OpenRaiser/Intern-Atlas huggingface dataset:
Multiset Bisimulations as a Common Framework for Ordinary and Probabilistic Bisimulations
Venue: arXiv cs.LO 2024
Year: 2024

Our concrete objective is to present both ordinary bisimulations and probabilistic bisimulations in a common coalgebraic framework based on multiset bisimulations. For that we show how to relate the underlying powerset and probabilistic distributions functors with the multiset functor by means of adequate natural transformations. This leads us to the general topic that we investigate in the paper: a natural transformation from a functor F to another G transforms F-bisimulations into G-bisimulations but, in general, it is not possible to express G-bisimulations in terms of F-bisimulations. However, they can be characterized by considering Hughes and Jacobs' notion of simulation, taking as the order on the functor F the equivalence induced by the epi-mono decomposition of the natural transformation relating F and G. We also consider the case of alternating probabilistic systems where non-deterministic and probabilistic choices are mixed, although only in a partial way, and extend all these results to categorical simulations.
Logics for Contravariant Simulations
Venue: arXiv cs.LO 2024
Year: 2024

Covariant-contravariant simulation and conformance simulation are two generalizations of the simple notion of simulation which aim at capturing the fact that it is not always the case that "the larger the number of behaviors, the better". Therefore, they can be considered to be more adequate to express the fact that a system is a correct implementation of some specification. We have previously shown that these two more elaborated notions fit well within the categorical framework developed to study the notion of simulation in a generic way. Now we show that their behaviors have also simple and natural logical characterizations, though more elaborated than those for the plain simulation semantics.
When Are Prime Formulae Characteristic?
Venue: arXiv cs.LO 2024
Year: 2024

In the setting of the modal logic that characterizes modal refinement over modal transition systems, Boudol and Larsen showed that the formulae for which model checking can be reduced to preorder checking, that is, the characteristic formulae, are exactly the consistent and prime ones. This paper presents general, sufficient conditions guaranteeing that characteristic formulae are exactly the consistent and prime ones. It is shown that the given conditions apply to various behavioural relations in the literature. In particular, characteristic formulae are exactly the prime and consistent ones for all the semantics in van Glabbeek's linear time-branching time spectrum.
FuseFormer: A Transformer for Visual and Thermal Image Fusion
Venue: arXiv cs.CV 2024
Year: 2024

Due to the lack of a definitive ground truth for the image fusion problem, the loss functions are structured based on evaluation metrics, such as the structural similarity index measure (SSIM). However, in doing so, a bias is introduced toward the SSIM and, consequently, the input visual band image. The objective of this study is to propose a novel methodology for the image fusion problem that mitigates the limitations associated with using classical evaluation metrics as loss functions. Our approach integrates a transformer-based multi-scale fusion strategy that adeptly addresses local and global context information. This integration not only refines the individual components of the image fusion process but also significantly enhances the overall efficacy of the method. Our proposed method follows a two-stage training approach, where an auto-encoder is initially trained to extract deep features at multiple scales in the first stage. For the second stage, we integrate our fusion block and change the loss function as mentioned. The multi-scale features are fused using a combination of Convolutional Neural Networks (CNNs) and Transformers. The CNNs are utilized to capture local features, while the Transformer handles the integration of general context features. Through extensive experiments on various benchmark datasets, our proposed method, along with the novel loss function definition, demonstrates superior performance compared to other competitive fusion algorithms.
An Information-Theoretic Approach to Analyze NLP Classification Tasks
Venue: arXiv cs.CL 2024
Year: 2024

Understanding the importance of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single element input or multiple element inputs to predict an output variable, where an element is a block of text. Each text element has two components: an associated semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the context influence on the output compared to the question influence reduces on more challenging datasets. In particular, more challenging contexts allow a greater variation in complexity of questions. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input text dominates (above 80\% for all datasets considered) compared to its linguistic realisation when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence
The Parallel Semantics Program Dependence Graph
Venue: arXiv cs.PL 2024
Year: 2024

