Reviewerly/exHarmony huggingface dataset:
#588 Beyond the binary: explaining the elusive nature of blood transfusion thresholds in advanced ovarian cancer cytoreductive surgery

<h3>Introduction/Background</h3> Institutional transfusion protocols are not universal, and a variety of transfusion policies may exist across participating institutions. As a result, there is no well-defined threshold for intraoperative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. According to a recent ESGO consensus guidance, many patients need chemotherapy, thus more liberal transfusion thresholds may be used. We developed a Machine Learning (ML)-prediction that could trigger a BT communication alert based on the extent of surgical cytoreduction. <h3>Methodology</h3> We analyzed prospectively collected data from 560 patients with advanced epithelial ovarian cancer (EOC) who underwent cytoreductive surgery at a UK tertiary center between 2014 and 2019. We excluded those with pre-operative anaemia and non-intact anticoagulation system, totaling 464 patients. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to model pre-operative, intra-operative, and human factors. We calculated the estimated blood volume (EBV) using the formula EBV= weight x 80 and set off 10%EBV as threshold for individual intervention. Based on the known estimated blood loss (EBL) we identified two groups. Receiver operating characteristic (ROC) curves were used for performance comparison. We used The SHapley Additive exPlanations (SHAP) framework to explain the predictive model. <h3>Results</h3> The model performance for the above threshold prediction was satisfactory (AUC 0.723, 95% CI 0.69–0.75). The top feature commonly shared between both interrogators was operative time (OT) (figure 1). Intra-operative blood loss of at least 20%EBV was associated with OT&gt;130 minutes, primary surgery, peritoneal carcinomatosis index &gt;8, surgical complexity score &gt;3, age of consultant surgeon &lt;48 years, and ascites. <h3>Conclusion</h3> Based on the EBV and EBL, we identified a threshold for potential individual intervention, regardless of BT policies. Precise prediction of blood requirements is not possible unless a rough estimate of OT is known in advance. <h3>Disclosures</h3> There are no conflict of interests.
Feature Extraction of English Semantic Translation Relying on Graph Regular Knowledge Recognition Algorithm

Under the background of big data, people are not only pursuing the quantity but also the accuracy of knowledge in acquiring knowledge, especially for English. Because of the ambiguity, variety, and irregularity of English translation, people's reading has brought a lot of trouble. This paper aims to study the feature extraction of English semantic translation and suggests a recognition algorithm that relies on graph common knowledge. Through the analysis of graph regularization and the construction of the model, the recognition algorithm is improved, and the feature extraction methods are compared and analyzed. At the same time, experiments are intended to investigate the improvement of the English semantic translation of the improved recognition algorithm after feature extraction. The experimental results in this paper show that the improved English semantic translation has increased by 10%-15% in terms of translation accuracy. This degree of improvement has great application significance in actual English semantic translation.
Fundamental Properties of Optical Diffractive Neural Networks Through Co-Design

Optical Diffractive Neural Networks (ODNNs) have emerged as a new class of AI systems that hold promise for fast and low energy classification of scenes. While these systems resemble electronic neural networks, they also have important differences because they need to satisfy constraints imposed by physical laws of light propagation and light-matter interactions. This brings a number of interesting fundamental questions regarding the ultimate performance that can be achieved, the optimal structure of materials, and even how effectively they can be trained. In this presentation, we will present our efforts to address these questions. In particular, we will discuss how co-design of the diffractive material, the system architecture, and the training algorithms is essential to achieve the best performance and also reveal underlying properties. For example, universal scaling of the performance emerges which differs from traditional electronic NNs. We will also discuss how the properties of the systems differs for coherent and incoherent light. Finally, the role of depth will also be addressed.
An innovative conceptual model for education and training on hybrid warfare

Abstract This study aims to present an innovative conceptual model of learning, teaching, and training that appears to be applicable to the field of hybrid warfare. It is based on integrating three critical elements such as reference curriculum, teaching methodology, and online support capabilities. By using qualitative research through correlating critical analysis of relevant sources, logical scheme, and cognitive task analysis, each descriptive element is analysed in order to provide an eloquent image of the proposed conceptual model. Also, to gain real feedback on the proposed model as well as on its constitutive elements, the quantitative research using statistical data grouping is performed. The analysis and interpretation of the collected data demonstrate not only a high interest in the designed conceptual model, but also a positive impact at the level of each descriptive element on the academic staff and students from the military and civilian university environment.
Defensive strategies against PCC attacks based on ideal (t,n)-secret sharing scheme

