OpenRaiser/Intern-Atlas huggingface dataset:
On Principled Local Optimization Methods for Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Federated Learning (FL), a distributed learning paradigm that scales on-device learning collaboratively, has emerged as a promising approach for decentralized AI applications. Local optimization methods such as Federated Averaging (FedAvg) are the most prominent methods for FL applications. Despite their simplicity and popularity, the theoretical understanding of local optimization methods is far from clear. This dissertation aims to advance the theoretical foundation of local methods in the following three directions. First, we establish sharp bounds for FedAvg, the most popular algorithm in Federated Learning. We demonstrate how FedAvg may suffer from a notion we call iterate bias, and how an additional third-order smoothness assumption may mitigate this effect and lead to better convergence rates. We explain this phenomenon from a Stochastic Differential Equation (SDE) perspective. Second, we propose Federated Accelerated Stochastic Gradient Descent (FedAc), the first principled acceleration of FedAvg, which provably improves the convergence rate and communication efficiency. Our technique uses on a potential-based perturbed iterate analysis, a novel stability analysis of generalized accelerated SGD, and a strategic tradeoff between acceleration and stability. Third, we study the Federated Composite Optimization problem, which extends the classic smooth setting by incorporating a shared non-smooth regularizer. We show that direct extensions of FedAvg may suffer from the "curse of primal averaging," resulting in slow convergence. As a solution, we propose a new primal-dual algorithm, Federated Dual Averaging, which overcomes the curse of primal averaging by employing a novel inter-client dual averaging procedure.
LPNL: Scalable Link Prediction with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs.
"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science
Venue: arXiv cs.CY 2024
Year: 2024

The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on samples of abstracts from preprint servers and compare against the volume of public discussions on Twitter mentioning these papers. We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter, thus producing a false consensus effect. This transpires with favorable papers being disproportionately amplified, along with an influx of new anti-consensus user sign-ups. Finally, our content analysis highlights that anti-consensus users misrepresent scientific findings or question scientists' integrity in their efforts to substantiate their claims.
Designing Redistribution Mechanisms for Reducing Transaction Fees in Blockchains
Venue: arXiv cs.GT 2024
Year: 2024

Blockchains deploy Transaction Fee Mechanisms (TFMs) to determine which user transactions to include in blocks and determine their payments (i.e., transaction fees). Increasing demand and scarce block resources have led to high user transaction fees. As these blockchains are a public resource, it may be preferable to reduce these transaction fees. To this end, we introduce Transaction Fee Redistribution Mechanisms (TFRMs) -- redistributing VCG payments collected from such TFM as rebates to minimize transaction fees. Classic redistribution mechanisms (RMs) achieve this while ensuring Allocative Efficiency (AE) and User Incentive Compatibility (UIC). Our first result shows the non-triviality of applying RM in TFMs. More concretely, we prove that it is impossible to reduce transaction fees when (i) transactions that are not confirmed do not receive rebates and (ii) the miner can strategically manipulate the mechanism. Driven by this, we propose \emph{Robust} TFRM (\textsf{R-TFRM}): a mechanism that compromises on an honest miner's individual rationality to guarantee strictly positive rebates to the users. We then introduce \emph{robust} and \emph{rational} TFRM (\textsf{R}$^2$\textsf{-TFRM}) that uses trusted on-chain randomness that additionally guarantees miner's individual rationality (in expectation) and strictly positive rebates. Our results show that TFRMs provide a promising new direction for reducing transaction fees in public blockchains.
Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques
Venue: arXiv cs.LG 2024
Year: 2024

Background and purpose: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. Methods: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. Results: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. Conclusions: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%. Keywords: Bagging; Multilayer Perceptron; Naive Bayes classifier; clinically isolated syndrome; diffusion tensor imaging; machine-learning; magnetic resonance imaging; multiple sclerosis; radiologically isolated syndrome.
POSTER: Towards Secure 5G Infrastructures for Production Systems
Venue: arXiv cs.CR 2024
Year: 2024

To meet the requirements of modern production, industrial communication increasingly shifts from wired fieldbus to wireless 5G communication. Besides tremendous benefits, this shift introduces severe novel risks, ranging from limited reliability over new security vulnerabilities to a lack of accountability. To address these risks, we present approaches to (i) prevent attacks through authentication and redundant communication, (ii) detect anomalies and jamming, and (iii) respond to detected attacks through device exclusion and accountability measures.
Rank-decreasing transductions
Venue: arXiv cs.LO 2024
Year: 2024

We propose to study transformations on graphs, and more generally structures, by looking at how the cut-rank (as introduced by Oum) of subsets is affected when going from the input structure to the output structure. We consider transformations in which the underlying sets are the same for both the input and output, and so the cut-ranks of subsets can be easily compared. The purpose of this paper is to give a characterisation of logically defined transductions that is expressed in purely structural terms, without referring to logic: transformations which decrease the cut-rank, in the asymptotic sense, are exactly those that can be defined in monadic second-order logic. This characterisation assumes that the transduction has inputs of bounded treewidth; we also show that the characterisation fails in the absence of any assumptions.
Evaluation of depth perception in crowded volumes
Venue: arXiv cs.GR 2024
Year: 2024

