OpenRaiser/Intern-Atlas huggingface dataset:
Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology
Venue: arXiv cs.LG 2023
Year: 2023

Psychiatry research seeks to understand the manifestations of psychopathology in behavior, as measured in questionnaire data, by identifying a small number of latent factors that explain them. While factor analysis is the traditional tool for this purpose, the resulting factors may not be interpretable, and may also be subject to confounding variables. Moreover, missing data are common, and explicit imputation is often required. To overcome these limitations, we introduce interpretability constrained questionnaire factorization (ICQF), a non-negative matrix factorization method with regularization tailored for questionnaire data. Our method aims to promote factor interpretability and solution stability. We provide an optimization procedure with theoretical convergence guarantees, and an automated procedure to detect latent dimensionality accurately. We validate these procedures using realistic synthetic data. We demonstrate the effectiveness of our method in a widely used general-purpose questionnaire, in two independent datasets (the Healthy Brain Network and Adolescent Brain Cognitive Development studies). Specifically, we show that ICQF improves interpretability, as defined by domain experts, while preserving diagnostic information across a range of disorders, and outperforms competing methods for smaller dataset sizes. This suggests that the regularization in our method matches domain characteristics. The python implementation for ICQF is available at \url{https://github.com/jefferykclam/ICQF}.
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Venue: arXiv cs.AI 2023
Year: 2023

Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is often hindered by the limited training labels. Given Earth imagery with limited training labels, a base deep neural network model, and a spatial knowledge base with label constraints, our problem is to infer the full labels while training the neural network. The problem is challenging due to the sparse and noisy input labels, spatial uncertainty within the label inference process, and high computational costs associated with a large number of sample locations. Existing works on neuro-symbolic models focus on integrating symbolic logic into neural networks (e.g., loss function, model architecture, and training label augmentation), but these methods do not fully address the challenges of spatial data (e.g., spatial uncertainty, the trade-off between spatial granularity and computational costs). To bridge this gap, we propose a novel Spatial Knowledge-Infused Hierarchical Learning (SKI-HL) framework that iteratively infers sample labels within a multi-resolution hierarchy. Our framework consists of a module to selectively infer labels in different resolutions based on spatial uncertainty and a module to train neural network parameters with uncertainty-aware multi-instance learning. Extensive experiments on real-world flood mapping datasets show that the proposed model outperforms several baseline methods. The code is available at \url{https://github.com/ZelinXu2000/SKI-HL}.
VR for Acupuncture? Exploring Needs and Opportunities for Acupuncture Training and Treatment in Virtual Reality
Venue: arXiv cs.HC 2023
Year: 2023

Acupuncture is a form of medicine that involves inserting needles into targeted areas of the body and requires knowledge of both Traditional Chinese Medicine (TCM) and Evidence-Based Medicine (EBM). The process of acquiring such knowledge and using it for practical treatment is challenging due to the need for a deep understanding of human anatomy and the ability to apply both TCM and EBM approaches. Visual aids have been introduced to aid in understanding the alignment of acupuncture points with key elements of the human body, and are indispensable tools for both learners and expert acupuncturists. However, they are often not enough to enable effective practice and fail to fully support the learning process. Novel approaches based on immersive visualization and Virtual Reality (VR) have shown promise in many healthcare settings due to their unique advantages in terms of realism and interactions, but it is still unknown whether and how VR can possibly be beneficial to acupuncture training and treatment. Following participatory design protocols such as observations and semi-structured interviews with eight doctors and nine students, we explore the needs and pain points of current acupuncture workflows at the intersection of EBM and TCM in China and the United States. We highlight opportunities for introducing VR in today's acupuncture training and treatment workflows, and discuss two design approaches that build on 11 specific challenges spanning education, diagnosis, treatment, and communication.
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
Venue: arXiv cs.LG 2023
Year: 2023

