OpenRaiser/Intern-Atlas huggingface dataset:
SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data
Venue: arXiv cs.LG 2024
Year: 2024

In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. In order to balance the data distribution before model training, oversampling methods are often used to generate data for a small number of classes to solve the problem of classifying unbalanced data. Most of the classical oversampling methods are based on the SMOTE technique, which only focuses on the local information of the data, and therefore the generated data may have the problem of not being realistic enough. In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise. problem of poor noise removal. In order to overcome the above problems, we propose a novel oversampling method SEMRes-DDPM.In the SEMRes-DDPM backward diffusion process, a new neural network structure SEMST-ResNet is used, which is suitable for tabular data and has good noise removal effect, and it can generate tabular data with higher quality. Experiments show that the SEMResNet network removes noise better than MLP; SEMRes-DDPM generates data distributions that are closer to the real data distributions than TabDDPM with CWGAN-GP; on 20 real unbalanced tabular datasets with 9 classification models, SEMRes-DDPM improves the quality of the generated tabular data in terms of three evaluation metrics (F1, G-mean, AUC) with better classification performance than other SOTA oversampling methods.
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
Venue: arXiv cs.CL 2024
Year: 2024

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes.
Can Generative Models Improve Self-Supervised Representation Learning?
Venue: arXiv cs.CV 2024
Year: 2024

The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the existing techniques, particularly those employing different augmentations of the same image, often rely on a limited set of simple transformations that cannot fully capture variations in the real world. This constrains the diversity and quality of samples, which leads to sub-optimal representations. In this paper, we introduce a framework that enriches the self-supervised learning (SSL) paradigm by utilizing generative models to produce semantically consistent image augmentations. By directly conditioning generative models on a source image, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for SSL. Our extensive experimental results on various joint-embedding SSL techniques demonstrate that our framework significantly enhances the quality of learned visual representations by up to 10\% Top-1 accuracy in downstream tasks. This research demonstrates that incorporating generative models into the joint-embedding SSL workflow opens new avenues for exploring the potential of synthetic data. This development paves the way for more robust and versatile representation learning techniques.
Electromagnetic Hybrid Beamforming for Holographic Communications
Venue: arXiv cs.IT 2024
Year: 2024

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviates issues related to a priori identification of system models that may become deficient under evolving system behaviors or shifting, unanticipated, environmental influences. Our proposed learning-based platform comprises a deep Koopman system model and a change point detector that provides guidance on domain shifts prompting relearning under severe exogenous and endogenous perturbations. Motion control of the autonomous system is achieved via an optimal controller design. The Koopman linearized model naturally lends itself to a linear-quadratic regulator (LQR) control design. We propose the C3D control architecture Cascade Control with Change Point Detection and Deep Koopman Learning. The framework is verified in station keeping task on an ASV in both simulation and real experiments. The approach achieved at least 13.9 percent improvement in mean distance error in all test cases compared to the methods that do not consider system changes.
An Event-Based Approach for the Conservative Compression of Covariance Matrices
Venue: arXiv cs.RO 2024
Year: 2024

This work introduces a flexible and versatile method for the data-efficient yet conservative transmission of covariance matrices, where a matrix element is only transmitted if a so-called triggering condition is satisfied for the element. Here, triggering conditions can be parametrized on a per-element basis, applied simultaneously to yield combined triggering conditions or applied only to certain subsets of elements. This allows, e.g., to specify transmission accuracies for individual elements or to constrain the bandwidth available for the transmission of subsets of elements. Additionally, a methodology for learning triggering condition parameters from an application-specific dataset is presented. The performance of the proposed approach is quantitatively assessed in terms of data reduction and conservativeness using estimate data derived from real-world vehicle trajectories from the InD-dataset, demonstrating substantial data reduction ratios with minimal over-conservativeness. The feasibility of learning triggering condition parameters is demonstrated.
A Tool for Automated Reasoning About Traces Based on Configurable Formal Semantics
Venue: arXiv cs.SE 2024
Year: 2024

We present Tarski, a tool for specifying configurable trace semantics to facilitate automated reasoning about traces. Software development projects require that various types of traces be modeled between and within development artifacts. For any given artifact (e.g., requirements, architecture models and source code), Tarski allows the user to specify new trace types and their configurable semantics, while, using the semantics, it automatically infers new traces based on existing traces provided by the user, and checks the consistency of traces. It has been evaluated on three industrial case studies in the automotive domain (https://modelwriter.github.io/Tarski/).
End-to-end solution for linked open data query logs analytics
Venue: arXiv cs.DB 2024
Year: 2024

