成像表明临床前和人类肿瘤是异质性的，即单个肿瘤可以表现出多个区域，在正常发育过程中均表现出不同的行为，也可以反应治疗。在对照组肿瘤中观察到的大变化可能会掩盖由于归因于变化原因的歧义而导致的显着治疗作用的检测。由于实验设计的局限性，而不是由于治疗衰竭，这可能会阻碍有效疗法的发展。描述了对成像信号中生物变异和异质性进行建模的改进方法。具体而言，线性泊松建模（LPM）在放疗前和72小时之前评估了两种结直肠癌的异种移植模型，在放疗前和72小时后评估了明显的扩散效率（ADC）的变化。使用基本ADC分布参数的常规t检验分析将测量变化的统计显着性与可实现的变化的统计显着性进行了比较。当LPM应用于治疗的肿瘤时，LPM检测到了高度显着的变化。与常规方法相比，所有肿瘤的分析对于所有肿瘤都很重要，相当于4倍的增益（即等同于样本量大16倍）。相比之下，只有使用t检验在队列水平上检测到极大的变化，从而限制了其在个性化医学中的潜在用途，并增加了测试过程中所需的动物数量。此外，LPM使每个异种移植模型估计响应和非反应组织的相对体积。对处理过的异种移植物的剩余分析提供了质量控制并确定了潜在的异常值，从而提高了对临床相关样本量的LPM数据的信心。

公民科学已成为初步数据处理任务的流行工具，例如识别和计算现代高分辨率图像中的月球影响陨石坑。但是，使用此类数据要求公民科学产品是可以理解和可靠的。污染和缺失的数据可以降低数据集的实用性，因此必须量化此类效果很重要。本文介绍了一种基于新开发的定量模式识别系统（线性泊松模型），用于估计Moonzoo Citizen Science Crater数据中的污染水平。证据将表明，有可能取消污染的效果，参考一些商定的地面真理，从而导致估计的火山口计数高度可重复。但是，还将表明，目前难以实现丢失数据的纠正。这些技术在Apollo 17站点的Moonzoo公民科学火山口注释中进行了测试，并从同一地区的本科和专家成果进行了测试。

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.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. As input data for the classifier, we used infor-mation obtained from a developed laboratory setup with MEMS sensors on a sealed platform with the ability to adjust its tilt angles. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for each core in the case of a linear, polynomial radial basis function. The inverse regularization parameter was used as a parameter. The proposed algorithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors, where good classification results were obtained when choosing the optimal values of hyperpa-rameters.

The deployment of neural networks on heterogeneous SoCs coupled with custom accelerators is a challenging task because of the lack of end-to-end software tools provided for these systems. Moreover, the already available low level schedules and mapping strategies provided by the accelerator developers for typical tensor operations are not necessarily the best possible ones for each particular use case. This is why frameworks which automatically test the performance of the generated code on a specific hardware configuration are of special interest. In this work, the integration between the code generation framework TVM and the systolic array-based accelerator Gemmini is presented. A generic schedule to offload the GEneral Matrix Multiply (GEMM) tensor operation onto Gemmini is detailed, and its suitability is tested by executing the AutoTVM tuning process on it. Our generated code achieves a peak throughput of 46 giga-operations per second (GOPs) under a 100 MHz clock on a Xilinx ZCU102 FPGA, outperforming previous work. Furthermore, the code generated by this integration was able to surpass the default hand-tuned schedules provided by the Gemmini developers in real-world workloads.

An algorithm and a program for detecting the boundaries of water bodies for the autopilot module of asurface robot are proposed. A method for detecting water objects on satellite maps by the method of finding a color in the HSV color space, using erosion, dilation - methods of digital image filtering is applied.The following operators for constructing contours on the image are investigated: the operators of Sobel,Roberts, Prewitt, and from them the one that detects the boundary more accurately is selected for thismodule. An algorithm for calculating the GPS coordinates of the contours is created. The proposed algorithm allows saving the result in a format suitable for the surface robot autopilot module.

Artificial Intelligence (AI) and its data-centric branch of machine learning (ML) have greatly evolved over the last few decades. However, as AI is used increasingly in real world use cases, the importance of the interpretability of and accessibility to AI systems have become major research areas. The lack of interpretability of ML based systems is a major hindrance to widespread adoption of these powerful algorithms. This is due to many reasons including ethical and regulatory concerns, which have resulted in poorer adoption of ML in some areas. The recent past has seen a surge in research on interpretable ML. Generally, designing a ML system requires good domain understanding combined with expert knowledge. New techniques are emerging to improve ML accessibility through automated model design. This paper provides a review of the work done to improve interpretability and accessibility of machine learning in the context of global problems while also being relevant to developing countries. We review work under multiple levels of interpretability including scientific and mathematical interpretation, statistical interpretation and partial semantic interpretation. This review includes applications in three areas, namely food processing, agriculture and health.

We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model. A key challenge is allowing the segmenter (which runs in real-time, synchronously with the decoder) to finalize the 2nd pass (which runs 900 ms behind real-time) without introducing user-perceived latency or deletion errors during inference. We propose a design where the neural segmenter is integrated with the causal 1st pass decoder to emit a end-of-segment (EOS) signal in real-time. The EOS signal is then used to finalize the non-causal 2nd pass. We experiment with different ways to finalize the 2nd pass, and find that a novel dummy frame injection strategy allows for simultaneous high quality 2nd pass results and low finalization latency. On a real-world long-form captioning task (YouTube), we achieve 2.4% relative WER and 140 ms EOS latency gains over a baseline VAD-based segmenter with the same cascaded encoder.

The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the latent space of a Deep Generative Model as a solution to these bottlenecks. To this end, we present the SemanticStyle Autoencoder (SSAE), a deep Generative Autoencoder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images. We present qualitative and quantitative results for the same and their analysis. This work shall serve as a guiding primer for future work.