OpenRaiser/Intern-Atlas huggingface dataset:
FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Federated learning (FL) effectively mitigates the data silo challenge brought about by policies and privacy concerns, implicitly harnessing more data for deep model training. However, traditional centralized FL models grapple with diverse multi-center data, especially in the face of significant data heterogeneity, notably in medical contexts. In the realm of medical image segmentation, the growing imperative to curtail annotation costs has amplified the importance of weakly-supervised techniques which utilize sparse annotations such as points, scribbles, etc. A pragmatic FL paradigm shall accommodate diverse annotation formats across different sites, which research topic remains under-investigated. In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation. In FedLPPA, a learnable universal knowledge prompt is maintained, complemented by multiple learnable personalized data distribution prompts and prompts representing the supervision sparsity. Integrated with sample features through a dual-attention mechanism, those prompts empower each local task decoder to adeptly adjust to both the local distribution and the supervision form. Concurrently, a dual-decoder strategy, predicated on prompt similarity, is introduced for enhancing the generation of pseudo-labels in weakly-supervised learning, alleviating overfitting and noise accumulation inherent to local data, while an adaptable aggregation method is employed to customize the task decoder on a parameter-wise basis. Extensive experiments on four distinct medical image segmentation tasks involving different modalities underscore the superiority of FedLPPA, with its efficacy closely parallels that of fully supervised centralized training. Our code and data will be available.
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
Venue: arXiv cs.CV 2024
Year: 2024

Recently, the usage of Contrastive Representation Learning (CRL) as a pre-training technique improves the performance of learning with noisy labels (LNL) methods. However, instead of pre-training, when trivially combining CRL loss with LNL methods as an end-to-end framework, the empirical experiments show severe degeneration of the performance. We verify through experiments that this issue is caused by optimization conflicts of losses and propose an end-to-end \textbf{PLReMix} framework by introducing a Pseudo-Label Relaxed (PLR) contrastive loss. This PLR loss constructs a reliable negative set of each sample by filtering out its inappropriate negative pairs, alleviating the loss conflicts by trivially combining these losses. The proposed PLR loss is pluggable and we have integrated it into other LNL methods, observing their improved performance. Furthermore, a two-dimensional Gaussian Mixture Model is adopted to distinguish clean and noisy samples by leveraging semantic information and model outputs simultaneously. Experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed method. Code is available at \url{https://github.com/lxysl/PLReMix}.
Autonomous Shuttle Operation for Vulnerable Populations: Lessons and Experiences
Venue: arXiv cs.RO 2024
Year: 2024

The increasing shortage of drivers poses a significant threat to vulnerable populations, particularly seniors and disabled individuals who heavily depend on public transportation for accessing healthcare services and social events. Autonomous Vehicles (AVs) emerge as a promising alternative, offering potential improvements in accessibility and independence for these groups. However, current designs and studies often overlook the unique needs and experiences of these populations, leading to potential accessibility barriers. This paper presents a detailed case study of an autonomous shuttle test specifically tailored for seniors and disabled individuals, conducted during the early stages of the COVID-19 pandemic. The service, which lasted 13 weeks, catered to approximately 1500 passengers in an urban setting, aiming to facilitate access to essential services. Drawing from the safety operator's experiences and direct observations, we identify critical user experience and safety challenges faced by vulnerable passengers. Based on our findings, we propose targeted initiatives to enhance the safety, accessibility, and user education of AV technology for seniors and disabled individuals. These include increasing educational opportunities to familiarize these groups with AV technology, designing AVs with a focus on diversity and inclusion, and improving training programs for AV operators to address the unique needs of vulnerable populations. Through these initiatives, we aim to bridge the gap in AV accessibility and ensure that these technologies benefit all members of society.
CustomSketching: Sketch Concept Extraction for Sketch-based Image Synthesis and Editing
Venue: arXiv cs.CV 2024
Year: 2024