Important advances in pillar domains are derived from exploiting query-logs which represents users interest and preferences. Deep understanding of users provides useful knowledge which can influence strongly decision-making. In this work, we want to extract valuable information from Linked Open Data (LOD) query-logs. LOD logs have experienced significant growth due to the large exploitation of LOD datasets. However, exploiting these logs is a difficult task because of their complex structure. Moreover, these logs suffer from many risks related to their Quality and Provenance, impacting their trust. To tackle these issues, we start by clearly defining the ecosystem of LOD query-logs. Then, we provide an end-to-end solution to exploit these logs. At the end, real LOD logs are used and a set of experiments are conducted to validate the proposed solution.
Semi-Supervised Multimodal Multi-Instance Learning for Aortic Stenosis Diagnosis
Venue: arXiv cs.CV 2024
Year: 2024

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), a new deep learning framework for automatic interpretation for structural heart diseases like AS. When deployed, SMMIL can combine information from two input modalities, spectral Dopplers and 2D cineloops, to produce a study-level AS diagnosis. During training, SMMIL can combine a smaller labeled set and an abundant unlabeled set of both modalities to improve its classifier. Experiments demonstrate that SMMIL outperforms recent alternatives at 3-level AS severity classification as well as several clinically relevant AS detection tasks.
Ensemble Language Models for Multilingual Sentiment Analysis
Venue: arXiv cs.CL 2024
Year: 2024

The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset. Moreover, We investigated four pretrained language models and proposed two ensemble language models. Our findings include monolingual models exhibiting superior performance and ensemble models outperforming the baseline while the majority voting ensemble outperforms the English language.
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model
Venue: arXiv cs.CV 2024
Year: 2024

Current pre-trained vision-language models, such as CLIP, have demonstrated remarkable zero-shot generalization capabilities across various downstream tasks. However, their performance significantly degrades when test inputs exhibit different distributions. In this paper, we explore the concept of test-time prompt tuning (TTPT), which facilitates the adaptation of the CLIP model to novel downstream tasks through a one-step unsupervised optimization that involves only test samples. Inspired by in-context learning in natural language processing (NLP), we propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks, which empowers a pre-trained vision-language model with labeled examples as context information on downstream task. Specifically, InCPL associates a new test sample with very few labeled examples (sometimes just one) as context information, enabling reliable label estimation for the test sample and facilitating model adaptation. To achieve this, InCPL employs an efficient language-to-vision translator to explore the textual prior information for visual prompt learning. Further, we introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples. Finally, we design a cyclic learning strategy for visual and textual prompts to ensure mutual synergy across different modalities. This enables a pre-trained, frozen CLIP model to adapt to any task using its learned adaptive prompt. Our method demonstrates superior performance and achieves state-of-the-art results across various downstream datasets.
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Venue: arXiv cs.CV 2024
Year: 2024

Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently optimized along a single modality path, may sacrifice the vision-language alignment of pre-trained models in return for improved performance on specific tasks and classes, leading to poorer generalization. In this paper, we first demonstrate that prompt tuning along only one single branch of CLIP (e.g., language or vision) is the reason why the misalignment occurs. Without proper regularization across the learnable parameters in different modalities, prompt learning violates the original pre-training constraints inherent in the two-tower architecture. To address such misalignment, we first propose feature shift, which is defined as the variation of embeddings after introducing the learned prompts, to serve as an explanatory tool. We dive into its relation with generalizability and thereafter propose RESTORE, a multi-modal prompt learning method that exerts explicit constraints on cross-modal consistency. To be more specific, to prevent feature misalignment, a feature shift consistency is introduced to synchronize inter-modal feature shifts by measuring and regularizing the magnitude of discrepancy during prompt tuning. In addition, we propose a "surgery" block to avoid short-cut hacking, where cross-modal misalignment can still be severe if the feature shift of each modality varies drastically at the same rate. It is implemented as feed-forward adapters upon both modalities to alleviate the misalignment problem. Extensive experiments on 15 datasets demonstrate that our method outperforms the state-of-the-art prompt tuning methods without compromising feature alignment.
IDEAS: Information-Driven EV Admission in Charging Station Considering User Impatience to Improve QoS and Station Utilization
Venue: arXiv cs.MA 2024
Year: 2024

