pwc-archive/papers-with-abstracts huggingface dataset:
Adapting the Biological SSVEP Response to Artificial Neural Networks
Authors: Emirhan Böge, Yasemin Gunindi, Erchan Aptoula, Nihan Alp, Huseyin Ozkan
Date: 2024-11-15 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, image-classification, Image Classification, Network Pruning, SSVEP

Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs and analyzing resulting neuron activations, this method enables fine-grained analysis of a network's decision-making processes. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. These findings suggest that ANNs exhibit behavior akin to biological brains in tuning to flickering frequencies, thereby opening avenues for neuron/filter importance assessment through frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence and addressing the lack of transparency in neural networks. Future research directions include developing novel loss functions to encourage biologically plausible behavior in ANNs.
Real-Time AI-Driven People Tracking and Counting Using Overhead Cameras
Authors: Ishrath Ahamed, Chamith Dilshan Ranathunga, Dinuka Sandun Udayantha, Benny Kai Kiat Ng, Chau Yuen
Date: 2024-11-15 00:00:00
Tasks: energy management, Management, Object, object-detection, Object Detection, Object Tracking

Accurate people counting in smart buildings and intelligent transportation systems is crucial for energy management, safety protocols, and resource allocation. This is especially critical during emergencies, where precise occupant counts are vital for safe evacuation. Existing methods struggle with large crowds, often losing accuracy with even a few additional people. To address this limitation, this study proposes a novel approach combining a new object tracking algorithm, a novel counting algorithm, and a fine-tuned object detection model. This method achieves 97% accuracy in real-time people counting with a frame rate of 20-27 FPS on a low-power edge computer.
Evidential Federated Learning for Skin Lesion Image Classification
Authors: Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato, Simone Palazzo, Ulas Bagci
Date: 2024-11-15 00:00:00
Tasks: Classification, Federated Learning, image-classification, Image Classification, Knowledge Distillation, Lesion Classification, Skin Lesion Classification

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision Transformer (ViT) models trained in an evidential learning framework to maximize class evidences. Crucially, knowledge sharing across federation clients is achieved only through knowledge distillation on attention maps generated by the local ViT models, ensuring enhanced privacy preservation compared to traditional parameter or synthetic image sharing methodologies. FedEvPrompt is optimized within a round-based learning paradigm, where each round involves training local models followed by attention maps sharing with all federation clients. Experimental validation conducted in a real distributed setting, on the ISIC2019 dataset, demonstrates the superior performance of FedEvPrompt against baseline federated learning algorithms and knowledge distillation methods, without sharing model parameters. In conclusion, FedEvPrompt offers a promising approach for federated learning, effectively addressing challenges such as data heterogeneity, imbalance, privacy preservation, and knowledge sharing.
KuaiFormer: Transformer-Based Retrieval at Kuaishou
Authors: Chi Liu, Jiangxia Cao, Rui Huang, Kai Zheng, Qiang Luo, Kun Gai, Guorui Zhou
Date: 2024-11-15 00:00:00
Tasks: Recommendation Systems, Retrieval

In large-scale content recommendation systems, retrieval serves as the initial stage in the pipeline, responsible for selecting thousands of candidate items from billions of options to pass on to ranking modules. Traditionally, the dominant retrieval method has been Embedding-Based Retrieval (EBR) using a Deep Neural Network (DNN) dual-tower structure. However, applying transformer in retrieval tasks has been the focus of recent research, though real-world industrial deployment still presents significant challenges. In this paper, we introduce KuaiFormer, a novel transformer-based retrieval framework deployed in a large-scale content recommendation system. KuaiFormer fundamentally redefines the retrieval process by shifting from conventional score estimation tasks (such as click-through rate estimate) to a transformer-driven Next Action Prediction paradigm. This shift enables more effective real-time interest acquisition and multi-interest extraction, significantly enhancing retrieval performance. KuaiFormer has been successfully integrated into Kuaishou App's short-video recommendation system since May 2024, serving over 400 million daily active users and resulting in a marked increase in average daily usage time of Kuaishou users. We provide insights into both the technical and business aspects of deploying transformer in large-scale recommendation systems, addressing practical challenges encountered during industrial implementation. Our findings offer valuable guidance for engineers and researchers aiming to leverage transformer models to optimize large-scale content recommendation systems.
Towards unearthing neglected climate innovations from scientific literature using Large Language Models
Authors: César Quilodrán-Casas, Christopher Waite, Nicole Alhadeff, Diyona Dsouza, Cathal Hughes, Larissa Kunstel-Tabet, Alyssa Gilbert
Date: 2024-11-15 00:00:00

Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist within scientific literature but remain underutilised. To address this gap, this study employs a curated dataset sourced from OpenAlex, a comprehensive repository of scientific papers. Utilising Large Language Models (LLMs), such as GPT4-o from OpenAI, we evaluate title-abstract pairs from scientific papers on seven dimensions, covering climate change mitigation potential, stage of technological development, and readiness for deployment. The outputs of the language models are then compared with human evaluations to assess their effectiveness in identifying promising yet overlooked climate innovations. Our findings suggest that these LLM-based models can effectively augment human expertise, uncovering climate solutions that are potentially impactful but with far greater speed, throughput and consistency. Here, we focused on UK-based solutions, but the workflow is region-agnostic. This work contributes to the discovery of neglected innovations in scientific literature and demonstrates the potential of AI in enhancing climate action strategies.
That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design
Authors: Anna Goldie, Azalia Mirhoseini, Jeff Dean
Date: 2024-11-15 00:00:00
Tasks: Deep Reinforcement Learning

In 2020, we introduced a deep reinforcement learning method capable of generating superhuman chip layouts, which we then published in Nature and open-sourced on GitHub. AlphaChip has inspired an explosion of work on AI for chip design, and has been deployed in state-of-the-art chips across Alphabet and extended by external chipmakers. Even so, a non-peer-reviewed invited paper at ISPD 2023 questioned its performance claims, despite failing to run our method as described in Nature. For example, it did not pre-train the RL method (removing its ability to learn from prior experience), used substantially fewer compute resources (20x fewer RL experience collectors and half as many GPUs), did not train to convergence (standard practice in machine learning), and evaluated on test cases that are not representative of modern chips. Recently, Igor Markov published a meta-analysis of three papers: our peer-reviewed Nature paper, the non-peer-reviewed ISPD paper, and Markov's own unpublished paper (though he does not disclose that he co-authored it). Although AlphaChip has already achieved widespread adoption and impact, we publish this response to ensure that no one is wrongly discouraged from innovating in this impactful area.
Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2
Authors: Arnav Mejari, Maitreya Vaghulade, Paarshva Chitaliya, Arya Telang, Lynette D'Mello
Date: 2024-11-15 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In recent years, the global and Indian government efforts in monitoring and collecting data related to the fisheries industry have witnessed significant advancements. Despite this wealth of data, there exists an untapped potential for leveraging artificial intelligence based technological systems to benefit Indian fishermen in coastal areas. To fill this void in the Indian technology ecosystem, the authors introduce Jal Anveshak. This is an application framework written in Dart and Flutter that uses a Llama 2 based Large Language Model fine-tuned on pre-processed and augmented government data related to fishing yield and availability. Its main purpose is to help Indian fishermen safely get the maximum yield of fish from coastal areas and to resolve their fishing related queries in multilingual and multimodal ways.
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
Authors: Tymofii Nikolaienko, Harshil Patel, Aniruddha Panda, Subodh Madhav Joshi, Stanislav Jaso, Kaushic Kalyanaraman
Date: 2024-11-15 00:00:00

Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equations to replace some or even all steps of multi-stage computational workflows, leading to their significant speed-up. However, wide adoption of PINNs is still hindered by reliability issues, particularly at extreme ends of the input parameter ranges. In this study, we demonstrate this in the context of a system of coupled non-linear differential reaction-diffusion and heat transfer equations related to Fischer-Tropsch synthesis, which are solved by a finite-difference method with a PINN used in evaluating their source terms. It is shown that the testing strategies traditionally used to assess the accuracy of neural networks as function approximators can overlook the peculiarities which ultimately cause instabilities of the finite-difference solver. We propose a domain knowledge-based modifications to the PINN architecture ensuring its correct asymptotic behavior. When combined with an improved numerical scheme employed as an initial guess generator, the proposed modifications are shown to recover the overall stability of the simulations, while preserving the speed-up brought by PINN as the workflow component. We discuss the possible applications of the proposed hybrid transport equation solver in context of chemical reactors simulations.
Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion
Authors: Dan He, Guofen Wang, Weisheng Li, Yucheng Shu, Wenbo Li, Lijian Yang, Yuping Huang, Feiyan Li
Date: 2024-11-15 00:00:00
Tasks: Decoder