We present a method to increase the dependability of cloud-based applications. Traditional Secret Sharing Schemes (SSSs) typically fail to counter the challenges brought on by Private Channel Cracking (PCC) and Illegal Participant (IP) attacks. To prevent these attacks, we suggest a closely-coupled (t,m,n) secret sharing method that combines m(m⩾t) shareholders. A polynomial-based (t,m,n)-ITSS scheme is presented, which uses a k-round Random Number Selection (RNS) process to strengthen resistance to PCC assaults. A common method to convert a perfect (t,n)-SS into a (t,m,n)-ITSS is explained, which greatly enhances the defense against PCC attacks and illegal participation. The presented strategy can enhance the dependability of cloud-based applications.
FPGA Deployment of Deep Neural Network for Modulation Recognition

Modulation recognition is an important task in intelligent communications, and modulation recognition using deep neural network has gained considerable attention due to the rapid advancement of deep learning technology. However, implementing modulation recognition using deep neural network on a general-purpose platform is time-consuming, which makes it difficult to meet the high-speed and real-time requirements of practical applications. To address the issue, this paper proposes a field-programmable gate array (FPGA) deployment system for modulation recognition network. The proposed system leverages parallel computing and pipeline methods to significantly enhance the inference speed of the modulation network, Experiment results show that FPGA as a hardware accelerator can run parallel calculations up to 139 times faster than the ARM Cortex-A9 processor.
Contrastive Disentangled Learning on Graph for Node Classification

Contrastive learning methods have attracted considerable attention due to their remarkable success in analyzing graph-structured data. Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals. Specifically, we introduce a disentangled graph encoder to enforce the framework to distinguish various latent factors corresponding to underlying semantic information and learn the disentangled node embeddings. Moreover, to overcome the heavy reliance on labels, we design two self-supervision signals, namely node specificity and channel independence, which capture informative knowledge without the need for labeled data, thereby guiding the automatic disentanglement of nodes. Finally, we perform node classification tasks on three citation networks by using the disentangled node embeddings, and the relevant analysis is provided. Experimental results validate the effectiveness of the proposed framework compared with various baselines.
A Multi-Agent Q-Learning with Value Function Approximation Based on Single-leader Multi-followers Stackelberg Game

Although multi-agent reinforcement learning (MARL) has made significant progress in dealing with complex tasks, the hypothesis that agents act simultaneously still limits the applicability of MARL in many real-world problems. In this work, we relax such hypothesis by proposing a single-leader multi-followers Stackelberg game model. In this model, the leader considers the policy of the followers, and the followers make the best response based on the leader's action. By combining the single-leader multi-followers Stackelberg game model with Q-learning, we propose a Q-learning algorithm based on the Stackelberg game. We test a series of games, including Lumberjack and Predator Prey, which are challenging for existing MARL algorithms. Experimental results show that our method achieves competitive advantages in terms of performance and convergence speed.
Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming

Abstract Discovering physics from data have the potential to advance our understanding and prediction of a system where the governing physics are unknown but experimental data are available. Physics-based models are conventionally obtained from rigorous first principles derivations such as conservation laws or knowledge-based phenomenological derivations. However, inferring physics-based models remains challenging for many real-world complex systems with intertwined and coupled multi-physics that are challenging to derive from first principles. In recent years, data-driven physics discovery has become an attractive research topic across many scientific disciplines due to the increasing richness of observational data and advances in machine learning. Various data-driven physics discovery algorithms have been proposed to extract simple but appropriate models governing the systems from the observed data. Among the proposed algorithms, genetic programming (GP) based symbolic regression (SR) plays a dominant role. Here, we build a general GP based SR platform to discover physical models from observations for real-world systems. The platform is used to solve an optimization problem where the outcome is a set of human-readable equations that the user can select from based on accuracy vs parsimony trades. The discovery process also considers the prior knowledge of experts, which manifests as constraints to enhance the embedding of physics and avoid overfitting. The discovered models not only have high interpretability but also demonstrate good extrapolation performance.
Estimating Post-OCR Denoising Complexity on Numerical Texts