Our work delves into user behaviour at Electric Vehicle(EV) charging stations during peak times, particularly focusing on how impatience drives balking (not joining queues) and reneging (leaving queues prematurely). We introduce an Agent-based simulation framework that incorporates user optimism levels (pessimistic, standard, and optimistic) in the queue dynamics. Unlike previous work, this framework highlights the crucial role of human behaviour in shaping station efficiency for peak demand. The simulation reveals a key issue: balking often occurs due to a lack of queue insights, creating user dilemmas. To address this, we propose real-time sharing of wait time metrics with arriving EV users at the station. This ensures better Quality of Service (QoS) with user-informed queue joining and demonstrates significant reductions in reneging (up to 94%) improving the charging operation. Further analysis shows that charging speed decreases significantly beyond 80%, but most users prioritize full charges due to range anxiety, leading to a longer queue. To address this, we propose a two-mode, two-port charger design with power-sharing options. This allows users to fast-charge to 80% and automatically switch to slow charging, enabling fast charging on the second port. Thus, increasing fast charger availability and throughput by up to 5%. As the mobility sector transitions towards intelligent traffic, our modelling framework, which integrates human decision-making within automated planning, provides valuable insights for optimizing charging station efficiency and improving the user experience. This approach is particularly relevant during the introduction phase of new stations, when historical data might be limited.
Probing Image Compression For Class-Incremental Learning
Venue: arXiv cs.CV 2024
Year: 2024

Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhanced data storage capacity but also potentially brings advantages to the development of continual machine learning (ML) systems, which learn new knowledge incrementally from sequential data. Continual ML systems often rely on storing representative samples, also known as exemplars, within a limited memory constraint to maintain the performance on previously learned data. These methods are known as memory replay-based algorithms and have proven effective at mitigating the detrimental effects of catastrophic forgetting. Nonetheless, the limited memory buffer size often falls short of adequately representing the entire data distribution. In this paper, we explore the use of image compression as a strategy to enhance the buffer's capacity, thereby increasing exemplar diversity. However, directly using compressed exemplars introduces domain shift during continual ML, marked by a discrepancy between compressed training data and uncompressed testing data. Additionally, it is essential to determine the appropriate compression algorithm and select the most effective rate for continual ML systems to balance the trade-off between exemplar quality and quantity. To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method. We conduct extensive experiments on CIFAR-100 and ImageNet datasets and show that our method significantly improves image classification accuracy in continual ML settings.
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

Deep reinforcement learning(DRL) has shown significant promise in a wide range of applications including computer games and robotics. Yet, training DRL policies consume extraordinary computing resources resulting in dense policies which are prone to overfitting. Moreover, inference with dense DRL policies limit their practical applications, especially in edge computing. Techniques such as pruning and singular value decomposition have been used with deep learning models to achieve sparsification and model compression to limit overfitting and reduce memory consumption. However, these techniques resulted in sub-optimal performance with notable decay in rewards. $L_1$ and $L_2$ regularization techniques have been proposed for neural network sparsification and sparse auto-encoder development, but their implementation in DRL environments has not been apparent. We propose a novel $L_0$-norm-regularization technique using an optimal sparsity map to sparsify DRL policies and promote their decomposition to a lower rank without decay in rewards. We evaluated our $L_0$-norm-regularization technique across five different environments (Cartpole-v1, Acrobat-v1, LunarLander-v2, SuperMarioBros-7.1.v0 and Surgical Robot Learning) using several on-policy and off-policy algorithms. We demonstrated that the $L_0$-norm-regularized DRL policy in the SuperMarioBros environment achieved 93% sparsity and gained 70% compression when subjected to low-rank decomposition, while significantly outperforming the dense policy. Additionally, the $L_0$-norm-regularized DRL policy in the Surgical Robot Learning environment achieved a 36% sparsification and gained 46% compression when decomposed to a lower rank, while being performant. The results suggest that our custom $L_0$-norm-regularization technique for sparsification of DRL policies is a promising avenue to reduce computational resources and limit overfitting.
Improved FPT Approximation Scheme and Approximate Kernel for Biclique-Free Max k-Weight SAT: Greedy Strikes Back
Venue: arXiv cs.DS 2024
Year: 2024

In the Max $k$-Weight SAT (aka Max SAT with Cardinality Constraint) problem, we are given a CNF formula with $n$ variables and $m$ clauses together with a positive integer $k$. The goal is to find an assignment where at most $k$ variables are set to one that satisfies as many constraints as possible. Recently, Jain et al. [SODA'23] gave an FPT approximation scheme (FPT-AS) with running time $2^{O\left(\left(dk/ε\right)^d\right)} \cdot (n + m)^{O(1)}$ for Max $k$-Weight SAT when the incidence graph is $K_{d,d}$-free. They asked whether a polynomial-size approximate kernel exists. In this work, we answer this question positively by giving an $(1 - ε)$-approximate kernel with $\left(\frac{d k}ε\right)^{O(d)}$ variables. This also implies an improved FPT-AS with running time $(dk/ε)^{O(dk)} \cdot (n + m)^{O(1)}$. Our approximate kernel is based mainly on a couple of greedy strategies together with a sunflower lemma-style reduction rule.