Efficient traffic signal control is critical for reducing traffic congestion and improving overall transportation efficiency. The dynamic nature of traffic flow has prompted researchers to explore Reinforcement Learning (RL) for traffic signal control (TSC). Compared with traditional methods, RL-based solutions have shown preferable performance. However, the application of RL-based traffic signal controllers in the real world is limited by the low sample efficiency and high computational requirements of these solutions. In this work, we propose DTLight, a simple yet powerful lightweight Decision Transformer-based TSC method that can learn policy from easily accessible offline datasets. DTLight novelly leverages knowledge distillation to learn a lightweight controller from a well-trained larger teacher model to reduce implementation computation. Additionally, it integrates adapter modules to mitigate the expenses associated with fine-tuning, which makes DTLight practical for online adaptation with minimal computation and only a few fine-tuning steps during real deployment. Moreover, DTLight is further enhanced to be more applicable to real-world TSC problems. Extensive experiments on synthetic and real-world scenarios show that DTLight pre-trained purely on offline datasets can outperform state-of-the-art online RL-based methods in most scenarios. Experiment results also show that online fine-tuning further improves the performance of DTLight by up to 42.6% over the best online RL baseline methods. In this work, we also introduce Datasets specifically designed for TSC with offline RL (referred to as DTRL). Our datasets and code are publicly available.
Finetuning an LLM on Contextual Knowledge of Classics for Q&A
Venue: arXiv cs.CL 2023
Year: 2023

The open-source publishing of large language models (LLMs) has created many possibilities for how anyone who understands language and has access to a computer can interact with significant tools of artificial intelligence, particularly in the context of learning and knowledge dissemination. However, the utility of these models in specialized fields like Classics is still largely unexplored. This project is an attempt to merge the knowledge of Classics with the capabilities of artificial intelligence by finetuning an LLM to cater to the specific needs of learners and professionals. The goal of this project is to develop an LLM that not only reproduces contextual knowledge accurately but also exhibits a consistent "personality" - and, indeed, has consistent propriety - to appeal to a diverse audience who possess differing levels of knowledge. A significant portion of this project was dedicated to refining the dataset, following the principle of "garbage in, garbage out," to ensure the model generates relevant, useful, and creative responses when given a prompt (a statement, question, or single word). After training and evaluation, my model's ability to handle a vast array of different types of inputs and prompting exceeded expectations for a 355M parameter model, though its occasional hallucinations (especially when set with a high temperature), particularly in its assertions about historical events or its own identity, make it seem somewhat capricious and more work in the form of continuous finetuning will be undertaken.
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation
Venue: arXiv cs.CV 2023
Year: 2023

Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction. Taking inspiration from the Chain-of-Thought prompting technique utilized in language models, we present an innovative concept known as Chain-of-Instruct Editing (CoIE), which enhances the capabilities of these models through step-by-step editing using a series of instructions. In particular, in the context of face manipulation, we leverage the contextual learning abilities of a pretrained Large Language Model (LLM), such as GPT-4, to generate a sequence of instructions from the original input, utilizing a purpose-designed 1-shot template. To further improve the precision of each editing step, we conduct fine-tuning on the editing models using our self-constructed instruction-guided face editing dataset, Instruct-CelebA. And additionally, we incorporate a super-resolution module to mitigate the adverse effects of editability and quality degradation. Experimental results across various challenging cases confirm the significant boost in multi-attribute facial image manipulation using chain-of-instruct editing. This is evident in enhanced editing success rates, measured by CLIPSim and Coverage metrics, improved by 17.86% and 85.45% respectively, and heightened controllability indicated by Preserve L1 and Quality metrics, improved by 11.58% and 4.93% respectively.
Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI
Venue: arXiv cs.IT 2023
Year: 2023

In conventional multiple-input multiple-output (MIMO) communication systems, the positions of antennas are fixed. To take full advantage of spatial degrees of freedom, a new technology called fluid antenna (FA) is proposed to obtain higher achievable rate and diversity gain. Most existing works on FA exploit instantaneous channel state information (CSI). However, in FA-assisted systems, it is difficult to obtain instantaneous CSI since changes in the antenna position will lead to channel variation. In this letter, we investigate a FA-assisted MIMO system using relatively slow-varying statistical CSI. Specifically, in the criterion of rate maximization, we propose an algorithmic framework for transmit precoding and transmit/receive FAs position designs with statistical CSI. Simulation results show that our proposed algorithm in FA-assisted systems significantly outperforms baselines in terms of rate performance.
Polar-Doc: One-Stage Document Dewarping with Multi-Scope Constraints under Polar Representation
Venue: arXiv cs.CV 2023
Year: 2023