Post-OCR processing has significantly improved over the past few years. However, these have been primarily beneficial for texts consisting of natural, alphabetical words, as opposed to documents of numerical nature such as invoices, payslips, medical certificates, etc. To evaluate the OCR post-processing difficulty of these datasets, we propose a method to estimate the denoising complexity of a text and evaluate it on several datasets of varying nature, and show that texts of numerical nature have a significant disadvantage. We evaluate the estimated complexity ranking with respect to the error rates of modern-day denoising approaches to show the validity of our estimator.
Optimizing Hyper Meta Learning Models

Optimizing hyper meta learning models is a critical task in the field of machine learning, as it can improve the performance, efficiency, and scalability of these models. In this chapter, the authors present an epic overview of the process of optimizing hyper meta learning models. They discuss the key steps involved in this process, including task selection, model architecture selection, hyperparameter optimization, model training, model evaluation, and deployment. They also explore the benefits of hyper meta learning models and their potential future applications in various fields. Finally, they highlight the challenges and limitations of hyper meta learning models and suggest future research directions to overcome these challenges and improve the effectiveness of these models.
EDA, Data Cleaning, and Feature Selection

Exploratory data analysis (EDA) and data cleaning are usually the first two topics in a traditional data mining course. So, it is legitimate to ask why I place them in Chapter 11 .
Theology and thermodynamics of life and mind in the scientific legacy of Nikolay Ivanovich Kobozev

Nikolay Ivanovich Kobozev (1903–1974), Professor of Moscow State University, famous Russian physico-chemist, specialist in the field of thermodynamics. In his works devoted to thermodynamics of information and thinking, he emphasized the incompatibility between the entropy law for molecular structures, including molecular machinery of the mind, and the law of identity for thinking. In order to compensate for the molecular entropy, the mind should receive a negative entropy or antientropy, which is defined on the basis of the extended concept of thermodynamic probability. The latter is defined as a number of procedures (algorithms) that can be used to produce a physico-chemical system from its elementary components. For non-algorithmic systems, the entropy is negative. In order to model processing and production of information, N. I. Kobozev postulated the existence in a brain of a strongly degenerate Fermi gas of superlight Ψ-particles (psychons), thus being one of the first scientists who emphasized the importance of quantum processes for the physics of mind. In all its levels, life contains an antientropic component, which distinguishes it from the non-living matter. According to Kobozev, ‘human mind cannot be the result of evolutionary transformation of information function into thinking. The latter must be given to us, and not acquired in the course of evolution’. Since the source of information is the God Himself, the N. I. Kobozev’s works correspond to the theocentric scientific worldview and they can well be considered as theological construction in natural sciences.
Large-range lithography nano-alignment without phase unwrapping by a dual-frequency moiré fringe heterodyne

Moiré fringe is an effective approach to realize nano-alignment. However, affected by short periodicity and phase unwrapping, moiré fringe technology has small alignment ranges and redundant algorithms, making it difficult to meet practical application requirements. To solve the problem, we propose a large-range lithography nano-alignment method without phase unwrapping by a dual-frequency moiré fringe heterodyne. This method obtains four sets of moiré fringes from the main and differential alignment marks and then calculates the misalignment information using the heterodyne method. In this approach, both large alignment range and high alignment accuracy are achieved while avoiding the phase unwrapping process. The experimental results verified the rationality and feasibility of the proposed method.
An Empirical Study of Retrieval-Enhanced Graph Neural Networks

Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Motivated by this, we want to explore how the retrieval idea can help augment the useful information learned in the graph neural networks, and we design a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. In GRAPHRETRIEVAL, for each input graph, similar graphs together with their ground-true labels are retrieved from an existing database. Thus they can act as a potential enhancement to complete various graph property predictive tasks. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs. Moreover, our empirical study also illustrates that retrieval enhancement is a promising remedy for alleviating the long-tailed label distribution problem.
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations

