Reviewerly/exHarmony huggingface dataset:
End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets

Privacy-preserving machine learning (PPML) promises to train machine learning (ML) models by combining data spread across multiple data silos. Theoretically, secure multiparty computation (MPC) allows multiple data owners to train models on their joint data without revealing the data to each other. However, the prior implementations of this secure training using MPC have three limitations: they have only been evaluated on CNNs, and LSTMs have been ignored; fixed point approximations have affected training accuracies compared to training in floating point; and due to significant latency overheads of secure training via MPC, its relevance for practical tasks with streaming data remains unclear. The motivation of this work is to report our experience of addressing the practical problem of secure training and inference of models for urban sensing problems, e.g., traffic congestion estimation, or air pollution monitoring in large cities, where data can be contributed by rival fleet companies while balancing the privacy-accuracy trade-offs using MPC-based techniques.Our first contribution is to design a custom ML model for this task that can be efficiently trained with MPC within a desirable latency. In particular, we design a GCN-LSTM and securely train it on time-series sensor data for accurate forecasting, within 7 minutes per epoch. As our second contribution, we build an end-to-end system of private training and inference that provably matches the training accuracy of cleartext ML training. This work is the first to securely train a model with LSTM cells. Third, this trained model is kept secret-shared between the fleet companies and allows clients to make sensitive queries to this model while carefully handling potentially invalid queries. Our custom protocols allow clients to query predictions from privately trained models in milliseconds, all the while maintaining accuracy and cryptographic security.
Context-aware augmentation for liver lesion segmentation: shape uniformity, expansion limit and fusion strategy

Data augmentation with context has been an effective way to increase the robustness and generalizability of deep learning models. However, to our knowledge, shape uniformity, expansion limit, and fusion strategy of context have yet to be comprehensively studied, particularly in lesion segmentation of medical images.To examine the impact of these factors, we take liver lesion segmentation based on the well-known deep learning architecture U-Net as an example and thoroughly vary the context shape, the expansion bandwidth as well as three representative fusion methods. In particular, the context shape includes rectangular, circular and polygonal, the expansion bandwidth is scaled by a maximum value of 2 compared to the lesion size, and the context fusion weighting strategy is composed of average, Gaussian and inverse Gaussian.Studies conducted on a newly constructed high-quality and large-volume dataset show that (I) uniform context improves lesion segmentation, (II) expanding the context with either 5 or 7 pixels yields the highest performance for liver lesion segmentation, depending on the lesion size, and (III) an unevenly distributed weighting strategy for context fusion is appreciated but in the opposite direction, depending on lesion size as well.Our findings and newly constructed dataset are expected to be useful for liver lesion segmentation, especially for small lesions.
If You Liked That, You Will Love This: On Sameness-Based Algorithmic Recommendation Systems

On Sameness-Based Algorithmic Recommendation SystemsEcho was a lovely nymph with a beautiful voice who got into trouble with the queen of the gods.According to Ovid, Jupiter had charged Echo with distracting his wife Juno with long conversations so that he could engage in his unfaithful escapades with various nymphs. 1 Unfortunately, Juno discovered the ruse and chastised Echo by taking away her voice, or rather, by making her lose the power to formulate her own thoughts, since the only thing she was thereafter allowed to utter were the last words of whatever was spoken to her.As if this tragedy was not enough, Echo fell in love with the self-absorbed Narcissus, who rejected her (as he did everybody) and caused her to fade away, so her bones turned to stone and the only part of her that remained was her voice.Narcissus did not go unpunished for his cruelty though, since Nemesis condemned him to fall in love with his own reflection in the water, so much so that he wasted away and ended up dying, even if he was changed into a flower by the gods.This myth is about divine punishment, losing humanity, the despair of repetition and the dangers of excessive mirroring.The stories of Echo and Narcissus also illustrate two complementary sides of algorithmic culture: the tension between the individual and collective dimensions of sameness that will articulate this chapter.Echo is desperate, condemned to waiting for the sounds that others make, in the hope that she can use their last words to express herself; she has lost her own language, the seductive tongue she was so adept at using.We can understand her frustration just by doing an online search.The algorithms there offer us the echoes of other people's answers, aggregated in massive calculations, whose distilled
Analysis of Neural Networks

This chapters presents some known theoretical results for neural networks, including some theoretical analysis that has been developed recently. We show that neural networks can be analyzed using kernel methods and L1 regularization methods that have been studied in previous chapters.
Blender’s Simulation Nodes

