Miracle-dz/UniFAR-FTUs huggingface dataset:
Left-right Discrepancy for Adversarial Attack on Stereo Networks

Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images. The similarity between these intermediate left-right features significantly impacts the accuracy of disparity estimation. In this paper, we introduce a novel adversarial attack approach that generates perturbation noise specifically designed to maximize the discrepancy between left and right image features. Extensive experiments demonstrate the superior capability of our method to induce larger prediction errors in stereo neural networks, e.g. outperforming existing state-of-the-art attack methods by 219% MAE on the KITTI dataset and 85% MAE on the Scene Flow dataset. Additionally, we extend our approach to include a proxy network black-box attack method, eliminating the need for access to stereo neural network. This method leverages an arbitrary network from a different vision task as a proxy to generate adversarial noise, effectively causing the stereo network to produce erroneous predictions. Our findings highlight a notable sensitivity of stereo networks to discrepancies in shallow layer features, offering valuable insights that could guide future research in enhancing the robustness of stereo vision systems.
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms

Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-prey dynamics have captured substantial interest and various simulations been tailored to unique requirements. To prevent further time-intensive developments, we introduce Aquarium, a comprehensive Multi-Agent Reinforcement Learning environment for predator-prey interaction, enabling the study of emergent behavior. Aquarium is open source and offers a seamless integration of the PettingZoo framework, allowing a quick start with proven algorithm implementations. It features physics-based agent movement on a two-dimensional, edge-wrapping plane. The agent-environment interaction (observations, actions, rewards) and the environment settings (agent speed, prey reproduction, predator starvation, and others) are fully customizable. Besides a resource-efficient visualization, Aquarium supports to record video files, providing a visual comprehension of agent behavior. To demonstrate the environment's capabilities, we conduct preliminary studies which use PPO to train multiple prey agents to evade a predator. In accordance to the literature, we find Individual Learning to result in worse performance than Parameter Sharing, which significantly improves coordination and sample-efficiency.
Knowledge Distillation for Closed-Source Language Models

Closed-source language models such as GPT-4 have achieved remarkable performance. Many recent studies focus on enhancing the capabilities of smaller models through knowledge distillation from closed-source language models. However, due to the incapability to directly access the weights, hidden states, and output distributions of these closed-source models, the distillation can only be performed by fine-tuning smaller models with data samples generated by closed-source language models, which constrains the effectiveness of knowledge distillation. In this paper, we propose to estimate the output distributions of closed-source language models within a Bayesian estimation framework, involving both prior and posterior estimation. The prior estimation aims to derive a prior distribution by utilizing the corpus generated by closed-source language models, while the posterior estimation employs a proxy model to update the prior distribution and derive a posterior distribution. By leveraging the estimated output distribution of closed-source language models, traditional knowledge distillation can be executed. Experimental results demonstrate that our method surpasses the performance of current models directly fine-tuned on data generated by closed-source language models.
Feature Selection via Maximizing Distances between Class Conditional Distributions

For many data-intensive tasks, feature selection is an important preprocessing step. However, most existing methods do not directly and intuitively explore the intrinsic discriminative information of features. We propose a novel feature selection framework based on the distance between class conditional distributions, measured by integral probability metrics (IPMs). Our framework directly explores the discriminative information of features in the sense of distributions for supervised classification. We analyze the theoretical and practical aspects of IPMs for feature selection, construct criteria based on IPMs. We propose several variant feature selection methods of our framework based on the 1-Wasserstein distance and implement them on real datasets from different domains. Experimental results show that our framework can outperform state-of-the-art methods in terms of classification accuracy and robustness to perturbations.
Image edge enhancement for effective image classification

Image classification has been a popular task due to its feasibility in real-world applications. Training neural networks by feeding them RGB images has demonstrated success over it. Nevertheless, improving the classification accuracy and computational efficiency of this process continues to present challenges that researchers are actively addressing. A widely popular embraced method to improve the classification performance of neural networks is to incorporate data augmentations during the training process. Data augmentations are simple transformations that create slightly modified versions of the training data and can be very effective in training neural networks to mitigate overfitting and improve their accuracy performance. In this study, we draw inspiration from high-boost image filtering and propose an edge enhancement-based method as means to enhance both accuracy and training speed of neural networks. Specifically, our approach involves extracting high frequency features, such as edges, from images within the available dataset and fusing them with the original images, to generate new, enriched images. Our comprehensive experiments, conducted on two distinct datasets CIFAR10 and CALTECH101, and three different network architectures ResNet-18, LeNet-5 and CNN-9 demonstrates the effectiveness of our proposed method.
Generating Maximal Configurations and Their Variants Using Code Metrics