A compiler's intermediate representation (IR) defines a program's execution plan by encoding its instructions and their relative order. Compiler optimizations aim to replace a given execution plan with a semantically-equivalent one that increases the program's performance for the target architecture. Alternative representations of an IR, like the Program Dependence Graph (PDG), aid this process by capturing the minimum set of constraints that semantically-equivalent execution plans must satisfy. Parallel programming like OpenMP extends a sequential execution plan by adding the possibility of running instructions in parallel, creating a parallel execution plan. Recently introduced parallel IRs, like TAPIR, explicitly encode a parallel execution plan. These new IRs finally make it possible for compilers to change the parallel execution plan expressed by programmers to better fit the target parallel architecture. Unfortunately, parallel IRs do not help compilers in identifying the set of parallel execution plans that preserve the original semantics. In other words, we are still lacking an alternative representation of parallel IRs to capture the minimum set of constraints that parallel execution plans must satisfy to be semantically-equivalent. Unfortunately, the PDG is not an ideal candidate for this task as it was designed for sequential code. We propose the Parallel Semantics Program Dependence Graph (PS-PDG) to precisely capture the salient program constraints that all semantically-equivalent parallel execution plans must satisfy. This paper defines the PS-PDG, justifies the necessity of each extension to the PDG, and demonstrates the increased optimization power of the PS-PDG over an existing PDG-based automatic-parallelizing compiler. Compilers can now rely on the PS-PDG to select different parallel execution plans while maintaining the same original semantics.
AI-generated faces influence gender stereotypes and racial homogenization
Venue: arXiv cs.CV 2024
Year: 2024

Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Stable Diffusion across six races, two genders, 32 professions, and eight attributes. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals significant racial homogenization, e.g., depicting nearly all Middle Eastern men as bearded, brown-skinned, and wearing traditional attire. We then propose debiasing solutions that allow users to specify the desired distributions of race and gender when generating images while minimizing racial homogenization. Finally, using a preregistered survey experiment, we find evidence that being presented with inclusive AI-generated faces reduces people's racial and gender biases, while being presented with non-inclusive ones increases such biases, regardless of whether the images are labeled as AI-generated. Taken together, our findings emphasize the need to address biases and stereotypes in text-to-image models.
End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands
Venue: arXiv cs.IT 2024
Year: 2024

This paper proposes and analyzes novel deep learning methods for downlink (DL) single-user multiple-input multiple-output (SU-MIMO) and multi-user MIMO (MU-MIMO) systems operating in time division duplex (TDD) mode. A motivating application is the 6G upper midbands (7-24 GHz), where the base station (BS) antenna arrays are large, user equipment (UE) array sizes are moderate, and theoretically optimal approaches are practically infeasible for several reasons. To deal with uplink (UL) pilot overhead and low signal power issues, we introduce the channel-adaptive pilot, as part of an analog channel state information feedback mechanism. Deep neural network (DNN)-generated pilots are used to linearly transform the UL channel matrix into lower-dimensional latent vectors. Meanwhile, the BS employs a second DNN that processes the received UL pilots to directly generate near-optimal DL precoders. The training is end-to-end which exploits synergies between the two DNNs. For MU-MIMO precoding, we propose a DNN structure inspired by theoretically optimum linear precoding. The proposed methods are evaluated against genie-aided upper bounds and conventional approaches, using realistic upper midband datasets. Numerical results demonstrate the potential of our approach to achieve significantly increased sum-rate, particularly at moderate to high signal-to-noise ratio (SNR) and when UL pilot overhead is constrained.
Bio-Inspired Compensatory Strategies for Damage to Flapping Robotic Propulsors
Venue: arXiv cs.RO 2024
Year: 2024

To maintain full autonomy, autonomous robotic systems must have the ability to self-repair. Self-repairing via compensatory mechanisms appears in nature: for example, some fish can lose even 76% of their propulsive surface without loss of thrust by altering stroke mechanics. However, direct transference of these alterations from an organism to a robotic flapping propulsor may not be optimal due to irrelevant evolutionary pressures. We instead seek to determine what alterations to stroke mechanics are optimal for a damaged robotic system via artificial evolution. To determine whether natural and machine-learned optima differ, we employ a cyber-physical system using a Covariance Matrix Adaptation Evolutionary Strategy to seek the most efficient trajectory for a given force. We implement an online optimization with hardware-in-the-loop, performing experimental function evaluations with an actuated flexible flat plate. To recoup thrust production following partial amputation, the most efficient learned strategy was to increase amplitude, increase frequency, increase the amplitude of angle of attack, and phase shift the angle of attack by approximately 110 degrees. In fish, only an amplitude increase is reported by majority in the literature. To recoup side-force production, a more challenging optimization landscape is encountered. Nesting of optimal angle of attack traces is found in the resultant-based reference frame, but no clear trend in amplitude or frequency are exhibited -- in contrast to the increase in frequency reported in insect literature. These results suggest that how mechanical flapping propulsors most efficiently adjust to damage of a flapping propulsor may not align with natural swimmers and flyers.
Fairness and efficiency trade-off in two-sided matching
Venue: arXiv cs.GT 2024
Year: 2024

