Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.
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We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Our method is training-free and does not rely on any label supervision. Two key designs enable us to employ an off-the-shelf text-to-image diffusion model as a useful dataset generator for object instances and mask annotations. First, we divide an image canvas into several regions and perform a single round of diffusion process to generate multiple instances simultaneously, conditioning on different text prompts. Second, we obtain corresponding instance masks by aggregating cross-attention maps associated with object prompts across layers and diffusion time steps, followed by simple thresholding and edge-aware refinement processing. Without bells and whistles, our MosaicFusion can produce a significant amount of synthetic labeled data for both rare and novel categories. Experimental results on the challenging LVIS long-tailed and open-vocabulary benchmarks demonstrate that MosaicFusion can significantly improve the performance of existing instance segmentation models, especially for rare and novel categories. Code will be released at //github.com/Jiahao000/MosaicFusion.
Memory interference may heavily inflate task execution times in Heterogeneous Systems-on-Chips (HeSoCs). Knowing worst-case interference is consequently fundamental for supporting the correct execution of time-sensitive applications. In most of the literature, worst-case interference is assumed to be generated by, and therefore is estimated through read-intensive synthetic workloads with no caching. Yet these workloads do not always generate worst-case interference. This is the consequence of the general results reported in this work. By testing on multiple architectures, we determined that the highest interference generation traffic pattern is actually hardware dependant, and that making assumptions could lead to a severe underestimation of the worst-case (in our case, of more than 9x).
Large language models (LLMs) have demonstrated great potential in natural language processing tasks within the financial domain. In this work, we present a Chinese Financial Generative Pre-trained Transformer framework, named CFGPT, which includes a dataset~(CFData) for pre-training and supervised fine-tuning, a financial LLM~(CFLLM) to adeptly manage financial texts, and a deployment framework~(CFAPP) designed to navigate real-world financial applications. The CFData comprising both a pre-training dataset and a supervised fine-tuning dataset, where the pre-training dataset collates Chinese financial data and analytics, alongside a smaller subset of general-purpose text with 584M documents and 141B tokens in total, and the supervised fine-tuning dataset is tailored for six distinct financial tasks, embodying various facets of financial analysis and decision-making with 1.5M instruction pairs and 1.5B tokens in total. The CFLLM, which is based on InternLM-7B to balance the model capability and size, is trained on CFData in two stage, continued pre-training and supervised fine-tuning. The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application. Our codes are released at //github.com/TongjiFinLab/CFGPT.
We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, such as Inception Score (IS), Frechet Inception Distance (FID), and the variants of Precision and Recall (P&R), heavily rely on supports that are estimated from sample features. However, the reliability of their estimation has not been seriously discussed (and overlooked) even though the quality of the evaluation entirely depends on it. In this paper, we propose Topological Precision and Recall (TopP&R, pronounced 'topper'), which provides a systematic approach to estimating supports, retaining only topologically and statistically important features with a certain level of confidence. This not only makes TopP&R strong for noisy features, but also provides statistical consistency. Our theoretical and experimental results show that TopP&R is robust to outliers and non-independent and identically distributed (Non-IID) perturbations, while accurately capturing the true trend of change in samples. To the best of our knowledge, this is the first evaluation metric focused on the robust estimation of the support and provides its statistical consistency under noise.
Fish tracking plays a vital role in understanding fish behavior and ecology. However, existing tracking methods face challenges in accuracy and robustness dues to morphological change of fish, occlusion and complex environment. This paper proposes FishMOT(Multiple Object Tracking for Fish), a novel fish tracking approach combining object detection and IoU matching, including basic module, interaction module and refind module. Wherein, a basic module performs target association based on IoU of detection boxes between successive frames to deal with morphological change of fish; an interaction module combines IoU of detection boxes and IoU of fish entity to handle occlusions; a refind module use spatio-temporal information uses spatio-temporal information to overcome the tracking failure resulting from the missed detection by the detector under complex environment. FishMOT reduces the computational complexity and memory consumption since it does not require complex feature extraction or identity assignment per fish, and does not need Kalman filter to predict the detection boxes of successive frame. Experimental results demonstrate FishMOT outperforms state-of-the-art multi-object trackers and specialized fish tracking tools in terms of MOTA, accuracy, computation time, memory consumption, etc.. Furthermore, the method exhibits excellent robustness and generalizability for varying environments and fish numbers. The simplified workflow and strong performance make FishMOT as a highly effective fish tracking approach. The source codes and pre-trained models are available at: //github.com/gakkistar/FishMOT
People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MateRobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model MateViT to perform pixel-wise semantic segmentation, simultaneously recognizing both objects and materials. Our methods achieve respective 40.2% and 51.1% of mIoU on COCOStuff-10K and DMS datasets, surpassing the previous method with +5.7% and +7.0% gains. Moreover, on the field test with participants, our wearable system reaches a score of 28 in the NASA-Task Load Index, indicating low cognitive demands and ease of use. Our MateRobot demonstrates the feasibility of recognizing material property through visual cues and offers a promising step towards improving the functionality of wearable robots for PVI. The source code has been made publicly available at //junweizheng93.github.io/publications/MATERobot/MATERobot.html.
Assigning repetitive and physically-demanding construction tasks to robots can alleviate human workers's exposure to occupational injuries. Transferring necessary dexterous and adaptive artisanal construction craft skills from workers to robots is crucial for the successful delegation of construction tasks and achieving high-quality robot-constructed work. Predefined motion planning scripts tend to generate rigid and collision-prone robotic behaviors in unstructured construction site environments. In contrast, Imitation Learning (IL) offers a more robust and flexible skill transfer scheme. However, the majority of IL algorithms rely on human workers to repeatedly demonstrate task performance at full scale, which can be counterproductive and infeasible in the case of construction work. To address this concern, this paper proposes an immersive, cloud robotics-based virtual demonstration framework that serves two primary purposes. First, it digitalizes the demonstration process, eliminating the need for repetitive physical manipulation of heavy construction objects. Second, it employs a federated collection of reusable demonstrations that are transferable for similar tasks in the future and can thus reduce the requirement for repetitive illustration of tasks by human agents. Additionally, to enhance the trustworthiness, explainability, and ethical soundness of the robot training, this framework utilizes a Hierarchical Imitation Learning (HIL) model to decompose human manipulation skills into sequential and reactive sub-skills. These two layers of skills are represented by deep generative models, enabling adaptive control of robot actions. By delegating the physical strains of construction work to human-trained robots, this framework promotes the inclusion of workers with diverse physical capabilities and educational backgrounds within the construction industry.
Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees.
Nowadays, Transformer language models (LMs) represent a fundamental component of the NLP research methodologies and applications. However, the development of such models specifically for the Russian language has received little attention. This paper presents a collection of 13 Russian Transformer LMs based on the encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) models in multiple sizes. Access to these models is readily available via the HuggingFace platform. We provide a report of the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian natural language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we hope to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language.
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose occupancy networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.
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