pwc-archive/papers-with-abstracts huggingface dataset:
MagicFace: Training-free Universal-Style Human Image Customized Synthesis
Authors: Yibin Wang, Weizhong Zhang, Cheng Jin
Date: 2024-08-14 00:00:00
Tasks: Attribute, Image Generation, Semantic Segmentation

Current human image customization methods leverage Stable Diffusion (SD) for its rich semantic prior. However, since SD is not specifically designed for human-oriented generation, these methods often require extensive fine-tuning on large-scale datasets, which renders them susceptible to overfitting and hinders their ability to personalize individuals with previously unseen styles. Moreover, these methods extensively focus on single-concept human image synthesis and lack the flexibility to customize individuals using multiple given concepts, thereby impeding their broader practical application. This paper proposes MagicFace, a novel training-free method for multi-concept universal-style human image personalized synthesis. Our core idea is to simulate how humans create images given specific concepts, i.e., first establish a semantic layout considering factors such as concepts' shape and posture, then optimize details by comparing with concepts at the pixel level. To implement this process, we introduce a coarse-to-fine generation pipeline, involving two sequential stages: semantic layout construction and concept feature injection. This is achieved by our Reference-aware Self-Attention (RSA) and Region-grouped Blend Attention (RBA) mechanisms. In the first stage, RSA enables the latent image to query features from all reference concepts simultaneously, extracting the overall semantic understanding to facilitate the initial semantic layout establishment. In the second stage, we employ an attention-based semantic segmentation method to pinpoint the latent generated regions of all concepts at each step. Following this, RBA divides the pixels of the latent image into semantic groups, with each group querying fine-grained features from the corresponding reference concept. Extensive experiments demonstrate the superiority of our MagicFace.
Exploring Retrieval Augmented Generation in Arabic
Authors: Samhaa R. El-Beltagy, Mohamed A. Abdallah
Date: 2024-08-14 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Text Generation

Recently, Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing, combining the strengths of retrieval-based and generation-based models to enhance text generation tasks. However, the application of RAG in Arabic, a language with unique characteristics and resource constraints, remains underexplored. This paper presents a comprehensive case study on the implementation and evaluation of RAG for Arabic text. The work focuses on exploring various semantic embedding models in the retrieval stage and several LLMs in the generation stage, in order to investigate what works and what doesn't in the context of Arabic. The work also touches upon the issue of variations between document dialect and query dialect in the retrieval stage. Results show that existing semantic embedding models and LLMs can be effectively employed to build Arabic RAG pipelines.
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image
Authors: Fan Yang, Sicheng Zhao, Yanhao Zhang, Haoxiang Chen, Hui Chen, Wenbo Tang, Haonan Lu, Pengfei Xu, Zhenyu Yang, Jungong Han, Guiguang Ding
Date: 2024-08-14 00:00:00
Tasks: Autonomous Driving, Logical Reasoning, Logical Reasoning Question Answering, parameter-efficient fine-tuning, Question Answering

Recent advancements in autonomous driving, augmented reality, robotics, and embodied intelligence have necessitated 3D perception algorithms. However, current 3D perception methods, particularly small models, struggle with processing logical reasoning, question-answering, and handling open scenario categories. On the other hand, generative multimodal large language models (MLLMs) excel in general capacity but underperform in 3D tasks, due to weak spatial and local object perception, poor text-based geometric numerical output, and inability to handle camera focal variations. To address these challenges, we propose the following solutions: Spatial-Enhanced Local Feature Mining for better spatial feature extraction, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations. We employ parameter-efficient fine-tuning for a pre-trained MLLM and develop LLMI3D, a powerful 3D perception MLLM. Additionally, we have constructed the IG3D dataset, which provides fine-grained descriptions and question-answer annotations. Extensive experiments demonstrate that our LLMI3D achieves state-of-the-art performance, significantly outperforming existing methods.
The Restaurant Meal Delivery Problem with Ghost Kitchens
Authors: Gal Neria, Florentin D Hildebrandt, Michal Tzur, Marlin W Ulmer
Date: 2024-08-14 00:00:00
Tasks: Scheduling

