Reviewerly/exHarmony huggingface dataset:
A target region extraction method for ultrasound medical images based on improved <scp>PRIDNet</scp> and <scp>UCTransNet</scp>

Abstract Computer‐aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region extraction method combining the improved PRIDNet and UCTransNet for ultrasound medical images is proposed. The method sequentially enhances and segments the target region of the image, thereby solving the problem of inconspicuous target region caused by noise and artifacts in ultrasound images. There are three key characteristics: (i) The feature extraction part of the enhancement network (improved PRIDNet) is redesigned for speckle noise to improve the network's ability to extract information and highlight the feature information of the target region in ultrasound images. (ii) The segmentation network with the addition of the underlying information on UCTransNet would effectively improves Channel‐wise Cross fusion Transformer (CCT) and decoder feature fusion capability. (iii) By combining the enhancement network with the segmentation network, we can further improve the segmentation accuracy of the target region in the presence of noise interference. The experiments conducted on both UFSU that we prepared and on some public datasets including BUSI, FHC, and CT2US have demonstrated that The proposed method attains MIoU, DSC, Acc, and HD of 96.34%, 98.12%, 99.35%, and 8.64 in CT2US, respectively. The method significantly surpasses those certain state‐of‐the‐art methods, demonstrating its potential to offer valuable guidance for clinical treatment. The code will be publicly released at https://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images .
An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.
A Randomized Optimal k-mer Indexing Approach for Efficient Parallel Genome Sequence Compression

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.
Weakly supervised instance segmentation via peak mining and filtering

Abstract Learning the full extent of pixel‐level instance response in a weakly supervised manner remains unsatisfactory. Peak response maps (PRMs) localizes the discriminative object regions but cannot provide complete instance information, suffering from incomplete segmentation and unreliable mask prediction by noisy proposal retrieval. This work tackles this challenging problem by mining diverse class peak responses that include more discriminative and complete object regions and retrieving more reliable proposals from noisy segment proposal galleries. First, the existing method is enhanced with two more classification branches, thus contributing to more diverse and abundant instance regions from peak response maps. The mined class peak responses from two of the branches are then merged to generate more complete peak response maps by a clustering approach in their deep feature space. Then, instance segmentation masks are retrieved from a noisy object segment proposal gallery with class confidence, which is calculated by a normal classifier to obtain cleaner mask prediction. Finally, the pseudo‐supervision can be used to train an instance segmentation network in a fully supervised manner. Experiments on the PASCAL VOC 2012 dataset and COCO dataset show that the approach works effectively and outperforms other counterparts by a margin of more than 6 %, 4%, and 3% with the mean average precision (mAP) at IoU threshold of 0.25, 0.5 and 0.75, respectively.
Physical scene understanding

Abstract Current AI systems still fail to match the flexibility, robustness, and generalizability of human intelligence: how even a young child can manipulate objects to achieve goals of their own invention or in cooperation, or can learn the essentials of a complex new task within minutes. We need AI with such embodied intelligence: transforming raw sensory inputs to rapidly build a rich understanding of the world for seeing, finding, and constructing things, achieving goals, and communicating with others. This problem of physical scene understanding is challenging because it requires a holistic interpretation of scenes, objects, and humans, including their geometry, physics, functionality, semantics, and modes of interaction, building upon studies across vision, learning, graphics, robotics, and AI. My research aims to address this problem by integrating bottom‐up recognition models, deep networks, and inference algorithms with top‐down structured graphical models, simulation engines, and probabilistic programs.
Towards Generalist Biomedical AI

BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, and interpret these data might better enable impactful applications ranging from scientific discovery to care delivery. MethodsTo catalyze development of these models, we curated MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks, such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduced Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. To further probe the capabilities and limitations of Med-PaLM M, we conducted a radiologist evaluation of model-generated (and human) chest x-ray reports. ResultsWe observed encouraging performance across model scales. Med-PaLM M reached performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. In a side-by-side ranking on 246 retrospective chest x-rays, clinicians expressed a pairwise preference for Med-PaLM Multimodal reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility. ConclusionsAlthough considerable work is needed to validate these models in real-world cases and understand if cross-modality generalization is possible, our results represent a milestone toward the development of generalist biomedical artificial intelligence systems. (Funded by Alphabet Inc. and/or a subsidiary thereof.)
Information Retrieval Systems in Healthcare

Healthcare systems generate an immense volume of data, which presents a unique opportunity to make a significant impact. This chapter examines the role of information retrieval systems in healthcare, specifically focusing on how text analysis can be utilized to enhance the understanding of medical data. By employing advanced text mining tools, this chapter demonstrates how we can extract valuable insights from these complex documents. It also presents text analytics as a solution-oriented approach, particularly beneficial in managing crises within healthcare systems and in making informed decisions based on accurate data analysis. The technical foundation of the study is rooted in the fields of natural language processing and artificial intelligence, with a focus on methodologies related to the semantics of words and text (e.g., text corpus, dictionaries, and text embeddings). Through this exploration, the chapter aims to highlight the transformative potential of text analysis and information retrieval systems in revolutionizing healthcare data understanding.
A robust bit-level image encryption based on Bessel map

