Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

Recent developments in sequence modeling have allowed continuation to be viewed as an autore-gressive task, to be modeled with a suitable tokenization scheme and a powerful sequence model like the ubiquitous Transformer [1]. A nonexhaustive list of prior work in this vein includes the Music Transformer [2], Museformer [3], FIGARO [4], and MuseCoco [5]. Most research in symbolic music modeling has so far focused on generalizing these techniques to--and improving performance on--long-sequence, multitrack, multi-instrument, and/or text-or attribute-controllable generative tasks. Typically, specialized techniques must be developed for these foundation models to handle these harder tasks, such as fine-and coarse-grained attention for long sequences [3], and text feature extraction techniques [4] and attribute augmentation [5] for controllability.

Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised knowledge for accurate Sentiment Analysis (SA). To address these challenges, this study explores how Large Language Models (LLMs) can integrate expert knowledge through in-context learning for SA, providing a scalable solution for sophisticated health data analysis. Specifically, we develop a structured codebook that systematically encodes expert interpretation guidelines, enabling LLMs to apply domain-specific knowledge through targeted prompting rather than extensive training. Six GPT models validated alongside DeepSeek and LLaMA 3.1 are compared with pre-trained language models (BioBERT variants) and lexicon-based methods, using 400 expert-annotated posts from two OHCs. LLMs achieve superior performance while demonstrating expert-level agreement. This high agreement, with no statistically significant difference from inter-expert agreement levels, suggests knowledge integration beyond surface-level pattern recognition. The consistent performance across diverse LLM models, supported by in-context learning, offers a promising solution for digital health analytics. This approach addresses the critical challenge of expert knowledge shortage in digital health research, enabling real-time, expert-quality analysis for patient monitoring, intervention assessment, and evidence-based health strategies.

The continued rise of neural networks and large language models in the more recent past has altered the natural language processing landscape, enabling new approaches towards typical language tasks and achieving mainstream success. Despite the huge success of large language models, many disadvantages still remain and through this work we assess the state of the art in five application scenarios with a particular focus on the future perspectives and sensible application scenarios of traditional and older approaches and techniques. In this paper we survey recent publications in the application scenarios classification, information and relation extraction, text simplification as well as text summarization. After defining our terminology, i.e., which features are characteristic for traditional techniques in our interpretation for the five scenarios, we survey if such traditional approaches are still being used, and if so, in what way they are used. It turns out that all five application scenarios still exhibit traditional models in one way or another, as part of a processing pipeline, as a comparison/baseline to the core model of the respective paper, or as the main model(s) of the paper. For the complete statistics, see https://zenodo.org/records/13683801

The overall novelty of the proposed model is limited to some extend. I think this module is very similar to meta-GNN. Both of them conduct adaptation on the support set, then do evaluation on the query set, though they employ prototype and MAML respectively. In my view, the overall model stands on the shoulder on some traditional approaches, and seems a bit incremental. Could some other approaches, such as fine-tune (which is often utilized as the comparison with meta-learning), solve this novel label problem?

Overfitting remains a significant challenge in deep learning, often arising from data outliers, noise, and limited training data. To address this, we propose Divide2Conquer (D2C), a novel technique to mitigate overfitting. D2C partitions the training data into multiple subsets and trains identical models independently on each subset. To balance model generalization and subset-specific learning, the model parameters are periodically aggregated and averaged during training. This process enables the learning of robust patterns while minimizing the influence of outliers and noise. Empirical evaluations on benchmark datasets across diverse deep-learning tasks demonstrate that D2C significantly enhances generalization performance, particularly with larger datasets. Our analysis includes evaluations of decision boundaries, loss curves, and other performance metrics, highlighting D2C's effectiveness both as a standalone technique and in combination with other overfitting reduction methods. We further provide a rigorous mathematical justification for D2C's underlying principles and examine its applicability across multiple domains. Finally, we explore the trade-offs associated with D2C and propose strategies to address them, offering a holistic view of its strengths and limitations. This study establishes D2C as a versatile and effective approach to combating overfitting in deep learning. Our codes are publicly available at: https://github.com/Saiful185/Divide2Conquer.

Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually requires underlying asset simulations, and it is challenging to select the best model for such simulations. This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging. Artificial market simulations can replicate the stylized facts of financial markets, and they seem to be a promising approach for deep hedging. We investigate the effectiveness of the proposed approach by comparing its results with those of the traditional approach, which uses mathematical finance models such as Brownian motion and Heston models for underlying asset simulations. The results show that the proposed approach can achieve almost the same level of performance as the traditional approach without mathematical finance models. Finally, we also reveal that the proposed approach has some limitations in terms of performance under certain conditions.

Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for such a predictive model to gain user trust and be deployed in practice. Since commercial vehicles operate differently as transportation tasks, ambient, and drivers vary, a heterogeneous population is expected when building an AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is the Simpson paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g. LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability.

In weed control, precision agriculture can help to greatly reduce the use of herbicides, resulting in both economical and ecological benefits. A key element is the ability to locate and segment all the plants from image data. Modern instance segmentation techniques can achieve this, however, training such systems requires large amounts of hand-labelled data which is expensive and laborious to obtain. Weakly supervised training can help to greatly reduce labelling efforts and costs. We propose panoptic one-click segmentation, an efficient and accurate offline tool to produce pseudo-labels from click inputs which reduces labelling effort. Our approach jointly estimates the pixel-wise location of all N objects in the scene, compared to traditional approaches which iterate independently through all N objects; this greatly reduces training time. Using just 10% of the data to train our panoptic one-click segmentation approach yields 68.1% and 68.8% mean object intersection over union (IoU) on challenging sugar beet and corn image data respectively, providing comparable performance to traditional one-click approaches while being approximately 12 times faster to train. We demonstrate the applicability of our system by generating pseudo-labels from clicks on the remaining 90% of the data. These pseudo-labels are then used to train Mask R-CNN, in a semi-supervised manner, improving the absolute performance (of mean foreground IoU) by 9.4 and 7.9 points for sugar beet and corn data respectively. Finally, we show that our technique can recover missed clicks during annotation outlining a further benefit over traditional approaches.

Over 5000 Dynamic World image are produced every day, whereas traditional approaches to building land cover data can take months or years to produce. As a result of leveraging a novel deep learning approach, based on Sentinel-2 Top of Atmosphere, Dynamic World offers global land cover updating every 2–5 days depending on location. A major benefit of an AI-powered approach is the model looks at an incoming Sentinel-2 satellite image and, for every pixel in the image, estimates the degree of tree cover, how built up a particular area is, or snow coverage if there's been a recent snowstorm, for example. As a result of the European Commission's Copernicus Program making European Space Agency Sentinel data freely and openly available, products like Dynamic World are able to offer 10m resolution land cover data. This is important because quantifying data in higher resolution produces more accurate results for what's really on the surface of the Earth.