The theory of two-sided matching has been extensively developed and applied to many real-life application domains. As the theory has been applied to increasingly diverse types of environments, researchers and practitioners have encountered various forms of distributional constraints. As a mechanism can handle a more general class of constraints, we can assign students more flexibly to colleges to increase students' welfare. However, it turns out that there exists a trade-off between students' welfare (efficiency) and fairness (which means no student has justified envy). Furthermore, this trade-off becomes sharper as the class of constraints becomes more general. The first contribution of this paper is to clarify the boundary on whether a strategyproof and fair mechanism can satisfy certain efficiency properties for each class of constraints. Our second contribution is to establish a weaker fairness requirement called envy-freeness up to $k$ peers (EF-$k$), which is inspired by a similar concept used in the fair division of indivisible items. EF-$k$ guarantees that each student has justified envy towards at most $k$ students. By varying $k$, EF-$k$ can represent different levels of fairness. We investigate theoretical properties associated with EF-$k$. Furthermore, we develop two contrasting strategyproof mechanisms that work for general hereditary constraints, i.e., one mechanism can guarantee a strong efficiency requirement, while the other can guarantee EF-$k$ for any fixed $k$. We evaluate the performance of these mechanisms through computer simulation.
A Single Simple Patch is All You Need for AI-generated Image Detection
Venue: arXiv cs.CV 2024
Year: 2024

The recent development of generative models unleashes the potential of generating hyper-realistic fake images. To prevent the malicious usage of fake images, AI-generated image detection aims to distinguish fake images from real images. However, existing method suffer from severe performance drop when detecting images generated by unseen generators. We find that generative models tend to focus on generating the patches with rich textures to make the images more realistic while neglecting the hidden noise caused by camera capture present in simple patches. In this paper, we propose to exploit the noise pattern of a single simple patch to identify fake images. Furthermore, due to the performance decline when handling low-quality generated images, we introduce an enhancement module and a perception module to remove the interfering information. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on public benchmarks.
Learning Network Representations with Disentangled Graph Auto-Encoder
Venue: arXiv cs.LG 2024
Year: 2024

The (variational) graph auto-encoder is widely used to learn representations for graph-structured data. However, the formation of real-world graphs is a complicated and heterogeneous process influenced by latent factors. Existing encoders are fundamentally holistic, neglecting the entanglement of latent factors. This reduces the effectiveness of graph analysis tasks, while also making it more difficult to explain the learned representations. As a result, learning disentangled graph representations with the (variational) graph auto-encoder poses significant challenges and remains largely unexplored in the current research. In this paper, we introduce the Disentangled Graph Auto-Encoder (DGA) and the Disentangled Variational Graph Auto-Encoder (DVGA) to learn disentangled representations. Specifically, we first design a disentangled graph convolutional network with multi-channel message-passing layers to serve as the encoder. This allows each channel to aggregate information about each latent factor. The disentangled variational graph auto-encoder's expressive capability is then enhanced by applying a component-wise flow to each channel. In addition, we construct a factor-wise decoder that takes into account the characteristics of disentangled representations. We improve the independence of representations by imposing independence constraints on the mapping channels for distinct latent factors. Empirical experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.
LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning
Venue: arXiv cs.CL 2024
Year: 2024

ChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-generated text detection models, such as based on BERT and RoBERTa, are prone to in-domain over-fitting, leading to poor out-of-domain (OOD) detection performance. In this paper, we first collected Chinese text responses generated by human experts and 9 types of LLMs, for which to multiple domains questions, and further created a dataset that mixed human-written sentences and sentences polished by LLMs. We then proposed LLM-Detector, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs. Our method leverages the wealth of knowledge LLMs acquire during pre-training, enabling them to detect the text they generate. Instruction tuning aligns the model's responses with the user's expected text detection tasks. Experimental results show that previous methods struggle with sentence-level AI-generated text detection and OOD detection. In contrast, our proposed method not only significantly outperforms baseline methods in both sentence-level and document-level text detection but also demonstrates strong generalization capabilities. Furthermore, since LLM-Detector is trained based on open-source LLMs, it is easy to customize for deployment.
A Comprehensive Survey on 3D Content Generation
Venue: arXiv cs.CV 2024
Year: 2024

Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is the most close visual modality to real-world 3D environment and carries enormous knowledge. The 3D content generation shows both academic and practical values while also presenting formidable technical challenges. This review aims to consolidate developments within the burgeoning domain of 3D content generation. Specifically, a new taxonomy is proposed that categorizes existing approaches into three types: 3D native generative methods, 2D prior-based 3D generative methods, and hybrid 3D generative methods. The survey covers approximately 60 papers spanning the major techniques. Besides, we discuss limitations of current 3D content generation techniques, and point out open challenges as well as promising directions for future work. Accompanied with this survey, we have established a project website where the resources on 3D content generation research are provided. The project page is available at https://github.com/hitcslj/Awesome-AIGC-3D.
Efficient Physically-based Simulation of Soft Bodies in Embodied Environment for Surgical Robot
Venue: arXiv cs.RO 2024
Year: 2024

Surgical robot simulation platform plays a crucial role in enhancing training efficiency and advancing research on robot learning. Much effort have been made by scholars on developing open-sourced surgical robot simulators to facilitate research. We also developed SurRoL formerly, an open-source, da Vinci Research Kit (dVRK) compatible and interactive embodied environment for robot learning. Despite its advancements, the simulation of soft bodies still remained a major challenge within the open-source platforms available for surgical robotics. To this end, we develop an interactive physically based soft body simulation framework and integrate it to SurRoL. Specifically, we utilized a high-performance adaptation of the Material Point Method (MPM) along with the Neo-Hookean model to represent the deformable tissue. Lagrangian particles are used to track the motion and deformation of the soft body throughout the simulation and Eulerian grids are leveraged to discretize space and facilitate the calculation of forces, velocities, and other physical quantities. We also employed an efficient collision detection and handling strategy to simulate the interaction between soft body and rigid tool of the surgical robot. By employing the Taichi programming language, our implementation harnesses parallel computing to boost simulation speed. Experimental results show that our platform is able to simulate soft bodies efficiently with strong physical interpretability and plausible visual effects. These new features in SurRoL enable the efficient simulation of surgical tasks involving soft tissue manipulation and pave the path for further investigation of surgical robot learning. The code will be released in a new branch of SurRoL github repo.
Parametric-Task MAP-Elites
Venue: arXiv cs.NE 2024
Year: 2024

Optimizing a set of functions simultaneously by leveraging their similarity is called multi-task optimization. Current black-box multi-task algorithms only solve a finite set of tasks, even when the tasks originate from a continuous space. In this paper, we introduce Parametric-Task MAP-Elites (PT-ME), a new black-box algorithm for continuous multi-task optimization problems. This algorithm (1) solves a new task at each iteration, effectively covering the continuous space, and (2) exploits a new variation operator based on local linear regression. The resulting dataset of solutions makes it possible to create a function that maps any task parameter to its optimal solution. We show that PT-ME outperforms all baselines, including the deep reinforcement learning algorithm PPO on two parametric-task toy problems and a robotic problem in simulation.
A Unified Framework for Center-based Clustering of Distributed Data
Venue: arXiv cs.LG 2024
Year: 2024

We develop a family of distributed center-based clustering algorithms that work over networks of users. In the proposed scenario, users contain a local dataset and communicate only with their immediate neighbours, with the aim of finding a clustering of the full, joint data. The proposed family, termed Distributed Gradient Clustering (DGC-$\mathcal{F}_ρ$), is parametrized by $ρ\geq 1$, controling the proximity of users' center estimates, with $\mathcal{F}$ determining the clustering loss. Our framework allows for a broad class of smooth convex loss functions, including popular clustering losses like $K$-means and Huber loss. Specialized to popular clustering losses like $K$-means and Huber loss, DGC-$\mathcal{F}_ρ$ gives rise to novel distributed clustering algorithms DGC-KM$_ρ$ and DGC-HL$_ρ$, while novel clustering losses based on Logistic and Fair functions lead to DGC-LL$_ρ$ and DGC-FL$_ρ$. We provide a unified analysis and establish several strong results, under mild assumptions. First, we show that the sequence of centers generated by the methods converges to a well-defined notion of fixed point, under any center initialization and value of $ρ$. Second, we prove that, as $ρ$ increases, the family of fixed points produced by DGC-$\mathcal{F}_ρ$ converges to a notion of consensus fixed points. We show that consensus fixed points of DGC-$\mathcal{F}_ρ$ are equivalent to fixed points of gradient clustering over the full data, guaranteeing a clustering of the full data is produced. For the special case of Bregman losses, we show that our fixed points converge to the set of Lloyd points. Extensive numerical experiments on synthetic and real data confirm our theoretical findings, show strong performance of our methods and demonstrate the usefulness and wide range of potential applications of our general framework, such as outlier detection.