压力溃疡在ICU患者中具有很高的患病率，但如果以初始阶段识别，则可预防。在实践中，布拉登规模用于分类高风险患者。本文通过使用MIMIC-III V1.4中可用的数据调查了在电子健康中使用机器学习记录数据的使用。制定了两个主要贡献：评估考虑在住宿期间所有预测的模型的新方法，以及用于机器学习模型的新培训方法。结果与现有技术相比，表现出卓越的性能;此外，所有型号在精密召回曲线中的每个工作点都超过了Braden刻度。 - - les \〜oes por按\〜ao possuem alta preval \ ^ encia em pacientes de Uti e s \〜ao preven \'iveis ao serem endicidificadas em Est \'agios Iniciais。 na pr \'atica materiza-se a escala de braden para classifica \ c {c} \〜ao de pacientes em risco。 Este Artigo Investiga o Uso de Apenizado de M \'Aquina Em Dados de Registros Eletr \ ^ Onicos Para Este Fim，Parir Da Base dados Mimic-III V1.4。 s \〜ao feitas duas contribui \ c {c} \〜oes principais：uma nova abordagem para a avalia \ c {c} \〜ao dos modelos e da escala da escala de braden levando em conta todas作为predi \ c {c} \ 〜oes feitas ao longo das interna \ c {c} \〜oes，euro novo m \'etodo de treinamento para os modelos de aprendizo de m \'aquina。 os结果os overidos superam o estado da arte everifica-se que os modelos superam意义a escala de braden em todos oS pontos de Opera \ c {c} \〜〜ao da curva de precis \〜ao por sensibilidade。

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted approaches to causality: Humes regularity, counterfactual, manipulation, and mechanisms. This short review is motivated by presenting simplified views and definitions and then supplements them with examples from various fields, including economics, education, medicine, politics, physics, and engineering. It is the hope that this short review comes in handy for new and interested readers with little knowledge of causality and causal inference.

A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification was realized. In this context, the non-stationarity of EEG signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. 418 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 75 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject and cross-session validation strategy and making use of other biosignals as support were excluded. On the basis of the selected papers' analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion on the different ML approaches involved. The studies with the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation.

Solving the challenges of automatic machine translation of Building Automation System text metadata is a crucial first step in efficiently deploying smart building applications. The vocabulary used to describe building metadata appears small compared to general natural languages, but each term has multiple commonly used abbreviations. Conventional machine learning techniques are inefficient since they need to learn many different forms for the same word, and large amounts of data must be used to train these models. It is also difficult to apply standard techniques such as tokenisation since this commonly results in multiple output tags being associated with a single input token, something traditional sequence labelling models do not allow. Finite State Transducers can model sequence-to-sequence tasks where the input and output sequences are different lengths, and they can be combined with language models to ensure a valid output sequence is generated. We perform a preliminary analysis into the use of transducer-based language models to parse and normalise building point metadata.

Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modeling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modeling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations.

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.

As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude. We also demonstrate that our approach yields implementable trajectories in an experiment with a Crazyflie quadrotor.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.