OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Low-Resolution Face Recognition via Bridge Distillation
Venue: arXiv cs.CV 2024
Year: 2024

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, ~934 and 763 faces per second on GPU, CPU and mobile phone, respectively.
Empowering Visual Artists with Tokenized Digital Assets with NFTs
Venue: arXiv cs.CR 2024
Year: 2024

The Non-Fungible Tokens (NFTs) has the transformative impact on the visual arts industry by examining the nexus between empowering art practices and leveraging blockchain technology. First, we establish the context for this study by introducing some basic but critical technological aspects and affordances of the blockchain domain. Second, we revisit the creative practices involved in producing traditional artwork, covering various types, production processes, trading, and monetization methods. Third, we introduce and define the key fundamentals of the blockchain ecosystem, including its structure, consensus algorithms, smart contracts, and digital wallets. Fourth, we narrow the focus to NFTs, detailing their history, mechanics, lifecycle, and standards, as well as their application in the art world. In particular, we outline the key processes for minting and trading NFTs in various marketplaces and discuss the relevant market dynamics and pricing. We also consider major security concerns, such as wash trading, to underscore some of the central cybersecurity issues facing this domain. Finally, we conclude by considering future research directions, emphasizing improvements in user experience, security, and privacy. Through this innovative research overview, which includes input from creative industry and cybersecurity sdomain expertise, we offer some new insights into how NFTs can empower visual artists and reshape the wider copyright industries.
Age-of-Information and Energy Optimization in Digital Twin Edge Networks
Venue: arXiv cs.NI 2024
Year: 2024

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.
A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems
Venue: arXiv cs.SE 2024
Year: 2024

The development of Machine Learning (ML)- and, more recently, of Deep Learning (DL)-intensive systems requires suitable choices, e.g., in terms of technology, algorithms, and hyper-parameters. Such choices depend on developers' experience, as well as on proper experimentation. Due to limited time availability, developers may adopt suboptimal, sometimes temporary choices, leading to a technical debt (TD) specifically related to the ML code. This paper empirically analyzes the presence of Self-Admitted Technical Debt (SATD) in DL systems. After selecting 100 open-source Python projects using popular DL frameworks, we identified SATD from their source comments and created a stratified sample of 443 SATD to analyze manually. We derived a taxonomy of DL-specific SATD through open coding, featuring seven categories and 41 leaves. The identified SATD categories pertain to different aspects of DL models, some of which are technological (e.g., due to hardware or libraries) and some related to suboptimal choices in the DL process, model usage, or configuration. Our findings indicate that DL-specific SATD differs from DL bugs found in previous studies, as it typically pertains to suboptimal solutions rather than functional (\eg blocking) problems. Last but not least, we found that state-of-the-art static analysis tools do not help developers avoid such problems, and therefore, specific support is needed to cope with DL-specific SATD.
Law-based and standards-oriented approach for privacy impact assessment in medical devices: a topic for lawyers, engineers and healthcare practitioners in MedTech
Venue: arXiv cs.CY 2024
Year: 2024

Background: The integration of the General Data Protection Regulation (GDPR) and the Medical Device Regulation (MDR) creates complexities in conducting Data Protection Impact Assessments (DPIAs) for medical devices. The adoption of non-binding standards like ISO and IEC can harmonize these processes by enhancing accountability and privacy by design. Methods: This study employs a multidisciplinary literature review, focusing on GDPR and MDR intersection in medical devices that process personal health data. It evaluates key standards, including ISO/IEC 29134 and IEC 62304, to propose a unified approach for DPIAs that aligns with legal and technical frameworks. Results: The analysis reveals the benefits of integrating ISO/IEC standards into DPIAs, which provide detailed guidance on implementing privacy by design, risk assessment, and mitigation strategies specific to medical devices. The proposed framework ensures that DPIAs are living documents, continuously updated to adapt to evolving data protection challenges. Conclusions: A unified approach combining European Union (EU) regulations and international standards offers a robust framework for conducting DPIAs in medical devices. This integration balances security, innovation, and privacy, enhancing compliance and fostering trust in medical technologies. The study advocates for leveraging both hard law and standards to systematically address privacy and safety in the design and operation of medical devices, thereby raising the maturity of the MedTech ecosystem.
Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network
Venue: arXiv cs.LG 2024
Year: 2024

Electrification of vehicles is a potential way of reducing fossil fuel usage and thus lessening environmental pollution. Electric Vehicles (EVs) of various types for different transport modes (including air, water, and land) are evolving. Moreover, different EV user groups (commuters, commercial or domestic users, drivers) may use different charging infrastructures (public, private, home, and workplace) at various times. Therefore, usage patterns and energy demand are very stochastic. Characterizing and forecasting the charging demand of these diverse EV usage profiles is essential in preventing power outages. Previously developed data-driven load models are limited to specific use cases and locations. None of these models are simultaneously adaptive enough to transfer knowledge of day-ahead forecasting among EV charging sites of diverse locations, trained with limited data, and cost-effective. This article presents a location-based load forecasting of EV charging sites using a deep Multi-Quantile Temporal Convolutional Network (MQ-TCN) to overcome the limitations of earlier models. We conducted our experiments on data from four charging sites, namely Caltech, JPL, Office-1, and NREL, which have diverse EV user types like students, full-time and part-time employees, random visitors, etc. With a Prediction Interval Coverage Probability (PICP) score of 93.62\%, our proposed deep MQ-TCN model exhibited a remarkable 28.93\% improvement over the XGBoost model for a day-ahead load forecasting at the JPL charging site. By transferring knowledge with the inductive Transfer Learning (TL) approach, the MQ-TCN model achieved a 96.88\% PICP score for the load forecasting task at the NREL site using only two weeks of data.
LLMs in Education: Novel Perspectives, Challenges, and Opportunities
Venue: arXiv cs.CL 2024
Year: 2024