The enhancement of unsupervised learning of sentence representations has been significantly achieved by the utility of contrastive learning. This approach clusters the augmented positive instance with the anchor instance to create a desired embedding space. However, relying solely on the contrastive objective can result in sub-optimal outcomes due to its inability to differentiate subtle semantic variations between positive pairs. Specifically, common data augmentation techniques frequently introduce semantic distortion, leading to a semantic margin between the positive pair. While the InfoNCE loss function overlooks the semantic margin and prioritizes similarity maximization between positive pairs during training, leading to the insensitive semantic comprehension ability of the trained model. In this paper, we introduce a novel Identical and Fraternal Twins of Contrastive Learning (named IFTCL) framework, capable of simultaneously adapting to various positive pairs generated by different augmentation techniques. We propose a Twins Loss to preserve the innate margin during training and promote the potential of data enhancement in order to overcome the sub-optimal issue. We also present proof-of-concept experiments combined with the contrastive objective to prove the validity of the proposed Twins Loss. Furthermore, we propose a hippocampus queue mechanism to restore and reuse the negative instances without additional calculation, which further enhances the efficiency and performance of the IFCL. We verify the IFCL framework on nine semantic textual similarity tasks with both English and Chinese datasets, and the experimental results show that IFCL outperforms state-of-the-art methods.
Tuning in to Neural Encoding: Linking Human Brain and Artificial Supervised Representations of Language

To understand the algorithm that supports the human brain’s language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding. However, most of these studies have focused on probing neural representations of Germanic languages, such as English, with unsupervised ANNs. In this paper, we propose to bridge the gap between human brain and supervised ANN representations of the Chinese language. Specifically, we investigate how task tuning influences a pretained Transformer for neural encoding and which tasks lead to the best encoding performances. We generate supervised representations on eight Natural Language Understanding (NLU) tasks using prompt-tuning, a technique that is seldom explored in neural encoding for language. We demonstrate that prompt-tuning yields representations that better predict neural responses to Chinese stimuli than traditional fine-tuning on four tasks. Furthermore, we discover that tasks that require a fine-grained processing of concepts and entities lead to representations that are most predictive of brain activation patterns. Additionally, we reveal that the proportion of tuned parameters highly influences the neural encoding performance of fine-tuned models. Overall, our experimental findings could help us better understand the relationship between supervised artificial and brain language representations.
MIATS:A Chinese Spelling Error Correction Algorithm Based on Multimodal Information Alignment of Three-Towers Structure

Chinese spelling correction (CSC) is a crucial task in natural language processing, aiming to detect and correct spelling errors in Chinese text. The improved performance of Chinese spelling errors correction algorithms can enhance the efficiency and accuracy of upstream and downstream Chinese natural language processing tasks, such as OCR, ASR, and translation.However, current methods based on neural networks are mostly limited to either using only contextual information to correct misspelled words or failing to fully utilize glyph and pinyin information. Therefore, we propose a multimodal approach to address the above issues. Specifically, a three-tower multimodal structure is used to extract glyph, pinyin, and semantic information, and a decoder composing of an error probability prediction network and a transformer network is employed to achieve cross-modal information interaction. Besides, an additional training task is used to achieve cross-modal information alignment. Experiments demonstrate that proposed network outperform most existing motheds.
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering

Supplying data augmentation to conversational question answering (CQA) can effectively improve model performance. However, there is less improvement from single-turn datasets in CQA due to the distribution gap between single-turn and multi-turn datasets. On the other hand, while numerous single-turn datasets are available, we have not utilized them effectively. To solve this problem, we propose a novel method to convert single-turn datasets to multi-turn datasets. The proposed method consists of three parts, namely, a QA pair Generator, a QA pair Reassembler, and a question Rewriter. Given a sample consisting of context and single-turn QA pairs, the Generator obtains candidate QA pairs and a knowledge graph based on the context. The Reassembler utilizes the knowledge graph to get sequential QA pairs, and the Rewriter rewrites questions from a conversational perspective to obtain a multi-turn dataset S2M. Our experiments show that our method can synthesize effective training resources for CQA. Notably, S2M ranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time of submission (Aug 24th, 2022).
On Explaining Agent Behaviour via Root Cause Analysis: A Formal Account Grounded in Theory of Mind

Inspired by a novel action-theoretic formalization of actual cause, Khan and Lespérance (2021) recently proposed a first account of causal knowledge that supports epistemic effects, models causal knowledge dynamics, and allows sensing actions to be causes of observed effects. To date, no other study has looked specifically at these issues. But their formalization is not sufficiently expressive to model explanations via causal analysis of mental states as it ignores a crucial aspect of theory of mind, namely motivations. In this paper, we build on their work to support causal reasoning about conative effects. In our framework, one can reason about causes of motivational states, and we allow motivation-altering actions to be causes of observed effects. We illustrate that this formalization along with a model of goal recognition can be utilized to explain agent behaviour in communicative multiagent contexts.