搜索排序的数据（{\ BF SOSD}，简而言之）是一种高度工程化的软件平台，用于基准获得学习索引，后者是通过将机器学习技术与经典组合在一起的方式搜索分类表的新颖且相当有效的提议算法。在这样的平台和相关的基准测试实验中，在自然和直观的选择之后，通过标准（教科书）二进制搜索过程执行最终搜索阶段。然而，最近的研究，不使用机器学习预测，表明统一的二进制搜索，简化以避免主循环中的\ vir {分支}，在要搜索的表格中的标准对应物时，性能优异地是相对较小的，例如，在L1或L2缓存中拟合。 k-ary搜索的类似结果即使在大桌子上也是如此。人们期望学习索引中的类似行为。通过一组广泛的实验，与现有技术相干，我们表明，对于学习的索引，并且就{\ bf sosd}软件而言，使用标准例程（二进制或k-ary搜索）在所有内部存储器级别上优​​于均匀的一个。这一事实提供了到目前为止所制作的自然选择的定量理由。我们的实验还表明，统一的二进制和k-ary搜索可能是有利的，以便在学习索引中节省空间，同时授予良好的性能。我们的研究结果是对这种新颖和快速增长的区域的方法有关，以及有兴趣在应用领域中使用学习索引的从业者，例如数据库和搜索引擎。

机器学习技术，与数据结构合并，导致学习静态索引，创新和强大的工具，用于加速二进制搜索，使用其他空间相对于被搜索到的表。这种空间致力于ML模型。虽然在他们的阶段，但由于分类表搜索程序的普遍性，它们在方法上和实际上很重要。在现代应用中，模型空间是一个关键因素，实际上，关于该领域的一个重大开放问题是评估一个人在多大程度上享受学习索引的速度，同时使用常量或几乎恒定的空间模型。我们通过（a）在此处介绍两个新模型，即表示为{\ bf ko-bfs}和{\ bf sy-rmi}; （b）通过系统地探索现有模型的层次结构的时间空间权衡，即{\ bf sosd}中的{\ bf sosd}中的时间表。我们记录了一种新颖且复杂的时空折衷图片，这对用户来说非常丰富。我们通过实验表明{\ bf ko-bfs}可以加快恒定空间中的插值搜索和统一二进制搜索。对于其他版本的二进制搜索，我们的第二种模型以及双标准{\ BF PGM}索引可以实现速度，其模型空间比表所拍摄的0.05 \％$的型号。有竞争力在时间空间与现有建议的权衡方面。 {\ bf sy-rmi}和bi-criteria {\ bf pgm}在内部内存层次结构的各个级别中相互作用。最后，我们的调查结果对设计者感兴趣，因为它们强调了对学习指标中的时空关系的进一步研究的需要。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning (DL) in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, explainable by design, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem.

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-ofthe-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.

In this paper, we introduce MINTIME, a video deepfake detection approach that captures spatial and temporal anomalies and handles instances of multiple people in the same video and variations in face sizes. Previous approaches disregard such information either by using simple a-posteriori aggregation schemes, i.e., average or max operation, or using only one identity for the inference, i.e., the largest one. On the contrary, the proposed approach builds on a Spatio-Temporal TimeSformer combined with a Convolutional Neural Network backbone to capture spatio-temporal anomalies from the face sequences of multiple identities depicted in a video. This is achieved through an Identity-aware Attention mechanism that attends to each face sequence independently based on a masking operation and facilitates video-level aggregation. In addition, two novel embeddings are employed: (i) the Temporal Coherent Positional Embedding that encodes each face sequence's temporal information and (ii) the Size Embedding that encodes the size of the faces as a ratio to the video frame size. These extensions allow our system to adapt particularly well in the wild by learning how to aggregate information of multiple identities, which is usually disregarded by other methods in the literature. It achieves state-of-the-art results on the ForgeryNet dataset with an improvement of up to 14% AUC in videos containing multiple people and demonstrates ample generalization capabilities in cross-forgery and cross-dataset settings. The code is publicly available at https://github.com/davide-coccomini/MINTIME-Multi-Identity-size-iNvariant-TIMEsformer-for-Video-Deepfake-Detection.

Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state information is often possible. In particular, we propose a method for partially observable reinforcement learning that uses a fully observable policy (which we call a state expert) during offline training to improve online performance. Based on Soft Actor-Critic (SAC), our agent balances performing actions similar to the state expert and getting high returns under partial observability. Our approach can leverage the fully-observable policy for exploration and parts of the domain that are fully observable while still being able to learn under partial observability. On six robotics domains, our method outperforms pure imitation, pure reinforcement learning, the sequential or parallel combination of both types, and a recent state-of-the-art method in the same setting. A successful policy transfer to a physical robot in a manipulation task from pixels shows our approach's practicality in learning interesting policies under partial observability.