Time series, sets of sequences in chronological order, are essential data in statistical research with many forecasting applications. Although recent performance in many Transformer-based models has been noticeable, long multi-horizon time series forecasting remains a very challenging task. Going beyond transformers in sequence translation and transduction research, we observe the effects of down-and-up samplings that can nudge temporal saliency patterns to emerge in time sequences. Motivated by the mentioned observation, in this paper, we propose a novel architecture, Temporal Saliency Detection (TSD), on top of the attention mechanism and apply it to multi-horizon time series prediction. We renovate the traditional encoder-decoder architecture by making as a series of deep convolutional blocks to work in tandem with the multi-head self-attention. The proposed TSD approach facilitates the multiresolution of saliency patterns upon condensed multi-heads, thus progressively enhancing complex time series forecasting. Experimental results illustrate that our proposed approach has significantly outperformed existing state-of-the-art methods across multiple standard benchmark datasets in many far-horizon forecasting settings. Overall, TSD achieves 31% and 46% relative improvement over the current state-of-the-art models in multivariate and univariate time series forecasting scenarios on standard benchmarks. The Git repository is available at https://github.com/duongtrung/time-series-temporal-saliency-patterns.

21世纪的现代旅游面临着许多挑战。这些挑战之一是太空有限地区的游客数量迅速增长，例如历史城市中心，博物馆或地理瓶颈，例如狭窄的山谷。在这种情况下，对特定领域内的旅游量和旅游流程的正确准确预测对于游客管理任务，例如游客流量控制和预防人满为患至关重要。静态流量控制方法，例如限制对热点或使用常规低级控制器的访问，无法解决问题。在本文中，我们通过使用旅游区提供的可用粒状数据，并将结果与​​Arima进行比较，并将结果与​​Arima进行比较经典统计方法。我们的结果表明，与Arima方法相比，深度学习模型可以产生更好的预测，同时具有更快的推理时间和能够结合其他输入功能。

生物识别技术，尤其是人脸识别，已成为全球身份管理系统的重要组成部分。在Biometrics的部署中，必须安全地存储生物信息，以保护用户的隐私是必要的。在此上下文中，生物识别密码系统旨在满足生物识别信息保护的关键要求，从而实现隐私保留存储和生物识别数据的比较。该工作调查了通过深卷积神经网络提取的面部特征向量的改进的模糊Vault方案的应用。为此，引入了一个特征转换方法，将固定长度的实值深度特征向量映射到整数值的功能集。作为所述特征变换的一部分，进行了对不同特征量化和二碳技术的详细分析。在关键绑定处，获得的特征集被锁定在可解释的改进的模糊拱顶中。对于关键检索，研究了不同多项式重建技术的效率。所提出的特征转换方法和模板保护方案是生物识别特性的不可知。在实验中，构造了基于可解释的改进的深面模糊Vault基础模板保护方案，采用用培训的最先进的深卷积神经网络提取的特征，该特征在接受附加角度损失（arcFace）。为了最佳配置，在Furet和FRGCV2面部数据库的跨数据库实验中实现了0.01％的假匹配速率低于1％以下的假非匹配率。平均而言，获得高达约28位的安全级别。这项工作提出了一个有效的面基模糊Vault方案，提供了面部参考数据的隐私保护以及从脸部的数字键推导。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.

Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system.

Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide, where knee OA takes more than 80% of commonly affected joints. Knee OA is not a curable disease yet, and it affects large columns of patients, making it costly to patients and healthcare systems. Etiology, diagnosis, and treatment of knee OA might be argued by variability in its clinical and physical manifestations. Although knee OA carries a list of well-known terminology aiming to standardize the nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes of the chronic joint disease, in practice there is a wide range of terminology associated with knee OA across different data sources, including but not limited to biomedical literature, clinical notes, healthcare literacy, and health-related social media. Among these data sources, the scientific articles published in the biomedical literature usually make a principled pipeline to study disease. Rapid yet, accurate text mining on large-scale scientific literature may discover novel knowledge and terminology to better understand knee OA and to improve the quality of knee OA diagnosis, prevention, and treatment. The present works aim to utilize artificial neural network strategies to automatically extract vocabularies associated with knee OA diseases. Our finding indicates the feasibility of developing word embedding neural networks for autonomous keyword extraction and abstraction of knee OA.

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems. The objective of this article is to introduce practitioners and researchers from the field of Information Systems (IS) to data-centric AI. We define relevant terms, provide key characteristics to contrast the data-centric paradigm to the model-centric one, and introduce a framework for data-centric AI. We distinguish data-centric AI from related concepts and discuss its longer-term implications for the IS community.

Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.

Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn pairwise interactions between each piece of information in each data type, thereby escalating model complexity beyond manageable scales. This has so far precluded a widespread use of multimodal deep learning. Here, we present a new technical approach of "learnable synergies", in which the model only selects relevant interactions between data modalities and keeps an "internal memory" of relevant data. Our approach is easily scalable and naturally adapts to multimodal data inputs from clinical routine. We demonstrate this approach on three large multimodal datasets from radiology and ophthalmology and show that it outperforms state-of-the-art models in clinically relevant diagnosis tasks. Our new approach is transferable and will allow the application of multimodal deep learning to a broad set of clinically relevant problems.