Personalization techniques for large text-to-image (T2I) models allow users to incorporate new concepts from reference images. However, existing methods primarily rely on textual descriptions, leading to limited control over customized images and failing to support fine-grained and local editing (e.g., shape, pose, and details). In this paper, we identify sketches as an intuitive and versatile representation that can facilitate such control, e.g., contour lines capturing shape information and flow lines representing texture. This motivates us to explore a novel task of sketch concept extraction: given one or more sketch-image pairs, we aim to extract a special sketch concept that bridges the correspondence between the images and sketches, thus enabling sketch-based image synthesis and editing at a fine-grained level. To accomplish this, we introduce CustomSketching, a two-stage framework for extracting novel sketch concepts. Considering that an object can often be depicted by a contour for general shapes and additional strokes for internal details, we introduce a dual-sketch representation to reduce the inherent ambiguity in sketch depiction. We employ a shape loss and a regularization loss to balance fidelity and editability during optimization. Through extensive experiments, a user study, and several applications, we show our method is effective and superior to the adapted baselines.
Confidence-Aware Multi-Field Model Calibration
Venue: arXiv cs.LG 2024
Year: 2024

Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the rapid shift of data distributions and intrinsic model biases. Calibration aims to address this issue by post-processing model predictions, and field-aware calibration can adjust model output on different feature field values to satisfy fine-grained advertising demands. Unfortunately, the observed samples corresponding to certain field values can be seriously limited to make confident calibrations, which may yield bias amplification and online disturbance. In this paper, we propose a confidence-aware multi-field calibration method, which adaptively adjusts the calibration intensity based on confidence levels derived from sample statistics. It also utilizes multiple fields for joint model calibration according to their importance to mitigate the impact of data sparsity on a single field. Extensive offline and online experiments show the superiority of our method in boosting advertising performance and reducing prediction deviations.
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
Venue: arXiv cs.CV 2024
Year: 2024

To address the problem of catastrophic forgetting due to the invisibility of old categories in sequential input, existing work based on relatively simple categorization tasks has made some progress. In contrast, video captioning is a more complex task in multimodal scenario, which has not been explored in the field of incremental learning. After identifying this stability-plasticity problem when analyzing video with sequential input, we originally propose a method to Mitigate Catastrophic Forgetting in class-incremental learning for multimodal Video Captioning (MCF-VC). As for effectively maintaining good performance on old tasks at the macro level, we design Fine-grained Sensitivity Selection (FgSS) based on the Mask of Linear's Parameters and Fisher Sensitivity to pick useful knowledge from old tasks. Further, in order to better constrain the knowledge characteristics of old and new tasks at the specific feature level, we have created the Two-stage Knowledge Distillation (TsKD), which is able to learn the new task well while weighing the old task. Specifically, we design two distillation losses, which constrain the cross modal semantic information of semantic attention feature map and the textual information of the final outputs respectively, so that the inter-model and intra-model stylized knowledge of the old class is retained while learning the new class. In order to illustrate the ability of our model to resist forgetting, we designed a metric CIDER_t to detect the stage forgetting rate. Our experiments on the public dataset MSR-VTT show that the proposed method significantly resists the forgetting of previous tasks without replaying old samples, and performs well on the new task.
Federated Learning for Estimating Heterogeneous Treatment Effects
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy making, education, and more. Current machine learning approaches for HTE require access to substantial amounts of data per treatment, and the high costs associated with interventions makes centrally collecting so much data for each intervention a formidable challenge. To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning. We show that even under a diversity of interventions and subject populations across clients, one can jointly learn a common feature representation, while concurrently and privately learning the specific predictive functions for outcomes under distinct interventions across institutions. Our framework and the associated algorithm are based on this insight, and leverage tabular transformers to map multiple input data to feature representations which are then used for outcome prediction via multi-task learning. We also propose a novel way of federated training of personalised transformers that can work with heterogeneous input feature spaces. Experimental results on real-world clinical trial data demonstrate the effectiveness of our method.
The SMART approach to instance-optimal online learning
Venue: arXiv cs.LG 2024
Year: 2024

