背景和目的：与生物医学分析相结合的人工智能（AI）方法在Pandemics期间具有关键作用，因为它有助于释放来自医疗保健系统和医生的压力压力。由于持续的Covid-19危机在具有茂密的人口和巴西和印度等测试套件中的国家恶化，放射性成像可以作为准确分类Covid-19患者的重要诊断工具，并在适当时期规定必要的治疗。通过这种动机，我们基于使用胸部X射线检测Covid-19感染肺的深度学习架构的研究。数据集：我们共收集了三种不同类标签的2470张图片，即健康的肺，普通肺炎和Covid-19感染的肺炎，其中470个X射线图像属于Covid-19类。方法：我们首先使用直方图均衡技术预处理所有图像，并使用U-Net架构进行它们。然后，VGG-16网络用于从预处理图像中的特征提取，该特征提取通过SMTE过采样技术进一步采样以实现平衡数据集。最后，使用具有10倍交叉验证的支持向量机（SVM）分类器分类类平衡功能，评估精度。结果和结论：我们的新方法结合了众所周知的预处理技术，特征提取方法和数据集平衡方法，使我们在2470 X射线图像的数据集中获得了Covid-19图像的优秀识别率为98％ 。因此，我们的模型适用于用于筛选目的的医疗保健设施。

Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understanding (SLU), the intents of the speaker are predicted directly from the speech signal without requiring intermediate text transcripts. As a result, the model can optimize directly for intent classification and avoid cascading errors from ASR. The end-to-end SLU system also helps in reducing the latency of the intent prediction model. Although many datasets are available publicly for text-to-intent tasks, the availability of labeled speech-to-intent datasets is limited, and there are no datasets available in the Indian accent. In this paper, we release the Skit-S2I dataset, the first publicly available Indian-accented SLU dataset in the banking domain in a conversational tonality. We experiment with multiple baselines, compare different pretrained speech encoder's representations, and find that SSL pretrained representations perform slightly better than ASR pretrained representations lacking prosodic features for speech-to-intent classification. The dataset and baseline code is available at \url{https://github.com/skit-ai/speech-to-intent-dataset}

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method.

Sentence simplification aims at making the structure of text easier to read and understand while maintaining its original meaning. This can be helpful for people with disabilities, new language learners, or those with low literacy. Simplification often involves removing difficult words and rephrasing the sentence. Previous research have focused on tackling this task by either using external linguistic databases for simplification or by using control tokens for desired fine-tuning of sentences. However, in this paper we purely use pre-trained transformer models. We experiment with a combination of GPT-2 and BERT models, achieving the best SARI score of 46.80 on the Mechanical Turk dataset, which is significantly better than previous state-of-the-art results. The code can be found at https://github.com/amanbasu/sentence-simplification.

Modern deep learning models are over-parameterized, where the optimization setup strongly affects the generalization performance. A key element of reliable optimization for these systems is the modification of the loss function. Sharpness-Aware Minimization (SAM) modifies the underlying loss function to guide descent methods towards flatter minima, which arguably have better generalization abilities. In this paper, we focus on a variant of SAM known as mSAM, which, during training, averages the updates generated by adversarial perturbations across several disjoint shards of a mini-batch. Recent work suggests that mSAM can outperform SAM in terms of test accuracy. However, a comprehensive empirical study of mSAM is missing from the literature -- previous results have mostly been limited to specific architectures and datasets. To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets. We provide a flexible implementation of mSAM and compare the generalization performance of mSAM to the performance of SAM and vanilla training on different image classification and natural language processing tasks. We also conduct careful experiments to understand the computational cost of training with mSAM, its sensitivity to hyperparameters and its correlation with the flatness of the loss landscape. Our analysis reveals that mSAM yields superior generalization performance and flatter minima, compared to SAM, across a wide range of tasks without significantly increasing computational costs.

Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.

ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous driving, it is common to treat all available training data equally. However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset. We present a method to predict the inherent difficulty of a driving situation given data collected from a fleet of autonomous vehicles deployed on public roads. We then demonstrate that this difficulty score can be used in a zero-shot transfer to generate curricula for an imitation-learning based planning agent. Compared to training on the entire unbiased training dataset, we show that prioritizing difficult driving scenarios both reduces collisions by 15% and increases route adherence by 14% in closed-loop evaluation, all while using only 10% of the training data.

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph. To employ large language models (LMs) for this task, existing approaches ``serialize'' the output graph as a flat list of nodes and edges. Although feasible, these serialized graphs strongly deviate from the natural language corpora that LMs were pre-trained on, hindering LMs from generating them correctly. In this paper, we show that when we instead frame structured commonsense reasoning tasks as code generation tasks, pre-trained LMs of code are better structured commonsense reasoners than LMs of natural language, even when the downstream task does not involve source code at all. We demonstrate our approach across three diverse structured commonsense reasoning tasks. In all these natural language tasks, we show that using our approach, a code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the target task (e.g., T5) and other strong LMs such as GPT-3 in the few-shot setting.

坐标测量机（CMM）一直是测量近50年或更长时间以上固体物体的准确性的基准。然而，随着3D扫描技术的出现，产生的点云的准确性和密度已接管。在这个项目中，我们不仅比较可在3D扫描软件中使用的不同算法，而且还比较了从相机和投影仪等现成组件中创建自己的3D扫描仪。我们的目标是：1。为3D扫描仪开发一个原型，以实现在对象的广泛类型上以最佳精度执行的系统。2.使用现成的组件最小化成本。3.到达非常接近CMM的准确性。