pwc-archive/papers-with-abstracts huggingface dataset:
Why do we need to complement the European Union Regional Innovation Scoreboard with an artificial intelligence tool for what-if policy analysis?
Authors: Vincenzo Lanzetta, Cristina Ponsiglione
Date: 2024-09-20 00:00:00

The European Union Regional Innovation Scoreboard (EURIS) is currently and broadly used for the definition of regional innovation policies by European policymakers; it is a regional innovation measuring tool for the analysis of each specific innovation indicator, from which it is possible to analyze the overtime evolution of each regional innovation indicator; according to the importance of the European Union Regional Innovation Scoreboard for innovation policy purposes, we state that European regional policymakers need integrative and synergistic methodological tools, with respect to the EURIS one, for innovation policy purposes. We state the need to integrate the current methodology of the European Regional Innovation Scoreboard with a Factorial K-means (FKM) tool for grouping purposes, and with a neural network (NN) tool for performing what-if policy analyses. Experimental results suggested that our proposed grouping/labeling methodologies are able to develop more compact groups, resulting in regions having better similarities, than the ones developed by the EURIS methodology. Experimental simulations, within the framework of our proposed what-if tool, highlight the potential usefulness of our methodological tool (neural network-based) for the understanding of the potential effectiveness of each possible and specific regional innovation policy to be implemented. We claim that our proposed FKM-NN tool could be used, by regional innovation policymakers, as a very effective synergistic instrument of the European Union Regional Innovation Scoreboard.
Boosting Zero-Shot Human-Object Interaction Detection with Vision-Language Transfer
Authors: Sandipan Sarma, Pradnesh Kalkar, Arijit Sur
Date: 2024-03-18 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024 3
Tasks: Human-Object Interaction Detection, Language Modeling, Language Modelling, Object, Zero-Shot Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is a crucial task that involves localizing interactive human-object pairs and identifying the actions being performed. Most existing HOI detectors are supervised in nature and lack the ability of zero-shot discovery of unseen interactions. Recently, transformer-based methods have superseded the traditional CNN detectors by aggregating image-wide context but still suffer from the long-tail distribution problem in HOI. In this work, our primary focus is improving HOI detection in images, particularly in zero-shot scenarios. We use an end-to-end transformer-based object detector to localize human-object pairs and yield visual features of actions and objects. Moreover, we adopt the text encoder from a popular visual-language model called CLIP with a novel prompting mechanism to extract semantic information for unseen actions and objects. Finally, we learn a strong visual-semantic
alignment and achieve state-of-the-art performance on the challenging HICO-DET dataset across five zero-shot settings, with up to 70.88% relative gains. Code is available at https://github.com/sandipan211/ZSHOI-VLT.
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network
Authors: Hamza Gbada, Karim Kalti, Mohamed Ali Mahjoub
Date: 2024-09-11 00:00:00
Proceeding: International Conference on Document Analysis and Recognition 2024 9
Tasks: Document Layout Analysis, document understanding, Graph Neural Network, Information Retrieval, Key Information Extraction, Node Classification

This paper presents a novel approach to information extraction (IE) from visually rich documents (VRD) by employing a directed weighted graph representation to capture relationships among various VRD components. In contrast to conventional methods relying on spatial proximity through Euclidean distance, our approach aims to enhance performance by introducing a novel representation of relationships using directed weighted graphs. The information extraction task from VRD is treated as a node classification problem, leveraging graph convolutional networks that process the VRD graphs. We conducted evaluations on five real-world datasets, showcasing notable results and performances that align with established norms.
Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism
Authors: Qiyuan Tian, Zhuoyue Wang, Xiaoling Cui
Date: 2024-09-20 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