We devise an online learning algorithm -- titled Switching via Monotone Adapted Regret Traces (SMART) -- that adapts to the data and achieves regret that is instance optimal, i.e., simultaneously competitive on every input sequence compared to the performance of the follow-the-leader (FTL) policy and the worst case guarantee of any other input policy. We show that the regret of the SMART policy on any input sequence is within a multiplicative factor $e/(e-1) \approx 1.58$ of the smaller of: 1) the regret obtained by FTL on the sequence, and 2) the upper bound on regret guaranteed by the given worst-case policy. This implies a strictly stronger guarantee than typical `best-of-both-worlds' bounds as the guarantee holds for every input sequence regardless of how it is generated. SMART is simple to implement as it begins by playing FTL and switches at most once during the time horizon to the worst-case algorithm. Our approach and results follow from an operational reduction of instance optimal online learning to competitive analysis for the ski-rental problem. We complement our competitive ratio upper bounds with a fundamental lower bound showing that over all input sequences, no algorithm can get better than a $1.43$-fraction of the minimum regret achieved by FTL and the minimax-optimal policy. We also present a modification of SMART that combines FTL with a ``small-loss" algorithm to achieve instance optimality between the regret of FTL and the small loss regret bound.
Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
Venue: arXiv cs.CL 2024
Year: 2024

While general-purpose large language models (LLMs) demonstrate proficiency on multiple tasks within the domain of translation, approaches based on open LLMs are competitive only when specializing on a single task. In this paper, we propose a recipe for tailoring LLMs to multiple tasks present in translation workflows. We perform continued pretraining on a multilingual mixture of monolingual and parallel data, creating TowerBase, followed by finetuning on instructions relevant for translation processes, creating TowerInstruct. Our final model surpasses open alternatives on several tasks relevant to translation workflows and is competitive with general-purpose closed LLMs. To facilitate future research, we release the Tower models, our specialization dataset, an evaluation framework for LLMs focusing on the translation ecosystem, and a collection of model generations, including ours, on our benchmark.
ADL4D: Towards A Contextually Rich Dataset for 4D Activities of Daily Living
Venue: arXiv cs.CV 2024
Year: 2024

Hand-Object Interactions (HOIs) are conditioned on spatial and temporal contexts like surrounding objects, previous actions, and future intents (for example, grasping and handover actions vary greatly based on objects proximity and trajectory obstruction). However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject interacting with one object only. This restricts the generalization of learning-based HOI methods trained on those datasets. We introduce ADL4D, a dataset of up to two subjects interacting with different sets of objects performing Activities of Daily Living (ADL) like breakfast or lunch preparation activities. The transition between multiple objects to complete a certain task over time introduces a unique context lacking in existing datasets. Our dataset consists of 75 sequences with a total of 1.1M RGB-D frames, hand and object poses, and per-hand fine-grained action annotations. We develop an automatic system for multi-view multi-hand 3D pose annotation capable of tracking hand poses over time. We integrate and test it against publicly available datasets. Finally, we evaluate our dataset on the tasks of Hand Mesh Recovery (HMR) and Hand Action Segmentation (HAS).
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Venue: arXiv cs.CL 2024
Year: 2024

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.
Factors that Affect Personalization of Robots for Older Adults
Venue: arXiv cs.RO 2024
Year: 2024

We introduce a taxonomy of important factors to consider when designing interactions with an assistive robot in a senior living facility. These factors are derived from our reflection on two field studies and are grouped into the following high-level categories: primary user (residents), care partners, robot, facility and external circumstances. We outline how multiple factors in these categories impact different aspects of personalization, such as adjusting interactions based on the unique needs of a resident or modifying alerts about the robot's status for different care partners. This preliminary taxonomy serves as a framework for considering how to deploy personalized assistive robots in the complex caregiving ecosystem.
Predicting machine failures from multivariate time series: an industrial case study
Venue: arXiv cs.LG 2024
Year: 2024

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets concerning the operation of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. The problem is formulated as a binary classification task that assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate telemetry time series. The results indicate that, in the considered scenarios, the dimension of the prediction windows plays a crucial role and highlight the effectiveness of DL approaches at classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches at classifying similar and repetitive patterns preceding a failure.
Stable LM 2 1.6B Technical Report
Venue: arXiv cs.CL 2024
Year: 2024

