预测可帮助企业分配资源并实现目标。在LinkedIn，产品所有者使用预测来设定业务目标，跟踪前景和监视健康。工程师使用预测有效地提供硬件。开发一种预测解决方案来满足这些需求，需要对各种时间序列进行准确，可解释的预测，并以次数至季度的频率。我们提出了Greykite，这是一个用于预测的开源Python库，已在LinkedIn上部署了二十多种用例。它的旗舰算法Silverkite提供了可解释的，快速且高度灵活的单变量预测，可捕获诸如时期增长和季节性，自相关，假期和回归剂等效果。该库通过促进数据探索，模型配置，执行和解释来实现自我服务的准确性和信任。我们的基准结果显示了来自各个域的数据集的现成速度和准确性。在过去的两年中，金融，工程和产品团队的资源计划和分配，目标设置和进度跟踪，异常检测和根本原因分析的资源团队一直信任灰金矿的预测。我们希望灰金矿对具有类似应用的预测从业者有用，这些应用需要准确，可解释的预测，这些预测捕获了与人类活动相关的时间序列共有的复杂动力学。

肺癌近年来一直是最普遍的疾病之一。根据该领域的研究，每年在美国确定超过20万个案件。不受控制的繁殖和肺细胞的生长导致恶性肿瘤形成。最近，深入学习算法，特别是卷积神经网络（CNN），已成为自动诊断疾病的高级方式。本文的目的是审查不同的模型，导致诊断早期肺癌的不同准确性和敏感性，并帮助该领域的医生和研究人员。这项工作的主要目的是确定基于深度学习的肺癌存在的挑战。经过系统地编写了调查，这些调查结合了定期的映射和文献综述，从2016年到2021年审查该领域的32次会议和期刊文章。在分析和审查条款后，正在回答条款中提出的问题。由于对相关文章的完全审查和系统化，本领域，这项研究优于该领域的其他综述文章。

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at https://github.com/mindflow-institue/DAEFormer.

A track-before-detect (TBD) particle filter-based method for detection and tracking of low observable objects based on a sequence of image frames in the presence of noise and clutter is studied. At each time instance after receiving a frame of image, first, some preprocessing approaches are applied to the image. Then, it is sent to the detection and tracking algorithm which is based on a particle filter. Performance of the approach is evaluated for detection and tracking of an object in different scenarios including noise and clutter.

Recent advances in language modeling have enabled new conversational systems. In particular, it is often desirable for people to make choices among specified options when using such systems. We address the problem of reference resolution, when people use natural expressions to choose between real world entities. For example, given the choice `Should we make a Simnel cake or a Pandan cake?' a natural response from a non-expert may be indirect: `let's make the green one'. Reference resolution has been little studied with natural expressions, thus robustly understanding such language has large potential for improving naturalness in dialog, recommendation, and search systems. We create AltEntities (Alternative Entities), a new public dataset of entity pairs and utterances, and develop models for the disambiguation problem. Consisting of 42K indirect referring expressions across three domains, it enables for the first time the study of how large language models can be adapted to this task. We find they achieve 82%-87% accuracy in realistic settings, which while reasonable also invites further advances.

The problem of reversing the compilation process, decompilation, is an important tool in reverse engineering of computer software. Recently, researchers have proposed using techniques from neural machine translation to automate the process in decompilation. Although such techniques hold the promise of targeting a wider range of source and assembly languages, to date they have primarily targeted C code. In this paper we argue that existing neural decompilers have achieved higher accuracy at the cost of requiring language-specific domain knowledge such as tokenizers and parsers to build an abstract syntax tree (AST) for the source language, which increases the overhead of supporting new languages. We explore a different tradeoff that, to the extent possible, treats the assembly and source languages as plain text, and show that this allows us to build a decompiler that is easily retargetable to new languages. We evaluate our prototype decompiler, Beyond The C (BTC), on Go, Fortran, OCaml, and C, and examine the impact of parameters such as tokenization and training data selection on the quality of decompilation, finding that it achieves comparable decompilation results to prior work in neural decompilation with significantly less domain knowledge. We will release our training data, trained decompilation models, and code to help encourage future research into language-agnostic decompilation.

Numerous models have tried to effectively embed knowledge graphs in low dimensions. Among the state-of-the-art methods, Graph Neural Network (GNN) models provide structure-aware representations of knowledge graphs. However, they often utilize the information of relations and their interactions with entities inefficiently. Moreover, most state-of-the-art knowledge graph embedding models suffer from scalability issues because of assigning high-dimensional embeddings to entities and relations. To address the above limitations, we propose a scalable general knowledge graph encoder that adaptively involves a powerful tensor decomposition method in the aggregation function of RGCN, a well-known relational GNN model. Specifically, the parameters of a low-rank core projection tensor, used to transform neighborhood entities in the encoder, are shared across relations to benefit from multi-task learning and incorporate relations information effectively. Besides, we propose a low-rank estimation of the core tensor using CP decomposition to compress the model, which is also applicable, as a regularization method, to other similar linear models. We evaluated our model on knowledge graph completion as a common downstream task. We train our model for using a new loss function based on contrastive learning, which relieves the training limitation of the 1-N method on huge graphs. We improved RGCN performance on FB15-237 by 0.42% with considerably lower dimensionality of embeddings.