Reviewerly/exHarmony huggingface dataset:
The role of natural language processing techniques versus conventional methods to gain ICI safety insights from unstructured EHR data.

136 Background: Studies using International Classification of Diseases (ICD) codes have aimed to characterize the safety profile of cancer therapies in the Real-World Data setting, but this approach has limitations including an inability to illustrate patient journey and offer insights into drug-Adverse Event (AE) causality. Using data from a cohort of ~9000 patients treated with immune checkpoint inhibitors (ICI) at Mayo Clinic, we recently demonstrated the feasibility of applying Augmented Curation (AC), a natural language processing (NLP)-based innovation, on unstructured electronic health record (EHR) data to detect and characterize immune related AEs (irAEs). While the ability to identify and extract clinical details from EHRs has been previously demonstrated, our approach focuses on applying these techniques to identify implied textual causality of drug-event pairs. This study aims to build upon our prior findings and compare the accuracy, speed, and resource savings enabled by AC analysis versus manual review of patient notes. Methods: We compared the prevalence of adrenal insufficiency, colitis, and hypophysitis (A.C.H) among ICI patients at Mayo Clinic using ICD codes and AC which leverages SciBERT to perform scientific NLP tasks. AC models trained to identify drug-adverse event relationships were used to assess irAEs and subsequent treatments and demonstrated strong performance with F1 scores of 0.85. Manual curation of relevant clinical data, i.e. medications and adverse events, was employed to create a “gold standard” A.C.H. cohort. Time required for manual review compared to AC was documented for overall comparison. Corticosteroid/immunosuppressant use and ICI discontinuations were used as proxies of severity. Results: In our cohort, 540 unique patients experienced at least one of the A.C.H. irAEs. A.C.H patients were found to receive corticosteroids for their respective irAE 79% of the time and discontinued the ICI due to the irAE 7.7% of the time. 6.1% of Colitis patients were treated with a 2L immunosuppressant. Similar to the previous study, AC showed higher sensitivity, PPV and NPV compared to ICD codes alone within the A.C.H. cohort. Sentence extraction and entity classification using AC models on all 9,000-patients took ~10 minutes, whereas manual review of this cohort would take roughly 9 weeks (~10 minutes per patient). Conclusions: The results from this study reinforce that AC models are a valuable tool to comprehensively extract information and to impute relationships between entities, such as therapies and AEs. The AC models demonstrated better accuracy at a much greater speed compared to manual review of patient EHRs. Leveraging AC models provides the ability to manipulate unstructured clinical text married to structured EHR data at scale while minimizing need for manual curation.
An Evaluation of Feature Selection Robustness on Class Noisy Data

With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving the efficiency, interpretability, and final performance of the induced models. In recent literature, several studies have examined the strengths and weaknesses of the available feature selection methods from different points of view. Still, little work has been performed to investigate how sensitive they are to the presence of noisy instances in the input data. This is the specific field in which our work wants to make a contribution. Indeed, since noise is arguably inevitable in several application scenarios, it would be important to understand the extent to which the different selection heuristics can be affected by noise, in particular class noise (which is more harmful in supervised learning tasks). Such an evaluation may be especially important in the context of class-imbalanced problems, where any perturbation in the set of training records can strongly affect the final selection outcome. In this regard, we provide here a two-fold contribution by presenting (i) a general methodology to evaluate feature selection robustness on class noisy data and (ii) an experimental study that involves different selection methods, both univariate and multivariate. The experiments have been conducted on eight high-dimensional datasets chosen to be representative of different real-world domains, with interesting insights into the intrinsic degree of robustness of the considered selection approaches.
Multi-Resolution 3D Rendering for High-Performance Web AR

