OpenRaiser/Intern-Atlas huggingface dataset:
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
Venue: arXiv cs.CV 2024
Year: 2024

The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite advancements, existing systems often miss rare cardiac anomalies that could be precursors to serious, life-threatening issues or alterations in the cardiac macro/microstructure. We address this gap by focusing on self-supervised anomaly detection (AD), training exclusively on normal ECGs to recognize deviations indicating anomalies. We introduce a novel self-supervised learning framework for ECG AD, utilizing a vast dataset of normal ECGs to autonomously detect and localize cardiac anomalies. It proposes a novel masking and restoration technique alongside a multi-scale cross-attention module, enhancing the model's ability to integrate global and local signal features. The framework emphasizes accurate localization of anomalies within ECG signals, ensuring the method's clinical relevance and reliability. To reduce the impact of individual variability, the approach further incorporates crucial patient-specific information from ECG reports, such as age and gender, thus enabling accurate identification of a broad spectrum of cardiac anomalies, including rare ones. Utilizing an extensive dataset of 478,803 ECG graphic reports from real-world clinical practice, our method has demonstrated exceptional effectiveness in AD across all tested conditions, regardless of their frequency of occurrence, significantly outperforming existing models. It achieved superior performance metrics, including an AUROC of 91.2%, an F1 score of 83.7%, a sensitivity rate of 84.2%, a specificity of 83.0%, and a precision of 75.6% with a fixed recall rate of 90%. It has also demonstrated robust localization capabilities, with an AUROC of 76.5% and a Dice coefficient of 65.3% for anomaly localization.
Fuzzy K-Means Clustering without Cluster Centroids
Venue: arXiv cs.LG 2024
Year: 2024

Fuzzy K-Means clustering is a critical technique in unsupervised data analysis. Unlike traditional hard clustering algorithms such as K-Means, it allows data points to belong to multiple clusters with varying degrees of membership, determined through iterative optimization to establish optimal cluster centers and memberships, thereby achieving fuzzy partitioning of data. However, the performance of popular Fuzzy K-Means algorithms is sensitive to the selection of initial cluster centroids and is also affected by noise when updating mean cluster centroids. To address these challenges, this paper proposes a novel Fuzzy \textit{K}-Means clustering algorithm that entirely eliminates the reliance on cluster centroids, obtaining membership metrics solely through distance matrix computation. This innovation enhances flexibility in distance measurement between sample points, thus improving the algorithm's performance and robustness. The paper also establishes theoretical connections between the proposed model and popular Fuzzy K-Means clustering techniques. Experimental results on several real datasets demonstrate the effectiveness of the algorithm.
A robust assessment for invariant representations
Venue: arXiv cs.LG 2024
Year: 2024

The performance of machine learning models can be impacted by changes in data over time. A promising approach to address this challenge is invariant learning, with a particular focus on a method known as invariant risk minimization (IRM). This technique aims to identify a stable data representation that remains effective with out-of-distribution (OOD) data. While numerous studies have developed IRM-based methods adaptive to data augmentation scenarios, there has been limited attention on directly assessing how well these representations preserve their invariant performance under varying conditions. In our paper, we propose a novel method to evaluate invariant performance, specifically tailored for IRM-based methods. We establish a bridge between the conditional expectation of an invariant predictor across different environments through the likelihood ratio. Our proposed criterion offers a robust basis for evaluating invariant performance. We validate our approach with theoretical support and demonstrate its effectiveness through extensive numerical studies.These experiments illustrate how our method can assess the invariant performance of various representation techniques.
QRscript: Embedding a Programming Language in QR codes to support Decision and Management
Venue: arXiv cs.NI 2024
Year: 2024