An improved model of medical image segmentation for brain tumor is discussed, which is a deep learning algorithm based on U-Net architecture. Based on the traditional U-Net, we introduce GSConv module and ECA attention mechanism to improve the performance of the model in medical image segmentation tasks. With these improvements, the new U-Net model is able to extract and utilize multi-scale features more efficiently while flexibly focusing on important channels, resulting in significantly improved segmentation results. During the experiment, the improved U-Net model is trained and evaluated systematically. By looking at the loss curves of the training set and the test set, we find that the loss values of both rapidly decline to the lowest point after the eighth epoch, and then gradually converge and stabilize. This shows that our model has good learning ability and generalization ability. In addition, by monitoring the change in the mean intersection ratio (mIoU), we can see that after the 35th epoch, the mIoU gradually approaches 0.8 and remains stable, which further validates the model. Compared with the traditional U-Net, the improved version based on GSConv module and ECA attention mechanism shows obvious advantages in segmentation effect. Especially in the processing of brain tumor image edges, the improved model can provide more accurate segmentation results. This achievement not only improves the accuracy of medical image analysis, but also provides more reliable technical support for clinical diagnosis.
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Authors: Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy, Balint Kovacs, Constantin Ulrich, Tassilo Wald, Maximilian Zenk, Stefan Denner, Fabian Isensee, Philipp Vollmuth, Jens Kleesiek, Klaus Maier-Hein
Date: 2024-09-20 00:00:00
Tasks: Inductive Bias, Lesion Detection, Lesion Segmentation

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level $F_1$ score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting.
FracGM: A Fast Fractional Programming Technique for Geman-McClure Robust Estimator
Authors: Bang-Shien Chen, Yu-Kai Lin, Jian-Yu Chen, Chih-Wei Huang, Jann-Long Chern, Ching-Cherng Sun
Date: 2024-09-21 00:00:00
Tasks: Point Cloud Registration

Robust estimation is essential in computer vision, robotics, and navigation, aiming to minimize the impact of outlier measurements for improved accuracy. We present a fast algorithm for Geman-McClure robust estimation, FracGM, leveraging fractional programming techniques. This solver reformulates the original non-convex fractional problem to a convex dual problem and a linear equation system, iteratively solving them in an alternating optimization pattern. Compared to graduated non-convexity approaches, this strategy exhibits a faster convergence rate and better outlier rejection capability. In addition, the global optimality of the proposed solver can be guaranteed under given conditions. We demonstrate the proposed FracGM solver with Wahba's rotation problem and 3-D point-cloud registration along with relaxation pre-processing and projection post-processing. Compared to state-of-the-art algorithms, when the outlier rates increase from 20% to 80%, FracGM shows 53% and 88% lower rotation and translation increases. In real-world scenarios, FracGM achieves better results in 13 out of 18 outcomes, while having a 19.43% improvement in the computation time.
Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction
Authors: Hossein Sholehrasa, Sanaz Saki Norouzi, Pascal Hitzler, Majid Jaberi-Douraki
Date: 2024-09-21 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Integrating structured knowledge from tabular formats poses significant challenges within natural language processing (NLP), mainly when dealing with complex, semi-structured tables like those found in the FeTaQA dataset. These tables require advanced methods to interpret and generate meaningful responses accurately. Traditional approaches, such as SQL and SPARQL, often fail to fully capture the semantics of such data, especially in the presence of irregular table structures like web tables. This paper addresses these challenges by proposing a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model. Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics. It effectively generates contextually accurate and detailed long-form answers from tables, showcasing its strength in complex data interpretation.
ControlEdit: A MultiModal Local Clothing Image Editing Method
Authors: Di Cheng, Yingjie Shi, ShiXin Sun, JiaFu Zhang, WeiJing Wang, Yu Liu
Date: 2024-09-23 00:00:00
Tasks: Self-Supervised Learning

Multimodal clothing image editing refers to the precise adjustment and modification of clothing images using data such as textual descriptions and visual images as control conditions, which effectively improves the work efficiency of designers and reduces the threshold for user design. In this paper, we propose a new image editing method ControlEdit, which transfers clothing image editing to multimodal-guided local inpainting of clothing images. We address the difficulty of collecting real image datasets by leveraging the self-supervised learning approach. Based on this learning approach, we extend the channels of the feature extraction network to ensure consistent clothing image style before and after editing, and we design an inverse latent loss function to achieve soft control over the content of non-edited areas. In addition, we adopt Blended Latent Diffusion as the sampling method to make the editing boundaries transition naturally and enforce consistency of non-edited area content. Extensive experiments demonstrate that ControlEdit surpasses baseline algorithms in both qualitative and quantitative evaluations.
MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator
Authors: Cong Wang, Zeming Chen, Shanshi Huang
Date: 2024-09-23 00:00:00
Tasks: Quantization