In the context of web augmented reality (AR), 3D rendering that maintains visual quality and frame rate requirements remains a challenge. The lack of a dedicated and efficient 3D format often results in the degraded visual quality of the original data and compromises the user experience. This paper examines the integration of web-streamable view-dependent representations of large-sized and high-resolution 3D models in web AR applications. The developed cross-platform prototype exploits the batched multi-resolution structures of the Nexus.js library as a dedicated lightweight web AR format and tests it against common formats and compression techniques. Built with AR.js and Three.js open-source libraries, it allows the overlay of the multi-resolution models by interactively adjusting the position, rotation and scale parameters. The proposed method includes real-time view-dependent rendering, geometric instancing and 3D pose regression for two types of AR: natural feature tracking (NFT) and location-based positioning for large and textured 3D overlays. The prototype achieves up to a 46% speedup in rendering time compared to optimized glTF models, while a 34 M vertices 3D model is visible in less than 4 s without degraded visual quality in slow 3D networks. The evaluation under various scenes and devices offers insights into how a multi-resolution scheme can be adopted in web AR for high-quality visualization and real-time performance.
YOLOv5s-DSD: An Improved Aerial Image Detection Algorithm Based on YOLOv5s

Due to the challenges of small detection targets, dense target distribution, and complex backgrounds in aerial images, existing object detection algorithms perform poorly in aerial image detection tasks. To address these issues, this paper proposes an improved algorithm called YOLOv5s-DSD based on YOLOv5s. Specifically, the SPDA-C3 structure is proposed and used to reduce information loss while focusing on useful features, effectively tackling the challenges of small detection targets and complex backgrounds. The novel decoupled head structure, Res-DHead, is introduced, along with an additional small object detection head, further improving the network's performance in detecting small objects. The original NMS is replaced by Soft-NMS-CIOU to address the issue of neighboring box suppression caused by dense object distribution. Finally, extensive ablation experiments and comparative tests are conducted on the VisDrone2019 dataset, and the results demonstrate that YOLOv5s-DSD outperforms current state-of-the-art object detection models in aerial image detection tasks. The proposed improved algorithm achieves a significant improvement compared with the original algorithm, with an increase of 17.4% in mAP@0.5 and 16.4% in mAP@0.5:0.95, validating the superiority of the proposed improvements.
Research on the Mobile Robot Map-Building Algorithm Based on Multi-Source Fusion

In this paper, the mobile robot position fusion algorithm is inaccurate. There is a delay, and the map-construction accuracy is not high; an improvement method is proposed. First, the Cartographer algorithm is optimized. Radius filtering is used for data processing after voxel filtering. In contrast, the idea of multi-sensor fusion is used to fuse the processed IMU data information. This improved method improves the efficiency of the algorithm and the accuracy of the positional pose fusion. We verify the effect of the algorithm applied to the environment map, respectively, in the experimental building promenade environment and the teaching building hall environment, and analyze and compare the effect of map construction before and after the improvement; the experiment proves that in the experimental building promenade environment, the absolute error of measuring and analyzing the obstacles reduces by 0.06 m, and the relative error decreases by 1.63%; in the teaching building hall environment, the absolute error of measuring and analyzing the longest side of the map decreases by 1.121 m and the relative error decreased by 5.52%. In addition, during the experimental operation, the CPU occupancy of the optimized algorithm is around 59.5%. In contrast, the CPU occupancy of the original algorithm is 67% on average, and sometimes it will soar to 75%. The experimental results prove that the algorithm in this paper significantly improves performance in all aspects when constructing real-time environment maps.
Alternative Formulations of Decision Rule Learning from Neural Networks

This paper extends recent work on decision rule learning from neural networks for tabular data classification. We propose alternative formulations to trainable Boolean logic operators as neurons with continuous weights, including trainable NAND neurons. These alternative formulations provide uniform treatments to different trainable logic neurons so that they can be uniformly trained, which enables, for example, the direct application of existing sparsity-promoting neural net training techniques like reweighted L1 regularization to derive sparse networks that translate to simpler rules. In addition, we present an alternative network architecture based on trainable NAND neurons by applying De Morgan’s law to realize a NAND-NAND network instead of an AND-OR network, both of which can be readily mapped to decision rule sets. Our experimental results show that these alternative formulations can also generate accurate decision rule sets that achieve state-of-the-art performance in terms of accuracy in tabular learning applications.
External Slot Relationship Memory for Multi-Domain Dialogue State Tracking

Dialogue state tracking is an essential component in multi-domain dialogue systems that aims to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample often has limitations and may introduce noise and have high time complexity issues. In this paper, we propose an external slot relation memory-based dialogue state tracking model (ER-DST). By utilizing external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Our method is evaluated on MultiWOZ 2.0 and MultiWOZ 2.1, achieving improvements of 0.23% and 0.39% over the baseline models, respectively, while reducing the complexity of the slot relationship learning component to O(n).
Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.
Hidden Danger Association Mining for Water Conservancy Projects Based on Task Scenario-Driven

