting task, and the ME Generation task.
RuleXAI—A package for rule-based explanations of machine learning model

The ability to use eXplainable Artificial Intelligence (XAI) methods is very important for both AI users and AI developers. This paper presents the RuleXAI library, which provides XAI methods based on rule-based models. The package presented can be applied to classification, regression and survival analysis tasks. RuleXAI operates on elementary rule conditions and enables the generation of global explanations, local explanations and the generation of a new data representation, simplifying data preprocessing. The explanations of model decisions that are generated by RuleXAI rely on feature relevance and provide information not only about the importance of attributes, but also about the importance of attribute values.
Double chain system for online and offline medical data sharing viaprivate and consortium blockchain: A system design study

With the informatization development and digital construction in the healthcare industry, electronic medical records and Internet medicine facilitate people's medical treatment. However, the current data storage method has the risk of data loss, leakage, and tampering, and can't support extensive and secure sharing of medical data. To realize effective and secure medical data storage and sharing among offline medical institutions and Internet medicine platforms, this study used a combined private blockchain and consortium blockchain to design a medical blockchain double-chain system (MBDS). This system can store encrypted medical data in distributed storage mode and systematically integrate the medical data of patients in offline medical institutions and Internet medicine platforms, to achieve equality, credibility, and data sharing among participating nodes. The MBDS system constructed in this study incorporated Internet medicine care services into the current healthcare system and provided new solutions and practical guidance for the future development of collaborative medical care. This study helped to solve the problems of medical data interconnection and resource sharing, improve the efficiency and effect of disease diagnosis, alleviate the contradiction between doctors and patients, and facilitate personal health management. This study has substantial theoretical and practical implications for the research and application of medical data storage and sharing.
Setting the stage for materials simulation using acoustic metamaterials digital quantum analogue computing platforms

Abstract We present a model of an externally driven acoustic metamaterial constituted of a nonlinear parallel array of coupled acoustic waveguides that supports logical phi-bits, classical analogues of quantum bits (qubit). Descriptions of correlated multiple phi-bit systems emphasize the importance of representations of phi-bit and multiple phi-bit vector states within the context of their corresponding Hilbert space. Experimental data are used to demonstrate the realization of the single phi-bit Hadamard gate and the phase shift gate. A three phi-bit system is also used to illustrate the development of multiple phi-bit gates as well as a simple quantum-like algorithm. These demonstrations set the stage for the implementation of a digital quantum analogue computing platform based on acoustic metamaterial that can implement quantum-like gates and may offer promise as an efficient platform for the simulation of materials.
Query-Aware Sequential Recommendation

Sequential recommenders aim to capture users' dynamic interests from their historical action sequences, but remain challenging due to data sparsity issues, as well as the noisy and complex relationships among items in a sequence. Several approaches have sought to alleviate these issues using side-information, such as item content (e.g., images), action types (e.g., click, purchase). While useful, we argue one of the main contextual signals is largely ignored-namely users' queries. When users browse and consume products (e.g., music, movies), their sequential interactions are usually a combination of queries, clicks (etc.). Most interaction datasets discard queries, and corresponding methods simply model sequential behaviors over items and thus ignore this critical context of user interactions.
Attention Regularized Laplace Graph for Domain Adaptation

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. However, current graph embedding DA methods suffer from two issues: 1). they are only concerned with preservation of the underlying data structures in the embedding and ignore sub-domain adaptation, which requires taking into account intra-class similarity and inter-class dissimilarity, thereby leading to negative transfer; 2). manifold learning is proposed across different feature/label spaces separately, thereby hindering unified comprehensive manifold learning. In this paper, starting from our previous DGA-DA, we propose a novel DA method, namely A ttention R egularized Laplace G raph-based D omain A daptation (ARG-DA), to remedy the aforementioned issues. Specifically, by weighting the importance across different sub-domain adaptation tasks, we propose the A ttention R egularized Laplace Graph for class aware DA, thereby generating the attention regularized DA. Furthermore, using a specifically designed FEEL strategy, our approach dynamically unifies alignment of the manifold structures across different feature/label spaces, thus leading to comprehensive manifold learning. Comprehensive experiments are carried out to verify the effectiveness of the proposed DA method, which consistently outperforms the state of the art DA methods on 7 standard DA benchmarks, i.e., 37 cross-domain image classification tasks including object, face, and digit images. An in-depth analysis of the proposed DA method is also discussed, including sensitivity, convergence, and robustness.
Don't Break my Flow: Effects of Switching Latency in Shooting Video Games

