语音中的自我监督学习涉及在大规模的未注释的语音语料库上训练语音表示网络，然后将学习的表示形式应用于下游任务。由于语音中SSL学习的大多数下游任务主要集中在语音中的内容信息上，因此最理想的语音表示形式应该能够将不需要的变化（例如说话者的变化）从内容中删除。但是，解开扬声器非常具有挑战性，因为删除说话者的信息也很容易导致内容丢失，而后者的损害通常远远超过了前者的好处。在本文中，我们提出了一种新的SSL方法，该方法可以实现扬声器分解而不会严重丢失内容。我们的方法是根据休伯特框架改编的，并结合了解开机制，以使教师标签和博学的代表规范化。我们在一组与内容相关的下游任务上评估了说话者分解的好处，并观察到我们的扬声器示词表示的一致且著名的性能优势。

神经科学家和机器学习研究人员通常引用对抗的例子，作为计算模型如何从生物感官系统发散的示例。最近的工作已经提出将生物启发组件添加到视觉神经网络中，作为提高其对抗性鲁棒性的一种方式。一种令人惊讶的有效组分，用于减少对抗性脆弱性是响应随机性，例如由生物神经元呈现的响应性随机性。在这里，使用最近开发的从计算神经科学的几何技术，我们研究了对抗性扰动如何影响标准，前列培训和生物学启发的随机网络的内部表示。我们为每种类型的网络找到了不同的几何签名，揭示了实现稳健表示的不同机制。接下来，我们将这些结果概括为听觉域，表明神经插值性也使听觉模型对对抗对抗扰动更鲁棒。随机网络的几何分析揭示了清洁和离前动脉扰动刺激的表示之间的重叠，并且定量表现出随机性的竞争几何效果在对抗和清洁性能之间调解权衡。我们的结果阐明了通过对外内培训和随机网络利用的强大感知的策略，并帮助解释了随机性如何有利于机器和生物计算。

已知深神经网络（DNN）容易受到对抗性攻击的影响，即对输入的不可察觉的扰动可以误导DNN在清洁图像上培训，以制造错误的预测。为了解决这一目标，对抗性训练是目前最有效的防御方法，通过增强速度设定的训练，在飞行中产生的对抗样本。有趣的是，我们首次发现，在随机初始化的网络中，在没有任何模型训练的随机初始化网络中，第一次发现具有天生稳健性，匹配或超越对抗训练网络的强大准确性的鲁棒准确性，表明对模型权重的对抗训练不是对抗性鲁棒性不可或缺。我们命名为强大的临时票故障票（RST），也是自然效率的那种。不同于流行的彩票假设，既不需要培训原始密集的网络也不需要训练。为了验证和理解这种迷人的发现，我们进一步开展了广泛的实验，以研究不同模型，数据集，稀疏模式和攻击下RST的存在性和性质，绘制关于DNNS鲁棒性与其初始化/过度分辨率之间的关系的洞察。此外，我们确定从同一随机初始化的密集网络绘制的不同稀疏比率的RST之间的差的对抗性转移性，并提出了一种随机切换不同RST之间的随机切换的随机性，作为基于顶部的新型防御方法第一次。我们相信我们对RST的调查结果已经开辟了一个新的视角，以研究模型稳健性并扩大彩票假设。

我们介绍了ThreedWorld（TDW），是交互式多模态物理模拟的平台。 TDW能够模拟高保真感官数据和富裕的3D环境中的移动代理和对象之间的物理交互。独特的属性包括：实时近光 - 真实图像渲染;对象和环境库，以及他们定制的例程;有效构建新环境课程的生成程序;高保真音频渲染;各种材料类型的现实物理相互作用，包括布料，液体和可变形物体;可定制的代理体现AI代理商;并支持与VR设备的人类交互。 TDW的API使多个代理能够在模拟中进行交互，并返回一系列表示世界状态的传感器和物理数据。我们在计算机视觉，机器学习和认知科学中的新兴的研究方向上提供了通过TDW的初始实验，包括多模态物理场景理解，物理动态预测，多代理交互，像孩子一样学习的模型，并注意研究人类和神经网络。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

Springs are efficient in storing and returning elastic potential energy but are unable to hold the energy they store in the absence of an external load. Lockable springs use clutches to hold elastic potential energy in the absence of an external load but have not yet been widely adopted in applications, partly because clutches introduce design complexity, reduce energy efficiency, and typically do not afford high-fidelity control over the energy stored by the spring. Here, we present the design of a novel lockable compression spring that uses a small capstan clutch to passively lock a mechanical spring. The capstan clutch can lock up to 1000 N force at any arbitrary deflection, unlock the spring in less than 10 ms with a control force less than 1 % of the maximal spring force, and provide an 80 % energy storage and return efficiency (comparable to a highly efficient electric motor operated at constant nominal speed). By retaining the form factor of a regular spring while providing high-fidelity locking capability even under large spring forces, the proposed design could facilitate the development of energy-efficient spring-based actuators and robots.