低估和控制生成模型的潜像是一个复杂的任务。在本文中，我们提出了一种新的学习方法，用于在预先训练的GaN的潜在空间中控制任何所需属性，以便相应地编辑合成和现实世界数据样本。我们执行SIM2REAL学习，依靠最小的样品来实现无限量的连续精确编辑。我们介绍了一种基于AutoEncoder的模型，该模型学习以编码图像之间的变化的语义作为编辑稍后编辑新样本的基础，实现了精确的期望结果 - 图1所示的示例。虽然先前的编辑方法依赖于潜伏的已知结构空格（例如，样式中的某些语义的线性），我们的方法本身不需要任何结构约束。我们在面部图像的域中演示了我们的方法：编辑不同的表达式，姿势和照明属性，实现最先进的结果。

For applications that require processing large amounts of text at inference time, Large Language Models (LLMs) are handicapped by their limited context windows, which are typically 2048 tokens. In-context learning, an emergent phenomenon in LLMs in sizes above a certain parameter threshold, constitutes one significant example because it can only leverage training examples that fit into the context window. Existing efforts to address the context window limitation involve training specialized architectures, which tend to be smaller than the sizes in which in-context learning manifests due to the memory footprint of processing long texts. We present Parallel Context Windows (PCW), a method that alleviates the context window restriction for any off-the-shelf LLM without further training. The key to the approach is to carve a long context into chunks (``windows'') that fit within the architecture, restrict the attention mechanism to apply only within each window, and re-use the positional embeddings among the windows. We test the PCW approach on in-context learning with models that range in size between 750 million and 178 billion parameters, and show substantial improvements for tasks with diverse input and output spaces. Our results motivate further investigation of Parallel Context Windows as a method for applying off-the-shelf LLMs in other settings that require long text sequences.

Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation. Existing approaches for SFDA focus on self-training usually including well-established entropy minimization techniques. One of the main challenges in SFDA is to reduce accumulation of errors caused by domain misalignment. A recent strategy successfully managed to reduce error accumulation by pseudo-labeling the target samples based on class-wise prototypes (centroids) generated by their clustering in the representation space. However, this strategy also creates cases for which the cross-entropy of a pseudo-label and the minimum entropy have a conflict in their objectives. We call this conflict the centroid-hypothesis conflict. We propose to reconcile this conflict by aligning the entropy minimization objective with that of the pseudo labels' cross entropy. We demonstrate the effectiveness of aligning the two loss objectives on three domain adaptation datasets. In addition, we provide state-of-the-art results using up-to-date architectures also showing the consistency of our method across these architectures.

Hebrew is a Morphological rich language, making its modeling harder than simpler language. Recent developments such as Transformers in general and Bert in particular opened a path for Hebrew models that reach SOTA results, not falling short from other non-MRL languages. We explore the cutting edge in this field performing style transfer, text generation and classification over news articles collected from online archives. Furthermore, the news portals that feed our collective consciousness are an interesting corpus to study, as their analysis and tracing might reveal insights about our society and discourse.

A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument, with different semantics performing the computation in different manners. In this work, we consider the problem of attack inference. That is, given a gradual semantics, a set of arguments with associated initial weights, and the final desirable acceptability degrees associated with each argument, we seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees. The main contribution of our work is to demonstrate that the associated decision problem, i.e., whether a set of attacks can exist which allows the final acceptability degrees to occur for given initial weights, is NP-complete for the weighted h-categoriser and cardinality-based semantics, and is polynomial for the weighted max-based semantics, even for the complete version of the problem (where all initial weights and final acceptability degrees are known). We then briefly discuss how this decision problem can be modified to find the attacks themselves and conclude by examining the partial problem where not all initial weights or final acceptability degrees may be known.

Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems.

Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.

本文介绍了信息性多臂强盗（IMAB）模型，在每个回合中，玩家选择手臂，观察符号，并以符号的自我信息形式获得未观察到的奖励。因此，手臂的预期奖励是产生其符号的源质量函数的香农熵。玩家的目标是最大程度地提高与武器的熵值相关的预期奖励。在假设字母大小是已知的假设下，为IMAB模型提出了两种基于UCB的算法，该算法考虑了插件熵估计器的偏差。第一种算法在熵估计中乐观地纠正了偏置项。第二算法依赖于数据依赖性置信区间，该置信区间适应具有较小熵值的源。性能保证是通过上限为每种算法的预期遗憾提供的。此外，在Bernoulli案例中，将这些算法的渐近行为与伪遗憾的Lai-Robbins的下限进行了比较。此外，在假设\ textit {cract}字母大小的假设下是未知的，而播放器仅知道其上方的宽度上限，提出了一种基于UCB的算法，在其中，玩家的目的是减少由该算法造成的遗憾。未知的字母尺寸在有限的时间方面。数字结果说明了论文中介绍的算法的预期遗憾。

这项研究提出了一种检测近距离红外（NIR）眼周眼图像的酒精消耗的方法。该研究的重点是确定外部因素（例如酒精对中枢神经系统（CNS））的影响。目的是分析这如何影响虹膜和学生运动，以及是否可以使用标准的Iris NIR相机捕获这些更改。本文提出了一个新型的融合胶囊网络（F-CAPSNET），以对饮酒受试者拍摄的虹膜NIR图像进行分类。结果表明，使用一半参数作为标准胶囊网络算法，F-CAPSNET算法可以检测IRIS NIR图像中的酒精消耗，精度为92.3％。这项工作是开发自动系统以估计“适合值班”并防止因饮酒而导致事故的一步。

联合学习（FL）是使用Edge设备上可能可用的私人数据训练机器学习模型的新兴范式。 FL的分布式操作引起了集中式机器学习中未遇到的挑战，包括需要保留本地数据集的隐私以及由于重复交换更新模型而导致的通信负载。这些挑战通常通过引起更新模型的某些失真的技术来单独解决，例如当地差异隐私（LDP）机制和有损压缩。在这项工作中，我们提出了一种方法创造的联合隐私增强和量化（JOPEQ），该隐私和量化共同实现了FL环境中的有损压缩和隐私增强。特别是，Jopeq利用基于随机晶格的矢量量化，这是一种通用压缩技术，其副产品失真在统计学上等同于加性噪声。通过使用专用的多元隐私保护噪声来增强模型更新，可以利用这种失真来增强隐私。我们表明，JOPEQ在持有所需的隐私级别的同时，根据所需的比特率同时量化数据，而不会特别影响学习模型的实用性。这是通过分析的LDP保证，失真和收敛范围的推导以及数值研究所示的。最后，我们从经验上断言，乔普克（Jopeq）拆除了已知的普通攻击，以利用隐私泄漏。