We present a smoothly broken power law functional form that accurately models and extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as the amount of compute used for training, number of model parameters, training dataset size, or upstream performance varies) for each task within a large and diverse set of upstream and downstream tasks, in zero-shot, prompted, and fine-tuned settings. This set includes large-scale vision and unsupervised language tasks, diffusion generative modeling of images, arithmetic, and reinforcement learning. When compared to other functional forms for neural scaling behavior, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. Code is available at https://github.com/ethancaballero/broken_neural_scaling_laws

对脑外伤（TBI）患者的准确预后很难为治疗，患者管理和长期护理提供信息至关重要。年龄，运动和学生反应性，缺氧和低血压以及计算机断层扫描（CT）的放射学发现等患者特征已被确定为TBI结果预测的重要变量。 CT是临床实践中选择的急性成像方式，因为其获取速度和广泛的可用性。但是，这种方式主要用于定性和半定量评估，例如马歇尔评分系统，该系统容易受到主观性和人为错误。这项工作探讨了使用最先进的，深度学习的TBI病变分割方法从常规获得的医院入院CT扫描中提取的成像生物标志物的预测能力。我们使用病变体积和相应的病变统计作为扩展TBI结果预测模型的输入。我们将我们提出的功能的预测能力与马歇尔分数进行比较，并与经典的TBI生物标志物配对。我们发现，在预测不利的TBI结果时，自动提取的定量CT功能的性能与Marshall分数相似或更好。利用自动地图集对齐，我们还确定额叶外病变是不良预后的重要指标。我们的工作可能有助于更好地理解TBI，并提供有关如何使用自动化神经影像分析来改善TBI后预测的新见解。

部署在医学成像任务上的机器学习模型必须配备分布外检测功能，以避免错误的预测。不确定依赖于深神经网络的分布外检测模型是否适合检测医学成像中的域移位。高斯流程可以通过其数学结构可靠地与分布数据点可靠地分开分发数据点。因此，我们为分层卷积高斯工艺提出了一个参数有效的贝叶斯层，该过程融合了在Wasserstein-2空间中运行的高斯过程，以可靠地传播不确定性。这直接用远距离的仿射操作员在分布中直接取代了高斯流程。我们对脑组织分割的实验表明，所得的架构接近了确定性分割算法（U-NET）的性能，而先前的层次高斯过程尚未实现。此外，通过将相同的分割模型应用于分布外数据（即具有病理学（例如脑肿瘤）的图像），我们表明我们的不确定性估计导致分布外检测，以优于以前的贝叶斯网络和以前的贝叶斯网络的功能基于重建的方法学习规范分布。为了促进未来的工作，我们的代码公开可用。

搅拌是痴呆症患病率高的神经精神症状之一，可以对日常生活（ADL）的活动产生负面影响，以及个体的独立性。检测搅拌剧集可以帮助提前及时地提供痴呆症（PLWD）的人们。分析搅拌剧集还将有助于识别可修改的因素，例如环境温度和睡眠中的睡眠，导致个体搅动。这项初步研究提出了一种监督学习模型，用于分析PLWD中搅动风险，使用家庭监控数据。家庭监控数据包括来自2019年4月2021年4月至6月至6月20日至6月20日至6月至6月间PLWD的运动传感器，生理测量和厨房电器的使用。我们应用经常性的深度学习模型，以识别验证和记录的临床监测团队验证和记录的搅拌集团。我们提出了评估拟议模型的功效的实验。拟议的模型平均召回79.78％的召回，27.66％的精确度和37.64％的F1分数在采用最佳参数时得分，表明识别搅动事件的良好能力。我们还使用机器学习模型讨论使用连续监测数据分析行为模式，并探索临床适用性以及敏感性和特异性监控应用之间的选择。

对于由缺陷线性回归中的标签噪声引起的预期平均平方概率，我们证明了无渐近分布的下限。我们的下部结合概括了过度公共数据（内插）制度的类似已知结果。与最先前的作品相比，我们的分析适用于广泛的输入分布，几乎肯定的全排列功能矩阵，允许我们涵盖各种类型的确定性或随机特征映射。我们的下限是渐近的锐利，暗示在存在标签噪声时，缺陷的线性回归不会在任何这些特征映射中围绕内插阈值进行良好的。我们详细分析了强加的假设，并为分析（随机）特征映射提供了理论。使用此理论，我们可以表明我们的假设对于具有（Lebesgue）密度的输入分布以及随机深神经网络给出的特征映射，具有Sigmoid，Tanh，SoftPlus或Gelu等分析激活功能。作为进一步的例子，我们示出了来自随机傅里叶特征和多项式内核的特征映射也满足我们的假设。通过进一步的实验和分析结果，我们补充了我们的理论。

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.