Reviewerly/exHarmony huggingface dataset:
Seeing both sides: context-aware heterogeneous graph matching networks for extracting-related arguments

Abstract Our research focuses on extracting exchanged views from dialogical documents through argument pair extraction (APE). The objective of this process is to facilitate comprehension of complex argumentative discourse by finding the related arguments. The APE comprises two stages: argument mining and argument matching. Researchers typically employ sequence labeling models for mining arguments and text matching models to calculate the relationships between them, thereby generating argument pairs. However, these approaches fail to capture long-distance contextual information and struggle to fully comprehend the complex structure of arguments. In our work, we propose the context-aware heterogeneous graph matching (HGMN) model for the APE task. First, we design a graph schema specifically tailored to argumentative texts, along with a heterogeneous graph attention network that effectively captures context information and structural information of arguments. Moreover, the text matching between arguments is converted into a graph matching paradigm and a multi-granularity graph matching model is proposed to handle the intricate relationships between arguments at various levels of granularity. In this way, the semantics of argument are modeled structurally and thus capture the complicated correlations between arguments. Extensive experiments are conducted to evaluate the HGMN model, including comparisons with existing methods and the GPT series of large language models (LLM). The results demonstrate that HGMN outperforms the state-of-the-art method.
A Remark on Concept Drift for Dependent Data

Concept drift, i.e., the change of the data generating distribution, can render machine learning models inaccurate. Several works address the phenomenon of concept drift in the streaming context usually assuming that consecutive data points are independent of each other. To generalize to dependent data, many authors link the notion of concept drift to time series. In this work, we show that the temporal dependencies are strongly influencing the sampling process. Thus, the used definitions need major modifications. In particular, we show that the notion of stationarity is not suited for this setup and discuss alternatives. We demonstrate that these alternative formal notions describe the observable learning behavior in numerical experiments.
Controllable Diffusion Models for Safety-Critical Driving Scenario Generation

Safety-critical driving scenarios are essential to the development and validation of autonomous driving algorithms. Currently, most of the data is acquired in naturalistic scenarios, resulting in a sparsity of the safety-critical cases. Consequently, synthetic scenario generation based on deep models becomes crucial to validate the risk and reduce the cost. However, previous works either fail to generate realistic scenarios with high quality or can hardly follow instructions to generate desired scenarios. In this paper, we propose a controllable diffusion model that operates on a naturalistic driving scenario to generate safety-critical cases with high fidelity and controllability. In particular, our proposed method encompasses a pre-trained text-to-image diffusion model and a bounding box to incorporate the conditions of category and position of generated objects, respectively. To enhance generation quality and mitigate boundary artifacts, we introduce a mask-aware adapter to better integrate the generated objects into the driving scenarios. Moreover, we propose a transformer encoder and region-guided cross attention to fuse the additional coordinate inputs into the diffusion model, while encouraging more interaction between different generated objects. Comparative analysis demonstrates that our method outperforms exciting work by offering more realistic, diverse and controllable synthetic scenarios and allowing for multiple objects generation with complex spatial relationship.
Energy optimization strategy for data center electrothermal coupling system based on task scheduling

With technologies such as 5G, AI and IoT developing, the DC industry has ushered in a new round of development and challenges. However, high power consumption has brought huge operating costs and environmental pressure to data center enterprises. How to reduce operating costs and environmental impact has become a key challenge for data center enterprises in the next step. This article proposes a framework for the management of electric heating energy within a data center microgrid, which achieves joint optimization scheduling and energy management of the power and thermal systems within the microgrid through load distribution in the data center. The numerical results demonstrate that the model established in this article effectively reduces system operating costs and reduces the use of fossil fuels.
Anomaly detection for automated inspection of power line insulators

Inspection of insulators is important to ensure reliable operation of the power system. Deep learning has recently been explored to automate the inspection process by leveraging aerial images captured by drones along with powerful object detection models. However, a purely object detection-based approach exhibits class imbalance-induced poor detection accuracy for faulty insulators, especially for incipient faults. In order to address this issue in a data-efficient manner, this article proposes a two-stage approach that leverages object detection in conjunction with anomaly detection to reliably detect faults in insulators. The article adopts an explainable deep neural network-based one-class classifier for anomaly detection, that reduces the reliance on plentifully available images of faulty insulators, that might be difficult to obtain in real-life applications. The anomaly detection model is trained with two datasets -- representing data abundant and data scarce scenarios -- in unsupervised and semi-supervised manner. The results suggest that including as few as six real anomalies in the training dataset significantly improves the performance of the model, and enables reliable detection of rarely occurring faults in insulators. An analysis of the explanations provided by the anomaly detection model reveals that the model is able to accurately identify faulty regions on the insulator disks, while also exhibiting some false predictions.
User Modeling in the Era of Large Language Models: Current Research and Future Directions