We introduce StableLM 2 1.6B, the first in a new generation of our language model series. In this technical report, we present in detail the data and training procedure leading to the base and instruction-tuned versions of StableLM 2 1.6B. The weights for both models are available via Hugging Face for anyone to download and use. The report contains thorough evaluations of these models, including zero- and few-shot benchmarks, multilingual benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time of publishing this report, StableLM 2 1.6B was the state-of-the-art open model under 2B parameters by a significant margin. Given its appealing small size, we also provide throughput measurements on a number of edge devices. In addition, we open source several quantized checkpoints and provide their performance metrics compared to the original model.
End-User Development for Human-Robot Interaction
Venue: arXiv cs.RO 2024
Year: 2024

End-user development (EUD) represents a key step towards making robotics accessible for experts and nonexperts alike. Within academia, researchers investigate novel ways that EUD tools can capture, represent, visualize, analyze, and test developer intent. At the same time, industry researchers increasingly build and ship programming tools that enable customers to interact with their robots. However, despite this growing interest, the role of EUD within HRI is not well defined. EUD struggles to situate itself within a growing array of alternative approaches to application development, such as robot learning and teleoperation. EUD further struggles due to the wide range of individuals who can be considered end users, such as independent third-party application developers, consumers, hobbyists, or even employees of the robot manufacturer. Key questions remain such as how EUD is justified over alternate approaches to application development, which contexts EUD is most suited for, who the target users of an EUD system are, and where interaction between a human and a robot takes place, amongst many other questions. We seek to address these challenges and questions by organizing the first End-User Development for Human-Robot Interaction (EUD4HRI) workshop at the 2024 International Conference of Human-Robot Interaction. The workshop will bring together researchers with a wide range of expertise across academia and industry, spanning perspectives from multiple subfields of robotics, with the primary goal being a consensus of perspectives about the role that EUD must play within human-robot interaction.
Adversarial Math Word Problem Generation
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have significantly transformed the educational landscape. As current plagiarism detection tools struggle to keep pace with LLMs' rapid advancements, the educational community faces the challenge of assessing students' true problem-solving abilities in the presence of LLMs. In this work, we explore a new paradigm for ensuring fair evaluation -- generating adversarial examples which preserve the structure and difficulty of the original questions aimed for assessment, but are unsolvable by LLMs. Focusing on the domain of math word problems, we leverage abstract syntax trees to structurally generate adversarial examples that cause LLMs to produce incorrect answers by simply editing the numeric values in the problems. We conduct experiments on various open- and closed-source LLMs, quantitatively and qualitatively demonstrating that our method significantly degrades their math problem-solving ability. We identify shared vulnerabilities among LLMs and propose a cost-effective approach to attack high-cost models. Additionally, we conduct automatic analysis to investigate the cause of failure, providing further insights into the limitations of LLMs.
Inducing Generalization across Languages and Tasks using Featurized Low-Rank Mixtures
Venue: arXiv cs.CL 2024
Year: 2024

Adapting pretrained large language models (LLMs) to various downstream tasks in tens or hundreds of human languages is computationally expensive. Parameter-efficient fine-tuning (PEFT) significantly reduces the adaptation cost, by tuning only a small amount of parameters. However, common PEFT methods LoRA (Hu et al., 2022) suffer from suboptimal performance on diverse dataset mixtures, due to aggressive parameter tying and negative interference among different datasets. In this work, we propose Featurized Low-rank Mixtures (FLix), a novel PEFT method designed for effective multitask multilingual adaptation. FLix associates each unique dataset feature, such as the dataset's language or task, with its own low-rank weight update parameters. By composing feature-specific parameters for each dataset, FLix can accommodate diverse dataset mixtures and generalize better to unseen datasets. Our experiments show that FLix leads to significant improvements over a variety of tasks for both supervised learning and zero-shot settings with gains of up to $14.2$ inexact match points in zero-shot semantic parsing.