Testing configurable systems continues to be challenging and costly. Generation of configurations for testing tends to use either techniques based on semantic sampling (e.g., logical formulas over configuration variables, often called presence conditions) or structural code metrics (e.g., code coverage). In this paper we describe our hybrid approaches that combine these two kinds of techniques to good effect. We present new configuration-generation algorithms that leverage constraint solving (SAT and MaxSAT) and configuration fuzzing, and implement our approach in a configuration-generation framework, CONFIZZ. CONFIZZ both enables the generation of maximal configurations (maximal sets of presence conditions that can be satisfied together) and performs code-metric guided configuration fuzzing. Results from evaluation on BusyBox, a highly configurable benchmark, show that our MaxSAT-based configuration generation achieves better coverage for several code metrics. Results also show that, when high coverage of multiple configurations is needed, CONFIZZ's presence-condition fuzzing outperforms alternatives.
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions

Despite the impressive capabilities of Deep Reinforcement Learning (DRL) agents in many challenging scenarios, their black-box decision-making process significantly limits their deployment in safety-sensitive domains. Several previous self-interpretable works focus on revealing the critical states of the agent's decision. However, they cannot pinpoint the error-prone states. To address this issue, we propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior by identify the error-prone states. At a high level, BET hypothesizes that states in which the agent consistently executes uniform decisions exhibit a reduced propensity for errors. To effectively model this phenomenon, BET expresses these states within neighborhoods, each defined by a curated set of representative states. Therefore, states positioned at a greater distance from these representative benchmarks are more prone to error. We evaluate BET in various popular RL environments and show its superiority over existing self-interpretable models in terms of explanation fidelity. Furthermore, we demonstrate a use case for providing explanations for the agents in StarCraft II, a sophisticated multi-agent cooperative game. To the best of our knowledge, we are the first to explain such a complex scenarios using a fully transparent structure.
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach

This paper addresses a major challenge to historical research on the 19th century. Large quantities of sources have become digitally available for the first time, while extraction techniques are lagging behind. Therefore, we researched machine learning (ML) models to recognise and extract complex data structures in a high-value historical primary source, the Schematismus. It records every single person in the Habsburg civil service above a certain hierarchical level between 1702 and 1918 and documents the genesis of the central administration over two centuries. Its complex and intricate structure as well as its enormous size have so far made any more comprehensive analysis of the administrative and social structure of the later Habsburg Empire on the basis of this source impossible. We pursued two central objectives: Primarily, the improvement of the OCR quality, for which we considered an improved structure recognition to be essential; in the further course, it turned out that this also made the extraction of the data structure possible. We chose Faster R-CNN as base for the ML architecture for structure recognition. In order to obtain the required amount of training data quickly and economically, we synthesised Hof- und Staatsschematismus-style data, which we used to train our model. The model was then fine-tuned with a smaller set of manually annotated historical source data. We then used Tesseract-OCR, which was further optimised for the style of our documents, to complete the combined structure extraction and OCR process. Results show a significant decrease in the two standard parameters of OCR-performance, WER and CER (where lower values are better). Combined structure detection and fine-tuned OCR improved CER and WER values by remarkable 71.98 percent (CER) respectively 52.49 percent (WER).
TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information in user profiles and job descriptions apart from user behavior features and job metadata. However, the general domain-oriented design struggles to capture the unique structural information within user profiles and job descriptions, leading to a loss of latent semantic correlations. We propose TAROT, a hierarchical multitask co-pretraining framework, to better utilize structural and semantic information for informative text embeddings. TAROT targets semi-structured text in profiles and jobs, and it is co-pretained with multi-grained pretraining tasks to constrain the acquired semantic information at each level. Experiments on a real-world LinkedIn dataset show significant performance improvements, proving its effectiveness in person-job fit tasks.
All the Way There and Back: Inertial-Based, Phone-in-Pocket Indoor Wayfinding and Backtracking Apps for Blind Travelers

We introduce two iOS apps that have been designed to support wayfinding and backtracking for blind travelers navigating in indoor building environments. Wayfinding involves determining and following a route through the building's corridors to reach a destination, and assumes that the app has access to the floor plan of the building. Backtracking one's route, on the other hand, requires no map knowledge. Our apps only use the inertial and magnetic sensors of the smartphone, and thus require no infrastructure modification (e.g., installation and support of BLE beacons). Unlike systems that use the phone's camera, users of our apps can conveniently keep their phone tucked inside a pocket, while interacting with the apps using a smartwatch. Routing directions are given via speech. Both apps were tested in a user study with seven blind participants, who used them while navigating a campus building.
Towards Efficient Diffusion-Based Image Editing with Instant Attention Masks

Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most existing solutions obtain these masks via manual operations or off-line processing, greatly reducing their efficiency. In this paper, we propose a novel and efficient image editing method for Text-to-Image (T2I) diffusion models, termed Instant Diffusion Editing (InstDiffEdit). In particular, InstDiffEdit aims to employ the cross-modal attention ability of existing diffusion models to achieve instant mask guidance during the diffusion steps. To reduce the noise of attention maps and realize the full automatics, we equip InstDiffEdit with a training-free refinement scheme to adaptively aggregate the attention distributions for the automatic yet accurate mask generation. Meanwhile, to supplement the existing evaluations of DIE, we propose a new benchmark called Editing-Mask to examine the mask accuracy and local editing ability of existing methods. To validate InstDiffEdit, we also conduct extensive experiments on ImageNet and Imagen, and compare it with a bunch of the SOTA methods. The experimental results show that InstDiffEdit not only outperforms the SOTA methods in both image quality and editing results, but also has a much faster inference speed, i.e., +5 to +6 times. Our code available at https://anonymous.4open. science/r/InstDiffEdit-C306
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Extensive experiments demonstrate the effectiveness of our method outperforming previous state-of-the-art methods on both indoor and outdoor 3D point cloud datasets (i.e., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI) using 1% and 1pt evaluation.
Inkjet printed intelligent reflecting surface (IRS) for indoor applications