Depth perception in volumetric visualization plays a crucial role in the understanding and interpretation of volumetric data. Numerous visualization techniques, many of which rely on physically based optical effects, promise to improve depth perception but often do so without considering camera movement or the content of the volume. As a result, the findings from previous studies may not be directly applicable to crowded volumes, where a large number of contained structures disrupts spatial perception. Crowded volumes therefore require special analysis and visualization tools with sparsification capabilities. Interactivity is an integral part of visualizing and exploring crowded spaces, but has received little attention in previous studies. To address this gap, we conducted a study to assess the impact of different rendering techniques on depth perception in crowded volumes, with a particular focus on the effects of camera movement. The results show that depth perception considering camera motion depends much more on the content of the volume than on the chosen visualization technique. Furthermore, we found that traditional rendering techniques, which have often performed poorly in previous studies, showed comparable performance to physically based methods in our study.
EndoGaussians: Single View Dynamic Gaussian Splatting for Deformable Endoscopic Tissues Reconstruction
Venue: arXiv cs.CV 2024
Year: 2024

The accurate 3D reconstruction of deformable soft body tissues from endoscopic videos is a pivotal challenge in medical applications such as VR surgery and medical image analysis. Existing methods often struggle with accuracy and the ambiguity of hallucinated tissue parts, limiting their practical utility. In this work, we introduce EndoGaussians, a novel approach that employs Gaussian Splatting for dynamic endoscopic 3D reconstruction. This method marks the first use of Gaussian Splatting in this context, overcoming the limitations of previous NeRF-based techniques. Our method sets new state-of-the-art standards, as demonstrated by quantitative assessments on various endoscope datasets. These advancements make our method a promising tool for medical professionals, offering more reliable and efficient 3D reconstructions for practical applications in the medical field.
Memoryless Strategies in Stochastic Reachability Games
Venue: arXiv cs.LO 2024
Year: 2024

We study concurrent stochastic reachability games played on finite graphs. Two players, Max and Min, seek respectively to maximize and minimize the probability of reaching a set of target states. We prove that Max has a memoryless strategy that is optimal from all states that have an optimal strategy. Our construction provides an alternative proof of this result by Bordais, Bouyer and Le Roux, and strengthens it, as we allow Max's action sets to be countably infinite.
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Venue: arXiv cs.CV 2024
Year: 2024

Current datasets for action recognition tasks face limitations stemming from traditional collection and generation methods, including the constrained range of action classes, absence of multi-viewpoint recordings, limited diversity, poor video quality, and labor-intensive manually collection. To address these challenges, we introduce GTAutoAct, a innovative dataset generation framework leveraging game engine technology to facilitate advancements in action recognition. GTAutoAct excels in automatically creating large-scale, well-annotated datasets with extensive action classes and superior video quality. Our framework's distinctive contributions encompass: (1) it innovatively transforms readily available coordinate-based 3D human motion into rotation-orientated representation with enhanced suitability in multiple viewpoints; (2) it employs dynamic segmentation and interpolation of rotation sequences to create smooth and realistic animations of action; (3) it offers extensively customizable animation scenes; (4) it implements an autonomous video capture and processing pipeline, featuring a randomly navigating camera, with auto-trimming and labeling functionalities. Experimental results underscore the framework's robustness and highlights its potential to significantly improve action recognition model training.
Model Predictive Wave Disturbance Rejection for Underwater Soft Robotic Manipulators
Venue: arXiv cs.RO 2024
Year: 2024

Inspired by the octopus and other animals living in water, soft robots should naturally lend themselves to underwater operations, as supported by encouraging validations in deep water scenarios. This work deals with equipping soft arms with the intelligence necessary to move precisely in wave-dominated environments, such as shallow waters where marine renewable devices are located. This scenario is substantially more challenging than calm deep water since, at low operational depths, hydrodynamic wave disturbances can represent a significant impediment. We propose a control strategy based on Nonlinear Model Predictive Control that can account for wave disturbances explicitly, optimising control actions by considering an estimate of oncoming hydrodynamic loads. The proposed strategy is validated through a set of tasks covering set-point regulation, trajectory tracking and mechanical failure compensation, all under a broad range of varying significant wave heights and peak spectral periods. The proposed control methodology displays positional error reductions as large as 84% with respect to a baseline controller, proving the effectiveness of the method. These initial findings present a first step in the development and deployment of soft manipulators for performing tasks in hazardous water environments.
Can GPT-3.5 Generate and Code Discharge Summaries?
Venue: arXiv cs.CL 2024
Year: 2024

Objective: To investigate GPT-3.5 in generating and coding medical documents with ICD-10 codes for data augmentation on low-resources labels. Materials and Methods: Employing GPT-3.5 we generated and coded 9,606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on a MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices were employed to determine within-family and outside-of-family coding errors in the latter codesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided self-generated data and real MIMIC-IV data. Clinical professionals evaluated the clinical acceptability of the generated documents. Results: Augmentation slightly hinders the overall performance of the models but improves performance for the generation candidate codes and their families, including one unseen in the baseline training data. Augmented models display lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the prompted descriptions, but performs poorly on real data. Evaluators note the correctness of generated concepts while suffering in variety, supporting information, and narrative. Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding. Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors. Discharge summaries generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives. They are unsuitable for clinical practice.
Dynamic Risk Management in Cyber Physical Systems
Venue: arXiv cs.SE 2024
Year: 2024