The role of large language models (LLMs) in education is an increasing area of interest today, considering the new opportunities they offer for teaching, learning, and assessment. This cutting-edge tutorial provides an overview of the educational applications of NLP and the impact that the recent advances in LLMs have had on this field. We will discuss the key challenges and opportunities presented by LLMs, grounding them in the context of four major educational applications: reading, writing, and speaking skills, and intelligent tutoring systems (ITS). This COLING 2025 tutorial is designed for researchers and practitioners interested in the educational applications of NLP and the role LLMs have to play in this area. It is the first of its kind to address this timely topic.
Generation of Complex 3D Human Motion by Temporal and Spatial Composition of Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we address the challenge of generating realistic 3D human motions for action classes that were never seen during the training phase. Our approach involves decomposing complex actions into simpler movements, specifically those observed during training, by leveraging the knowledge of human motion contained in GPTs models. These simpler movements are then combined into a single, realistic animation using the properties of diffusion models. Our claim is that this decomposition and subsequent recombination of simple movements can synthesize an animation that accurately represents the complex input action. This method operates during the inference phase and can be integrated with any pre-trained diffusion model, enabling the synthesis of motion classes not present in the training data. We evaluate our method by dividing two benchmark human motion datasets into basic and complex actions, and then compare its performance against the state-of-the-art.
Reinforcement Learning with Lie Group Orientations for Robotics
Venue: arXiv cs.RO 2024
Year: 2024

Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations, especially in learning pipelines involving, for example, artificial neural networks. In this paper, we investigate reinforcement learning with orientations and propose a simple modification of the network's input and output that adheres to the Lie group structure of orientations. As a result, we obtain an easy and efficient implementation that is directly usable with existing learning libraries and achieves significantly better performance than other common orientation representations. We briefly introduce Lie theory specifically for orientations in robotics to motivate and outline our approach. Subsequently, a thorough empirical evaluation of different combinations of orientation representations for states and actions demonstrates the superior performance of our proposed approach in different scenarios, including: direct orientation control, end effector orientation control, and pick-and-place tasks.
Differentiable Collision-Supervised Tooth Arrangement Network with a Decoupling Perspective
Venue: arXiv cs.CV 2024
Year: 2024

Tooth arrangement is an essential step in the digital orthodontic planning process. Existing learning-based methods use hidden teeth features to directly regress teeth motions, which couples target pose perception and motion regression. It could lead to poor perceptions of three-dimensional transformation. They also ignore the possible overlaps or gaps between teeth of predicted dentition, which is generally unacceptable. Therefore, we propose DTAN, a differentiable collision-supervised tooth arrangement network, decoupling predicting tasks and feature modeling. DTAN decouples the tooth arrangement task by first predicting the hidden features of the final teeth poses and then using them to assist in regressing the motions between the beginning and target teeth. To learn the hidden features better, DTAN also decouples the teeth-hidden features into geometric and positional features, which are further supervised by feature consistency constraints. Furthermore, we propose a novel differentiable collision loss function for point cloud data to constrain the related gestures between teeth, which can be easily extended to other 3D point cloud tasks. We propose an arch-width guided tooth arrangement network, named C-DTAN, to make the results controllable. We construct three different tooth arrangement datasets and achieve drastically improved performance on accuracy and speed compared with existing methods.
Research Citations Building Trust in Wikipedia
Venue: arXiv cs.DL 2024
Year: 2024

The use of Wikipedia citations in scholarly research has been the topic of much inquiry over the past decade. A cross-publisher study (Taylor & Francis and University of Michigan Press) convened by Digital Science was established in late 2022 to explore author sentiment towards Wikipedia as a trusted source of information. A short survey was designed to poll published authors about views and uses of Wikipedia and explore how the increased addition of research citations in Wikipedia might help combat misinformation in the context of increasing public engagement with and access to validated research sources. With 21,854 surveys sent, targeting 40,402 papers mentioned in Wikipedia, a total of 750 complete surveys from 60 countries were included in this analysis. In general, responses revealed a positive sentiment towards research citation in Wikipedia and the researcher engagement practices. However, our sub analysis revealed statistically significant differences when comparison articles vs books and across disciplines, but not open vs closed access. This study will open the door to further research and deepen our understanding of authors perceived trustworthiness of the representation of their research in Wikipedia.
Sampling Latent Material-Property Information From LLM-Derived Embedding Representations
Venue: arXiv cs.CL 2024
Year: 2024