A passive, low-cost, paper-based intelligent reflecting surface (IRS) is designed to reflect a signal in a desired direction to overcome non-line-of-sight scenarios in indoor environments. The IRS is fabricated using conductive silver ink printed on a paper with a specific nanoparticle arrangement, yielding a cost effective paper-based IRS that can easily be mass-produced. Full-wave numerical simulation results were consistent with measurements results, demonstrating the IRS's ability to reflect incident wave into a desired nonspecular direction based on the inkjet-printed design and materials.
Acoustic Three-dimensional Chern Insulators with Arbitrary Chern Vectors

The Chern vector is a vectorial generalization of the scalar Chern number, being able to characterize the topological phase of three-dimensional (3D) Chern insulators. Such a vectorial generalization extends the applicability of Chern-type bulk-boundary correspondence from one-dimensional (1D) edge states to two-dimensional (2D) surface states, whose unique features, such as forming nontrivial torus knots or links in the surface Brillouin zone, have been demonstrated recently in 3D photonic crystals. However, since it is still unclear how to achieve an arbitrary Chern vector, so far the surface-state torus knots or links can emerge, not on the surface of a single crystal as in other 3D topological phases, but only along an internal domain wall between two crystals with perpendicular Chern vectors. Here, we extend the 3D Chern insulator phase to acoustic crystals for sound waves, and propose a scheme to construct an arbitrary Chern vector that allows the emergence of surface-state torus knots or links on the surface of a single crystal. These results provide a complete picture of bulk-boundary correspondence for Chern vectors, and may find use in novel applications in topological acoustics.
Supersymmetry in Quantum Mechanics by Generalized Uncertainty Principle

In this paper, we study supersymmetry in quantum mechanics using the generalized uncertainty principle (GUP), or in other words, generalized supersymmetry in quantum mechanics. We construct supersymmetry in the generalized form of the momentum operator, which is derived from GUP. By generalizing the creation and annihilation operators, we can transform the supersymmetry into a generalized state. In the following, we address the challenge of solving the Schr\"odinger equation for the generalized Hamiltonian. To overcome this difficulty, we employ perturbation theory to establish a relationship between the creation and annihilation operators. By solving this equation analytically and utilizing wave functions and energy levels, we can generate new potentials using the creation and annihilation operators of the wave functions and energy levels for the newer potentials.
Hunting young stars in the Galactic centre

Context. The Galactic centre stands out as the most prolific star-forming environment of the Galaxy when averaged over volume. In the last 30 million years, it has witnessed the formation of ∼106 M⊙ of stars. However, crowding and high extinction hamper their detection and, up to now, only a small fraction of the expected mass of young stars has been identified. Aims. We aim to detect hidden young stars at the Galactic centre by analysing the stellar population in Sagittarius (Sgr) C. This is a region at the western edge of the nuclear stellar disc whose HII emission makes it a perfect candidate to host young stars. Methods.We built dereddened luminosity functions for Sgr C and a control field in the central region of the nuclear stellar disc, and fitted them with a linear combination of theoretical models to analyse their stellar population. Results. We find that Sgr C hosts several 105 M⊙ of young stars. We compared our results with the recently discovered young stellar population in Sgr B1, which is situated at the opposite edge of the nuclear stellar disc. We estimated that the Sgr C young stars are ∼20 Myr old, and likely show the next evolutionary step of the slightly younger stars in Sgr B1. Our findings contribute to addressing the discrepancy between the expected and the detected number of young stars in the Galactic centre, and shed light on their evolution in this extreme environment. As a secondary result, we find an intermediate-age stellar population in Sgr C (∼50% of its stellar mass with an age of between 2 and 7 Gyr), which is not present in the innermost regions of the nuclear stellar disc (dominated by stars > 7 Gyr). This supports the existence of an age gradient and favours an inside-out formation of the nuclear stellar disc.
An Axiomatic Approach to Model-Agnostic Concept Explanations

Concept explanation is a popular approach for examining how human-interpretable concepts impact the predictions of a model. However, most existing methods for concept explanations are tailored to specific models. To address this issue, this paper focuses on model-agnostic measures. Specifically, we propose an approach to concept explanations that satisfy three natural axioms: linearity, recursivity, and similarity. We then establish connections with previous concept explanation methods, offering insight into their varying semantic meanings. Experimentally, we demonstrate the utility of the new method by applying it in different scenarios: for model selection, optimizer selection, and model improvement using a kind of prompt editing for zero-shot vision language models.
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot

Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals' practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.