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Introducing IHARDS-CNN: A Cutting-Edge Deep Learning Method for Human Activity Recognition Using Wearable Sensors
No full textHuman activity recognition, facilitated by smart devices, has recently garnered significant attention. Deep learning algorithms have become pivotal in daily activities, sports, and healthcare. Nevertheless, addressing the challenge of extracting features from sensor data processing necessitates the utilization of diverse algorithms in isolation, subsequently transforming them into a standard mode. This research introduces a novel approach called IHARDS-CNN, amalgamating data from three distinct datasets (UCI-HAR, WISDM, and KU-HAR) for human activity recognition. The data collected from sensors embedded in smartwatches or smartphones encompass five daily activity classes. This study initially outlines the dataset integration approach, follows with a comprehensive statistical analysis, and assesses dataset accuracy. The proposed methodology employs a one-dimensional deep convolutional neural network for classification. Compared to extant activity recognition methods, this approach stands out for its high speed, reduced detection steps, and absence of the need to aggregate classified results. Despite fewer detection steps, empirical results demonstrate an impressive accuracy of nearly 100%, marking it the highest among existing methods. Evaluation outcomes further highlight superior classification performance when compared to analogous architectures.18 pages, 9 figures, 9 Table
Observational features of the rotating Bardeen black hole surrounded by perfect fluid dark matter
No full textBy employing ray-tracing techniques, we investigate the shadow images of rotating Bardeen black holes surrounded by perfect fluid dark matter. In this work, two models are considered for the background light source, namely the celestial light source model and the thin accretion disk model. Regarding the celestial light source, the investigation focuses on the impact of variations in relevant parameters and observed inclination on the contour and size of the shadow. For the thin accretion disk model, the optical appearance of a black hole is evidently contingent upon the radiative properties exhibited by the accretion disk, as well as factors such as observed inclination and relevant parameters governing spacetime. With an increasing observation inclination, the observed flux of direct and lensed images of the accretion disk gradually converge towards the lower region of the image, while an increase in the dark matter parameter significantly expands the region encompassing both direct and lensed images. Furthermore, the predominant effect is redshift at lower observation angles, whereas the blueshift effect only becomes apparent at higher observation angles. Simultaneously, the increase in the observation inclination will amplify the redshift effect, whereas an increase in the magnetic charge , rotation parameter and the absolute value of dark matter parameter will attenuate the redshift effect observed in the image. These observations of a rotating Bardeen black hole surrounded by perfect fluid dark matter could provide a convenient way to distinguish it from other black hole models.32 pages, 12 figures
Do Captioning Metrics Reflect Music Semantic Alignment?
No full textMusic captioning has emerged as a promising task, fueled by the advent of advanced language generation models. However, the evaluation of music captioning relies heavily on traditional metrics such as BLEU, METEOR, and ROUGE which were developed for other domains, without proper justification for their use in this new field. We present cases where traditional metrics are vulnerable to syntactic changes, and show they do not correlate well with human judgments. By addressing these issues, we aim to emphasize the need for a critical reevaluation of how music captions are assessed.International Society for Music Information Retrieval (ISMIR) 2024, Late Breaking Demo (LBD
Moral Persuasion in Large Language Models: Evaluating Susceptibility and Ethical Alignment
No full textWe explore how large language models (LLMs) can be influenced by prompting them to alter their initial decisions and align them with established ethical frameworks. Our study is based on two experiments designed to assess the susceptibility of LLMs to moral persuasion. In the first experiment, we examine the susceptibility to moral ambiguity by evaluating a Base Agent LLM on morally ambiguous scenarios and observing how a Persuader Agent attempts to modify the Base Agent\u27s initial decisions. The second experiment evaluates the susceptibility of LLMs to align with predefined ethical frameworks by prompting them to adopt specific value alignments rooted in established philosophical theories. The results demonstrate that LLMs can indeed be persuaded in morally charged scenarios, with the success of persuasion depending on factors such as the model used, the complexity of the scenario, and the conversation length. Notably, LLMs of distinct sizes but from the same company produced markedly different outcomes, highlighting the variability in their susceptibility to ethical persuasion
Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking
No full textThe paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer Encoder between several classification heads. One head is responsible for binary classification between human-written and machine-generated texts, while the other heads are auxiliary multiclass classifiers for texts of different domains from particular datasets. As multiclass heads were trained to distinguish the domains presented in the data, they provide a better understanding of the samples. This approach led us to achieve the first place in the official ranking with 83.07% macro F1-score on the test set and bypass the baseline by 10%. We further study obtained system through ablation, error and representation analyses, finding that multi-task learning outperforms single-task mode and simultaneous tasks form a cluster structure in embeddings space
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
No full textThe search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CORR) and hydrogen evolution reactions (HER) at current densities up to mA/cm. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on 20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening.38 pages, 22 figure
A Potential Game Perspective in Federated Learning
No full textFederated learning (FL) is an emerging paradigm for training machine learning models across distributed clients. Traditionally, in FL settings, a central server assigns training efforts (or strategies) to clients. However, from a market-oriented perspective, clients may independently choose their training efforts based on rational self-interest. To explore this, we propose a potential game framework where each client\u27s payoff is determined by their individual efforts and the rewards provided by the server. The rewards are influenced by the collective efforts of all clients and can be modulated through a reward factor. Our study begins by establishing the existence of Nash equilibria (NEs), followed by an investigation of uniqueness in homogeneous settings. We demonstrate a significant improvement in clients\u27 training efforts at a critical reward factor, identifying it as the optimal choice for the server. Furthermore, we prove the convergence of the best-response algorithm to compute NEs for our FL game. Finally, we apply the training efforts derived from specific NEs to a real-world FL scenario, validating the effectiveness of the identified optimal reward factor
Tackling prediction tasks in relational databases with LLMs
No full textThough large language models (LLMs) have demonstrated exceptional performance across numerous problems, their application to predictive tasks in relational databases remains largely unexplored. In this work, we address the notion that LLMs cannot yield satisfactory results on relational databases due to their interconnected tables, complex relationships, and heterogeneous data types. Using the recently introduced RelBench benchmark, we demonstrate that even a straightforward application of LLMs achieves competitive performance on these tasks. These findings establish LLMs as a promising new baseline for ML on relational databases and encourage further research in this direction
Quantifying Scalar Field Dynamics with DESI 2024 Y1 BAO measurements
No full textQuintessence scalar fields are a natural candidate for evolving dark energy. Unlike the phenomenological parameterization of the dark energy equation of state, they cannot accommodate the phantom regime of dark energy . The tension under CDM remains noticeable (), when replacing two of the DESI BAO redshift bins with effective redshifts , and with comparable BOSS DR 12 BAO measurements at , and . Canonical scalar fields as dark energy are successful in mitigating that tension.15 pages, 12 figures, v2 matches journal versio
Partially Unitary Learning
No full textThe problem of an optimal mapping between Hilbert spaces of and of based on a set of wavefunction measurements (within a phase) , , is formulated as an optimization problem maximizing the total fidelity subject to probability preservation constraints on (partial unitarity). The constructed operator can be considered as an to quantum channel; it is a partially unitary rectangular matrix (an isometry) of dimension transforming operators as . An iterative algorithm for finding the global maximum of this optimization problem is developed, and its application to a number of problems is demonstrated. A software product implementing the algorithm is available from the authors.A working algorithm implementing Partially Unitary Learning arXiv:2212.14810 has been developed and generalized. See arXiv:2407.04406 for further generalization to density matrix mapping