This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration. Modularized from the state-of-the-art CIM simulator NeuroSim, MICSim provides a highly configurable simulation framework supporting multiple quantization algorithms, diverse circuit/architecture designs, and different memory devices. This modular approach also allows MICSim to be effectively extended to accommodate new designs. MICSim natively supports evaluating accelerators' software and hardware performance for CNNs and Transformers in Python, leveraging the popular PyTorch and HuggingFace Transformers frameworks. These capabilities make MICSim highly adaptive when simulating different networks and user-friendly. This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation. Also, MICSim can achieve a 9x - 32x speedup of NeuroSim through a statistic-based average mode proposed by this work.
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
Authors: Zeyu Zhu, Peisong Wang, Qinghao Hu, Gang Li, Xiaoyao Liang, Jian Cheng
Date: 2024-09-23 00:00:00
Tasks: GPU

Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution to train GNN on large graphs with billions of nodes and edges, the sampling-based training is widely adopted by existing training frameworks. However, through an in-depth analysis, we observe that the efficiency of existing sampling-based training frameworks is still limited due to the key bottlenecks lying in all three phases of sampling-based training, i.e., subgraph sample, memory IO, and computation. To this end, we propose FastGL, a GPU-efficient Framework for accelerating sampling-based training of GNN at Large scale by simultaneously optimizing all above three phases, taking into account both GPU characteristics and graph structure. Specifically, by exploiting the inherent overlap within graph structures, FastGL develops the Match-Reorder strategy to reduce the data traffic, which accelerates the memory IO without incurring any GPU memory overhead. Additionally, FastGL leverages a Memory-Aware computation method, harnessing the GPU memory's hierarchical nature to mitigate irregular data access during computation. FastGL further incorporates the Fused-Map approach aimed at diminishing the synchronization overhead during sampling. Extensive experiments demonstrate that FastGL can achieve an average speedup of 11.8x, 2.2x and 1.5x over the state-of-the-art frameworks PyG, DGL, and GNNLab, respectively.Our code is available at https://github.com/a1bc2def6g/fastgl-ae.
AlphaZip: Neural Network-Enhanced Lossless Text Compression
Authors: Swathi Shree Narashiman, Nitin Chandrachoodan
Date: 2024-09-23 00:00:00
Tasks: Benchmarking, Data Compression, Language Modeling, Language Modelling, Large Language Model, Text Compression

Data compression continues to evolve, with traditional information theory methods being widely used for compressing text, images, and videos. Recently, there has been growing interest in leveraging Generative AI for predictive compression techniques. This paper introduces a lossless text compression approach using a Large Language Model (LLM). The method involves two key steps: first, prediction using a dense neural network architecture, such as a transformer block; second, compressing the predicted ranks with standard compression algorithms like Adaptive Huffman, LZ77, or Gzip. Extensive analysis and benchmarking against conventional information-theoretic baselines demonstrate that neural compression offers improved performance.
Sparsity-Enhanced Multilayered Non-Convex Regularization with Epigraphical Relaxation for Debiased Signal Recovery
Authors: Akari Katsuma, Seisuke Kyochi, Shunsuke Ono, Ivan Selesnick
Date: 2024-09-23 00:00:00

This paper proposes a precise signal recovery method with multilayered non-convex regularization, enhancing sparsity/low-rankness for high-dimensional signals including images and videos. In optimization-based signal recovery, multilayered convex regularization functions based on the L1 and nuclear-norms not only guarantee a global optimal solution but also offer more accurate estimation than single-layered ones, thanks to their faithful modeling of structured sparsity and low-rankness in high-dimensional signals. However, these functions are known to yield biased solutions (estimated with smaller amplitude values than the true ones). To address this issue, multilayered non-convex regularization functions have been considered, although they face their own challenges: 1) their closed-form proximity operators are unavailable, and 2) convergence may result in a local optimal solution. In this paper, we resolve the two issues with an approach based on epigraphical relaxation (ER). First, ER decomposes a multilayered non-convex regularization function into the outermost function and epigraph constraints for the inner functions, facilitating the computation of proximity operators. Second, the relaxed regularization functions by ER are integrated into a non-convexly regularized convex optimization model to estimate a global optimal solution with less bias. Numerical experiments demonstrate the bias reduction achieved by the proposed method in image recovery and principal component analysis.
R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models
Authors: Viet Dung Nguyen, Zhizhuo Yang, Christopher L. Buckley, Alexander Ororbia
Date: 2024-09-21 00:00:00