Latency is inherently part of every interactive computing system and particularly important for video games. Previous work shows that constant latency above 25 ms reduces game experience and player performance. However, latency in the wild varies and is never constant due to multiple factors, such as updates in routing tables, users changing their location, or the system's workload. It is unclear if switching latency impairs the gaming experience stronger than a constant high latency. To elucidate, we conducted an experiment with 264 participants playing a shooting video game induced with 0 ms, 33 ms, and 66 ms controlled latency. While playing, the game switched between different latency levels based on three frequencies. Our analysis shows that switching latency significantly impaired the participants' flow. Additionally, we found effects on the perceived tension, the experienced challenge, and the players' performance. We conclude that games should prioritize constant latency, even if that entails artificially adding latency.
Oral epithelial cell segmentation from fluorescent multichannel cytology images using deep learning

Cytology is a proven, minimally-invasive cancer screening and surveillance strategy. Given the high incidence of oral cancer globally, there is a need to develop a point-of-care, automated, cytology-based screening tool. Oral cytology image analysis has multiple challenges such as, presence of debris, blood cells, artefacts, and clustered cells, which necessitate a skilled expertise for single-cell detection of atypical cells for diagnosis. The main objective of this study is to develop a semantic segmentation model for Single Epithelial Cell (SEC) separation from fluorescent, multichannel, microscopic oral cytology images and classify the segmented images. We have used multi-channel, fluorescent, microscopic images (number of images; n = 2730), which were stained differentially for cytoplasm and nucleus. The cytoplasmic and cell membrane markers used in the study were Mackia Amurensis Agglutinin (MAA; n: 2364) and Sambucus Nigra Agglutinin-1 (SNA-1; n: 366) with a nuclear stain DAPI. The cytology images were labelled for SECs, cluster of cells, artefacts, and blood cells. In this study, we used encoder-decoder models based on the well-established U-Net architecture, modified U-Net and ResNet-34 for multi-class segmentation. The experiments were performed with different class combinations of data to reduce imbalance. The derived MAA dataset (n: 14,706) of SEC, cluster, and artefacts/blood cells were used for developing a classification model. InceptionV3 model and a new custom Convolutional-Neural-Network (CNN) model (Artefact-Net) were trained to classify SNA-1 marker stained segmented images (n:6101). For segmentation models, Intersection Over Union (IoU) and F1 score were used as the evaluation matrices, while the classification models were evaluated using the conventional classification metrics like precision, recall and F1-Score. The U-Net and the modified U-Net models gave the best IoU overall (0.73–0.76) as well as for SEC segmentation (079). The images segmented using the modified U-Net model were classified by Artefact-Net and Inception V3 model with F1 scores of 0.96 and 0.95 respectively. The Artefact-Net, when compared to InceptionV3, provided a better precision and F1 score in classifying clusters (Precision: 0.91 vs 0.80; F1: 0.91 vs 0.86). This study establishes a pipeline for SEC segmentation with the segmented component containing only single cells. The pipline will enable automated, cytology-based early detection with reduced bias.
Verified compilation of Quantum oracles

Quantum algorithms often apply classical operations, such as arithmetic or predicate checks, over a quantum superposition of classical data; these so-called oracles are often the largest components of a quantum program. To ease the construction of efficient, correct oracle functions, this paper presents VQO, a high-assurance framework implemented with the Coq proof assistant. The core of VQO is OQASM, the oracle quantum assembly language. OQASM operations move qubits between two different bases via the quantum Fourier transform, thus admitting important optimizations, but without inducing entanglement and the exponential blowup that comes with it. OQASM’s design enabled us to prove correct VQO’s compilers—from a simple imperative language called OQIMP to OQASM, and from OQASM to SQIR, a general-purpose quantum assembly language—and allowed us to efficiently test properties of OQASM programs using the QuickChick property-based testing framework. We have used VQO to implement a variety of arithmetic and geometric operators that are building blocks for important oracles, including those used in Shor’s and Grover’s algorithms. We found that VQO’s QFT-based arithmetic oracles require fewer qubits, sometimes substantially fewer, than those constructed using “classical” gates; VQO’s versions of the latter were nevertheless on par with or better than (in terms of both qubit and gate counts) oracles produced by Quipper, a state-of-the-art but unverified quantum programming platform.
C2QA - Bosonic Qiskit