Embedding a programming language in a QR code is a new and extremely promising opportunity, as it makes devices and objects smarter without necessarily requiring an Internet connection. In this paper, all the steps needed to translate a program written in a high-level programming language to its binary representation encoded in a QR code, and the opposite process that, starting from the QR code, executes it by means of a virtual machine, have been carefully detailed. The proposed programming language was named QRscript, and can be easily extended so as to integrate new features. One of the main design goals was to produce a very compact target binary code. In particular, in this work we propose a specific sub-language (a dialect) that is aimed at encoding decision trees. Besides industrial scenarios, this is useful in many other application fields. The reported example, related to the configuration of an industrial networked device, highlights the potential of the proposed technology, and permits to better understand all the translation steps.
A Note on LoRA
Venue: arXiv cs.LG 2024
Year: 2024

LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy. This note extends the original LoRA paper by offering new perspectives that were not initially discussed and presents a series of insights for deploying LoRA at scale. Without introducing new experiments, we aim to improve the understanding and application of LoRA.
Quantitative Weakest Hyper Pre: Unifying Correctness and Incorrectness Hyperproperties via Predicate Transformers
Venue: arXiv cs.LO 2024
Year: 2024

We present a novel \emph{weakest pre calculus} for \emph{reasoning about quantitative hyperproperties} over \emph{nondeterministic and probabilistic} programs. Whereas existing calculi allow reasoning about the expected value that a quantity assumes after program termination from a \emph{single initial state}, we do so for \emph{initial sets of states} or \emph{initial probability distributions}. We thus (i)~obtain a weakest pre calculus for hyper Hoare logic and (ii)~enable reasoning about so-called \emph{hyperquantities} which include expected values but also quantities (e.g. variance) out of scope of previous work. As a byproduct, we obtain a novel strongest post for weighted programs that extends both existing strongest and strongest liberal post calculi. Our framework reveals novel dualities between forward and backward transformers, correctness and incorrectness, as well as nontermination and unreachability.
Legibot: Generating Legible Motions for Service Robots Using Cost-Based Local Planners
Venue: arXiv cs.RO 2024
Year: 2024

With the increasing presence of social robots in various environments and applications, there is an increasing need for these robots to exhibit socially-compliant behaviors. Legible motion, characterized by the ability of a robot to clearly and quickly convey intentions and goals to the individuals in its vicinity, through its motion, holds significant importance in this context. This will improve the overall user experience and acceptance of robots in human environments. In this paper, we introduce a novel approach to incorporate legibility into local motion planning for mobile robots. This can enable robots to generate legible motions in real-time and dynamic environments. To demonstrate the effectiveness of our proposed methodology, we also provide a robotic stack designed for deploying legibility-aware motion planning in a social robot, by integrating perception and localization components.
Reconstructing Retinal Visual Images from 3T fMRI Data Enhanced by Unsupervised Learning
Venue: arXiv cs.CV 2024
Year: 2024

The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality fMRI scans. In response to these constraints, we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN), leveraging unpaired training across two distinct fMRI datasets in 7T and 3T, respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper, we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data, highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject.
Rollbot: a Spherical Robot Driven by a Single Actuator
Venue: arXiv cs.RO 2024
Year: 2024

Here we present Rollbot, the first spherical robot capable of controllably maneuvering on 2D plane with a single actuator. Rollbot rolls on the ground in circular pattern and controls its motion by changing the curvature of the trajectory through accelerating and decelerating its single motor and attached mass. We present the theoretical analysis, design, and control of Rollbot, and demonstrate its ability to move in a controllable circular pattern and follow waypoints.
On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
Venue: arXiv cs.LG 2024
Year: 2024

In one view of the classical game of prediction with expert advice with binary outcomes, in each round, each expert maintains an adversarially chosen belief and honestly reports this belief. We consider a recently introduced, strategic variant of this problem with selfish (reputation-seeking) experts, where each expert strategically reports in order to maximize their expected future reputation based on their belief. In this work, our goal is to design an algorithm for the selfish experts problem that is incentive-compatible (IC, or \emph{truthful}), meaning each expert's best strategy is to report truthfully, while also ensuring the algorithm enjoys sublinear regret with respect to the expert with the best belief. Freeman et al. (2020) recently studied this problem in the full information and bandit settings and obtained truthful, no-regret algorithms by leveraging prior work on wagering mechanisms. While their results under full information match the minimax rate for the classical ("honest experts") problem, the best-known regret for their bandit algorithm WSU-UX is $O(T^{2/3})$, which does not match the minimax rate for the classical ("honest bandits") setting. It was unclear whether the higher regret was an artifact of their analysis or a limitation of WSU-UX. We show, via explicit construction of loss sequences, that the algorithm suffers a worst-case $Ω(T^{2/3})$ lower bound. Left open is the possibility that a different IC algorithm obtains $O(\sqrt{T})$ regret. Yet, WSU-UX was a natural choice for such an algorithm owing to the limited design room for IC algorithms in this setting.
Power Series Composition in Near-Linear Time
Venue: arXiv cs.SC 2024
Year: 2024

