Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network-Based Approximation of Coverage Path Planning Results for Parking Lots

Parking lots have wide variety of shapes because of surrounding environment and the objects inside the parking lot, such as trees, manholes, etc. In the case of paving the parking lot, as much area as possible should be covered by the construction vehicle to reduce the need for manual workforce. Thus, the coverage path planning (CPP) problem is formulated. The CPP of the parking lots is a complex problem with constraints regarding various issues, such as dimensions of the construction vehicle and data processing time and resources. A strategy based on convolutional neural networks (CNNs) for the fast estimation of the CPP’s average track length, standard deviation of track lengths, and number of tracks was suggested in this article. Two datasets of different complexity were generated to analyze the suggested approach. The first case represented a simple case with a working polygon constructed out of several rectangles with applied shear and rotation transformations. The second case represented a complex geometry generated out of rectangles and ellipses, narrow construction area, and obstacles. The results were compared with the linear regression models, with the area of the working polygon as an input. For both generated datasets, the strategy to use an approximator to estimate outcomes led to more accurate results compared to the respective linear regression models. The suggested approach enables us to have rough estimates of a large number of geometries in a short period of time and organize the working process, for example, planning construction time and price, choosing the best decomposition of the working polygon, etc.
The Ethics of the Personal Digital Twin

This chapter explains how we have developed our unique Digisheds™ theory of change, and packaged it within a bias-free blended learning 'playbook' which can be delivered informally within both physical and remote learning settings. Our Digisheds™ employability pathways provide training and wraparound support to long-term unemployed and hard to reach groups. Due to the significant level of mistrust of publicly funded education and training provision by these learners, we find it necessary to explain how using a personal digital twin used as an avatar can showcase how equitable career opportunities could be curated using human centered design at scale. A 'digital twin' is a virtual model that is designed to accurately reflect a physical object by producing data about different aspects of the physical object's capacity, capability, and performance. Once our learners discover, using their avatar, more about the ethics behind how their digital activities are being used (often without their knowledge and consent), they start to become more motivated to exert their own autonomy over the legibility, agency, and potential for negotiability related to where and how their data is to be retained.
Artificial intelligence for breast cancer screening: breathtaking results and a word of caution

In The Lancet Oncology, Kristina Lång and colleagues report a prespecified clinical safety analysis of the MASAI trial, 1 Lång K Josefsson V Larsson AM et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence (MASAI) trial: a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023; 24: 936-944 Google Scholar investigating the use of artificial intelligence (AI) as a supplement to radiologist reading of mammograms in breast cancer screening. Conducted within the Swedish national breast screening programme, participants were randomly assigned (1:1) to either AI-supported screening (intervention group) or standard double reading without AI (control group). In the intervention group, the AI algorithm was used to triage screening mammograms to provide a level of risk of breast cancer from 1 to 10, with 10 having the highest risk. Screening examinations given a risk score of 1–9 were assigned to AI-assisted single reading, and those with a score of 10 were assigned to AI-assisted double reading. In the analysis, which was prespecified to occur after 80 000 women had been enrolled, there were 244 screen-detected cancers (6·1 per 1000 participants) and 46 345 screen readings in the intervention group (n=39 996 participants), and 203 cancers (5·1 per 1000) and 83 231 screen readings in the control group (n=40 024). Compared with the control group, the screen-reading workload of radiologists was reduced by 44·3% using AI-assisted techniques. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy studyAI-supported mammography screening resulted in a similar cancer detection rate compared with standard double reading, with a substantially lower screen-reading workload, indicating that the use of AI in mammography screening is safe. The trial was thus not halted and the primary endpoint of interval cancer rate will be assessed in 100 000 enrolled participants after 2-years of follow up. Full-Text PDF Digital solutions for early breast cancer detectionBy 2040 , more than 3 million new cases of breast cancer and 1 million deaths are estimated to occur each year worldwide. Identifying those at greatest risk of developing life-threatening breast cancers is a public-health priority. In this issue of The Lancet Digital Health, Ash Kieran Clift and colleagues developed and evaluated four prognostic models to predict 10-year risk of breast cancer mortality in women aged 20–90 years. The best performing model could be used to identify women at high risk of mortality, including those too young for breast cancer screening. Full-Text PDF Open Access
Deflectometry based calibration of a deformable mirror for aberration correction and remote focusing in microscopy