With the rapid development of water conservancy engineering and infrastructure construction, there are many safety hazards in the construction process of water conservancy engineering, so it is of great significance to study the potential hazards in the construction process. In this context, this paper proposes a task scenario-based association mining method for hydraulic engineering hidden danger records. By analyzing transaction characteristics, the traditional Apriori algorithm is improved to optimize pruning results and generate hidden danger association rules. The research results of this paper have been successfully applied to the investigation and management of hidden dangers in the Xinmenghe dredging project. Based on the mapping of association rules driven by task scenarios, hidden dangers association rules in specific task scenarios are mined to assist construction safety managers in hidden dangers investigation, which reduces the complexity of the algorithm, reduces the running time of the algorithm and improves the efficiency of the algorithm.
Semantic CMR Synthesis: Generating Coherent Short- and Long-Axis Images with Corresponding Multi-Class Anatomical Labels

We propose to use a combination of the StyleGAN2, ADA and DatasetGAN methods to produce synthetic short- and long-axis view cardiac magnetic resonance (CMR) images accompanied with corresponding 11-class tissue masks. The image generator networks are trained on datasets of approximately 1850 and 5000 unlabelled images, for short- and long-axis images respectively. The segmentation networks are trained on only 30 manually annotated synthetic images in total. We further demonstrate a proof-of-concept method for generating coherent long- and short-axis images of the same synthetic patient.
Graph contextualized self-attention network for software service sequential recommendation

With the broad application of software services, an increasing number of developers are turning to social coding sites for constructing their applications or conducting further research. These software services generate spatiotemporal data with numerous unique features. GitHub, being the world's largest code hosting platform, is essential to efficiently provide recommendation services for its users. In order to make accurate recommendations and establish effective user–item and item–item relationships, we propose a Graph Contextualized Self-attention Network for Software Service Sequential Recommendation (GCSAN). This model captures global repository-to-repository relationships based on contextual information and recommends suitable repositories to users. Specifically, we leverage the relationships between repositories in all behavior sequences and graph embedding technique to alleviate the data sparsity problem. Moreover, we employ a self attention mechanism to capture user's repository preferences at different time points, assigning varying weights accordingly. Finally, the experimental results on real-world datasets demonstrate the superior performance of our proposed model compared to benchmark recommendation methods.
Simultaneous learning of group-wise registration and joint segmentation of cardiac cine MRI with sparse annotation

Numerous deep learning methods have been proposed for cardiac cine MRI segmentation, while most of them require laborious annotation for supervised training. Herein, we propose an approach to perform group-wise registration and joint segmentation of cardiac cine images, by training a registration network in a self-supervised manner to align dynamic images to their mean image space and also a segmentation network in a weakly-supervised manner using sparsely-annotated data and predicted motions from the registration network. By training these two (registration and segmentation) networks simultaneously, our proposed joint learning approach provides better segmentations than the direct segmentation network.
MIRTorch: A PyTorch-powered Differentiable Toolbox for Fast Image Reconstruction and Scan Protocol Optimization

MIRTorch (Michigan Image Reconstruction Toolbox for PyTorch) is an image reconstruction toolbox implemented with native Python/PyTorch. It provides fast iterative and data-driven image reconstruction on both CPUs and GPUs. Researchers can rapidly develop new model-based and learning-based methods (i.e., unrolled neural networks) with its convenient abstraction layers. With the full support of auto-differentiation, one may optimize imaging protocols, such as sampling patterns and image reconstruction parameters with gradient-based methods.
Stone as Witness

