NeuDA: Neural Deformable Anchor for High-Fidelity Implicit Surface Reconstruction

This paper studies implicit surface reconstruction leveraging differentiable ray casting. Previous works such as IDR and NeuS overlook the spatial context in 3D space when predicting and rendering the surface, thereby may fail to capture sharp local topologies such as small holes and structures. To mitigate the limitation, we propose a flexible neural implicit representation leveraging hierarchical voxel grids, namely Neural Deformable Anchor (NeuDA), for high-fidelity surface reconstruction. NeuDA maintains the hierarchical anchor grids where each vertex stores a 3D position (or anchor) instead of the direct embedding (or feature). We optimize the anchor grids such that different local geometry structures can be adaptively encoded. Besides, we dig into the frequency encoding strategies and introduce a simple hierarchical positional encoding method for the hierarchical anchor structure to flexibly exploit the properties of high-frequency and low-frequency geometry and appearance. Experiments on both the DTU and BlendedMVS datasets demonstrate that NeuDA can produce promising mesh surfaces.Comment: Accepted to CVPR 2023, project page: https://3d-front-future.github.io/neud

Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.Comment: Accepted at AAAI 202

Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection

Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to their myriad of practical applications, yet their adoption has been constrained by issues of fact-conflicting hallucinations across web platforms. The assessment of factuality in text, produced by LLMs, remains inadequately explored, extending not only to the judgment of vanilla facts but also encompassing the evaluation of factual errors emerging in complex inferential tasks like multi-hop, and etc. In response, we introduce FactCHD, a fact-conflicting hallucination detection benchmark meticulously designed for LLMs. Functioning as a pivotal tool in evaluating factuality within "Query-Respons" contexts, our benchmark assimilates a large-scale dataset, encapsulating a broad spectrum of factuality patterns, such as vanilla, multi-hops, comparison, and set-operation patterns. A distinctive feature of our benchmark is its incorporation of fact-based chains of evidence, thereby facilitating comprehensive and conducive factual reasoning throughout the assessment process. We evaluate multiple LLMs, demonstrating the effectiveness of the benchmark and current methods fall short of faithfully detecting factual errors. Furthermore, we present TRUTH-TRIANGULATOR that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence. The benchmark dataset and source code will be made available in https://github.com/zjunlp/FactCHD.Comment: Work in progres

Abnormal Liver Function Tests Were Associated With Adverse Clinical Outcomes: An Observational Cohort Study of 2,912 Patients With COVID-19

Background and Aim: The impact of liver function test (LFTs) abnormality on adverse clinical outcomes in coronavirus disease 2019 (COVID-19) patients remains controversial. The aim of this study was to assess the impact of abnormal LFTs on clinical outcomes in a large cohort of hospitalized patients with COVID-19.Methods: We retrospectively collected data on 2,912 consecutive patients with COVID-19 who were admitted to a makeshift hospital in China between 5 February and 23 March 2020. The association between LFTs abnormalities (baseline and peak values) and clinical outcomes was measured by using Cox regression models.Results: On admission 1,414 patients (48.6%) had abnormal LFTs, with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), alkaline phosphatase (ALP), and gamma-glutamyltransferase (GGT) elevation in 662 (22.7%), 221 (7.6%), 52 (1.8%), 135 (4.6%), and 536 (18.5%) patients, respectively, and hypoalbuminemia in 737 (25.3%) patients. During a median 13 (IQR: 8–19) days of hospitalization, 61 patients (2.1%) died, 106 patients (3.6%) admitted to intensive care unit (ICU), and 75 patients (2.6%) required mechanical ventilation. After adjustment for confounders, baseline abnormal LFTs were independently associated with increased risks of mortality (adjusted HR 3.66, 95%CI 1.64–8.19, p = 0.002), ICU admission (adjusted HR 3.12 95%CI 1.86–5.23, p < 0.001), and mechanical ventilation (adjusted HR 3.00, 95%CI 1.63–5.52, p < 0.001), which was homogeneous across the severity of COVID-19 infection. Among the parameters of LTFs, the associations with the outcomes were more pronounced for AST and albumin abnormality. In contrast, ALT elevation was not significantly associated with those outcomes. Similar results were observed for peak values of LFTs during hospitalization.Conclusions: Abnormality of AST, albumin, TBIL, ALP, and GGT but not ALT were independently associated with adverse outcomes

Data-Free Evaluation of User Contributions in Federated Learning

Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device\u27s private data and computing resources. A critical issues is to evaluate individual users\u27 contributions so that (1) users\u27 effort in model training can be compensated with proper incentives and (2) malicious and low-quality users can be detected and removed. The state-of-the-art solutions require a representative test dataset for the evaluation purpose, but such a dataset is often unavailable and hard to synthesize. In this paper, we propose a method called Pairwise Correlated Agreement (PCA) based on the idea of peer prediction to evaluate user contribution in FL without a test dataset. PCA achieves this using the statistical correlation of the model parameters uploaded by users. We then apply PCA to designing (1) a new federated learning algorithm called Fed-PCA, and (2) a new incentive mechanism that guarantees truthfulness. We evaluate the performance of PCA and Fed-PCA using the MNIST dataset and a large industrial product recommendation dataset. The results demonstrate that our Fed-PCA outperforms the canonical FedAvg algorithm and other baseline methods in accuracy, and at the same time, PCA effectively incentivizes users to behave truthfully