The practical benefits of hybrid quantum information processing hardware that contains continuous-variable objects (bosonic modes such as mechanical or electromagnetic oscillators) in addition to traditional (discrete-variable) qubits have recently been demonstrated by experiments with bosonic codes that reach the break-even point for quantum error correction [1-5] and by efficient Gaussian boson sampling simulation of the Franck-Condon spectra of triatomic molecules [6] that is well beyond the capabilities of current qubit-only hardware. The goal of this Co-design Center for Quantum Advantage (C2QA) project is to develop an instruction set architecture (ISA) for hybrid qubit/bosonic mode systems that contains an inventory of the fundamental operations and measurements that are possible in such hardware. The corresponding abstract machine model (AMM) would also contain a description of the appropriate error models associated with the gates, measurements and time evolution of the hardware. This information has been implemented as an extension of Qiskit. Qiskit is an open-source software development toolkit (SDK) for simulating the quantum state of a quantum circuit on a system with Python 3.7+ and for running the same circuits on prototype hardware within the IBM Quantum Lab. We introduce the Bosonic Qiskit software to enable the simulation of hybrid qubit/bosonic systems using the existing Qiskit software development kit [7]. This implementation can be used for simulating new hybrid systems, verifying proposed physical systems, and modeling systems larger than can currently be constructed. We also cover tutorials and example use cases included within the software to study Jaynes-Cummings models, bosonic Hubbard models, plotting Wigner functions and animations, and calculating maximum likelihood estimations using Wigner functions.
ReCell: replicating recurrent cell for auto-regressive pose generation

This paper describes FineMotion’s gesture generating system entry for the GENEA Challenge 2022. Our system is based on auto-regressive approach imitating recurrent cell. Combined with a special windowed auto-encoder and training approach this system generates plausible gestures appropriate to input speech.
COPER: Continuous Patient State Perceiver

In electronic health records (EHRs), irregular time-series (ITS) occur naturally due to patient health dynamics, reflected by irregular hospital visits, diseases/conditions and the necessity to measure different vitals signs at each visit etc. ITS present challenges in training machine learning algorithms which mostly are built on assumption of coherent fixed dimensional feature space. In this paper, we propose a novel COntinuous patient state PERceiver model, called COPER, to cope with ITS in EHRs. COPER uses Perceiver model and the concept of neural ordinary differential equations (ODEs) to learn the continuous time dynamics of patient state, i.e., continuity of input space and continuity of output space. The neural ODEs help COPER to generate regular time-series to feed to Perceiver model which has the capability to handle multi-modality large-scale inputs. To evaluate the performance of the proposed model, we use in-hospital mortality prediction task on MIMIC-III dataset and carefully design experiments to study irregularity. The results are compared with the baselines which prove the efficacy of the proposed model.
Price of MEV

Maximal (also miner) extractable value, or MEV, usually refers to the value that privileged players can extract by strategically ordering, censoring, and placing transactions in a blockchain. Each blockchain network, which we refer to as a domain, has its own consensus, ordering, and block-creation mechanisms, which gives rise to different optimal strategies to extract MEV. The strategic behaviour of rational players, known as searchers, lead to MEV games that have different impacts and externalities in each domain. Several ordering mechanisms, which determine the inclusion and position of transactions in a block, have been considered to construct alternative games to organise MEV extraction, and minimize negative externalities; examples include sealed bid auctions, first input first output, and private priority gas auctions. However, to date, no sufficiently formal and abstract definition of MEV games have been made. In this paper, we take a step toward the formalization of MEV games and compare different ordering mechanisms and their externalities. In particular, we attempt to formalize games that arise from common knowledge MEV opportunities, such as arbitrage and sandwich attacks. In defining these games, we utilise a theoretical framework that provides groundwork for several important roles and concepts, such as the searcher, sequencer, domain, and bundle. We also introduce the price of MEV as the price of anarchy of MEV games, a measure that provides formal comparison between different ordering mechanisms.
PSI from Ring-OLE

Private set intersection (PSI) is one of the most extensively studied instances of secure computation. PSI allows two parties to compute the intersection of their input sets without revealing anything else. Other useful variants include PSI-Payload, where the output includes payloads associated with members of the intersection, and PSI-Sum, where the output includes the sum of the payloads instead of individual ones.