User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality. The user models enable personalization and suspiciousness detection in many online applications such as recommendation, education, and healthcare. Two common types of user data are text and graph, as the data usually contain a large amount of user-generated content (UGC) and online interactions. The research of text and graph mining is developing rapidly, contributing many notable solutions in the past two decades. Recently, large language models (LLMs) have shown superior performance on generating, understanding, and even reasoning over text data. The approaches of user modeling have been equipped with LLMs and soon become outstanding. This article summarizes existing research about how and why LLMs are great tools of modeling and understanding UGC. Then it reviews a few categories of large language models for user modeling (LLM-UM) approaches that integrate the LLMs with text and graph-based methods in different ways. Then it introduces specific LLM-UM techniques for a variety of UM applications. Finally, it presents remaining challenges and future directions in the LLM-UM research. We maintain the reading list at: https://github.com/TamSiuhin/LLM-UM-Reading
GazeMoDiff: Gaze-guided Diffusion Model for Stochastic Human Motion Prediction

Human motion prediction is important for virtual reality (VR) applications, e.g., for realistic avatar animation. Existing methods have synthesised body motion only from observed past motion, despite the fact that human gaze is known to correlate strongly with body movements and is readily available in recent VR headsets. We present GazeMoDiff -- a novel gaze-guided denoising diffusion model to generate stochastic human motions. Our method first uses a graph attention network to learn the spatio-temporal correlations between eye gaze and human movements and to fuse them into cross-modal gaze-motion features. These cross-modal features are injected into a noise prediction network via a cross-attention mechanism and progressively denoised to generate realistic human full-body motions. Experimental results on the MoGaze and GIMO datasets demonstrate that our method outperforms the state-of-the-art methods by a large margin in terms of average displacement error (15.03% on MoGaze and 9.20% on GIMO). We further conducted an online user study to compare our method with state-of-the-art methods and the responses from 23 participants validate that the motions generated by our method are more realistic than those from other methods. Taken together, our work makes a first important step towards gaze-guided stochastic human motion prediction and guides future work on this important topic in VR research.
MixRT: Mixed Neural Representations For Real-Time NeRF Rendering

Neural Radiance Field (NeRF) has emerged as a leading technique for novel view synthesis, owing to its impressive photorealistic reconstruction and rendering capability. Nevertheless, achieving real-time NeRF rendering in large-scale scenes has presented challenges, often leading to the adoption of either intricate baked mesh representations with a substantial number of triangles or resource-intensive ray marching in baked representations. We challenge these conventions, observing that high-quality geometry, represented by meshes with substantial triangles, is not necessary for achieving photorealistic rendering quality. Consequently, we propose MixRT, a novel NeRF representation that includes a low-quality mesh, a view-dependent displacement map, and a compressed NeRF model. This design effectively harnesses the capabilities of existing graphics hardware, thus enabling real-time NeRF rendering on edge devices. Leveraging a highly-optimized WebGL-based rendering framework, our proposed MixRT attains real-time rendering speeds on edge devices (over 30 FPS at a resolution of 1280 x 720 on a MacBook M1 Pro laptop), better rendering quality (0.2 PSNR higher in indoor scenes of the Unbounded-360 datasets), and a smaller storage size (less than 80% compared to state-of-the-art methods).
Towards Inherently Interpretable Deep Learning for Accelerating Scientific Discoveries in Climate Science

While deep learning models have high representation power and promising performances, there is often a lack of evidence to interpret potential reasons behind the predictions, which is a major concern limiting their usability for scientific discovery. We propose a Neural Additive Convolutional Neural Network (NA-CNN) to enhance the interpretability of the model to facilitate scientific discoveries in climate science. To investigate the interpretation quality of NA-CNN, we perform experiments on the El Niño identification task where the ground truth for El Niño patterns is known and can be used for validation. Experiment results show that compared to Spatial Attention and state-of-the-art post-hoc explanation techniques, NA-CNN has higher interpretation precision, remarkably improved physical consistency, and reduced redundancy. These qualities provide an encouraging ground for domain scientists to focus their analysis on potentially relevant patterns and derive laws governing phenomena with unknown physical processes.
Unveiling the Neural Mirage in the Pursuit of Transcendent Intelligence

