神经记录的进展现在在前所未有的细节中研究神经活动的机会。潜在的变量模型（LVMS）是用于分析各种神经系统和行为的丰富活动的有希望的工具，因为LVM不依赖于活动与外部实验变量之间的已知关系。然而，目前缺乏标准化目前阻碍了对神经元群体活性的LVM进行的进展，导致采用临时方式进行和比较方法。为协调这些建模工作，我们为神经人群活动的潜在变量建模介绍了基准套件。我们从认知，感官和机动领域策划了四种神经尖峰活动的数据集，以促进适用于这些地区各地的各种活动的模型。我们将无监督的评估视为用于评估数据集的模型的共同框架，并应用几个显示基准多样性的基线。我们通过评估释放此基准。 http://neurallatents.github.io.

Twenty20板球，有时是二十20，经常缩写为T20，是板球的一小部分。在一场二十二十比赛中，两支球员组成的两支球队都有一局，最多仅限20分。这个版本的板球尤其是不可预测的，这是它最近在近期越来越受欢迎的原因之一。但是，在本文中，我们尝试了四种不同的方法来预测T20板球比赛的结果。具体来说，我们要考虑：以前的竞争团队参与者的绩效统计数据，从知名的板球统计网站获得的球员的评分，以相似的性能统计数据和基于ELO基于ELO的方法来汇率玩家。我们通过使用逻辑回归，支持向量机，贝叶斯网络，决策树，随机森林来比较每种方法的性能。

我们提出了一个新型的基于流动合成的视觉致毒框架，从而为微型航空车辆（MAV）避免了远距离的障碍物（MAV）在高大的摩天大楼中飞行。最近的基于深度学习的框架使用光流进行高精度的视觉伺服。在本文中，我们探讨了一个问题：我们可以为这些高精度视觉服务方法设计替代流，从而导致避免障碍？我们重新审视显着性的概念，以识别其他竞争摩天大楼和建筑物之间的攻击线中的高层建筑物作为碰撞障碍。合成的流程用于取代显着对象分割掩码。该流程得以计算，以至于视觉伺服控制器在障碍物周围安全地操纵MAV。在这种方法中，我们使用基于多步跨凝结法（CEM）的伺服控制来实现流量收敛，从而导致避免障碍物。我们使用这种新颖的管道来成功，持久地进行高层建筑，并在模拟和现实的现实世界中实现目标。我们进行了广泛的实验，并将我们的方法与光流和基于短距离的障碍物回避方法进行比较，以证明所提出的框架的优点。可以在https://sites.google.com/view/munocular-obstacle/home上找到其他可视化。

在手术视频中自动识别外科手术阶段是手术工作流程分析中的一项基本任务。在本报告中，我们提出了一种基于变压器的方法，该方法利用了2阶段推理管道的校准置信度得分，该方法根据校准的置信度水平动态切换基线模型和单独训练的过渡模型。我们的方法的表现优于Cholec80数据集上的基线模型，并且可以应用于各种动作分割方法。

In this paper we explore the task of modeling (semi) structured object sequences; in particular we focus our attention on the problem of developing a structure-aware input representation for such sequences. In such sequences, we assume that each structured object is represented by a set of key-value pairs which encode the attributes of the structured object. Given a universe of keys, a sequence of structured objects can then be viewed as an evolution of the values for each key, over time. We encode and construct a sequential representation using the values for a particular key (Temporal Value Modeling - TVM) and then self-attend over the set of key-conditioned value sequences to a create a representation of the structured object sequence (Key Aggregation - KA). We pre-train and fine-tune the two components independently and present an innovative training schedule that interleaves the training of both modules with shared attention heads. We find that this iterative two part-training results in better performance than a unified network with hierarchical encoding as well as over, other methods that use a {\em record-view} representation of the sequence \cite{de2021transformers4rec} or a simple {\em flattened} representation of the sequence. We conduct experiments using real-world data to demonstrate the advantage of interleaving TVM-KA on multiple tasks and detailed ablation studies motivating our modeling choices. We find that our approach performs better than flattening sequence objects and also allows us to operate on significantly larger sequences than existing methods.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

The generalisation performance of a convolutional neural networks (CNN) is majorly predisposed by the quantity, quality, and diversity of the training images. All the training data needs to be annotated in-hand before, in many real-world applications data is easy to acquire but expensive and time-consuming to label. The goal of the Active learning for the task is to draw most informative samples from the unlabeled pool which can used for training after annotation. With total different objective, self-supervised learning which have been gaining meteoric popularity by closing the gap in performance with supervised methods on large computer vision benchmarks. self-supervised learning (SSL) these days have shown to produce low-level representations that are invariant to distortions of the input sample and can encode invariance to artificially created distortions, e.g. rotation, solarization, cropping etc. self-supervised learning (SSL) approaches rely on simpler and more scalable frameworks for learning. In this paper, we unify these two families of approaches from the angle of active learning using self-supervised learning mainfold and propose Deep Active Learning using BarlowTwins(DALBT), an active learning method for all the datasets using combination of classifier trained along with self-supervised loss framework of Barlow Twins to a setting where the model can encode the invariance of artificially created distortions, e.g. rotation, solarization, cropping etc.

Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.

Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.