Multimodal image fusion (MMIF) aims to integrate information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing methods tend to prioritize natural image fusion and focus on information complementary and network training strategies. They ignore the essential distinction between natural and medical image fusion and the influence of underlying components. This paper dissects the significant differences between the two tasks regarding fusion goals, statistical properties, and data distribution. Based on this, we rethink the suitability of the normalization strategy and convolutional kernels for end-to-end MMIF.Specifically, this paper proposes a mixture of instance normalization and group normalization to preserve sample independence and reinforce intrinsic feature correlation.This strategy promotes the potential of enriching feature maps, thus boosting fusion performance. To this end, we further introduce the large kernel convolution, effectively expanding receptive fields and enhancing the preservation of image detail. Moreover, the proposed multipath adaptive fusion module recalibrates the decoder input with features of various scales and receptive fields, ensuring the transmission of crucial information. Extensive experiments demonstrate that our method exhibits state-of-the-art performance in multiple fusion tasks and significantly improves downstream applications. The code is available at https://github.com/HeDan-11/LKC-FUNet.
VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos
Authors: Weihao Zhong, Yinhao Xiao, Minghui Xu, Xiuzhen Cheng
Date: 2024-11-15 00:00:00
Tasks: Fake News Detection, Large Language Model, Misinformation

Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information. However, these platforms have also emerged as significant conduits for the rapid spread of misinformation, as fake news and rumors can leverage the visual appeal and wide reach of short videos to circulate extensively among audiences. Existing fake news detection methods mainly rely on single-modal information, such as text or images, or apply only basic fusion techniques, limiting their ability to handle the complex, multi-layered information inherent in short videos. To address these limitations, this paper presents a novel fake news detection method based on multimodal information, designed to identify misinformation through a multi-level analysis of video content. This approach effectively utilizes different modal representations to generate a unified textual description, which is then fed into a large language model for comprehensive evaluation. The proposed framework successfully integrates multimodal features within videos, significantly enhancing the accuracy and reliability of fake news detection. Experimental results demonstrate that the proposed approach outperforms existing models in terms of accuracy, robustness, and utilization of multimodal information, achieving an accuracy of 90.93%, which is significantly higher than the best baseline model (SV-FEND) at 81.05%. Furthermore, case studies provide additional evidence of the effectiveness of the approach in accurately distinguishing between fake news, debunking content, and real incidents, highlighting its reliability and robustness in real-world applications.
MOT FCG++: Enhanced Representation of Spatio-temporal Motion and Appearance Features
Authors: Yanzhao Fang
Date: 2024-11-15 00:00:00
Tasks: Multi-Object Tracking, Object Tracking, State Estimation

The goal of multi-object tracking (MOT) is to detect and track all objects in a scene across frames, while maintaining a unique identity for each object. Most existing methods rely on the spatial-temporal motion features and appearance embedding features of the detected objects in consecutive frames. Effectively and robustly representing the spatial and appearance features of long trajectories has become a critical factor affecting the performance of MOT. We propose a novel approach for appearance and spatial-temporal motion feature representation, improving upon the hierarchical clustering association method MOT FCG. For spatialtemporal motion features, we first propose Diagonal Modulated GIoU, which more accurately represents the relationship between the position and shape of the objects. Second, Mean Constant Velocity Modeling is proposed to reduce the effect of observation noise on target motion state estimation. For appearance features, we utilize a dynamic appearance representation that incorporates confidence information, enabling the trajectory appearance features to be more robust and global. Based on the baseline model MOT FCG, we have realized further improvements in the performance of all. we achieved 63.1 HOTA, 76.9 MOTA and 78.2 IDF1 on the MOT17 test set, and also achieved competitive performance on the MOT20 and DanceTrack sets.
MicroCrackAttentionNeXt: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks through Feature Visualization
Authors: Fatahlla Moreh, Yusuf Hasan, Bilal Zahid Hussain, Mohammad Ammar, Sven Tomforde
Date: 2024-11-15 00:00:00
Tasks: Decoder