Restaurant meal delivery has been rapidly growing in the last few years. The main challenges in operating it are the temporally and spatially dispersed stochastic demand that arrives from customers all over town as well as the customers' expectation of timely and fresh delivery. To overcome these challenges a new business concept emerged, "Ghost kitchens". This concept proposes synchronized food preparation of several restaurants in a central complex, exploiting consolidation benefits. However, dynamically scheduling food preparation and delivery is challenging and we propose operational strategies for the effective operations of ghost kitchens. We model the problem as a sequential decision process. For the complex, combinatorial decision space of scheduling order preparations, consolidating orders to trips, and scheduling trip departures, we propose a large neighborhood search procedure based on partial decisions and driven by analytical properties. Within the large neighborhood search, decisions are evaluated via a value function approximation, enabling anticipatory and real-time decision making. We show the effectiveness of our method and demonstrate the value of ghost kitchens compared to conventional meal delivery systems. We show that both integrated optimization of cook scheduling and vehicle dispatching, as well as anticipation of future demand and decisions, are essential for successful operations. We further derive several managerial insights, amongst others, that companies should carefully consider the trade-off between fast delivery and fresh food.
Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator
Authors: Federico Nicolas Peccia, Svetlana Pavlitska, Tobias Fleck, Oliver Bringmann
Date: 2024-08-14 00:00:00

The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions associated with cloud servers and mitigating risks related to sharing sensitive data. But deploying Convolutional Neural Networks (CNNs) on non-off-the-shelf edge devices remains a complex and labor-intensive task. In this paper, we present and end-to-end workflow for deployment of CNNs on Field Programmable Gate Arrays (FPGAs) using the Gemmini accelerator, which we modified for efficient implementation on FPGAs. We describe how we leverage the use of open source software on each optimization step of the deployment process, the customizations we added to them and its impact on the final system's performance. We were able to achieve real-time performance by deploying a YOLOv7 model on a Xilinx ZCU102 FPGA with an energy efficiency of 36.5 GOP/s/W. Our FPGA-based solution demonstrates superior power efficiency compared with other embedded hardware devices, and even outperforms other FPGA reference implementations. Finally, we present how this kind of solution can be integrated into a wider system, by testing our proposed platform in a traffic monitoring scenario.
A Quantum-Inspired Analysis of Human Disambiguation Processes
Authors: Daphne Wang
Date: 2024-08-14 00:00:00

Formal languages are essential for computer programming and are constructed to be easily processed by computers. In contrast, natural languages are much more challenging and instigated the field of Natural Language Processing (NLP). One major obstacle is the ubiquity of ambiguities. Recent advances in NLP have led to the development of large language models, which can resolve ambiguities with high accuracy. At the same time, quantum computers have gained much attention in recent years as they can solve some computational problems faster than classical computers. This new computing paradigm has reached the fields of machine learning and NLP, where hybrid classical-quantum learning algorithms have emerged. However, more research is needed to identify which NLP tasks could benefit from a genuine quantum advantage. In this thesis, we applied formalisms arising from foundational quantum mechanics, such as contextuality and causality, to study ambiguities arising from linguistics. By doing so, we also reproduced psycholinguistic results relating to the human disambiguation process. These results were subsequently used to predict human behaviour and outperformed current NLP methods.
DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization
Authors: Zhuoyue Wan, Yuanfeng Song, Shuaimin Li, Chen Jason Zhang, Raymond Chi-Wing Wong
Date: 2024-08-14 00:00:00
Tasks: Data Visualization, Language Modeling, Language Modelling, Natural Language Queries

Data visualization (DV) is the fundamental and premise tool to improve the efficiency in conveying the insights behind the big data, which has been widely accepted in existing data-driven world. Task automation in DV, such as converting natural language queries to visualizations (i.e., text-to-vis), generating explanations from visualizations (i.e., vis-to-text), answering DV-related questions in free form (i.e. FeVisQA), and explicating tabular data (i.e., table-to-text), is vital for advancing the field. Despite their potential, the application of pre-trained language models (PLMs) like T5 and BERT in DV has been limited by high costs and challenges in handling cross-modal information, leading to few studies on PLMs for DV. We introduce DataVisT5, a novel PLM tailored for DV that enhances the T5 architecture through a hybrid objective pre-training and multi-task fine-tuning strategy, integrating text and DV datasets to effectively interpret cross-modal semantics. Extensive evaluations on public datasets show that DataVisT5 consistently outperforms current state-of-the-art models on various DV-related tasks. We anticipate that DataVisT5 will not only inspire further research on vertical PLMs but also expand the range of applications for PLMs.
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space
Authors: Xiaoyang Yu, Youfang Lin, Shuo Wang, Kai Lv, Sheng Han
Date: 2024-08-14 00:00:00
Tasks: Multi-agent Reinforcement Learning, SMAC, SMAC+