A chaotic map plays a critical role in image encryption (IME). The map used to generate chaotic sequences should perform high dynamic characteristics. In this study, a new chaotic system depending on the Bessel function, so-called Bessel map, and a novel Bessel map-based IME scheme are proposed for the IME. The Bessel map has three control parameters, which provide superior ergodicity and diversity. Bessel map has also order degree rather than Sine map, which is used as a control parameter boosting the security. The chaotic characteristic of the Bessel map is verified through different reliable measurements such as bifurcation diagram, trajectory phase, Lyapunov exponent (LE), sample entropy (SE), permutation entropy (PE), and 0-1 test. Then, the Bessel map is employed in a new bitwise IME (BIME) scheme based on bit-level permutation and diffusion processes. The Bessel map-based BIME is validated through various simulated cryptanalyses and cyberattacks as well as compared with the state-of-the-art schemes. The achieved results demonstrate that the BIME based on the Bessel map ensures the most secure ciphered images thanks to the excellent randomness and complexity performance.
Asynchronous Threshold ECDSA With Batch Processing

Threshold Elliptic Curve Digital Signature Algorithm (ECDSA) has attracted a lot of attention due to the wide applications of ECDSA in crypto asset. Although several variants of threshold signature protocols can provide functions, such as key generation and signing, they suffer from two shortfalls. First, these schemes only discuss a single signature computation task in a synchronous algorithm context, which is difficult to adapt to real crypto-asset applications, such as custody. Second, these schemes are computing intensive and not scalable, hence can hardly support large-scale processing operations in real life even after traditional optimization, such as multithreading, is applied. In this article, we propose an innovative computation method called asynchronous threshold ECDSA with batch processing, based on the interactive threshold signature protocols. The method provides a reliable solution for critical operational scenarios, such as threshold signing and distributed key generation (DKG) in crypto-asset custody, and can be a future reference in secure data distribution mechanisms. The performance and scalability of our methods are validated through a benchmark testing.
Multitask Particle Swarm Optimization with Heterogeneous Domain Adaptation

The main goal of multitask optimization (MTO) is the parallel optimization of multiple different tasks. However, since different tasks in the MTO problem usually have heterogeneous characteristics, it is difficult to realize the positive knowledge transfer among tasks, resulting in poor convergence. To cope with this problem, a multi-task particle swarm optimization with a heterogeneous domain adaptation strategy (MTPSO-HDA) is proposed to transfer positive knowledge among heterogeneous tasks. First, a nonlinear mapping between the source task and the target task is constructed based on the adaptive kernel function. Then, source tasks are mapped to the target task space to reduce the differences among heterogeneous tasks. Second, a multi-source domain adaptive strategy based on fitness landscape similarity is designed to implement domain adaptation. Then, the importance of each source domain is quantitatively described to reduce the differences between multiple source domains and a target domain and achieve domain adaptation among heterogeneous tasks. Third, a heterogeneous multitask particle swarm optimization mechanism is introduced to facilitate positive knowledge transfer among heterogeneous tasks. Then, an appropriate evolutionary mechanism is designed according to the fitness landscape similarity to achieve positive knowledge transfer. Finally, to assess the effectiveness of the MTPSO-HDA algorithm, some experiments are designed based on some benchmark problems and real-world application of wastewater treatment process. The results demonstrate that the proposed MTPSO-HDA algorithm can promote positive knowledge transfer among heterogeneous tasks to improve convergence.
Cybercrime

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Neural Architecture Search via Multi-Hashing Embedding and Graph Tensor Networks for Multilingual Text Classification

Neural architecture search (NAS) has been demonstrated to be promising in deep learning for text classification. Most existing NAS algorithms, however, are proposed for single-language text classification. They may become ineffective when extended to multilingual text classification because the differences in the syntactic structure and contextual semantics of different languages increase the computational complexity of NAS search. To address the above issue, this article proposes a differential neural architecture search approach using multi-hashing embedding for multilingual text representation. A multi-hashing network capable of processing heterogeneous graph information is constructed so that cross-language syntactic and contextual semantic information can be effectively represented. In addition, a neural tensor network with multi-hashing embedding is adopted as a continuous encoder to estimate the probability for each candidate operation in the search space, and reparameterization-based gradient search is employed to efficiently search for network architectures for multilingual text representation. Our experimental results on two multilingual text classification datasets demonstrate that the proposed approach outperforms the state-of-the-art NAS methods for text classification in terms of both classification accuracy and computational efficiency.
Multi-Hop Task Routing in Vehicle-Assisted Collaborative Edge Computing