Adaptive optics (AO) techniques enhance the capability of optical microscopy through precise control of wavefront modulations to compensate phase aberrations and improves image quality. However, the aberration correction is often limited due to the lack of dynamic range in existing calibration methods, such as interferometry or Shack-Hartmann (SH) wavefront sensors. Here, we use deflectometry (DF) as a calibration method for a deformable mirror (DM) to extend the available range of aberration correction. We characterised the dynamic range and accuracy of the DF-based calibration of DMs depending on the spatial frequency of the test pattern used in DF. We also demonstrated the capability of large magnitude phase control for remote-focusing over a range larger than was possible with SH sensing.
CDANER: Contrastive Learning with Cross-domain Attention for Few-shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) aims to recognize unseen name entities based on a tiny support set that consists of seen name entities and labels, which is obviously different from traditional supervised NER methods. Contrastive learning has become a popular solution for few-shot NER, which improves the robustness of NER to handle unlabeled entities by learning a similarity metric to measure the semantic similarity between test samples and entity labels. However, existing contrastive learning based NER methods individually learn the word embedding in source and target domains, ignoring connections between entities with the same label and limiting the effectiveness of contrast learning. In this paper, we propose a novel few-shot NER framework that jointly models different domain texts and optimizes a generalized objective of differentiating between words in all stages. The proposed model builds the cross-domain attention layer to enhance the feature representations of words and transfer the entity similarity information from the source domain to the target domain. This significantly reduces the divergence between entities with same label. Experimental results on the largest Few-shot NER dataset show that CDANER significantly outperforms all baseline methods, which verifies the effectiveness and robustness of the proposed model.
Knowledge Graph Enhanced Sentential Relation Extraction via Dual Heterogeneous Graph Context Selection

Sentential relation extraction is a type of relation extraction task whose goal is to extract semantic relations between entities from a single sentence. Compared with other variants of relation extraction, it often suffers from limitations of semantic contextual information. Due to the presence of knowledge graphs, many approaches propose to augment the semantics of sentences with the knowledge of entities, thus improving the performance of relation extraction. Despite their success, existing methods still suffer from two weaknesses: (1) existing approaches aggregate sentences, entities and their attribute values into a heterogeneous information graph, but do not consider the types of edges; (2) existing methods dynamically select knowledge based only on the structural features of the graph, without considering the features of the nodes themselves. To address these two problems, we propose a dual heterogeneous graph context selection method for knowledge graph enhanced sentential relation extraction. Specifically, to solve the first problem, we employ an edge-aware graph convolutional network to learn the representations of the heterogeneous graph with considering the types of edges. To solve the second problem, we propose dual graph context selection to select the useful context by considering the graph structure and node feature representation together. Experiments conducted on the Wikidata-RE dataset demonstrate the effectiveness of the method.
RFDG: Reinforcement Federated Domain Generalization

During the training process of federated learning models, the domain information of the target test data on the server can differ greatly from the training data of each client, leading to a decrease in the performance of the federated model. Additionally, due to privacy protection during federated training, clients cannot see the target domain test data, and the distribution information of the target data cannot be used. This poses a new challenge for federated learning. Domain generalization techniques are often used in centralized frameworks to resolve such problems. In recent years, the domain generalization method based on feature decorrelation has enabled models to learn knowledge with a stronger generalization ability in unseen target domain data. However, existing methods require data centralization in the feature decorrelation process, which conflicts with data privacy protection in federated learning. To address these issues, we propose Reinforcement Federated Domain Generalization (RFDG), which incorporates domain generalization in federated learning via reinforcement learning. RFDG can improve the generalization ability of the federated model of unseen target domain test data. We design a reinforcement federated feature decorrelation policy that uses reinforcement learning technology to transform the sample reweight work into a parameterized sample reweight policy that can be shared among federated learning clients. We develop reinforcement federated experience replay techniques to supplement the feature information loss of local data due to the mini-batch mechanism during the policy learning process. When the policy is shared by each client, those features can be decorrelated from a global perspective, allowing the model to focus on capturing the fundamental association between features and labels to learn domain-invariant knowledge. We verified the effectiveness of our method through extensive experiments using four publicly available datasets.
Lee-Yang zeros and quantum Fisher information matrix in a nonlinear system