In a multi-agent system (MAS), action semantics indicates the different influences of agents' actions toward other entities, and can be used to divide agents into groups in a physically heterogeneous MAS. Previous multi-agent reinforcement learning (MARL) algorithms apply global parameter-sharing across different types of heterogeneous agents without careful discrimination of different action semantics. This common implementation decreases the cooperation and coordination between agents in complex situations. However, fully independent agent parameters dramatically increase the computational cost and training difficulty. In order to benefit from the usage of different action semantics while also maintaining a proper parameter-sharing structure, we introduce the Unified Action Space (UAS) to fulfill the requirement. The UAS is the union set of all agent actions with different semantics. All agents first calculate their unified representation in the UAS, and then generate their heterogeneous action policies using different available-action-masks. To further improve the training of extra UAS parameters, we introduce a Cross-Group Inverse (CGI) loss to predict other groups' agent policies with the trajectory information. As a universal method for solving the physically heterogeneous MARL problem, we implement the UAS adding to both value-based and policy-based MARL algorithms, and propose two practical algorithms: U-QMIX and U-MAPPO. Experimental results in the SMAC environment prove the effectiveness of both U-QMIX and U-MAPPO compared with several state-of-the-art MARL methods.
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Authors: Milan Papež, Martin Rektoris, Tomáš Pevný, Václav Šmídl
Date: 2024-08-14 00:00:00

Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undirected cyclic graphs. This assumption of a generic graph structure brings various computational challenges, and, more importantly, the presence of non-linearities in neural networks does not permit tractable probabilistic inference. We address these problems by proposing sum-product-set networks, an extension of probabilistic circuits from unstructured tensor data to tree-structured graph data. To this end, we use random finite sets to reflect a variable number of nodes and edges in the graph and to allow for exact and efficient inference. We demonstrate that our tractable model performs comparably to various intractable models based on neural networks.
Do GPT Language Models Suffer From Split Personality Disorder? The Advent Of Substrate-Free Psychometrics
Authors: Peter Romero, Stephen Fitz, Teruo Nakatsuma
Date: 2024-08-14 00:00:00
Tasks: Language Modeling, Language Modelling

Previous research on emergence in large language models shows these display apparent human-like abilities and psychological latent traits. However, results are partly contradicting in expression and magnitude of these latent traits, yet agree on the worrisome tendencies to score high on the Dark Triad of narcissism, psychopathy, and Machiavellianism, which, together with a track record of derailments, demands more rigorous research on safety of these models. We provided a state of the art language model with the same personality questionnaire in nine languages, and performed Bayesian analysis of Gaussian Mixture Model, finding evidence for a deeper-rooted issue. Our results suggest both interlingual and intralingual instabilities, which indicate that current language models do not develop a consistent core personality. This can lead to unsafe behaviour of artificial intelligence systems that are based on these foundation models, and are increasingly integrated in human life. We subsequently discuss the shortcomings of modern psychometrics, abstract it, and provide a framework for its species-neutral, substrate-free formulation.
The Complexity of Manipulation of k-Coalitional Games on Graphs
Authors: Hodaya Barr, Yohai Trabelsi, Sarit Kraus, Liam Roditty, Noam Hazon
Date: 2024-08-14 00:00:00

In many settings, there is an organizer who would like to divide a set of agents into $k$ coalitions, and cares about the friendships within each coalition. Specifically, the organizer might want to maximize utilitarian social welfare, maximize egalitarian social welfare, or simply guarantee that every agent will have at least one friend within his coalition. However, in many situations, the organizer is not familiar with the friendship connections, and he needs to obtain them from the agents. In this setting, a manipulative agent may falsely report friendship connections in order to increase his utility. In this paper, we analyze the complexity of finding manipulation in such $k$-coalitional games on graphs. We also introduce a new type of manipulation, socially-aware manipulation, in which the manipulator would like to increase his utility without decreasing the social welfare. We then study the complexity of finding socially-aware manipulation in our setting. Finally, we examine the frequency of socially-aware manipulation and the running time of our algorithms via simulation results.
RTAT: A Robust Two-stage Association Tracker for Multi-Object Tracking
Authors: Song Guo, Rujie Liu, Narishige Abe
Date: 2024-08-14 00:00:00
Tasks: Edge Classification, Multi-Object Tracking, Object Tracking

Data association is an essential part in the tracking-by-detection based Multi-Object Tracking (MOT). Most trackers focus on how to design a better data association strategy to improve the tracking performance. The rule-based handcrafted association methods are simple and highly efficient but lack generalization capability to deal with complex scenes. While the learnt association methods can learn high-order contextual information to deal with various complex scenes, but they have the limitations of higher complexity and cost. To address these limitations, we propose a Robust Two-stage Association Tracker, named RTAT. The first-stage association is performed between tracklets and detections to generate tracklets with high purity, and the second-stage association is performed between tracklets to form complete trajectories. For the first-stage association, we use a simple data association strategy to generate tracklets with high purity by setting a low threshold for the matching cost in the assignment process. We conduct the tracklet association in the second-stage based on the framework of message-passing GNN. Our method models the tracklet association as a series of edge classification problem in hierarchical graphs, which can recursively merge short tracklets into longer ones. Our tracker RTAT ranks first on the test set of MOT17 and MOT20 benchmarks in most of the main MOT metrics: HOTA, IDF1, and AssA. We achieve 67.2 HOTA, 84.7 IDF1, and 69.7 AssA on MOT17, and 66.2 HOTA, 82.5 IDF1, and 68.1 AssA on MOT20.
Towards Few-shot Self-explaining Graph Neural Networks
Authors: Jingyu Peng, Qi Liu, Linan Yue, Zaixi Zhang, Kai Zhang, Yunhao Sha
Date: 2024-08-14 00:00:00

Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as medicine. A promising approach is the self-explaining method, which outputs explanations along with predictions. However, existing self-explaining models require a large amount of training data, rendering them unavailable in few-shot scenarios. To address this challenge, in this paper, we propose a Meta-learned Self-Explaining GNN (MSE-GNN), a novel framework that generates explanations to support predictions in few-shot settings. MSE-GNN adopts a two-stage self-explaining structure, consisting of an explainer and a predictor. Specifically, the explainer first imitates the attention mechanism of humans to select the explanation subgraph, whereby attention is naturally paid to regions containing important characteristics. Subsequently, the predictor mimics the decision-making process, which makes predictions based on the generated explanation. Moreover, with a novel meta-training process and a designed mechanism that exploits task information, MSE-GNN can achieve remarkable performance on new few-shot tasks. Extensive experimental results on four datasets demonstrate that MSE-GNN can achieve superior performance on prediction tasks while generating high-quality explanations compared with existing methods. The code is publicly available at https://github.com/jypeng28/MSE-GNN.
An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems
Authors: Taeyoung Yun, Kanghoon Lee, Sujin Yun, Ilmyung Kim, Won-Woo Jung, Min-Cheol Kwon, Kyujin Choi, Yoohyeon Lee, Jinkyoo Park
Date: 2024-08-14 00:00:00
Tasks: Bayesian Optimization, Management

Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light management systems rely on human-crafted decisions, which may not adapt well to diverse traffic patterns. In this paper, we delve into two pivotal design components of the traffic light management system that can be dynamically adjusted to various traffic conditions: phase combination and phase time allocation. While numerous studies have sought an efficient strategy for managing traffic lights, most of these approaches consider a fixed traffic pattern and are limited to relatively small road networks. To overcome these limitations, we introduce a novel and practical framework to formulate the optimization of such design components using an offline meta black-box optimization. We then present a simple yet effective method to efficiently find a solution for the aforementioned problem. In our framework, we first collect an offline meta dataset consisting of pairs of design choices and corresponding congestion measures from various traffic patterns. After collecting the dataset, we employ the Attentive Neural Process (ANP) to predict the impact of the proposed design on congestion across various traffic patterns with well-calibrated uncertainty. Finally, Bayesian optimization, with ANP as a surrogate model, is utilized to find an optimal design for unseen traffic patterns through limited online simulations. Our experiment results show that our method outperforms state-of-the-art baselines on complex road networks in terms of the number of waiting vehicles. Surprisingly, the deployment of our method into a real-world traffic system was able to improve traffic throughput by 4.80\% compared to the original strategy.
On-the-fly Synthesis for LTL over Finite Traces: An Efficient Approach that Counts
Authors: Shengping Xiao, Yongkang Li, Shufang Zhu, Jun Sun, Jianwen Li, Geguang Pu, Moshe Y. Vardi
Date: 2024-08-14 00:00:00

We present an on-the-fly synthesis framework for Linear Temporal Logic over finite traces (LTLf) based on top-down deterministic automata construction. Existing approaches rely on constructing a complete Deterministic Finite Automaton (DFA) corresponding to the LTLf specification, a process with doubly exponential complexity relative to the formula size in the worst case. In this case, the synthesis procedure cannot be conducted until the entire DFA is constructed. This inefficiency is the main bottleneck of existing approaches. To address this challenge, we first present a method for converting LTLf into Transition-based DFA (TDFA) by directly leveraging LTLf semantics, incorporating intermediate results as direct components of the final automaton to enable parallelized synthesis and automata construction. We then explore the relationship between LTLf synthesis and TDFA games and subsequently develop an algorithm for performing LTLf synthesis using on-the-fly TDFA game solving. This algorithm traverses the state space in a global forward manner combined with a local backward method, along with the detection of strongly connected components. Moreover, we introduce two optimization techniques -- model-guided synthesis and state entailment -- to enhance the practical efficiency of our approach. Experimental results demonstrate that our on-the-fly approach achieves the best performance on the tested benchmarks and effectively complements existing tools and approaches.