The depiction of stones that speak has long been used as a literary and philosophical device to reflect upon the limitations of human language (i.e., language as a petrification of thought and action). Jacques Rancière has described stone’s capacity to bear witness as a form of “mute speech,” noting how “any stone can also be language,” as a part of the “testimony that mute things bear to mankind’s activity.” In exploring the character of this form of testimony, and asking how we hear it, this article examines the function of the Stone Guest in the legend of Don Juan, across Molière’s and Pushkin’s theatrical versions, a short film version by Marina Fomenko, and Mozart and Da Ponte’s operatic version, so revered by Kierkegaard and others. The character of the Stone Guest is often seen as casting judgement against the aesthetic mode of life, yet the power of the character lies not in his ghostly humanity or sense of retribution, but in his stoniness – his capacity to bear witness, just as a stone monument bears witness or commemorates a past trauma. In a number of versions of the Don Juan story, the Stone Guest is announced by approaching footsteps or knocking. This acousmatic device mirrors the uncanny separation of sound and source in opera – the way in which music mediates between the conflicting imperatives of language and the corporeal or material aspects of the singing voice, lending opera its vaunted mechanical qualities. Using stones that speak as a heuristic, the article draws together ideas about the limitations of language and the mechanical qualities of opera in order to excavate the auditory affordances of the stone’s form of testimony, in all its inorganic liveliness.
Logistics Robot Path Planning: A* Algorithm Operation and Analysis

Abstract With the rapid development of the e-commerce market, traditional logistics and warehousing methods are far from meeting the needs of the industry. Logistics robots have great potential in the area of intelligent warehousing. Inspired by this, this paper introduces the path planning principle of A* algorithm and the application of A* algorithm to find the path in the field of logistics robots. The principle of the A* algorithm is to select the grid with the least cost for each step from the starting point until the end point is reached. And it can be applied to the motion control of AGV and other logistics robots.
Medical Image Segmentation Based on U-Net

Abstract Traditional statistical methods, although having a solid theoretical foundation, have been challenged in terms of their efficiency as well as their generalization ability in the face of the ever-increasing amount of massive data. With the rise of deep learning in recent years, the use of new tools such as convolutional neural networks to get information from data has become a new option. In particular, in the field of imaging, segmentation of medical images is important for tasks such as determining the type of disease and the location of lesions, which are excellent application areas for deep learning. U-Net is a particularly important deep model structure with good results for segmentation of medical images. However, there is a lack of discussions on the application of U-Net in the clinical field. In this paper, we introduce traditional image segmentation methods and U-Net, analyze the advantages of deep learning techniques in the field of image segmentation. In addition, we applied U-Net to the problem of cell segmentation and segmentation of covid-19 CT images, showing the potential of U-Net for clinical applications.
Leveraging Inverse Probability Weighting Method to Missing Not at Random Problem

Abstract In nowadays recommender system field. The selection bias is ubiquitous to most of the data. Most real-world rating data is sparse and missing not at random (MNAR). MNAR data make it difficult to accurately estimate the performance of prediction model as well as learn an optimal prediction model. Investigation on overcoming the MNAR is becoming the mainstream in recommendation field. Plenty of approaches has been proposed to solve this problem. And many of them make the use of the propensity of observing each user-item event. Many of the approaches are suffering from the variance of the propensity. This paper proposes two approaches, summary of inversed propensity (SIP) and clip to make the propensity calculated by logistic regression more effective. And real-world-dataset based experiment will show the performance of these two approaches.
Self-Supervised Approach to Addressing Zero-Shot Learning Problem

Abstract In recent years, self-supervised learning has had significant success in applications involving computer vision and natural language processing. The type of pretext task is important to this boost in performance. One common pretext task is the measure of similarity and dissimilarity between pairs of images. In this scenario, the two images that make up the negative pair are visibly different to humans. However, in entomology, species are nearly indistinguishable and thus hard to differentiate. In this study, we explored the performance of a Siamese neural network using contrastive loss by learning to push apart embeddings of bumblebee species pair that are dissimilar, and pull together similar embeddings. Our experimental results show a 61% F1-score on zero-shot instances, a performance showing 11% improvement on samples of classes that share intersections with the training set.
Diversity of Models Based on Sequence to Sequence for Neural Machine Translation Tasks

Abstract One of the most critical areas of Natural Language Processing study is machine translation, which realizes the translation process from source language to target language. After a rapid development, neural machine translation has become mainstream. Most of the tasks of natural language generation are implemented based on sequence to sequence model. The current technology of machine translation is the use of recurrent neural networks and attention mechanisms, which is essentially a set of encoding and decoding techniques. In order to handle the issue of excessive information compression introduced by decoder, attention mechanism is proposed. Transformer is also built with attention, which not only computes faster, but also achieves better results on translation tasks. In this paper, a series of comparison of these models and description of the performance on the dataset were shown.