Although research has produced promising results demonstrating the utility of active inference (AIF) in Markov decision processes (MDPs), there is relatively less work that builds AIF models in the context of environments and problems that take the form of partially observable Markov decision processes (POMDPs). In POMDP scenarios, the agent must infer the unobserved environmental state from raw sensory observations, e.g., pixels in an image. Additionally, less work exists in examining the most difficult form of POMDP-centered control: continuous action space POMDPs under sparse reward signals. In this work, we address issues facing the AIF modeling paradigm by introducing novel prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. Empirically, we show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate. The code in support of this work can be found at https://github.com/NACLab/robust-active-inference.
Region Mixup
Authors: Saptarshi Saha, Utpal Garain
Date: 2024-09-23 00:00:00
Tasks: Data Augmentation

This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images.
Dynamic 2D Gaussians: Geometrically accurate radiance fields for dynamic objects
Authors: Shuai Zhang, Guanjun Wu, Xinggang Wang, Bin Feng, Wenyu Liu
Date: 2024-09-21 00:00:00

Reconstructing objects and extracting high-quality surfaces play a vital role in the real world. Current 4D representations show the ability to render high-quality novel views for dynamic objects but cannot reconstruct high-quality meshes due to their implicit or geometrically inaccurate representations. In this paper, we propose a novel representation that can reconstruct accurate meshes from sparse image input, named Dynamic 2D Gaussians (D-2DGS). We adopt 2D Gaussians for basic geometry representation and use sparse-controlled points to capture 2D Gaussian's deformation. By extracting the object mask from the rendered high-quality image and masking the rendered depth map, a high-quality dynamic mesh sequence of the object can be extracted. Experiments demonstrate that our D-2DGS is outstanding in reconstructing high-quality meshes from sparse input. More demos and code are available at https://github.com/hustvl/Dynamic-2DGS.
RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
Authors: Jiachi Chen, Qingyuan Zhong, Yanlin Wang, Kaiwen Ning, Yongkun Liu, Zenan Xu, Zhe Zhao, Ting Chen, Zibin Zheng
Date: 2024-09-23 00:00:00
Tasks: Benchmarking, Code Generation

The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused by malicious developers to create malicious code. Several previous studies have focused on the ability of LLMs to resist the generation of harmful content that violates human ethical standards, such as biased or offensive content. However, there is no research evaluating the ability of LLMs to resist malicious code generation. To fill this gap, we propose RMCBench, the first benchmark comprising 473 prompts designed to assess the ability of LLMs to resist malicious code generation. This benchmark employs two scenarios: a text-to-code scenario, where LLMs are prompted with descriptions to generate code, and a code-to-code scenario, where LLMs translate or complete existing malicious code. Based on RMCBench, we conduct an empirical study on 11 representative LLMs to assess their ability to resist malicious code generation. Our findings indicate that current LLMs have a limited ability to resist malicious code generation with an average refusal rate of 40.36% in text-to-code scenario and 11.52% in code-to-code scenario. The average refusal rate of all LLMs in RMCBench is only 28.71%; ChatGPT-4 has a refusal rate of only 35.73%. We also analyze the factors that affect LLMs' ability to resist malicious code generation and provide implications for developers to enhance model robustness.
LegiLM: A Fine-Tuned Legal Language Model for Data Compliance
Authors: Linkai Zhu, Lu Yang, Chaofan Li, Shanwen Hu, Lu Liu, Bin Yin
Date: 2024-09-09 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Legal Reasoning

Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise. This paper introduces LegiLM, a novel legal language model specifically tailored for consulting on data or information compliance. LegiLM leverages a pre-trained GDPR Fines dataset and has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. By incorporating a specialized dataset that includes global data protection laws, meticulously annotated policy documents, and relevant privacy policies, LegiLM is optimized for addressing data compliance challenges. The model integrates advanced legal reasoning methods and information retrieval enhancements to enhance accuracy and reliability in practical legal consulting scenarios. Our evaluation using a custom benchmark dataset demonstrates that LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications, setting a new benchmark for AI-driven legal compliance solutions. Our resources are publicly available at https://github.com/DAOLegalAI/LegiLM