The distribution of Lee-Yang zeros not only matters in thermodynamics and quantum mechanics, but also in mathematics. Hereby we propose a nonlinear quantum toy model and discuss the distribution of corresponding Lee-Yang zeros. Utilizing the coupling between a probe qubit and the nonlinear system, all Lee-Yang zeros can be detected in the dynamics of the probe qubit by tuning the coupling strength and linear coefficient of the nonlinear system. Moreover, the analytical expression of the quantum Fisher information matrix at the Lee-Yang zeros is provided and an interesting phenomenon is discovered. Both the coupling strength and temperature can simultaneously attain their precision limits at the Lee-Yang zeros. However, the probe qubit cannot work as a thermometer at a Lee-Yang zero if it sits on the unit circle.
A Synthetic Data Generation Framework for Grounded Dialogues

Training grounded response generation models often requires a large collection of grounded dialogues.However, it is costly to build such dialogues.In this paper, we present a synthetic data generation framework (SynDG) for grounded dialogues.The generation process utilizes large pre-trained language models and freely available knowledge data (e.g., Wikipedia pages, persona profiles, etc.).The key idea of designing SynDG is to consider dialogue flow and coherence in the generation process.Specifically, given knowledge data, we first heuristically determine a dialogue flow, which is a series of knowledge pieces.Then, we employ T5 to incrementally turn the dialogue flow into a dialogue.To ensure coherence of both the dialogue flow and the synthetic dialogue, we design a two-level filtering strategy, at the flow-level and the utterance-level respectively.Experiments on two public benchmarks show that the synthetic grounded dialogue data produced by our framework is able to significantly boost model performance in both full training data and low-resource scenarios.
VBD-NLP at BioLaySumm Task 1: Explicit and Implicit Key Information Selection for Lay Summarization on Biomedical Long Documents

We describe our systems participated in the Bio-LaySumm 2023 Task 1, which aims at automatically generating lay summaries of scientific articles in a simplified way so that its content becomes easier to comprehend for non-expert readers.Our approaches are based on selecting key information by both explicit and implicit strategies.For explicit selection strategies, we conduct extractive summarization based on selecting key sentences for training abstractive summarization models.For implicit selection strategies, we utilize a method based on a factorized energy-based model, which is able to extract important information from long documents to generate summaries and achieve promising results.We build our systems using sequence-to-sequence models, which enable us to leverage powerful and biomedical domain pre-trained language models and apply different strategies to generate lay summaries from long documents.We conducted various experiments to carefully investigate the effects of different aspects of this long-document summarization task such as extracting different document lengths and utilizing different pre-trained language models.We achieved the third rank in the shared task (and the second rank excluding the baseline submission of the organizers).
AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning

Argument generation is an important but challenging task in computational argumentation.Existing studies have mainly focused on generating individual short arguments, while research on generating long and coherent argumentative essays is still under-explored.In this paper, we propose a new task, Argumentative Essay Generation (AEG).Given a writing prompt, the goal of AEG is to automatically generate an argumentative essay with strong persuasiveness.We construct a large-scale dataset, ArgEssay, for this new task and establish a strong model based on a dual-decoder Transformer architecture.Our proposed model contains two decoders, a planning decoder (PD) and a writing decoder (WD), where PD is used to generate a sequence for essay content planning and WD incorporates the planning information to write an essay.Further, we pre-train this model on a large news dataset to enhance the plan-and-write paradigm.Automatic and human evaluation results show that our model can generate more coherent and persuasive essays with higher diversity and less repetition compared to several baselines. 1
SAR-optical feature matching: A large-scale patch dataset and a deep local descriptor

Image matching is a key prerequisite for image fusion. Currently, deep learning methods had shown great potential in matching. However, these methods mainly focus on optical images or area-based matching and their performance highly relies on the diversity of the training sets. This paper makes two major contributions to the synthetic aperture radar (SAR)-optical image feature matching task. First, we create a high-quality open SAR and optical patch dataset called SOPatch, which contains more than 650 000 matching patch pairs. SOPatch is generated from a variety of satellite images (Sentinel-1, Sentinel-2, Gaofen-3, etc.) and contains a rich set of features (mountains, lakes, buildings, farmland, bare land, etc.), which can provide robustness to sensor and localization changes and train a model with good generalization ability. Similar to the HPatches (Balntas et al., 2017), we also add position and rotation noise to make the dataset more realistic and robust. Second, a local descriptor for SAR-optical matching called SODescNet is proposed. We first weaken the speckle noise of SAR by multiscale dilated convolution and channel attention mechanisms and then use DenseNet-CSP (cross stage partial) as the backbone for feature description. To reduce the difficulty of network training, we first use a Siamese network for pre-training and then fine-tune it via a pseudo-Siamese network. Extensive experiments show that our SODescNet is comparable or superior to state-of-the-arts.
A Unification of the Coding Theory and OAQEC Perspectives on Hybrid Codes

