Miracle-dz/UniFAR-FTUs huggingface dataset:
Case-Based Asynchronous Interactive Modules in Undergraduate Medical Education

Undergraduate medical education traditionally consists of 2 years of lecture-based courses followed by 2 years of clinical clerkships. However, over the past couple decades, undergraduate medical education has been evolving toward non-lecture-based integrated curriculums, requiring a collaborative curriculum. Additionally, e-learning platforms have become efficacious and essential to delivering education asynchronously to students. At Thomas Jefferson University, the Pathology and Obstetrics and Gynecology departments collaborated to create a pilot series of case-based asynchronous interactive modules to teach gynecologic pathology in a clinical context, while interweaving other educational components, such as evidence-based medicine, clinical skills, and basic sciences. The case-based asynchronous interactive modules were given to third-year medical students during their obstetrics and gynecology clerkship. Students interpreted histologic and clinical images while being evaluated on clinical management skills, gynecologic diagnoses, general principles of population health and pathology. Sixty-eight students from 3 blocks completed a pre and posttest. All participants showed improvement in interpreting gynecologic pathology in routine clinical scenarios as well as improved case-based decision-making, with an average score increase by 5.7%. Learner feedback was positive, with suggestions to apply this method to other medical specialties, particularly radiology. Asynchronous interactive modules are an efficacious and popular method of pathology education.
Program Classification Using Gated Graph Attention Neural Network for Online Programming Service

The online programing services, such as Github,TopCoder, and EduCoder, have promoted a lot of social interactions among the service users. However, the existing social interactions is rather limited and inefficient due to the rapid increasing of source-code repositories, which is difficult to explore manually. The emergence of source-code mining provides a promising way to analyze those source codes, so that those source codes can be relatively easy to understand and share among those service users. Among all the source-code mining attempts,program classification lays a foundation for various tasks related to source-code understanding, because it is impossible for a machine to understand a computer program if it cannot classify the program correctly. Although numerous machine learning models, such as the Natural Language Processing (NLP) based models and the Abstract Syntax Tree (AST) based models, have been proposed to classify computer programs based on their corresponding source codes, the existing works cannot fully characterize the source codes from the perspective of both the syntax and semantic information. To address this problem, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the AST,and applies an improved GNN model to the integrated graph, so as to achieve the state-of-art program classification accuracy. The experiment results have shown that the proposed work can classify programs with accuracy over 97%.
Second-Generation Antidiabetic Sulfonylureas Inhibit Candida albicans and Candidalysin-Mediated Activation of the NLRP3 Inflammasome

Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans. ABSTRACT Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans. Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3−/− cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.
RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience

Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.
Revealing the Microscopic Structure of Human Renal Cell Carcinoma in Three Dimensions

For fully characterizing renal cell carcinoma (RCC), information about the 3D tissue microstructure is essential. Histopathology, which represents the current diagnostic gold standard, is destructive and only provides 2D information. 3D X-ray histology endeavors to overcome these limitations by generating 3D data. In a laboratory environment, most techniques struggle with limited resolution and the weak X-ray attenuation contrast of soft tissue. We recently developed a laboratory-based method combining nanoscopic X-ray CT with a cytoplasm-specific X-ray stain. Here, we present the application of this method to human RCC biopsies. The NanoCT slices enable pathological characterization of crucial structures by reproducing tissue morphology with a similar detail level as corresponding histological light microscopy images. Beyond that, our data offer deeper insights into the 3D configuration of the tumor. By demonstrating the compatibility of the X-ray stain with standard pathological stains, we highlight the feasibility of integrating staining based NanoCT into the pathological routine.
Adaptive Trajectory Planning and optimization at Limits of Handling

In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles. We employ concepts from adaptive model predictive control for run-time adaptation of tire force constraints that are imposed by local traction conditions. To solve the resulting optimization problem for real-time control synthesis with such time varying constraints, we propose a novel numerical scheme based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP), which we call Sampling Augmented Adaptive RTI (SAA-RTI). Sampling augmentation of conventional RTI-SQP provides additional feasible candidate trajectories for warm-starting the optimization procedure. Thus, the proposed SAA-RTI algorithm enables real time constraint adaptation and reduces sensitivity to local minima. Through extensive numerical simulations we demonstrate that our method increases the vehicle’s capacity to avoid accidents in scenarios with unanticipated obstacles and locally varying traction, compared to equivalent non-adaptive control schemes and traditional planning and tracking approaches.
Building an Affordances Map With Interactive Perception

Robots need to understand their environment to perform their task. If it is possible to pre-program a visual scene analysis process in closed environments, robots operating in an open environment would benefit from the ability to learn it through their interaction with their environment. This ability furthermore opens the way to the acquisition of affordances maps in which the action capabilities of the robot structure its visual scene understanding. We propose an approach to build such affordances maps by relying on an interactive perception approach and an online classification for a real robot equipped with two arms with 7 degrees of freedom. Our system is modular and permits to learn maps from different skills. In the proposed formalization of affordances, actions and effects are related to visual features, not objects, thus our approach does not need a prior definition of the concept of object. We have tested the approach on three action primitives and on a real PR2 robot.
Application of Google Cloud Platform in Astrophysics

The availability of new Cloud Platform offered by Google motivated us to propose nine Proof of Concepts (PoC) aiming to demonstrated and test the capabilities of the platform in the context of scientifically-driven tasks and requirements. We review the status of our initiative by illustrating 3 out of 9 successfully closed PoC that we implemented on Google Cloud Platform. In particular, we illustrate a cloud architecture for deployment of scientific software as microservice coupling Google Compute Engine with Docker and Pub/Sub to dispatch heavily parallel simulations. We detail also an experiment for HPC based simulation and workflow executions of data reduction pipelines (for the TNG-GIANO-B spectrograph) deployed on GCP. We compare and contrast our experience with on-site facilities comparing advantages and disadvantages both in terms of total cost of ownership and reached performances.
MAKE-METHOD-LAMBDA Revisited