Document dewarping, aiming to eliminate geometric deformation in photographed documents to benefit text recognition, has made great progress in recent years but is still far from being solved. While Cartesian coordinates are typically leveraged by state-of-the-art approaches to learn a group of deformation control points, such representation is not efficient for dewarping model to learn the deformation information. In this work, we explore Polar coordinates representation for each point in document dewarping, namely Polar-Doc. In contrast to most current works adopting a two-stage pipeline typically, Polar representation enables a unified point regression framework for both segmentation and dewarping network in one single stage. Such unification makes the whole model more efficient to learn under an end-to-end optimization pipeline, and also obtains a compact representation. Furthermore, we propose a novel multi-scope Polar-Doc-IOU loss to constrain the relationship among control points as a grid-based regularization under the Polar representation. Visual comparisons and quantitative experiments on two benchmarks show that, with much fewer parameters than the other mainstream counterparts, our one-stage model with multi-scope constraints achieves new state-of-the-art performance on both pixel alignment metrics and OCR metrics. Source codes will be available at \url{*****}.
LMD: Faster Image Reconstruction with Latent Masking Diffusion
Venue: arXiv cs.CV 2023
Year: 2023

As a class of fruitful approaches, diffusion probabilistic models (DPMs) have shown excellent advantages in high-resolution image reconstruction. On the other hand, masked autoencoders (MAEs), as popular self-supervised vision learners, have demonstrated simpler and more effective image reconstruction and transfer capabilities on downstream tasks. However, they all require extremely high training costs, either due to inherent high temporal-dependence (i.e., excessively long diffusion steps) or due to artificially low spatial-dependence (i.e., human-formulated high mask ratio, such as 0.75). To the end, this paper presents LMD, a faster image reconstruction framework with latent masking diffusion. First, we propose to project and reconstruct images in latent space through a pre-trained variational autoencoder, which is theoretically more efficient than in the pixel-based space. Then, we combine the advantages of MAEs and DPMs to design a progressive masking diffusion model, which gradually increases the masking proportion by three different schedulers and reconstructs the latent features from simple to difficult, without sequentially performing denoising diffusion as in DPMs or using fixed high masking ratio as in MAEs, so as to alleviate the high training time-consumption predicament. Our approach allows for learning high-capacity models and accelerate their training (by 3x or more) and barely reduces the original accuracy. Inference speed in downstream tasks also significantly outperforms the previous approaches.
On the privacy of federated Clustering: A Cryptographic View
Venue: arXiv cs.CR 2023
Year: 2023

The privacy concern in federated clustering has attracted considerable attention in past decades. Many privacy-preserving clustering algorithms leverage cryptographic techniques like homomorphic encryption or secure multiparty computation, to guarantee full privacy, i.e., no additional information is leaked other than the final output. However, given the iterative nature of clustering algorithms, consistently encrypting intermediate outputs, such as centroids, hampers efficiency. This paper delves into this intricate trade-off, questioning the necessity of continuous encryption in iterative algorithms. Using the federated K-means clustering as an example, we mathematically formulate the problem of reconstructing input private data from the intermediate centroids as a classical cryptographic problem called hidden subset sum problem (HSSP)-extended from an NP-complete problem called subset sum problem (SSP). Through an in-depth analysis, we show that existing lattice-based HSSP attacks fail in reconstructing the private data given the knowledge of intermediate centroids, thus it is secure to reveal them for the sake of efficiency. To the best of our knowledge, our work is the first to cast federated clustering's privacy concerns as a cryptographic problem HSSP such that a concrete and rigorous analysis can be conducted.
Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning
Venue: arXiv cs.CV 2023
Year: 2023

Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. Under such a paradigm, accurate BEV representation construction relies on reliable depth estimation for multi-camera images. However, existing approaches exhaustively predict depths for every pixel without prioritizing objects, which are precisely the entities requiring detection in the 3D space. To this end, we propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector. First, a category-specific structural priors mining approach is proposed for enhancing the efficacy of monocular depth generation. Besides, a self-boosting learning strategy is further proposed to encourage the model to place more emphasis on challenging objects in computation-expensive temporal stereo matching. Together they provide advanced depth estimation results for high-quality BEV features construction, benefiting the ultimate 3D detection. The proposed method achieves state-of-the-art performances on the challenging nuScenes benchmark, and extensive experimental results demonstrate the effectiveness of our designs.
Improving search relevance of Azure Cognitive Search by Bayesian optimization
Venue: arXiv cs.IR 2023
Year: 2023

Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a Service" cloud products in recent years. However, one of the major challenges for ACS users is to improve the relevance of the search results for their specific usecases. In this paper, we propose a novel method to find the optimal ACS configuration that maximizes search relevance for a specific usecase (product search, document search...) The proposed solution improves key online marketplace metrics such as click through rates (CTR) by formulating the search relevance problem as hyperparameter tuning. We have observed significant improvements in real-world search call to action (CTA) rate in multiple marketplaces by introducing optimized weights generated from the proposed approach.
Mono3DVG: 3D Visual Grounding in Monocular Images
Venue: arXiv cs.CV 2023
Year: 2023

We introduce a novel task of 3D visual grounding in monocular RGB images using language descriptions with both appearance and geometry information. Specifically, we build a large-scale dataset, Mono3DRefer, which contains 3D object targets with their corresponding geometric text descriptions, generated by ChatGPT and refined manually. To foster this task, we propose Mono3DVG-TR, an end-to-end transformer-based network, which takes advantage of both the appearance and geometry information in text embeddings for multi-modal learning and 3D object localization. Depth predictor is designed to explicitly learn geometry features. The dual text-guided adapter is proposed to refine multiscale visual and geometry features of the referred object. Based on depth-text-visual stacking attention, the decoder fuses object-level geometric cues and visual appearance into a learnable query. Comprehensive benchmarks and some insightful analyses are provided for Mono3DVG. Extensive comparisons and ablation studies show that our method significantly outperforms all baselines. The dataset and code will be publicly available at: https://github.com/ZhanYang-nwpu/Mono3DVG.
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models.
Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds
Venue: arXiv cs.RO 2023
Year: 2023

Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the full potential of MPs remains to be attained via adaptable, large-scale MP libraries. In this paper, we propose a set of seven fundamental operations to incrementally learn, improve, and re-organize MP libraries. To showcase their applicability, we provide explicit formulations of the spatial operations for libraries composed of Via-Point Movement Primitives (VMPs). By building on Riemannian manifold theory, our approach enables the incremental learning of all parameters of position and orientation VMPs within a library. Moreover, our approach stores a fixed number of parameters, thus complying with the essential principles of incremental learning. We evaluate our approach to incrementally learn a VMP library from motion capture data provided sequentially.
Safeguarding the safeguards: How best to promote AI alignment in the public interest
Venue: arXiv cs.CY 2023
Year: 2023

AI alignment work is important from both a commercial and a safety lens. With this paper, we aim to help actors who support alignment efforts to make these efforts as effective as possible, and to avoid potential adverse effects. We begin by suggesting that institutions that are trying to act in the public interest (such as governments) should aim to support specifically alignment work that reduces accident or misuse risks. We then describe four problems which might cause alignment efforts to be counterproductive, increasing large-scale AI risks. We suggest mitigations for each problem. Finally, we make a broader recommendation that institutions trying to act in the public interest should think systematically about how to make their alignment efforts as effective, and as likely to be beneficial, as possible.
Leveraging User Simulation to Develop and Evaluate Conversational Information Access Agents
Venue: arXiv cs.IR 2023
Year: 2023

We observe a change in the way users access information, that is, the rise of conversational information access (CIA) agents. However, the automatic evaluation of these agents remains an open challenge. Moreover, the training of CIA agents is cumbersome as it mostly relies on conversational corpora, expert knowledge, and reinforcement learning. User simulation has been identified as a promising solution to tackle automatic evaluation and has been previously used in reinforcement learning. In this research, we investigate how user simulation can be leveraged in the context of CIA. We organize the work in three parts. We begin with the identification of requirements for user simulators for training and evaluating CIA agents and compare existing types of simulator regarding these. Then, we plan to combine these different types of simulators into a new hybrid simulator. Finally, we aim to extend simulators to handle more complex information seeking scenarios.
Kimad: Adaptive Gradient Compression with Bandwidth Awareness
Venue: arXiv cs.LG 2023
Year: 2023

In distributed training, communication often emerges as a bottleneck. In response, we introduce Kimad, a solution that offers adaptive gradient compression. By consistently monitoring bandwidth, Kimad refines compression ratios to match specific neural network layer requirements. Our exhaustive tests and proofs confirm Kimad's outstanding performance, establishing it as a benchmark in adaptive compression for distributed deep learning.