pwc-archive/papers-with-abstracts huggingface dataset:
TTTS: Tree Test Time Simulation for Enhancing Decision Tree Robustness against Adversarial Examples
Authors: Seffi Cohen, Ofir Arbili, Yisroel Mirsky, Lior Rokach
Date: 2024-03-24 00:00:00
Proceeding: AAAI Conference on Artificial Intelligence 2024 3
Tasks: Classification

Decision trees are widely used for addressing learning tasks involving tabular data. Yet, they are susceptible to adversarial attacks. In this paper, we present Tree Test Time Simulation (TTTS), a novel inference-time methodology that incorporates Monte Carlo simulations into decision trees to enhance their robustness. TTTS introduces a probabilistic modification to the decision path, without altering the underlying tree structure. Our comprehensive empirical analysis of 50 datasets yields promising results. Without the presence of any attacks, TTTS has successfully improved model performance from an AUC of 0.714 to 0.773. Under the challenging conditions of white-box attacks, TTTS demonstrated its robustness by boosting performance from an AUC of 0.337 to 0.680. Even when subjected to black-box attacks, TTTS maintains high accuracy and enhances the model's performance from an AUC of 0.628 to 0.719. Compared to defenses such as Feature Squeezing, TTTS proves to be much more effective. We also found that TTTS exhibits similar robustness in decision forest settings across different attacks.
The Power of Pixels: Exploring the Potential of CNNs for Expected Goals (xG) in Football
Authors: Matteo Matteotti and Hadi Sotudeh
Date: 2024-07-22 00:00:00
Proceeding: Preprint 2024 7

Expected Goals (xG) is a popular metric in football that estimates the likelihood of a shot resulting in a goal. Traditional xG models are useful and are continuously improved incorporating more attributes and data. Leveraging the potential shown by deep learning models in image recognition tasks, we present xG-CNN, a convolutional neural network that computes xG based on the spatial distribution of the opponents in front of the shooter. We describe the input image representation and CNN architecture employed and explain how we trained/validated it using a publicly available dataset. Our goal is to determine whether a CNN-based xG model outperforms previous models. Despite the initial expectations, the model with an AUC ROC of 0.801 did not outperform the publicly announced state-of-the-art models. This could be due to the sample size or not incorporating additional context, such as body type, in the input representations or through separate CNN models. The approach presented here is also relevant to similar metrics and to promote good scientific practices, we have provided the code and data used to generate the results.
Difflare: Removing Image Lens Flare with Latent Diffusion Model
Authors: Tianwen Zhou, Qihao Duan, Zitong Yu
Date: 2024-07-20 00:00:00
Tasks: Flare Removal

The recovery of high-quality images from images corrupted by lens flare presents a significant challenge in low-level vision. Contemporary deep learning methods frequently entail training a lens flare removing model from scratch. However, these methods, despite their noticeable success, fail to utilize the generative prior learned by pre-trained models, resulting in unsatisfactory performance in lens flare removal. Furthermore, there are only few works considering the physical priors relevant to flare removal. To address these issues, we introduce Difflare, a novel approach designed for lens flare removal. To leverage the generative prior learned by Pre-Trained Diffusion Models (PTDM), we introduce a trainable Structural Guidance Injection Module (SGIM) aimed at guiding the restoration process with PTDM. Towards more efficient training, we employ Difflare in the latent space. To address information loss resulting from latent compression and the stochastic sampling process of PTDM, we introduce an Adaptive Feature Fusion Module (AFFM), which incorporates the Luminance Gradient Prior (LGP) of lens flare to dynamically regulate feature extraction. Extensive experiments demonstrate that our proposed Difflare achieves state-of-the-art performance in real-world lens flare removal, restoring images corrupted by flare with improved fidelity and perceptual quality. The codes will be released soon.
Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
Authors: Xin Ma, Yaohui Wang, Gengyun Jia, Xinyuan Chen, Yuan-Fang Li, Cunjian Chen, Yu Qiao
Date: 2024-07-22 00:00:00
Tasks: Image Animation