The paper proposes a new approach to AI, called Cognitive Artificial Intelligence (CAI), which is inspired by the human brain. CAI aims to develop AI systems that can think and act rationally, closely mirroring human cognition. The proposed approach, called Knowledge-Expanding System (KES), enables AI systems to acquire profound knowledge and evolve over time, surpassing the intellectual capacities of even the most gifted human minds. The realization of Transcendent Intelligence in AI systems has profound implications for humanity, necessitating thoughtful consideration of ethical aspects and the responsible development and deployment of Transcendent AI for the benefit of humanity.
SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting

Novel view synthesis has shown rapid progress recently, with methods capable of producing evermore photo-realistic results. 3D Gaussian Splatting has emerged as a particularly promising method, producing high-quality renderings of static scenes and enabling interactive viewing at real-time frame rates. However, it is currently limited to static scenes only. In this work, we extend 3D Gaussian Splatting to reconstruct dynamic scenes. We model the dynamics of a scene using a tunable MLP, which learns the deformation field from a canonical space to a set of 3D Gaussians per frame. To disentangle the static and dynamic parts of the scene, we learn a tuneable parameter for each Gaussian, which weighs the respective MLP parameters to focus attention on the dynamic parts. This improves the model's ability to capture dynamics in scenes with an imbalance of static to dynamic regions. To handle scenes of arbitrary length whilst maintaining high rendering quality, we introduce an adaptive window sampling strategy to partition the sequence into windows based on the amount of movement in the sequence. We train a separate dynamic Gaussian Splatting model for each window, allowing the canonical representation to change, thus enabling the reconstruction of scenes with significant geometric or topological changes. Temporal consistency is enforced using a fine-tuning step with self-supervising consistency loss on randomly sampled novel views. As a result, our method produces high-quality renderings of general dynamic scenes with competitive quantitative performance, which can be viewed in real-time with our dynamic interactive viewer.
Peer-to-Peer Learning + Consensus with Non-IID Data

Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs of training with distributed consensus steps. This process leads to model parameter drift/divergence amongst participating devices in both IID and non-IID settings. We observe that model drift results in significant oscillations in test performance evaluated after local training and consensus phases. We then identify factors that amplify performance oscillations and demonstrate that our novel approach, P2PL with Affinity, dampens test performance oscillations in non-IID settings without incurring any additional communication cost.
Enhancing Code Prediction Transformer with AST Structural Matrix

Deep learning Transformer architectures play a critical role in developing advanced code prediction models, which are essential in modern Integrated Development Environments (IDEs). Nevertheless, these architectures encounter a significant challenge in effectively capturing and utilizing the structural information present in Abstract Syntax Trees (ASTs). To tackle this challenge, we propose an innovative approach that leverages AST structural matrices to enhance Transformers for source code prediction. Specifically, we integrate three types of AST structural matrices - the R matrix, A&S matrix, and MVG matrix - into the attention module of the Transformer to effectively capture the structural information within ASTs. To ensure optimal utilization, we have devised a range of strategies and integration methods tailored specifically for the attention module. To assess the effectiveness of our proposal, we conduct empirical studies using a standard Python dataset. The results demonstrate that incorporating AST structural matrices significantly enhances the accuracy of code prediction models, leading to an overall improvement from 73.18% to 75.10%. Furthermore, we conduct an in-depth analysis of the impact of each matrix type, offering a comprehensive understanding of their application scenarios. This insightful analysis provides valuable guidance on how to effectively leverage each matrix type in various contexts.
Encoder Activation Diffusion and Decoder Transformer Fusion Network for Medical Image Segmentation

Over the years, medical image segmentation has played a vital role in assisting healthcare professionals in disease treatment. Convolutional neural networks have demonstrated remarkable success in this domain. Among these networks, the encoder-decoder architecture stands out as a classic and effective model for medical image segmentation. However, several challenges remain to be addressed, including segmentation issues arising from indistinct boundaries, difficulties in segmenting images with irregular shapes, and accurate segmentation of lesions with small targets. To address these limitations, we propose Encoder Activation Diffusion and Decoder Transformer Fusion Network (ADTF). Specifically, we propose a novel Lightweight Convolution Modulation (LCM) formed by a gated attention mechanism, using convolution to encode spatial features. LCM replaces the convolutional layer in the encoder-decoder network. Additionally, to enhance the integration of spatial information and dynamically extract more valuable high-order semantic information, we introduce Activation Diffusion Blocks after the encoder (EAD), so that the network can segment a complete medical segmentation image. Furthermore, we utilize a Transformer-based multi-scale feature fusion module on the decoder (MDFT) to achieve global interaction of multi-scale features. To validate our approach, we conduct experiments on multiple medical image segmentation datasets. Experimental results demonstrate that our model outperforms other state-of-the-art (SOTA) methods on commonly used evaluation metrics.
Age differentially mediates the relationship between plasma biomarkers and cognition detected by digital screening