Collaborative edge computing has emerged as a novel paradigm that allows edge servers (ESs) to share data and computing resources, effectively mitigating network congestion in traditional multi-access edge computing (MEC) scenarios. However, existing research in collaborative edge computing often limits offloading to only one hop, which may lead to suboptimal computing resource sharing due to challenges such as poor channel conditions or high computing workload at ESs located just one hop away. To address this limitation and enable more efficient computing resource utilization, we propose a multi-hop MEC approach that leverages omnipresent vehicles in urban areas to create a data transportation network for task delivery. Here, we propose a general multi-hop task offloading framework for vehicle-assisted collaborative edge computing where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we formulate an aggregated service throughput maximization problem by designing the task routing path subject to end-to-end latency requirements, spectrum, and computing resources. To efficiently address the curse of dimensionality problem due to vehicular mobility and channel variability, we develop a deep reinforcement learning, i.e., multi-agent deep deterministic policy gradient, based multi-hop task routing approach. Numerical results demonstrate that the proposed algorithm outperforms existing benchmark schemes.
Distinct but correct: generating diversified and entity-revised medical response

Medical dialogue generation (MDG) is applied for building medical dialogue systems for intelligent consultation. Such systems can communicate with patients in real time, thereby improving the efficiency of clinical diagnosis. However, predicting correct entities and correctly generating distinct responses remain a great challenge. Inspired by actual doctors’ responses to patients, we consider MDG a two-stage task: entity prediction and dialogue generation. For entity prediction, we design an ent-mac post pre-training strategy by leveraging external medical entity knowledge to enhance the pre-trained model. For dialogue generation, we propose an entity-aware fusion MDG method in which predicted entities are integrated into the dialogue generation model through different encoding fusion mechanisms, using information from different sources. Because the diverse beam search algorithm can produce responses with entities that deviate from the predicted entities, an entity-revised diverse beam search is proposed to correct the entities entailed in the generated responses and make the generated responses more distinct. The experimental results on the China Conference on Knowledge Graph and Semantic Computing 2021 (A/B tests) and the International Conference on Learning Representations 2021 (online test) datasets show that the proposed method outperforms several state-of-the-art methods, which demonstrates its practicability and effectiveness.
Multitask Kernel-based Learning with Logic Constraints

This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervised examples. In particular, we consider a multi-task learning scheme, where multiple unary predicates on the feature space are to be learned by kernel machines and a higher level abstract representation consists of logic clauses on these predicates, known to hold for any input. A general approach is presented to convert the logic clauses into a continuous implementation, that processes the outputs computed by the kernel-based predicates. The learning task is formulated as a primal optimization problem of a loss function that combines a term measuring the fitting of the supervised examples, a regularization term, and a penalty term that enforces the constraints on both supervised and unsupervised examples. The proposed semi-supervised learning framework is particularly suited for learning in high dimensionality feature spaces, where the supervised training examples tend to be sparse and generalization difficult. Unlike for standard kernel machines, the cost function to optimize is not generally guaranteed to be convex. However, the experimental results show that it is still possible to find good solutions using a two stage learning schema, in which first the supervised examples are learned until convergence and then the logic constraints are forced. Some promising experimental results on artificial multi-task learning tasks are reported, showing how the classification accuracy can be effectively improved by exploiting the a priori rules and the unsupervised examples.
Joint Gaussian Distribution and Attention for Time-Aware Recommendation Systems

Sequential models have achieved admirable success in recommendation systems. However, most sequential models typically only consider the chronological order of items through timestamps and ignore the relative distances in the sequence, which weakens the temporal relationships between items. To address this issue, we propose a temporal recommendation system using the Gaussian distribution and attention mechanism, which considers the sequentiality and interaction among items. Technically, we first deploy the word vector space along the time dimension as sequence features. Then, we use the Gaussian process to effectively represent the duration influence of items and the context interaction between items as high-level features. Finally, an innovative attention mechanism is used to capture the hidden correlation relationships between representation subspaces of different levels of features. Experiments conducted on two widely used real public datasets show that our model outperforms the state-of-the-art recommendation systems.
Progressive Learning of 3D Reconstruction Network From 2D GAN Data

This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they are not strictly multi-view consistent and sometimes GANs output distorted images. This results in degraded reconstruction qualities. In this work, to overcome these limitations of generated datasets, we have two main contributions which lead us to achieve state-of-the-art results on challenging objects: 1) A robust multi-stage learning scheme that gradually relies more on the models own predictions when calculating losses, 2) A novel adversarial learning pipeline with online pseudo-ground truth generations to achieve fine details. Our work provides a bridge from 2D supervisions of GAN models to 3D reconstruction models and removes the expensive annotation efforts. We show significant improvements over previous methods whether they were trained on GAN generated multi-view images or on real images with expensive annotations. Please visit our web-page for 3D visuals: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://research.nvidia.com/labs/adlr/progressive-3d-learning</uri> .