Geometry Nodes is a procedural modeling and animation system that has been part of Blender since 2021, with Blender 2.92. It was initially focused on set dressing, procedural modeling and initial hair grooming. However in Blender 3.6 similation nodes were finally added. The class will be a hands-on show and tell of Geometry Nodes with particular emphasis on the brand new simulation systems.
Incidencia de los espacios escolares sobre la regulación emocional y el aprendizaje en contextos de diversidad social y cultural

Interaction spaces influence emotional regulation, due to the link between bodies, emotions and places. On the one hand, a series of emotions are projected on a certain space, on the other hand, spaces have the capacity to generate different emotional responses in people. The objective of this research was to identify the direct and indirect effects of the organization of school spaces on emotional regulation and learning. Eleven key informants participated, divided into professionals who work or have demonstrated their experience in the school educational context. The design was non-probabilistic and an analysis of the level of incidence in first and second order was carried out, in order to find the forgotten effects in the explanation of the object of study. The results show that the main indirect effects are that of the school infrastructure both on the development of respect for others and on the ability to control negative emotions, both mediated by the learning environment. On the other hand, the use of the Mapuzungun language in the school educational context has indirect effects on the behavior and regulation of the emotions of the students, having as main mediator the interpersonal relationships that occur in the school educational context.
Using 3D realistic blood vessel structures and machine learning for MR vascular Fingerprinting

MR vascular fingerprinting aims at mapping cerebral vascular properties such as blood volume and blood oxygenation. We propose to improve the technique by generating dictionaries based on 3D vascular networks segmented from whole brain high-resolution (3 &amp;micro;m isotropic) microscopy datasets. In order to compensate for the limited number of available data and long computing times, we used a machine-learning reconstruction process to generalize our results and tested our approach in healthy, stroke and tumor animal models. Our results show high quality maps with expected contrast and baseline values in healthy animals as well as expected trends in pathological tissues.
Combination of Multichannel Blind Deconvolution (MALBEC) and GRAPPA for Highly Accelerated 3D Imaging

Most parallel imaging methods require calibration data for reconstruction. Low-rank-based methods allow calibration-free reconstruction from randomly undersampled, multi-channel data. This abstract presents a novel reconstruction method to combine multichannel blind deconvolution (MALBEC), a calibration-free method, and GRAPPA, a calibration-based method for highly accelerated imaging. The method sequentially performs MALBEC and GRAPPA with specially designed sampling masks such that the benefits of low-rank structure and the availability of calibration data can be utilized jointly. Our results demonstrate that the proposed method can achieve an acceleration factor that is the product of the factors achieved by MALBEC and GRAPPA alone.
Kidney segmentation in MR images using CT-trained ResUNet and transfer learning

Kidney segmentation is often necessary for analyzing renal MRI data. Deep learning approach shows much promise, but typically requires large numbers of images for model training. In this study, we explored the feasibility of segmenting MRI images using ResUNet pre-trained with CT images and fine-tuning with transfer learning. The fine-tuning step used 60 MRI images (from 5 subjects) only. The trained model performed excellently in segmenting an independent set of MRI images, with DICE similarity of 0.94 and volume error of 13%&amp;plusmn;9%. This study demonstrates the power of transfer learning in utilizing images of a different modality in kidney segmentation.
The Correlation of Vocabulary Mastery and Sentence Structure with Students' Writing Ability

This study aims to describe the relationship between vocabulary mastery and sentence structure with the ability to write descriptions of the eighth grade students of SMPN 02 Waway Karya, East Lampung. The author uses the correlational method, meaning that data are collected after all the events in question have taken place. The techniques used in collecting research data are questionnaires, tests, and observations. Based on the results of the study, it can be concluded that, (1) there is a positive and significant correlation between sentence structure and the ability to write descriptions, indicated by a correlation coefficient of 0.766 and a coefficient of determination (R2) of 0.587 or 58.7%, (2) there is a positive and significant correlation between vocabulary mastery and the ability to write descriptions as indicated by a correlation coefficient of 0.662 and a coefficient of determination (R2) of 0.438 or 43.8%, (3) there is a positive and significant relationship between vocabulary mastery and sentence structure and ability of writing a description shown by the correlation coefficient of 0.804 and the coefficient of determination (R2) of 0.646 or 64.6%.
RANCANG BANGUN APLIKASI PELAYANAN KEBIDANAN BERBASIS WEB DENGAN METODE OOP (OBJECT ORIENTED PROGRAMMING) PADA PRAKTIK MANDIRI BIDAN NY.SARI

