Computational social choice is a research area at the intersection of social choice (the science of collective decision-making) and computer science, which focuses on the algorithmic analysis of problems related topreference aggregation and elicitation(Brandt etal.,2013).

Symmetric property estimation is a fundamental and well studied problem in machine learning and statistics.

The per-label distribution is typically long-tailed (Tarekegn et al., 2021; De Alvis and Seneviratne, 2024): head labels dominate while tail labels appear sporadically. This imbalance is exacerbated in MLC because (i) co-occurring labels make resampling risky, and (ii) metrics like mAP favor head labels. As a result, standard optimizers (Ridnik et al., 2021) often learn head-biased boundaries, achieving high scores while failing on tail labels-problematic for safety-critical applications. In practice the per-label sample counts follow a heavy-tailed distribution: a handful of head labels dominate the data, whereas the vast majority of tail labels appear only sporadically, as shown in Figure 1. This long-tail imbalance (Tarekegn et al., 2021; De Alvis and Seneviratne, 2024) is particularly severe in the multi-label regime because (i) multiple labels co-occur within a single instance, so naïve resampling can destroy cross-label correlations, and (ii) evaluation metrics such as mAP or micro-F1 are disproportionately influenced by head labels, starving tail classes of gradient signal. Consequently, conventional optimizers (Ridnik et al., 2021) that target average loss or accuracy often learn a head-biased decision boundary, yielding high headline scores while silently failing on the tail-an outcome that is unacceptable in safety-critical or comprehensive retrieval scenarios(Barandas et al., 2024).

Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning.

In this paper, we demonstrate that "the decision boundaries of a DNN can only exist as long We clarify the main points raised by the reviewers here below. We further shed more light on the relationship between adv. Nevertheless, we never claim that, within the discr. In fact, we agree that the margin associated to different discr. Overall, however, we firmly believe that the invariant dirs.

Symmetric property estimation is a fundamental and well studied problem in machine learning and statistics.

Direct RAW-based object detection offers great promise by utilizing RAW data (unprocessed sensor data), but faces inherent challenges due to its wide dynamic range and linear response, which tends to suppress crucial object details. In particular, existing enhancement methods are almost all performed in the spatial domain, making it difficult to effectively recover these suppressed details from the skewed pixel distribution of RAW images. To address this limitation, we turn to the frequency domain, where features, such as object contours and textures, can be naturally separated based on frequency. In this paper, we propose Space-Frequency Aware RAW Image Object Detection Enhancer (SFAE), a novel framework that synergizes spatial and frequency representations. Our contribution is threefold. The first lies in the ``spatialization" of frequency bands. Different from the traditional paradigm of directly manipulating abstract spectra in deep networks, our method inversely transforms individual frequency bands back into tangible spatial maps, thus preserving direct physical intuition. Then the cross-domain fusion attention module is developed to enable deep multimodal interactions between these maps and the original spatial features. Finally, the framework performs adaptive nonlinear adjustments by predicting and applying different gamma parameters for the two domains.

-- Accurate classification of respiratory sounds requires deep learning models that effectively capture fine-grained acoustic features and long-range temporal dependencies. Convolutional Neural Networks (CNNs) are well-suited for extracting local time-frequency patterns but are limited in modeling global context. In contrast, transformer-based models can capture long-range dependencies, albeit with higher computational demands. To address these limitations, we propose a compact CNN-Temporal Self-Attention (CNN-TSA) network that integrates lightweight self-attention into an efficient CNN backbone. Central to our approach is a Frequency Band Selection (FBS) module that suppresses noisy and non-informative frequency regions, substantially improving accuracy and reducing FLOPs by up to 50%. We also introduce age-specific models to enhance robustness across diverse patient groups. Evaluated on the SPRSound-2022/2023 and ICBHI-2017 lung sound datasets, CNN-TSA with FBS sets new benchmarks on SPRSound and achieves state-of-the-art performance on ICBHI, all with a significantly smaller computational footprint. Furthermore, integrating FBS into an existing transformer baseline yields a new record on ICBHI, confirming FBS as an effective drop-in enhancement. These results demonstrate that our framework enables reliable, real-time respiratory sound analysis suitable for deployment in resource-constrained settings. ESPIRA TORY diseases remain a leading source of global morbidity and mortality, highlighting the demand for precise diagnostic tools [1]. Lung-sound analysis plays a crucial role in assessing pulmonary function, as respiratory acoustics reflect pulmonary status [2]; yet, conventional auscultation is constrained by the clinician's subjective interpretation [3]. This research was partially funded by Mohammed VI Polytechnic University (UM6P) through the i-Respire research project.