口吃是一种复杂的言语障碍，会对个人有效沟通的能力产生负面影响。口吃（PWS）的人通常在这种情况下遭受很大的痛苦，并通过治疗寻求帮助。流利的塑形是一种治疗方法，PWSS学会修改他们的语音以帮助他们克服口吃。即使在治疗后，掌握这种语音技术也需要时间和练习。治疗后不久，对成功的评估很高，但复发率很高。为了能够长期监视语音行为，检测口吃事件和语音修改的能力可以帮助PWSS和语音病理学家跟踪流利程度。监测可以通过检测流利度的失误来提早进行干预的能力。据我们所知，没有公共数据集可用，其中包含接受口吃疗法的人的演讲，这些疗法改变了口语风格。这项工作介绍了Kassel Fluency（KSOF），这是一项基于疗法的数据集，其中包含超过5500个PWSS。这些剪辑标记为六种与口吃相关的事件类型：块，延长，声音重复，单词重复，插入和 - 特定于治疗 - 语音修改。音频是在Kasseler Stottertherapie研究所期间记录的。该数据将根据要求提供用于研究目的。

Transformers have become the state-of-the-art neural network architecture across numerous domains of machine learning. This is partly due to their celebrated ability to transfer and to learn in-context based on few examples. Nevertheless, the mechanisms by which Transformers become in-context learners are not well understood and remain mostly an intuition. Here, we argue that training Transformers on auto-regressive tasks can be closely related to well-known gradient-based meta-learning formulations. We start by providing a simple weight construction that shows the equivalence of data transformations induced by 1) a single linear self-attention layer and by 2) gradient-descent (GD) on a regression loss. Motivated by that construction, we show empirically that when training self-attention-only Transformers on simple regression tasks either the models learned by GD and Transformers show great similarity or, remarkably, the weights found by optimization match the construction. Thus we show how trained Transformers implement gradient descent in their forward pass. This allows us, at least in the domain of regression problems, to mechanistically understand the inner workings of optimized Transformers that learn in-context. Furthermore, we identify how Transformers surpass plain gradient descent by an iterative curvature correction and learn linear models on deep data representations to solve non-linear regression tasks. Finally, we discuss intriguing parallels to a mechanism identified to be crucial for in-context learning termed induction-head (Olsson et al., 2022) and show how it could be understood as a specific case of in-context learning by gradient descent learning within Transformers.

We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. The intruders' objective is to breach the target boundary without being captured by the defender. As soon as the current intruder breaches the target or gets captured by the defender, the next intruder appears at a random location on a fixed circle surrounding the target. Therefore, the defender's final location at the end of the current game becomes its initial location for the next game. Thus, the players pick strategies that are advantageous for the current as well as for the future games. Depending on the information available to the players, each game is divided into two phases: partial information and full information phase. Under some assumptions on the sensing and speed capabilities, we analyze the agents' strategies in both phases. We derive equilibrium strategies for both the players to optimize the capture percentage using the notions of engagement surface and capture circle. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders.

We analyze the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several particularities such as the concentric circular ring shape of the objects and high precision requirements due to which existing methods don't perform sufficiently well. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD.

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.

In the past years, artificial neural networks (ANNs) have become the de-facto standard to solve tasks in communications engineering that are difficult to solve with traditional methods. In parallel, the artificial intelligence community drives its research to biology-inspired, brain-like spiking neural networks (SNNs), which promise extremely energy-efficient computing. In this paper, we investigate the use of SNNs in the context of channel equalization for ultra-low complexity receivers. We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE). For conversion of real-world data into spike signals we introduce a novel ternary encoding and compare it with traditional log-scale encoding. We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. We highlight that mainly the conversion of the channel output to spikes introduces a small performance penalty. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.

噪声的去除或取消对成像和声学具有广泛的应用。在日常生活中，Denoising甚至可能包括对地面真理不忠的生成方面。但是，对于科学应用，denoing必须准确地重现地面真相。在这里，我们展示了如何通过深层卷积神经网络来定位数据，从而以定量精度出现弱信号。特别是，我们研究了晶体材料的X射线衍射。我们证明，弱信号是由电荷排序引起的，在嘈杂的数据中微不足道的信号，在DeNo的数据中变得可见和准确。通过对深度神经网络的监督培训，具有成对的低噪声数据，可以通过监督培训来实现这一成功。这样，神经网络就可以了解噪声的统计特性。我们证明，使用人造噪声（例如泊松和高斯）不会产生这种定量准确的结果。因此，我们的方法说明了一种实用的噪声过滤策略，可以应用于具有挑战性的获取问题。

我们考虑使用随时间变化的贝叶斯优化（TVBO）依次优化时间变化的目标函数的问题。在这里，关键挑战是应对旧数据。当前的TVBO方法需要事先了解恒定的变化率。但是，变化率通常既不知道也不恒定。我们提出了一种事件触发的算法，ET-GP-UCB，该算法检测在线目标函数的变化。事件触发器基于高斯过程回归中使用的概率统一误差界。触发器会自动检测目标函数发生重大变化时。然后，该算法通过重置累积数据集来适应时间更改。我们为ET-GP-UCB提供了遗憾的界限，并在数值实验中显示了它与最先进算法具有竞争力，即使它不需要有关时间变化的知识。此外，如果变更率误指出，ET-GP-UCB的表现要优于这些竞争基准，并且我们证明它很容易适用于各种情况，而无需调整超参数。

强大的控制器确保在不确定性下设计但以绩效为代价的反馈回路中的稳定性。最近提出的基于学习的方法可以减少时间不变系统的模型不确定性，从而改善使用数据的稳健控制器的性能。但是，实际上，许多系统在随着时间的变化形式表现出不确定性，例如，由于重量转移或磨损，导致基于学习的控制器的性能或不稳定降低。我们提出了一种事件触发的学习算法，该算法决定何时在LQR问题中以罕见或缓慢的变化在LQR问题中学习。我们的关键想法是在健壮的控制器和学习的控制器之间切换。对于学习，我们首先使用概率模型通过蒙特卡洛估计来近似学习阶段的最佳长度。然后，我们根据LQR成本的力矩生成功能设计了不确定系统的统计测试。该测试检测到控制下的系统的变化，并在控制性能由于系统变化而恶化时触发重新学习。在数值示例中，我们证明了与鲁棒控制器基线相比的性能提高。

变化的条件或环境会导致系统动态随着时间而变化。为了确保最佳控制性能，控制器应适应这些更改。当不明变化的基本原因和时间未知时，我们需要依靠在线数据进行适应。在本文中，我们将使用随时间变化的贝叶斯优化（TVBO）在不断变化的环境中在线调整控制器，并使用有关控制目标及其更改的适当先验知识。两种属性是许多在线控制器调整问题的特征：首先，由于系统动力学的变化，例如通过磨损，它们在目标上表现出增量和持久的变化。其次，优化问题是调谐参数中的凸。当前的TVBO方法不会明确考虑这些属性，从而通过过度探索参数空间导致调谐性能和许多不稳定的控制器。我们建议使用不确定性注入（UI）的新型TVBO遗忘策略，该策略结合了增量和持久变化的假设。控制目标通过时间结构域中的维也纳工艺建模为使用UI的时空高斯过程（GP）。此外，我们通过与线性不等式约束的GP模型明确对空间维度中的凸度假设进行建模。在数值实验中，我们表明我们的模型优于TVBO中的最新方法，表现出减少的遗憾和更少的不稳定参数配置。