Vector embeddings derived from large language models (LLMs) show promise in capturing latent information from the literature. Interestingly, these can be integrated into material embeddings, potentially useful for data-driven predictions of materials properties. We investigate the extent to which LLM-derived vectors capture the desired information and their potential to provide insights into material properties without additional training. Our findings indicate that, although LLMs can be used to generate representations reflecting certain property information, extracting the embeddings requires identifying the optimal contextual clues and appropriate comparators. Despite this restriction, it appears that LLMs still have the potential to be useful in generating meaningful materials-science representations.
On Randomized Computational Models and Complexity Classes: a Historical Overview
Venue: arXiv cs.LO 2024
Year: 2024

Since their appearance in the 1950s, computational models capable of performing probabilistic choices have received wide attention and are nowadays pervasive in almost every areas of computer science. Their development was also inextricably linked with inquiries about computation power and resource issues. Although most crucial notions in the field are well-known, the related terminology is sometimes imprecise or misleading. The present work aims to clarify the core features and main differences between machines and classes developed in relation to randomized computation. To do so, we compare the modern definitions with original ones, recalling the context in which they first appeared, and investigate the relations linking probabilistic and counting models.
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
Venue: arXiv cs.CV 2024
Year: 2024

Significant work has been conducted in the domain of food computing, yet these studies typically focus on single tasks such as t2t (instruction generation from food titles and ingredients), i2t (recipe generation from food images), or t2i (food image generation from recipes). None of these approaches integrate all modalities simultaneously. To address this gap, we introduce a novel food computing foundation model that achieves true multimodality, encompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. By leveraging large language models (LLMs) and pre-trained image encoder and decoder models, our model can perform a diverse array of food computing-related tasks, including food understanding, food recognition, recipe generation, and food image generation. Compared to previous models, our foundation model demonstrates a significantly broader range of capabilities and exhibits superior performance, particularly in food image generation and recipe generation tasks. We open-sourced ChefFusion at GitHub.
Mixture of Prompt Learning for Vision Language Models
Venue: arXiv cs.CV 2024
Year: 2024

As powerful pre-trained vision-language models (VLMs) like CLIP gain prominence, numerous studies have attempted to combine VLMs for downstream tasks. Among these, prompt learning has been validated as an effective method for adapting to new tasks, which only requiring a small number of parameters. However, current prompt learning methods face two challenges: first, a single soft prompt struggles to capture the diverse styles and patterns within a dataset; second, fine-tuning soft prompts is prone to overfitting. To address these challenges, we propose a mixture of soft prompt learning method incorporating a routing module. This module is able to capture a dataset's varied styles and dynamically selects the most suitable prompts for each instance. Additionally, we introduce a novel gating mechanism to ensure the router selects prompts based on their similarity to hard prompt templates, which both retaining knowledge from hard prompts and improving selection accuracy. We also implement semantically grouped text-level supervision, initializing each soft prompt with the token embeddings of manually designed templates from its group and applied a contrastive loss between the resulted text feature and hard prompt encoded text feature. This supervision ensures that the text features derived from soft prompts remain close to those from their corresponding hard prompts, preserving initial knowledge and mitigating overfitting. Our method has been validated on 11 datasets, demonstrating evident improvements in few-shot learning, domain generalization, and base-to-new generalization scenarios compared to existing baselines. The code will be available at \url{https://anonymous.4open.science/r/mocoop-6387}
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization
Venue: arXiv cs.SE 2024
Year: 2024

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings capable of leveraging valuable knowledge from distributed and isolated datasets is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, demonstrating the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, highlighting their potential risks in leaking data. Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaboratively trained code models. We show that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating privacy or copyright. Our study further explores effectiveness and memorization patterns in incremental learning, emphasizing the sequence in which individual participant datasets are introduced. We also identify cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with recommendations for practitioners and researchers to optimize multisource datasets, propelling cross-organizational collaboration forward.
PhysMamba: Efficient Remote Physiological Measurement with SlowFast Temporal Difference Mamba
Venue: arXiv cs.CV 2024
Year: 2024

Facial-video based Remote photoplethysmography (rPPG) aims at measuring physiological signals and monitoring heart activity without any contact, showing significant potential in various applications. Previous deep learning based rPPG measurement are primarily based on CNNs and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range spatio-temporal dependencies, while Transformers also struggle with modeling long video sequences with high complexity. Recently, the state space models (SSMs) represented by Mamba are known for their impressive performance on capturing long-range dependencies from long sequences. In this paper, we propose the PhysMamba, a Mamba-based framework, to efficiently represent long-range physiological dependencies from facial videos. Specifically, we introduce the Temporal Difference Mamba block to first enhance local dynamic differences and further model the long-range spatio-temporal context. Moreover, a dual-stream SlowFast architecture is utilized to fuse the multi-scale temporal features. Extensive experiments are conducted on three benchmark datasets to demonstrate the superiority and efficiency of PhysMamba. The codes are available at https://github.com/Chaoqi31/PhysMamba