Abstract Background Plasma biomarkers are emerging as a useful tool to measure neurodegeneration, particularly in patients with Alzheimer’s Disease (AD). Digital cognitive testing is also increasingly used to detect cognitive changes in AD. Age is a major determinant of performance at cognitive testing and can affect the levels of different plasma biomarkers. However, little is known about how age mediates the relationship between plasma biomarkers and cognition. Methods 38 AD and 48 age‐matched controls were recruited from the Oxford Centre for Cognitive Disorders. We used a fully remote digital visual short‐term memory (STM) task ( Figure 1 ) and extracted eight cognitive metrics ( Figure 1 ): Identification Accuracy (percentage of correctly identified items), Absolute Error (distance from the response location to the original item), Target detection (probability of correctly identifying the target), Misbinding (erroneously localizing an item to the remembered location of another item in memory), Guessing (random guessing), Identification and Localization Time (time to identify and drag the correct object to its remembered location), and memory Imprecision (width of the distribution around the target). Plasma p‐tau 181, GFAP, NFL, Aβ40 and Aβ42 were measured, and the Aβ42/40 ratio was calculated. In Model 1 ( Figure 2 ) the correlation between digital cognitive metrics and plasma biomarkers is shown, while in Model 2 age was regressed out. A mediation analysis was then performed to look at the effect of age as a mediator. Results In Model 1, most cognitive metrics on the STM task were strongly associated with p‐tau 181, GFAP and NFL. Identification Accuracy was also negatively correlated with the Aβ42/40 ratio. No association with Aβ42 or Aβ40 was found. However, regressing out age had a profound impact on these correlations, particularly for GFAP and NFL, while p‐tau 181 was not significantly affected ( Figure 2 and 3 ). The mediation analysis ( Figure 3 ), shown for Identification Accuracy, confirmed that age had a more profound impact on GFAP and NFL compared to p‐tau 181. Conclusions These new digital metrics show a strong correlation with plasma biomarkers. Finally, age is an important mediator between plasma biomarkers and cognition, but that varies across different biomarkers.
Mobile Based Application Design of the Catholic Church of the Redeemer of Sumbawa Using the Waterfall Method

The church is a gathering place for Christians to carry out worship. The Sumbawa Redeemer Catholic Church is a church that originates from Sumbawa Besar Regency, West Nusa Tenggara Province. This Catholic Church is the only church in Sumbawa Regency. The purpose of this research is to make the Application of the Catholic Church of the Redeemer of Sumbawa. using the waterfall design method and the ADDIE model. The data collection method was carried out using the Unified Modeling Language (UML) method, use cases and activity diagrams ere used as analysis and design tools. The programming languages used include Kotlin and Java, the software used includes the IDE Android Studio 4.0, MS. This research yielded results, namely a program that can be used to make it easier for people to find information about the Sumbawa Redeemer Catholic Church
DCSP-RCNN: A Network for Person Detection with Color, Size and Pattern Characteristic Parsing

Traditional human parsing models rely on anchor boxes and do not analyze pixels at the pixel level. This has the potential to restrict their performance. To solve these short-comings, a new model known as DCSP-RCNN( A Network for Person Detection with Color, Size and Pattern Characteristic Parsing) was developed. It analyses pixels at a fine-grained level using two subnetworks, a detection head and an edge-guided parsing module. This allows it to outperform earlier models. DCSP-RCNN additionally employs a refinement head, as well as distinct colour, size, and pattern characteristics, to improve the quality of the semantic parsing outputs. This is accomplished by combining an innovative loss function with both bounding box-level and part-level semantic parsing quality. Experiments on multiple human parsing datasets (CCIHP) reveal that DCSP-RCNN outperforms the best current architectures. A Network for Person Detection with Color, Size and Pattern Characteristic Parsing