There is an advantage in simultaneously transmitting both classical and quantum information over a quantum channel compared to sending independent transmissions. The successful implementation of simultaneous transmissions of quantum and classical information will require the development of hybrid quantum-classical error-correcting codes, known as hybrid codes. The characterization of hybrid codes has been performed from a coding theory perspective and an operator algebra quantum error correction (OAQEC) perspective. First, we demonstrate that these two perspectives are equivalent and that the coding theory characterization is a specific case of the OAQEC model. Second, we include a generalization of the quantum Hamming bound for hybrid error-correcting codes. We discover a necessary condition for developing non-trivial hybrid codes—they must be degenerate. Finally, we construct an example of a non-trivial degenerate 4-qubit hybrid code.
EfCNN-Net: Smart Detection of Colon and Lung Cancer using Histopathological Images

In low- and middle-income nations where there is little access to health care and cancer screening, the death rate from lung cancer is highest. Contrarily, colon cancer is the subsequent most prevalent cancer worldwide and the third biggest illness overall, accounting for 935,000 fatalities in 2022. The death rate from colon cancer varies by region, with developed nations reporting the highest rates. The odds of survival for lung and colon cancer can be considerably increased with early detection and treatment. Therefore, it’s critical to promote routine cancer screening and prompt treatment while raising awareness of the risk factors linked to these malignancies. Cancer can be found through histopathological image analysis, which makes it possible to spot morphological abnormalities in tissue samples. In this study, we suggest a unique method for using histopathological scans to identify lung and colon tumors. The recommended approach uses convolutional neural networks (CNNs) for feature extraction and categorization. The underlying technology of this method is deep learning. To assess the suggested method and show its efficacy in highly accurate lung and colon cancer detection, we used the LC25000 Lung and colon histopathology imaging collection. On the basis of the parameters and accuracy, we have also contrasted the various image analysis methods. Our findings imply that the EfficientNet algorithm has the potential to increase the precision of cancer detection and support the creation of more efficient therapeutic approaches.
Semantic Segmentation and Depth Estimation of Urban Road Scene Images Using Multi-Task Networks

In autonomous driving, environment perception is an important step in understanding the driving scene. Objects in images captured through a vehicle camera can be detected and classified using semantic segmentation and depth estimation methods. Both these tasks are closely related to each other and this association helps in building a multi-task neural network where a single network is used to generate both views from a given monocular image. This approach gives the flexibility to include multiple related tasks in a single network. It helps reduce multiple independent networks and improve the performance of all related tasks. The main aim of our research presented in this paper is to build a multi-task deep learning network for simultaneous semantic segmentation and depth estimation from monocular images. Two decoder-focused U-N et-based multi-task networks that use a pre-trained Resnet-50 and DenseNet-121 which shared encoder and task-specific decoder networks with Attention Mechanisms are considered. We also employed multi-task optimization strategies such as equal weighting and dynamic weight averaging during the training of the models. The corresponding models' performance is evaluated using mean IoU for semantic segmentation and Root Mean Square Error for depth estimation. From our experiments, we found that the performance of these multi-task networks is on par with the corresponding single-task networks.
Incentivization and Aggregation Schemes for Federated Learning Applications

Currently, the data collected by the Internet of Things (IoT) still relies on the cloud-centric data aggregation and processing approach for preparing machine learning models. This approach puts the privacy of the participants at risk. In this paper, federated learning (FL) is proposed for privacy-preserving collaborative model training on data distributed across IoT users. To motivate participants, we must incentivize the whole process by rewarding each participant for their contribution to the training process of the federated learning model. The process of collective training takes place over a long duration of time and multiple iterations. However, participants in the training process may have varying levels of willingness to participate (WTP) and may contribute duplicate or poor-quality data. Therefore, in each iteration, participants must be rewarded based on their contribution in that specific iteration. In this paper, a methodology to reward each participant based on their contribution and a model aggregation technique are proposed. The aggregation technique uses Polyak-averaging to aggregate weights of local models, with the weightage assigned to each local model being proportional to its accuracy on the test dataset. Performance evaluation shows that the federated learning model formed using our aggregation approach achieves the performance level of machine learning as we perform more iterations and performs slightly better than the model formed using the FedAvg algorithm. Additionally, our incentivization methodology provides better performance-based rewards compared to other profit-sharing schemes.