The application of Information Technology in the field of Health has penetrated the obstetric sector. Butin reality there are still obstetric practices that have not had a positive impact on advances ininformation technology, one of them is the Ny.Sari’s Obstetric Clinic which in its service activities stillapplies a manual system and often encounters obstacles such as the difficulty of finding old patientdata, obstetric care documentation that meets storage, and report recapitulation takes a long time. Thegoal of this research is to develop a web-based midwifery service application that is expected tofacilitate the activities of midwifery services in the independent practice of Ny.Sari’s Obstetric Clinicmore effectively. The method used is OOP (Object Oriented Programming) method with UML (Unifiedmodeling Language) modeling to design Use Case Diagrams, Activity Diagrams, Class Diagrams, andSequence Diagrams. The stages of using the SDLC (System Development Life Cycle) method start fromthe requirement analysis, design, development, implementation and testing. The web-based obstetricsservice application requirements consist of the actors requirement, functional requirement, and nonfunctionals requirement. The results of the design of obstetric service application are currently madeattractive user interface design to facilitate the operation and implemented in the form of a website thatconsists of login menu, Dashboard, patient menu, service menu, queue menu and report menu. Withthe application of this obstetric service, service activities at Ny.Sari’s obstetric clinic become easier andmore effective
Real-time, multiplexed rendering of lymphoma diagnostic panels from unstained tissue sections using virtual staining.

115 Background: Lymphoproliferative disorders are complex, heterogeneous diseases with an ever expanding array of subtypes and variants requiring stepwise assay approach to arrive at a precise diagnosis. While extensive characterization of lesional cells and the microenvironment (TME) via immunophenotyping and genotyping remains the gold standard, a full workup may be unachievable due to limited input tissue, turnaround time pressure, or unavailability of particular markers in-house. Even when available, the interpretation of large numbers of markers by pathologists is limited by the ability to mentally spatially align markers by recall-superimposition of single-plex markers, limiting the discovery of patterns relevant for novel treatments strategies. Here we present a deep-learning based virtual staining method which transforms a single unstained tissue section into a panel of perfectly aligned virtual immunohistochemical (IHC) stained images. Methods: 4um unstained sections were cut from 30 formalin-fixed paraffin-embedded blocks. Using a standard commercial slide scanner (Axio Scan.Z1, Zeiss), autofluorescence images were captured from unstained sections and paired with brightfield images captured from the same sections after chemical staining with H&amp;E or various IHC stains of interest. The virtual staining was performed by a deep neural network trained as a conditional GAN using accurately co-registered patches of paired images . The training dataset comprised seven types of lymphoma plus benign lymphoid tissues. Results: We successfully created models for virtual CD3, CD8, CD20 and CD30 IHC stains as well as H&amp;E. Analysis by board certified pathologists confirmed consistent and accurate results between virtual and IHC stained slides in a range of lymphomas and reactive lymphoid tissue. We quantitatively evaluated the co-expression of CD3/CD8 and the spatial distribution of all four markers in eight holdout slides. We demonstrated that our virtual multiplex IHC staining achieves comparable performance for WSI scoring and cellular localization with actual staining across lymphoma types. Conclusions: We present the feasibility of spatially aligned, rapid virtual H&amp;E and IHC staining of four different markers from single, label-free autofluorescence images using deep-learning techniques. To the best of our knowledge, this is the first report of a panel of virtual IHC stains, perfectly aligned and with overlaid H&amp;E structural context, for analysis of neoplastic and reactive lymphoid tissues. Our technique provides an opportunity for a rapid, comprehensive histopathology and immunophenotyping workflow. Multiplex virtual staining greatly expands diagnostic and discovery opportunities: high accuracy TME profiling, seamless downstream computational image analysis, and screening of large biomarker pools in retrospective analysis of clinical trials.
Hippocampus Shape Characterization with 3D Zernike Transformation in Clinical Alzheimer’s Disease Progression

Alzheimer&amp;rsquo;s disease (AD) is a neurodegenerative disease and the most common cause of dementia among older adults. We implemented 3D Zernike transformation to characterize the shape changes of hippocampus in 428 older subjects with high-quality T 1 -weighted volumetric brain scans. The hippocampus shape features characterized with 3D Zernike transformation, in complement to volume measures, may serve as a novel imaging marker to monitor clinical AD progression.
Longitudinal Multiple Sclerosis Lesion Segmentation Using Pre-activation U-Net

Automated segmentation of new multiple sclerosis (MS) lesions in MRI data is crucial for monitoring and quantifying MS progression. Manual delineation of such lesions is laborious and time-consuming since experts need to deal with 3D images and numerous small lesions. We propose a 3D encoder-decoder architecture with pre-activation blocks to segment new MS lesions in longitudinal FLAIR images. We also applied intensive data augmentation and deep supervision to mitigate the limited data and the class imbalance problem. The proposed model, called Pre-U-Net, achieved a Dice score of 0.62 and a sensitivity of 0.58 on the public challenge MSSEG-2 dataset.
Design of a single screw extruder machine for 3d printing filament production application