Cyber Physical Systems (CPS) enable new kinds of applications as well as significant improvements of existing ones in numerous different application domains. A major trait of upcoming CPS is an increasing degree of automation up to the point of autonomy, as there is a huge potential for economic success as well as for ecologic and societal improvements. However, to unlock the full potential of such (cooperative and automated) CPS, we first need to overcome several significant engineering challenges, where safety assurance is a particularly important one. Unfortunately, established safety assurance methods and standards do not live up to this task, as they have been designed with closed and less complex systems in mind. This paper structures safety assurance challenges of cooperative automated CPS, provides an overview on our vision of dynamic risk management and describes already existing building blocks.
Large Malaysian Language Model Based on Mistral for Enhanced Local Language Understanding
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we present significant advancements in the pretraining of Mistral 7B, a large-scale language model, using a dataset of 32.6 GB, equivalent to 1.1 billion tokens. We explore the impact of extending the context length, releasing models with context lengths of 4096 and 32768 tokens, and further refining performance with a specialized 16384 context length instruction-tuned model, we called it Malaysian Mistral. Our experiments demonstrate the efficacy of continue pretraining and the influence of extended context lengths on Mistral 7B's language understanding capabilities. Additionally, we release a model specifically tuned with a 16384 context length instruction, showcasing its potential for capturing nuanced language intricacies. Furthermore, our research contributes to the benchmarking of Malaysian Mistral against prominent language models, including ChatGPT3.5 and Claude 2. We present compelling results indicating Malaysian Mistral's superior performance on Tatabahasa (Malay grammar) test set, particularly when fine-tuned with instructions. All models released at https://huggingface.co/collections/mesolitica/malaysian-mistral-7b-6528f2ec825f4bba46c1700c
SPARC-LoRa: A Scalable, Power-efficient, Affordable, Reliable, and Cloud Service-enabled LoRa Networking System for Agriculture Applications
Venue: arXiv cs.NI 2024
Year: 2024

With the rapid development of cloud and edge computing, Internet of Things (IoT) applications have been deployed in various aspects of human life. In this paper, we design and implement a holistic LoRa-based IoT system with LoRa communication capabilities, named SPARC-LoRa, which consists of field sensor nodes and a gateway connected to the Internet. SPARC-LoRa has the following important features. First, the proposed wireless network of SPARC-LoRa is even-driven and using off-the-shelf microcontroller and LoRa communication modules with a customized PCB design to integrate all the hardware. This enables SPARC-LoRa to achieve low power consumption, long range communication, and low cost. With a new connection-based upper layer protocol design, the scalability and communication reliability of SPARC-loRa can be achieved. Second, an open source software including sensor nodes and servers is designed based on Docker container with cloud storage, computing, and LTE functionalities. In order to achieve reliable wireless communication under extreme conditions, a relay module is designed and applied to SPARC-LoRa to forward the data from sensor nodes to the gateway node. The system design and implementation is completely open source and hosted on the DigitalOcean Droplet Cloud. Hence, the proposed system enables further research and applications in both academia and industry. The proposed system has been tested in real fields under different and extreme environmental conditions in Salt Lake City, Utah and the University of Nebraska-Lincoln. The experimental results validate the features of SPARC-LoRa including low power, reliability, and cloud services provided by SPARC-LoRa.
Distributed matrix multiplication with straggler tolerance using algebraic function fields
Venue: arXiv cs.IT 2024
Year: 2024

The problem of straggler mitigation in distributed matrix multiplication (DMM) is considered for a large number of worker nodes and a fixed small finite field. Polynomial codes and matdot codes are generalized by making use of algebraic function fields (i.e., algebraic functions over an algebraic curve) over a finite field. The construction of optimal solutions is translated to a combinatorial problem on the Weierstrass semigroups of the corresponding algebraic curves. Optimal or almost optimal solutions are provided. These have the same computational complexity per worker as classical polynomial and matdot codes, and their recovery thresholds are almost optimal in the asymptotic regime (growing number of workers and a fixed finite field).
CNN architecture extraction on edge GPU
Venue: arXiv cs.CR 2024
Year: 2024

Neural networks have become popular due to their versatility and state-of-the-art results in many applications, such as image classification, natural language processing, speech recognition, forecasting, etc. These applications are also used in resource-constrained environments such as embedded devices. In this work, the susceptibility of neural network implementations to reverse engineering is explored on the NVIDIA Jetson Nano microcomputer via side-channel analysis. To this end, an architecture extraction attack is presented. In the attack, 15 popular convolutional neural network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is analyzed during the inference operation of the neural networks. The results of the analysis show that neural network architectures are easily distinguishable using deep learning-based side-channel analysis.