生物视觉系统的神经基础在实验上研究很具有挑战性，特别是因为相对于视觉输入，神经元活性变得越来越非线性。人工神经网络（ANN）可以为改善我们对这一复杂系统的理解提供各种目标，不仅充当硅中新假设产生的感觉皮层的预测数字双胞胎，而且还融合了生物启发的建筑主题，以逐步桥接桥梁生物和机器视觉之间的差距。该鼠标最近已成为研究视觉信息处理的流行模型系统，但是尚未确定识别鼠标视觉系统最新模型的标准化大规模基准。为了填补这一空白，我们提出了感官基准竞赛。我们从小鼠初级视觉皮层中收集了一个大规模数据集，其中包含七个小鼠的28,000多个神经元的反应，并通过数千个自然图像刺激，以及同时的行为测量，包括跑步速度，瞳孔扩张和眼动。基准挑战将基于固定测试集​​中神经元响应的预测性能对模型进行对模型，其中包括两个模型输入的轨道，仅限于刺激（感觉到）或刺激加行为（感觉符号+）。我们提供一个起始套件，以降低进入障碍的障碍，包括教程，预训练的基线模型以及带有一条线命令以进行数据加载和提交的API。我们希望将其视为定期挑战和数据发布的起点，也是衡量鼠标视觉系统及其他大规模神经系统识别模型中进度的标准工具。

了解细胞类型的多样性及其在大脑中的功能是神经科学中的关键挑战之一。大规模数据集的出现引起了细胞类型分类的不偏不倚和定量方法。我们提出了GraphDino，一种学习神经元3D形态的低尺寸表示的纯粹数据驱动方法。 GraphDino是一种新的图形表示，用于在变压器模型上利用自我监督学习的空间图表。它在节点与经典图卷积处理之间的注意力全局交互之间平滑地插值。我们表明，该方法能够屈服于与基于手动特征的分类相当的形态细胞型聚类，并且对两种不同物种和皮质区域的专家标记的细胞类型表示良好的对应关系。我们的方法适用于在数据集中的样本是图形和图形级嵌入的设置中的神经科学中。

通常通过培训用于固定的对象类的模型来解决图像分割。稍后包含附加类或更复杂的查询是昂贵的，因为它需要重新培训包含这些表达式的数据集上的模型。在这里，我们提出了一个系统，该系统可以基于测试时间的任意提示生成图像分割。提示可以是文本或图像。这种方法使我们能够为三个常见的分段任务创建一个统一的模型（训练一次），这具有不同的挑战：引用表达式分割，零拍分段和单次分割。我们构建在剪辑模型中作为骨干，我们使用基于变压器的解码器扩展，该解码器能够致密预测。在对PhraseCut数据集的扩展版本进行培训之后，我们的系统基于自由文本提示符或表达查询的附加图像生成图像的二进制分段映射。详细分析了基于图像的提示的不同变体。这种新型混合输入允许不仅针对上述三个分段任务的动态调整，而是可以制定文本或图像查询的任何二进制分段任务。最后，我们发现我们的系统适应涉及可承受能力或属性的广义查询。源代码：https://ecterlab.org/code/clipseg

Random graph models with community structure have been studied extensively in the literature. For both the problems of detecting and recovering community structure, an interesting landscape of statistical and computational phase transitions has emerged. A natural unanswered question is: might it be possible to infer properties of the community structure (for instance, the number and sizes of communities) even in situations where actually finding those communities is believed to be computationally hard? We show the answer is no. In particular, we consider certain hypothesis testing problems between models with different community structures, and we show (in the low-degree polynomial framework) that testing between two options is as hard as finding the communities. In addition, our methods give the first computational lower bounds for testing between two different `planted' distributions, whereas previous results have considered testing between a planted distribution and an i.i.d. `null' distribution.

Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language processing. Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text. The approach leverages a pre-trained large language model (LLM), GPT-3, that is fine-tuned on approximately 500 pairs of prompts (inputs) and completions (outputs). Information is extracted either from single sentences or across sentences in abstracts/passages, and the output can be returned as simple English sentences or a more structured format, such as a list of JSON objects. We demonstrate that LLMs trained in this way are capable of accurately extracting useful records of complex scientific knowledge for three representative tasks in materials chemistry: linking dopants with their host materials, cataloging metal-organic frameworks, and general chemistry/phase/morphology/application information extraction. This approach represents a simple, accessible, and highly-flexible route to obtaining large databases of structured knowledge extracted from unstructured text. An online demo is available at http://www.matscholar.com/info-extraction.