Diffusion models have achieved great progress in image animation due to powerful generative capabilities. However, maintaining spatio-temporal consistency with detailed information from the input static image over time (e.g., style, background, and object of the input static image) and ensuring smoothness in animated video narratives guided by textual prompts still remains challenging. In this paper, we introduce Cinemo, a novel image animation approach towards achieving better motion controllability, as well as stronger temporal consistency and smoothness. In general, we propose three effective strategies at the training and inference stages of Cinemo to accomplish our goal. At the training stage, Cinemo focuses on learning the distribution of motion residuals, rather than directly predicting subsequent via a motion diffusion model. Additionally, a structural similarity index-based strategy is proposed to enable Cinemo to have better controllability of motion intensity. At the inference stage, a noise refinement technique based on discrete cosine transformation is introduced to mitigate sudden motion changes. Such three strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Compared to previous methods, Cinemo offers simpler and more precise user controllability. Extensive experiments against several state-of-the-art methods, including both commercial tools and research approaches, across multiple metrics, demonstrate the effectiveness and superiority of our proposed approach.
Norface: Improving Facial Expression Analysis by Identity Normalization
Authors: Hanwei Liu, Rudong An, Zhimeng Zhang, Bowen Ma, Wei zhang, Yan Song, Yujing Hu, Wei Chen, Yu Ding
Date: 2024-07-22 00:00:00
Tasks: Classification, Emotion Recognition, Facial Action Unit Detection, Facial Emotion Recognition, Facial Expression Recognition (FER), Mixture-of-Experts

Facial Expression Analysis remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition (FER) tasks. Norface consists of a normalization network and a classification network. First, the carefully designed normalization network struggles to directly remove the above task-irrelevant noise, by maintaining facial expression consistency but normalizing all original images to a common identity with consistent pose, and background. Then, these additional normalized images are fed into the classification network. Due to consistent identity and other factors (e.g. head pose, background, etc.), the normalized images enable the classification network to extract useful expression information more effectively. Additionally, the classification network incorporates a Mixture of Experts to refine the latent representation, including handling the input of facial representations and the output of multiple (AU or emotion) labels. Extensive experiments validate the carefully designed framework with the insight of identity normalization. The proposed method outperforms existing SOTA methods in multiple facial expression analysis tasks, including AU detection, AU intensity estimation, and FER tasks, as well as their cross-dataset tasks. For the normalized datasets and code please visit {https://norface-fea.github.io/}.
Swings and slippery slopes: Reflecting on processes of higher education curriculum in the space of globalization and a pandemic
Authors: Rob Townsend, Gerald Doyle, Sharon Sperling
Date: 2024-03-06 00:00:00
Proceeding: Environment and Public Health Research 2024 3

The Asia-Pacific is the most expansive region for social services and health care, ranging from New Zealand in the south to the border of the Russian Federation in the north. Professional education in human services, social work, and allied health is rapidly expanding in this region and globally as the power and influence around these professions ‘swings’ between different countries in the region. The globalization of social and health care issues is challenging professional higher education and accreditation processes to adjust to producing education graduates who are global professionals, multi-lingual, culturally responsive, and able to work in diverse community contexts and within the ‘slippery slopes’ of social and economic change. This article explores the development of a new social work curriculum and course for an international higher education provider that was implemented in 2022 and aims to meet the challenges of intercultural learning and skills development in the new plural-lingual and fragmented global contexts. The ethnographic study reveals that education organizations and educators can advocate for and develop globalized, internationalized social work and social care curricula in this unsteady context when supported to do so by regulatory authorities.
Pediatric Wrist Fracture Detection in X-rays via YOLOv10 Algorithm and Dual Label Assignment System
Authors: Ammar Ahmed, Abdul Manaf
Date: 2024-07-22 00:00:00
Tasks: Fracture detection, object-detection, Object Detection

Wrist fractures are highly prevalent among children and can significantly impact their daily activities, such as attending school, participating in sports, and performing basic self-care tasks. If not treated properly, these fractures can result in chronic pain, reduced wrist functionality, and other long-term complications. Recently, advancements in object detection have shown promise in enhancing fracture detection, with systems achieving accuracy comparable to, or even surpassing, that of human radiologists. The YOLO series, in particular, has demonstrated notable success in this domain. This study is the first to provide a thorough evaluation of various YOLOv10 variants to assess their performance in detecting pediatric wrist fractures using the GRAZPEDWRI-DX dataset. It investigates how changes in model complexity, scaling the architecture, and implementing a dual-label assignment strategy can enhance detection performance. Experimental results indicate that our trained model achieved mean average precision (mAP@50-95) of 51.9\% surpassing the current YOLOv9 benchmark of 43.3\% on this dataset. This represents an improvement of 8.6\%. The implementation code is publicly available at https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection
AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description
Authors: Junyu Xie, Tengda Han, Max Bain, Arsha Nagrani, Gül Varol, Weidi Xie, Andrew Zisserman
Date: 2024-07-22 00:00:00
Tasks: Sentence

