MARCO排名数据集已广泛用于培训IR任务的深度学习模型，在不同的零射击方案上实现了相当大的效果。但是，这种类型的资源是英语以外的语言的稀缺。在这项工作中，我们呈现MMARCO，MS Marco段落的多语言版本，该数据集包括使用机器翻译创建的13种语言。我们通过微调单语和多语言重新排名模型以及此数据集的密集多语言模型进行了评估。实验结果表明，在我们翻译的数据集上微调微调的多语言模型可以单独对原始英文版的模型进行微调的卓越效果。我们蒸馏的多语言RE-RANKER与非蒸馏模型具有竞争力，而参数较少的5.4倍。最后，我们展现了翻译质量和检索效果之间的正相关性，提供了证据，即翻译方法的改进可能导致多语言信息检索的改进。翻译的数据集和微调模型可在https://github.com/unicamp-dl/mmarco.git上获得。

Bi-encoders and cross-encoders are widely used in many state-of-the-art retrieval pipelines. In this work we study the generalization ability of these two types of architectures on a wide range of parameter count on both in-domain and out-of-domain scenarios. We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models. Our experiments show that increasing model size results in marginal gains on in-domain test sets, but much larger gains in new domains never seen during fine-tuning. Furthermore, we show that cross-encoders largely outperform bi-encoders of similar size in several tasks. In the BEIR benchmark, our largest cross-encoder surpasses a state-of-the-art bi-encoder by more than 4 average points. Finally, we show that using bi-encoders as first-stage retrievers provides no gains in comparison to a simpler retriever such as BM25 on out-of-domain tasks. The code is available at https://github.com/guilhermemr04/scaling-zero-shot-retrieval.git

在多语言甚至单语言中鉴定的模型的零拍跨语言能力刺激了许多假设，以解释这一有趣的经验结果。但是，由于预处理的成本，大多数研究都使用公共模型的公共模型，其预处理方法（例如代币化，语料库规模和计算预算的选择）可能会大不相同。当研究人员对自己的模型预识时，他们通常会在预算有限的情况下这样做，并且与SOTA模型相比，最终的模型的表现可能明显不足。这些实验差异导致有关这些模型跨语性能力的性质的各种不一致的结论。为了帮助对该主题进行进一步研究，我们发布了10个单语字节级模型，并在相同的配置下进行了严格审慎的概述，并具有大型计算预算（相当于V100的420天）和Corpora，比原始BERT大4倍。由于它们不含令牌，因此消除了看不见的令牌嵌入的问题，从而使研究人员可以在具有不同脚本的语言中尝试更广泛的跨语言实验。此外，我们释放了在不自然语言文本上预测的两个模型，这些模型可用于理智检查实验。关于质量检查和NLI任务的实验表明，我们的单语模型实现了多语言的竞争性能，因此可以加强我们对语言模型中跨语性可传递性的理解。

最近的工作表明，小型蒸馏语言模型是强大的竞争对手，这些模型是在广泛的信息检索任务中更大且较慢的数量级。由于潜伏期的限制，这使蒸馏而密集的模型是在现实世界检索应用程序中部署的首选选择。在这项工作中，我们通过证明参数和早期查询文档互动的数量在检索模型的概括能力中起着重要作用来质疑这种做法。我们的实验表明，增加模型大小会导致内域测试集的边际增长，但是在微调过程中从未见过的新领域的增长幅度更大。此外，我们表明，在几个任务中，Rerankers在很大程度上都超过了相似大小的密集。我们最大的重读者在基准-IR（BEIR）的18个数据集中的12个数据集中达到了最新技术，并超过了先前的最新水平。最后，我们确认内域的有效性不是零弹性有效性的良好指标。代码可从https://github.com/guilhermemr04/scaling-zero-shot-retrieval.git获得。

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects.

Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have developed radio resource management mechanisms using various optimisation engines. In this work, we consider the problem of overall ergodic spectral efficiency maximisation in the context of uplink-downlink data power control in cell-free networks. To solve this problem in large networks, and to address convergence-time limitations, we apply scalable multi-objective Bayesian optimisation. Furthermore, we discuss how an intersection of multi-fidelity emulation and Bayesian optimisation can improve radio resource management in cell-free networks.

Knowledge graph (KG) link prediction aims to infer new facts based on existing facts in the KG. Recent studies have shown that using the graph neighborhood of a node via graph neural networks (GNNs) provides more useful information compared to just using the query information. Conventional GNNs for KG link prediction follow the standard message-passing paradigm on the entire KG, which leads to over-smoothing of representations and also limits their scalability. On a large scale, it becomes computationally expensive to aggregate useful information from the entire KG for inference. To address the limitations of existing KG link prediction frameworks, we propose a novel retrieve-and-read framework, which first retrieves a relevant subgraph context for the query and then jointly reasons over the context and the query with a high-capacity reader. As part of our exemplar instantiation for the new framework, we propose a novel Transformer-based GNN as the reader, which incorporates graph-based attention structure and cross-attention between query and context for deep fusion. This design enables the model to focus on salient context information relevant to the query. Empirical results on two standard KG link prediction datasets demonstrate the competitive performance of the proposed method.

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.