We present an algebraic algorithm that computes the composition of two power series in softly linear time complexity. The previous best algorithms are $\mathop{\mathrm O}(n^{1+o(1)})$ by Kedlaya and Umans (FOCS 2008) and an $\mathop{\mathrm O}(n^{1.43})$ algebraic algorithm by Neiger, Salvy, Schost and Villard (JACM 2023). Our algorithm builds upon the recent Graeffe iteration approach to manipulate rational power series introduced by Bostan and Mori (SOSA 2021).
Bidirectional Long-Range Parser for Sequential Data Understanding
Venue: arXiv cs.CV 2024
Year: 2024

The transformer is a powerful data modelling framework responsible for remarkable performance on a wide range of tasks. However, they are limited in terms of scalability as it is suboptimal and inefficient to process long-sequence data. To this purpose we introduce BLRP (Bidirectional Long-Range Parser), a novel and versatile attention mechanism designed to increase performance and efficiency on long-sequence tasks. It leverages short and long range heuristics in the form of a local sliding window approach combined with a global bidirectional latent space synthesis technique. We show the benefits and versatility of our approach on vision and language domains by demonstrating competitive results against state-of-the-art methods on the Long-Range-Arena and CIFAR benchmarks together with ablations demonstrating the computational efficiency.
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Venue: arXiv cs.LG 2024
Year: 2024

Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs' reliability and robustness. Traditionally, research has addressed OOD detection and adversarial robustness as separate challenges. This survey focuses on the intersection of these two areas, examining how the research community has investigated them together. Consequently, we identify two key research directions: robust OOD detection and unified robustness. Robust OOD detection aims to differentiate between in-distribution (ID) data and OOD data, even when they are adversarially manipulated to deceive the OOD detector. Unified robustness seeks a single approach to make DNNs robust against both adversarial attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on the concept of distributional shifts. This framework clarifies how robust OOD detection and unified robustness relate to other research areas addressing distributional shifts, such as OOD detection, open set recognition, and anomaly detection. Subsequently, we review existing work on robust OOD detection and unified robustness. Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.
ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
Venue: arXiv cs.AI 2024
Year: 2024

Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent also presents a challenge. The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents. Several algorithms, including CBM, CBS-TA, and ITA-CBS, optimally solve the TAPF problem, with ITA-CBS being the leading algorithm for minimizing flowtime. However, the only existing bounded-suboptimal algorithm ECBS-TA is derived from CBS-TA rather than ITA-CBS. So, it faces the same issues as CBS-TA, such as searching through multiple constraint trees and spending too much time on finding the next-best target assignment. We introduce ITA-ECBS, the first bounded-suboptimal variant of ITA-CBS. Transforming ITA-CBS to its bounded-suboptimal variant is challenging because different constraint tree nodes can have different assignments of targets to agents. ITA-ECBS uses focal search to achieve efficiency and determines target assignments based on a new lower bound matrix. We show that it runs faster than ECBS-TA in 87.42% of 54,033 test cases.
LightFF: Lightweight Inference for Forward-Forward Algorithm
Venue: arXiv cs.LG 2024
Year: 2024

The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. This has led to a new generation of forward-only techniques, including the Forward-Forward algorithm. In this paper, we propose a lightweight inference scheme specifically designed for DNNs trained using the Forward-Forward algorithm. We have evaluated our proposed lightweight inference scheme in the case of the MNIST and CIFAR datasets, as well as two real-world applications, namely, epileptic seizure detection and cardiac arrhythmia classification using wearable technologies, where complexity overheads/energy consumption is a major constraint, and demonstrate its relevance. Our code is available at https://github.com/AminAminifar/LightFF.
Supervised Gradual Machine Learning for Aspect Category Detection
Venue: arXiv cs.CL 2024
Year: 2024

Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence. The state-of-the-art approaches for ACD use Deep Neural Networks (DNNs) to address the problem as a multi-label classification task. However, learning category-specific representations heavily rely on the amount of labeled examples, which may not readily available in real-world scenarios. In this paper, we propose a novel approach to tackle the ACD task by combining DNNs with Gradual Machine Learning (GML) in a supervised setting. we aim to leverage the strength of DNN in semantic relation modeling, which can facilitate effective knowledge transfer between labeled and unlabeled instances during the gradual inference of GML. To achieve this, we first analyze the learned latent space of the DNN to model the relations, i.e., similar or opposite, between instances. We then represent these relations as binary features in a factor graph to efficiently convey knowledge. Finally, we conduct a comparative study of our proposed solution on real benchmark datasets and demonstrate that the GML approach, in collaboration with DNNs for feature extraction, consistently outperforms pure DNN solutions.
Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration
Venue: arXiv cs.HC 2024
Year: 2024

Algorithmic Recourse aims to provide actionable explanations, or recourse plans, to overturn potentially unfavourable decisions taken by automated machine learning models. In this paper, we propose an interaction paradigm based on a guided interaction pattern aimed at both eliciting the users' preferences and heading them toward effective recourse interventions. In a fictional task of money lending, we compare this approach with an exploratory interaction pattern based on a combination of alternative plans and the possibility of freely changing the configurations by the users themselves. Our results suggest that users may recognize that the guided interaction paradigm improves efficiency. However, they also feel less freedom to experiment with "what-if" scenarios. Nevertheless, the time spent on the purely exploratory interface tends to be perceived as a lack of efficiency, which reduces attractiveness, perspicuity, and dependability. Conversely, for the guided interface, more time on the interface seems to increase its attractiveness, perspicuity, and dependability while not impacting the perceived efficiency. That might suggest that this type of interfaces should combine these two approaches by trying to support exploratory behavior while gently pushing toward a guided effective solution.
Scheduling Multi-Server Jobs is Not Easy
Venue: arXiv cs.DS 2024
Year: 2024

The problem of online scheduling of multi-server jobs is considered, where there are a total of $K$ servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, the number of servers from which it needs concurrent service and an online algorithm has to make scheduling decisions using only causal information, with the goal of minimizing the response/flow time. The worst case input model is considered and the performance metric is the competitive ratio. For the case, when all job processing time (sizes) are the same, we show that the competitive ratio of any deterministic/randomized algorithm is at least $Ω(K)$ and propose an online algorithm whose competitive ratio is at most $K+1$. With equal job sizes, we also consider the resource augmentation regime where an online algorithm has access to more servers than an optimal offline algorithm. With resource augmentation, we propose a simple algorithm and show that it has a competitive ratio of $1$ when provided with $2K$ servers with respect to an optimal offline algorithm with $K$ servers. With unequal job sizes, we propose an online algorithm whose competitive ratio is at most $2K \log (K w_{\max})$, where $w_{\max}$ is the maximum size of any job.
Multi-Task Learning for Features Extraction in Financial Annual Reports
Venue: arXiv cs.CL 2024
Year: 2024

For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information. This textual data can provide valuable weak signals, for example through stylistic features, which can complement the quantitative data on financial performance or on Environmental, Social and Governance (ESG) criteria. In this work, we use various multi-task learning methods for financial text classification with the focus on financial sentiment, objectivity, forward-looking sentence prediction and ESG-content detection. We propose different methods to combine the information extracted from training jointly on different tasks; our best-performing method highlights the positive effect of explicitly adding auxiliary task predictions as features for the final target task during the multi-task training. Next, we use these classifiers to extract textual features from annual reports of FTSE350 companies and investigate the link between ESG quantitative scores and these features.