Our objective is to generate Audio Descriptions (ADs) for both movies and TV series in a training-free manner. We use the power of off-the-shelf Visual-Language Models (VLMs) and Large Language Models (LLMs), and develop visual and text prompting strategies for this task. Our contributions are three-fold: (i) We demonstrate that a VLM can successfully name and refer to characters if directly prompted with character information through visual indications without requiring any fine-tuning; (ii) A two-stage process is developed to generate ADs, with the first stage asking the VLM to comprehensively describe the video, followed by a second stage utilising a LLM to summarise dense textual information into one succinct AD sentence; (iii) A new dataset for TV audio description is formulated. Our approach, named AutoAD-Zero, demonstrates outstanding performance (even competitive with some models fine-tuned on ground truth ADs) in AD generation for both movies and TV series, achieving state-of-the-art CRITIC scores.
HyperbolicLR: Epoch insensitive learning rate scheduler
Authors: Tae-Geun Kim
Date: 2024-07-21 00:00:00
Tasks: image-classification, Image Classification, Operator learning, Time Series Forecasting

This study proposes two novel learning rate schedulers -- Hyperbolic Learning Rate Scheduler (HyperbolicLR) and Exponential Hyperbolic Learning Rate Scheduler (ExpHyperbolicLR) -- to address the epoch sensitivity problem that often causes inconsistent learning curves in conventional methods. By leveraging the asymptotic behavior of hyperbolic curves, the proposed schedulers maintain more stable learning curves across varying epoch settings. Specifically, HyperbolicLR applies this property directly in the epoch-learning rate space, while ExpHyperbolicLR extends it to an exponential space. We first determine optimal hyperparameters for each scheduler on a small number of epochs, fix these hyperparameters, and then evaluate performance as the number of epochs increases. Experimental results on various deep learning tasks (e.g., image classification, time series forecasting, and operator learning) demonstrate that both HyperbolicLR and ExpHyperbolicLR achieve more consistent performance improvements than conventional schedulers as training duration grows. These findings suggest that our hyperbolic-based schedulers offer a more robust and efficient approach to deep network optimization, particularly in scenarios constrained by computational resources or time.
ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning
Authors: Senbin Zhu, Hanjie Zhao, Xingren Wang, Shanhong Liu, Yuxiang Jia, Hongying Zan
Date: 2024-07-22 00:00:00
Tasks: Aspect-Based Sentiment Analysis, In-Context Learning, Prediction, Sentiment Analysis

The DimABSA task requires fine-grained sentiment intensity prediction for restaurant reviews, including scores for Valence and Arousal dimensions for each Aspect Term. In this study, we propose a Coarse-to-Fine In-context Learning(CFICL) method based on the Baichuan2-7B model for the DimABSA task in the SIGHAN 2024 workshop. Our method improves prediction accuracy through a two-stage optimization process. In the first stage, we use fixed in-context examples and prompt templates to enhance the model's sentiment recognition capability and provide initial predictions for the test data. In the second stage, we encode the Opinion field using BERT and select the most similar training data as new in-context examples based on similarity. These examples include the Opinion field and its scores, as well as related opinion words and their average scores. By filtering for sentiment polarity, we ensure that the examples are consistent with the test data. Our method significantly improves prediction accuracy and consistency by effectively utilizing training data and optimizing in-context examples, as validated by experimental results.
Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models
Authors: Xiao Liu, Xiaoliu Guan, Yu Wu, Jiaxu Miao
Date: 2024-07-22 00:00:00
Tasks: Data Augmentation, Memorization