This study aims to determine the optimal 3D Printer filament extruder machine design, to determine the design of the extruder components for filament production, to determine the single screw design used for filament production. The research method used is creating flow chart, the result of the research is the calculation of the diameter of the 3D Printer filament of 1.77 mm, by measuring the diameter of the filament by taking a 2 meters long sample to be measured every 10 cm. the most influential components in this research are diameter, length of single screw extruder, length of filament winding process, production capacity and total electric power. With a screw speed of 5 RPM, 1.5 kg of ready-to-use 3D printer filament is produced within 1 hour of production. So there, is an error between the calculation and the real condition of 15%.
Interprofessionality in Health Care

Introduction: Interprofessional education in health has been revealed as an important strategy of professional training, in which professionals are able to develop teamwork that seeks to achieve integrality in health care. Objective: Tounderstand interprofessional education in health from the perspective of health education. Method: A bibliographic review with a reflective approach was carried out published between 2011 and 2021 in the SciElo, Pubmed and CAPS databases, using specific descriptors, in Portuguese and English languages, in February 2022. The titles and abstracts were analyzed, followed by the selection of articles that related to the theme, and 7 articles were included in the final sample. Subsequently, the reflection on the theme was carried out. Results and Discussions: From the selection of the articles, it was possible to understand that there is a long trajectory in the implementation of changes in teaching in the context of health education, and there is a need to improve the teaching-service-community integration, as well as to prioritize interdisciplinarity and interprofessionality, enabling cooperation for the exercise of transformative practices. Final considerations: this study revealed the urgent need for the implementation, strengthening and support of interinstitutional partnerships, the promotion of public policies that qualify the faculty for the development of interprofessional education and the strengthening of relations between the university, health institutions and the community. In addition, it pointed out the need to promote a culture of collaboration and teamwork among health professionals, valuing the diversity of knowledge and skills in order to achieve more comprehensive results to improve the results and quality of health care.
Elaboration-Generating Commonsense Question Answering at Scale

In question answering requiring common sense, language models (e.g., GPT-3) have been used to generate text expressing background knowledge that helps improve performance.Yet the cost of working with such models is very high; in this work, we finetune smaller language models to generate useful intermediate context, referred to here as elaborations.Our framework alternates between updating two language models-an elaboration generator and an answer predictor-allowing each to influence the other.Using less than 0.5% of the parameters of GPT-3, our model outperforms alternatives with similar sizes and closes the gap with GPT-3 on four commonsense question answering benchmarks.Human evaluations show that the quality of the generated elaborations is high. 1
Automated Digitization of Unstructured Medical Prescriptions

Automated digitization of prescription images is a critical prerequisite to scale digital healthcare services such as online pharmacies. This is challenging in emerging markets since prescriptions are not digitized at source and patients lack the medical expertise to interpret prescriptions to place orders. In this paper, we present prescription digitization system for online medicine ordering built with minimal supervision. Our system uses a modular pipeline comprising a mix of ML and rule-based components for (a) image to text extraction, (b) segmentation into blocks and medication items, (c) medication attribute extraction, (d) matching against medicine catalog, and (e) shopping cart building. Our approach efficiently utilizes multiple signals like layout, medical ontologies, and semantic embeddings via LayoutLMv2 model to yield substantial improvement relative to strong baselines on medication attribute extraction. Our pipeline achieves +5.9% gain in precision@3 and +5.6% in recall@3 over catalog-based fuzzy matching baseline for shopping cart building for printed prescriptions.
PersLEARN: Research Training through the Lens of Perspective Cultivation

Scientific research is inherently shaped by its authors' perspectives, influenced by various factors such as their personality, community, or society.Junior researchers often face challenges in identifying the perspectives reflected in the existing literature and struggle to develop their own viewpoints.In response to this issue, we introduce PersLEARN , a tool designed to facilitate the cultivation of scientific perspectives, starting from a basic seed idea and progressing to a well-articulated framework.By interacting with a prompt-based model, researchers can develop their perspectives explicitly.Our human study reveals that scientific perspectives developed by students using PersLEARN exhibit a superior level of logical coherence and depth compared to those that did not.Furthermore, our pipeline outperforms baseline approaches across multiple domains of literature from various perspectives.These results suggest that PersLEARN could help foster a greater appreciation of diversity in scientific perspectives as an essential component of research training. 1