Ever since the first microscope by Zacharias Janssen in the late 16th century, scientists have been inventing new types of microscopes for various tasks. Inventing a novel architecture demands years, if not decades, worth of scientific experience and creativity. In this work, we introduce Differentiable Microscopy ($\partial\mu$), a deep learning-based design paradigm, to aid scientists design new interpretable microscope architectures. Differentiable microscopy first models a common physics-based optical system however with trainable optical elements at key locations on the optical path. Using pre-acquired data, we then train the model end-to-end for a task of interest. The learnt design proposal can then be simplified by interpreting the learnt optical elements. As a first demonstration, based on the optical 4-$f$ system, we present an all-optical quantitative phase microscope (QPM) design that requires no computational post-reconstruction. A follow-up literature survey suggested that the learnt architecture is similar to the generalized phase contrast method developed two decades ago. Our extensive experiments on multiple datasets that include biological samples show that our learnt all-optical QPM designs consistently outperform existing methods. We experimentally verify the functionality of the optical 4-$f$ system based QPM design using a spatial light modulator. Furthermore, we also demonstrate that similar results can be achieved by an uninterpretable learning based method, namely diffractive deep neural networks (D2NN). The proposed differentiable microscopy framework supplements the creative process of designing new optical systems and would perhaps lead to unconventional but better optical designs.
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这项工作介绍了一个新颖的知识蒸馏框架,用于分类任务,其中可用并考虑到现有子类信息。在具有少量类或二进制检测的分类任务中,从教师到学生的信息量受到限制,从而限制了知识蒸馏的效用。通过利用类中可能的子类信息可以提高性能。为此,我们提出了所谓的子类知识蒸馏(SKD),这是将预测子类知识从老师转移到较小学生的过程。在老师的课堂逻辑中不存在的有意义的信息,而是在子类徽标中存在(例如,课堂内的相似之处)将通过SKD传达给学生,然后将提高学生的表现。从分析上,我们衡量教师可以通过SKD向学生提供多少额外信息,以证明我们工作的功效。开发的框架是在临床应用中评估的,即结直肠息肉分类。这是两个类别和每个类的许多子类的实际问题。在此应用程序中,使用临床医生提供的注释来根据注释标签的学习方式来定义子类。接受SKD框架训练的轻巧,低复杂的学生的F1得分为85.05%,提高了1.47%,比学生分别接受和没有常规知识蒸馏的学生获得了2.10%的收益。接受和没有SKD的学生之间的2.10%的F1得分差距可以通过额外的子类知识来解释,即,每个样本的额外的0.4656标签位可以在我们的实验中转移。
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使用(半)自动显微镜生成的大规模电子显微镜(EM)数据集已成为EM中的标准。考虑到大量数据,对所有数据的手动分析都是不可行的,因此自动分析至关重要。自动分析的主要挑战包括分析和解释生物医学图像的注释,并与实现高通量相结合。在这里,我们回顾了自动计算机技术的最新最新技术以及分析细胞EM结构的主要挑战。关于EM数据的注释,分割和可扩展性,讨论了过去五年来开发的高级计算机视觉,深度学习和软件工具。自动图像采集和分析的集成将允许用纳米分辨率对毫米范围的数据集进行高通量分析。
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这项工作介绍了一个新颖的知识蒸馏框架,用于分类任务,其中可用并考虑到现有子类信息。在具有少数类或二进制检测的分类任务(两个类)中,从教师到学生网络传递的信息量受到限制,从而限制了知识蒸馏的效用。可以通过利用有关分类任务中可用类中可能子类的信息来提高性能。为此,我们提出了所谓的子类知识蒸馏(SKD)框架,这是将子类的预测知识从大型教师模型转移到较小的学生的过程。通过SKD,其他有意义的信息不在教师的课堂逻辑中,而是在子类中存在(例如,课堂内的相似之处)将被传达给学生并提高其表现。从数学上讲,我们测量老师可以通过SKD框架为学生提供多少额外信息。开发的框架是在临床应用中评估的,即结直肠息肉分类。在此应用程序中,临床医生提供的注释用于根据注释标签的学习方式来定义子类。接受拟议框架培训的轻巧,低复杂性学生的F1得分为85.05%,比在没有常规知识蒸馏的情况下训练的学生分别提高了2.14%和1.49%的增长。这些结果表明,额外的子类知识(即我们实验中的培训样本0.4656标签位)可以提供有关教师概括的更多信息,因此SKD可以从使用更多信息中受益于提高学生的表现。
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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
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While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.
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This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.
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In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87.
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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.
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We propose an ensemble approach to predict the labels in linear programming word problems. The entity identification and the meaning representation are two types of tasks to be solved in the NL4Opt competition. We propose the ensembleCRF method to identify the named entities for the first task. We found that single models didn't improve for the given task in our analysis. A set of prediction models predict the entities. The generated results are combined to form a consensus result in the ensembleCRF method. We present an ensemble text generator to produce the representation sentences for the second task. We thought of dividing the problem into multiple small tasks due to the overflow in the output. A single model generates different representations based on the prompt. All the generated text is combined to form an ensemble and produce a mathematical meaning of a linear programming problem.
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