Diffusion models, known for their tremendous ability to generate novel and high-quality samples, have recently raised concerns due to their data memorization behavior, which poses privacy risks. Recent approaches for memory mitigation either only focused on the text modality problem in cross-modal generation tasks or utilized data augmentation strategies. In this paper, we propose a novel training framework for diffusion models from the perspective of visual modality, which is more generic and fundamental for mitigating memorization. To facilitate ``forgetting'' of stored information in diffusion model parameters, we propose an iterative ensemble training strategy by splitting the data into multiple shards for training multiple models and intermittently aggregating these model parameters. Moreover, practical analysis of losses illustrates that the training loss for easily memorable images tends to be obviously lower. Thus, we propose an anti-gradient control method to exclude the sample with a lower loss value from the current mini-batch to avoid memorizing. Extensive experiments and analysis on \crnote{four} datasets are conducted to illustrate the effectiveness of our method, and results show that our method successfully reduces memory capacity while even improving the performance slightly. Moreover, to save the computing cost, we successfully apply our method to fine-tune the well-trained diffusion models by limited epochs, demonstrating the applicability of our method. Code is available in https://github.com/liuxiao-guan/IET_AGC.
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA
Authors: Yuan Pu, Zhuolun He, Tairu Qiu, Haoyuan Wu, Bei Yu
Date: 2024-07-22 00:00:00
Tasks: Benchmarking, Contrastive Learning, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases, which is extensively employed in document-grounded question-answering (QA) tasks. Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. Furthermore, we have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform. Experimental results demonstrate that our proposed RAG flow and techniques have achieved superior performance on ORD-QA as well as on a commercial tool, compared with state-of-the-arts. The ORD-QA benchmark and the training dataset for our customized RAG flow are open-source at https://github.com/lesliepy99/RAG-EDA.
Token-Picker: Accelerating Attention in Text Generation with Minimized Memory Transfer via Probability Estimation
Authors: Junyoung Park, Myeonggu Kang, Yunki Han, Yanggon Kim, Jaekang Shin, Lee-Sup Kim
Date: 2024-07-21 00:00:00
Tasks: Text Generation

The attention mechanism in text generation is memory-bounded due to its sequential characteristics. Therefore, off-chip memory accesses should be minimized for faster execution. Although previous methods addressed this by pruning unimportant tokens, they fall short in selectively removing tokens with near-zero attention probabilities in each instance. Our method estimates the probability before the softmax function, effectively removing low probability tokens and achieving an 12.1x pruning ratio without fine-tuning. Additionally, we present a hardware design supporting seamless on-demand off-chip access. Our approach shows 2.6x reduced memory accesses, leading to an average 2.3x speedup and a 2.4x energy efficiency.
Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference
Authors: Joyjit Kundu, Wenzhe Guo, Ali BanaGozar, Udari De Alwis, Sourav Sengupta, Puneet Gupta, Arindam Mallik
Date: 2024-07-19 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model

Aligning future system design with the ever-increasing compute needs of large language models (LLMs) is undoubtedly an important problem in today's world. Here, we propose a general performance modeling methodology and workload analysis of distributed LLM training and inference through an analytical framework that accurately considers compute, memory sub-system, network, and various parallelization strategies (model parallel, data parallel, pipeline parallel, and sequence parallel). We validate our performance predictions with published data from literature and relevant industry vendors (e.g., NVIDIA). For distributed training, we investigate the memory footprint of LLMs for different activation re-computation methods, dissect the key factors behind the massive performance gain from A100 to B200 ($\sim$ 35x speed-up closely following NVIDIA's scaling trend), and further run a design space exploration at different technology nodes (12 nm to 1 nm) to study the impact of logic, memory, and network scaling on the performance. For inference, we analyze the compute versus memory boundedness of different operations at a matrix-multiply level for different GPU systems and further explore the impact of DRAM memory technology scaling on inference latency. Utilizing our modeling framework, we reveal the evolution of performance bottlenecks for both LLM training and inference with technology scaling, thus, providing insights to design future systems for LLM training and inference.
Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
Authors: Shubha R. Kharel, Prashansa Mukim, Piotr Maj, Grzegorz W. Deptuch, Shinjae Yoo, Yihui Ren, Soumyajit Mandal
Date: 2024-07-18 00:00:00
Tasks: Bayesian Optimization

Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt power, and nanosecond-scale speed while providing clear accuracy advantages over traditional architectures. Finding ideal solutions means identifying optimal AI and ASIC design choices from a design space that has explosively expanded during the merger of these domains, creating non-trivial couplings which together act upon a small set of solutions as constraints tighten. It is impractical, if not impossible, to manually determine ideal choices among possibilities that easily exceed billions even in small-size problems. Existing methods to bridge this gap have leveraged theoretical understanding of hardware to f architecture search. However, the assumptions made in computing such theoretical metrics are too idealized to provide sufficient guidance during the difficult search for a practical implementation. Meanwhile, theoretical estimates for many other crucial metrics (like delay) do not even exist and are similarly variable, dependent on parameters of the process design kit (PDK). To address these challenges, we present a study that employs intelligent search using multi-objective Bayesian optimization, integrating both neural network search and ASIC synthesis in the loop. This approach provides reliable feedback on the collective impact of all cross-domain design choices. We showcase the effectiveness of our approach by finding several Pareto-optimal design choices for effective and efficient neural networks that perform real-time feature extraction from input pulses within the individual pixels of a readout ASIC.