Suppose we are given an $n$-dimensional order-3 symmetric tensor $T \in (\mathbb{R}^n)^{\otimes 3}$ that is the sum of $r$ random rank-1 terms. The problem of recovering the rank-1 components is possible in principle when $r \lesssim n^2$ but polynomial-time algorithms are only known in the regime $r \ll n^{3/2}$. Similar "statistical-computational gaps" occur in many high-dimensional inference tasks, and in recent years there has been a flurry of work on explaining the apparent computational hardness in these problems by proving lower bounds against restricted (yet powerful) models of computation such as statistical queries (SQ), sum-of-squares (SoS), and low-degree polynomials (LDP). However, no such prior work exists for tensor decomposition, largely because its hardness does not appear to be explained by a "planted versus null" testing problem. We consider a model for random order-3 tensor decomposition where one component is slightly larger in norm than the rest (to break symmetry), and the components are drawn uniformly from the hypercube. We resolve the computational complexity in the LDP model: $O(\log n)$-degree polynomial functions of the tensor entries can accurately estimate the largest component when $r \ll n^{3/2}$ but fail to do so when $r \gg n^{3/2}$. This provides rigorous evidence suggesting that the best known algorithms for tensor decomposition cannot be improved, at least by known approaches. A natural extension of the result holds for tensors of any fixed order $k \ge 3$, in which case the LDP threshold is $r \sim n^{k/2}$.

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.

我们提供了证据表明，学到的密度功能理论（``dft'）的力场已准备好进行基态催化剂发现。我们的关键发现是，尽管预测的力与地面真相有很大差异，但使用从超过50 \％的评估系统中使用RPBE功能的能量与使用RPBE功能相似或较低能量的力量的力量与使用RPBE功能相似或较低的力量放松。这具有令人惊讶的含义，即学习的潜力可能已经准备好在挑战性的催化系统中替换DFT，例如在Open Catalyst 2020数据集中发现的电位。此外，我们表明，在局部谐波能量表面上具有与目标DFT能量相同的局部谐波能量表面训练的力场也能够在50 \％的情况下找到较低或相似的能量结构。与在真实能量和力量训练的标准模型相比，这种``简易电位''的收敛步骤更少，这进一步加速了计算。它的成功说明了一个关键：即使模型具有高力误差，学到的电位也可以定位能量最小值。结构优化的主要要求仅仅是学到的电位具有正确的最小值。由于学到的电位与系统大小的速度快速且尺寸为线性，因此我们的结果开辟了快速找到大型系统基础状态的可能性。

在不失去先前学习的情况下学习新任务和技能（即灾难性遗忘）是人为和生物神经网络的计算挑战，但是人工系统努力与其生物学类似物达成平等。哺乳动物的大脑采用众多神经手术来支持睡眠期间的持续学习。这些是人工适应的成熟。在这里，我们研究了建模哺乳动物睡眠的三个不同组成部分如何影响人工神经网络中的持续学习：（1）在非比型眼运动（NREM）睡眠期间观察到的垂直记忆重播过程； （2）链接到REM睡眠的生成记忆重播过程； （3）已提出的突触降压过程，以调整信噪比和支持神经保养。在评估持续学习CIFAR-100图像分类基准上的性能时，我们发现将所有三个睡眠组件的包含在内。在以后的任务期间，训练和灾难性遗忘在训练过程中提高了最高准确性。尽管某些灾难性遗忘在网络培训过程中持续存在，但更高水平的突触缩减水平会导致更好地保留早期任务，并进一步促进随后培训期间早期任务准确性的恢复。一个关键的要点是，在考虑使用突触缩小范围的水平时，手头有一个权衡 - 更具侵略性的缩减更好地保护早期任务，但较少的缩减可以增强学习新任务的能力。中级水平可以在训练过程中与最高的总体精度达到平衡。总体而言，我们的结果都提供了有关如何适应睡眠组件以增强人工连续学习系统的洞察力，并突出了未来神经科学睡眠研究的领域，以进一步进一步进行此类系统。

给定尺寸$ d $中的独立标准高斯点$ v_1，\ ldots，v_n $，对于$（n，d）$的值（n，d）$的值很高，概率很高，同时通过所有要点？将椭圆形拟合到随机点的基本问题与低级别矩阵分解，独立的组件分析和主成分分析有连接。基于有力的数值证据，桑德森，帕里洛和威尔斯基[Proc。关于决策和控制会议，第6031-6036页，2013年]猜想，椭圆形拟合问题的问题从可行的到不可行的$ n $增加，并在$ n \ sim d^2/4处急剧阈值$。我们通过为某些$ n = \ omega（\，d^2/\ log^5（d）\，）$构建合适的椭圆形来解决这个猜想，从而改善了Ghosh等人的先前工作。 [Proc。关于计算机科学基础的研讨会，第954-965、2020页]，需要$ n = o（d^{3/2}）$。我们的证明证明了Saunderson等人的最小二乘结构的可行性。使用对特定非标准随机矩阵的特征向量和特征值进行仔细的分析。