Micro Crack detection using deep neural networks (DNNs) through an automated pipeline using wave fields interacting with the damaged areas is highly sought after. These high-dimensional spatio-temporal crack data are limited, and these datasets have large dimensions in the temporal domain. The dataset presents a substantial class imbalance, with crack pixels constituting an average of only 5% of the total pixels per sample. This extreme class imbalance poses a challenge for deep learning models with the different micro-scale cracks, as the network can be biased toward predicting the majority class, generally leading to poor detection accuracy. This study builds upon the previous benchmark SpAsE-Net, an asymmetric encoder-decoder network for micro-crack detection. The impact of various activation and loss functions were examined through feature space visualization using the manifold discovery and analysis (MDA) algorithm. The optimized architecture and training methodology achieved an accuracy of 86.85%.
DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models
Authors: Atsushi Kudo
Date: 2024-11-15 00:00:00

Numerical weather prediction (NWP) centers around the world operate a variety of NWP models, and recent advances in AI-driven NWP models have increased the availability of diverse NWP outputs. While this expansion holds the potential to improve forecast accuracy, it also raises a critical challenge of identifying the most reliable predictions for specific forecast scenarios. Traditional approaches, such as ensemble or weighted averaging, combine multiple NWP outputs but often generate unrealistic atmospheric fields, complicating the production of reliable and consistent forecasts in operational settings. In this study, we introduce DeepMedcast, a deep learning method that generates intermediate forecast, or "medcast", between two or more NWP outputs. Unlike ensemble averaging, DeepMedcast can provide consistent and explainable medcast without distorting meteorological fields. This paper details the methodology and case studies of DeepMedcast, discussing its advantages and potential contributions to operational forecasting.
Graph-based Complexity for Causal Effect by Empirical Plug-in
Authors: Rina Dechter, Annie Raichev, Alexander Ihler, Jin Tian
Date: 2024-11-15 00:00:00

This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable causal query can be estimated from an expression over the observed variables, called the estimand. The estimand can then be evaluated by plugging in probabilities computed empirically from data. In contrast to conventional wisdom, which assumes that high dimensional probabilistic functions will lead to exponential evaluation time of the estimand. We show that computation can be done efficiently, potentially in time linear in the data size, depending on the estimand's hypergraph. In particular, we show that both the treewidth and hypertree width of the estimand's structure bound the evaluation complexity of the plug-in estimands, analogous to their role in the complexity of probabilistic inference in graphical models. Often, the hypertree width provides a more effective bound, since the empirical distributions are sparse.
Orca: Enhancing Role-Playing Abilities of Large Language Models by Integrating Personality Traits
Authors: Yuxuan Huang
Date: 2024-11-15 00:00:00

Large language models has catalyzed the development of personalized dialogue systems, numerous role-playing conversational agents have emerged. While previous research predominantly focused on enhancing the model's capability to follow instructions by designing character profiles, neglecting the psychological factors that drive human conversations. In this paper, we propose Orca, a framework for data processing and training LLMs of custom characters by integrating personality traits. Orca comprises four stages: (1) Personality traits inferring, leverage LLMs to infer user's BigFive personality trait reports and scores. (2) Data Augment, simulate user's profile, background story, and psychological activities. (3) Dataset construction, personality-conditioned instruction prompting (PCIP) to stimulate LLMs. (4) Modeling and Training, personality-conditioned instruction tuning (PTIT and PSIT), using the generated data to enhance existing open-source LLMs. We introduce OrcaBench, the first benchmark for evaluating the quality of content generated by LLMs on social platforms across multiple scales. Our experiments demonstrate that our proposed model achieves superior performance on this benchmark, demonstrating its excellence and effectiveness in perceiving personality traits that significantly improve role-playing abilities. Our Code is available at https://github.com/Aipura/Orca.
EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis
Authors: Ruoyu Chen, Weiyi Zhang, Bowen Liu, Xiaolan Chen, Pusheng Xu, Shunming Liu, Mingguang He, Danli Shi
Date: 2024-11-15 00:00:00

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, especially for rare diseases. Here, we introduce EyeDiff, a text-to-image model designed to generate multimodal ophthalmic images from natural language prompts and evaluate its applicability in diagnosing common and rare diseases. EyeDiff is trained on eight large-scale datasets using the advanced latent diffusion model, covering 14 ophthalmic image modalities and over 80 ocular diseases, and is adapted to ten multi-country external datasets. The generated images accurately capture essential lesional characteristics, achieving high alignment with text prompts as evaluated by objective metrics and human experts. Furthermore, integrating generated images significantly enhances the accuracy of detecting minority classes and rare eye diseases, surpassing traditional oversampling methods in addressing data imbalance. EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field.