The Common Lisp metaobject protocol specifies a generic function named make-method-lambda to be called at macro-expansion time of the macro defmethod . In an article by Costanza and Herzeel, a number of problems with this generic function are discussed, and a solution is proposed. In this paper, we show that the alleged problems are due to the fact that existing implementations do not include proper compile-time processing of the associated macro defgeneric , and that with proper compile-time processing, the problems indicated in the paper by Costanza and Herzeel simply vanish. The main characteristic of our proposed solution is for the compile-time side effects of defgeneric to include saving the name of the method class given as an option to that macro call. With this additional information, no difference exists between the behavior of direct evaluation and that of file compilation of a defgeneric form and a defmethod form mentioning the same name of the generic function.
Publicness, Privacy and Confidentiality in the Single-Serving Quantum Broadcast Channel

The 2-receiver broadcast channel with primary and third-party receivers is studied. The messages are classified into public, private and confidential. The messages in the public class are messages intended for both receivers. The private messages are intended for the primary receiver with no secrecy requirements imposed upon them. And the confidential messages are aimed exclusively to the primary receiver such that they must not be accessible to the other receiver. The encoder performs the necessary encryption by virtue of local randomness whose rate is assumed to be limited. We find an achievability region on the trade-off between the rates of the three messages and the source of randomness in the one-shot regime of a quantum broadcast channel.
From Low-Level Events to Activities - A Session-Based Approach (Extended Version)

Process-Mining techniques aim to use event data about past executions to gain insight into how processes are executed. While these techniques are proven to be very valuable, they are less successful to reach their goal if the process is flexible and, hence, events can potentially occur in any order. Furthermore, information systems can record events at very low level, which do not match the high-level concepts known at business level. Without abstracting sequences of events to high-level concepts, the results of applying process mining (e.g., discovered models) easily become very complex and difficult to interpret, which ultimately means that they are of little use. A large body of research exists on event abstraction but typically a large amount of domain knowledge is required to be fed in, which is often not readily available. Other abstraction techniques are unsupervised, which give lower accuracy. This paper puts forward a technique that requires limited domain knowledge that can be easily provided. Traces are divided in sessions, and each session is abstracted as one single high-level activity execution. The abstraction is based on a combination of automatic clustering and visualization methods. The technique was assessed on two case studies that evidently exhibits a large amount of behavior. The results clearly illustrate the benefits of the abstraction to convey knowledge to stakeholders.
A reference architecture for integrating the Industrial Internet of Things in the Industry 4.0

There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both business reports and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc). At present, the literature is missing a design process for integrating these constantly evolving systems and technologies in a clear and understandable step by step model. This paper contributes with a new reference architecture model for the integration of these systems and technologies. The reference architecture model is based on grouping of future and present techniques and presenting the design process through a new hierarchical framework and a new cascading model. With the application of the grounded theory, the hierarchical framework and the cascading model detail a new process for creating a taxonomy of categories and grouping of concepts into integration design. The new design process is tested and versified with an empirical review of Industry 4.0 frameworks and results with a new 5 levels reference architecture step by step model for the integration of these related systems and technologies (Industrial Internet of Things, Cyber Physical Systems, and Industry 4.0). We review 118 academic and industry papers published between 2010 and 2019. Then, we report the results of a qualitative empirical study that correlates academic literature with 14 world leading Industry 4.0 frameworks and initiatives. We therefore propose an architectural model that offers a better understanding of the systems integration between the Industrial Internet of Things and Industry 4.0.
Towards a scatter-gather architecture: hardware and software issues

The on-node performance of High performance computing (HPC) applications is traditionally dominated by memory operations. Put simply, memory is what these applications "do. " Unfortunately, they don't do it well. Caches, our first line of attack in the battle for memory performance, often throw away most of the data they fetch before using it. Processor cores, one of our most expensive resources, spend an inordinate amount of time performing simple address computations. Addressing these issues will require new approaches to how on-chip memory is organized and how memory operations are performed. Under Project 38, a joint Department of Energy / Department of Defense architectural resarch project, we have focused on exploring what a flexible in-memory scatter-gather architecture could look like in the context of several important HPC applications.
Exploring OpenStreetMap Availability for Driving Environment Understanding

With the great achievement of artificial intelligence, vehicle technologies have advanced significantly from human centric driving towards fully automated driving. An intelligent vehicle should be able to understand the driver's perception of the environment as well as controlling behavior of the vehicle. Since high digital map information has been available to provide rich environmental context about static roads, buildings and traffic infrastructures, it would be worthwhile to explore map data capability for driving task understanding. Alternative to commercial used maps, the OpenStreetMap (OSM) data is a free open dataset, which makes it unique for the exploration research. This study is focused on two tasks that leverage OSM for driving environment understanding. First, driving scenario attributes are retrieved from OSM elements, which are combined with vehicle dynamic signals for the driving event recognition. Utilizing steering angle changes and based on a Bi-directional Recurrent Neural Network (Bi-RNN), a driving sequence is segmented and classified as lane-keeping, lane-change-left, lane-change-right, turn-left, and turn-right events. Second, for autonomous driving perception, OSM data can be used to render virtual street views, represented as prior knowledge to fuse with vision/laser systems for road semantic segmentation. Five different types of road masks are generated from OSM, images, and Lidar points, and fused to characterize the drivable space at the driver's perspective. An alternative data-driven approach is based on a Fully Convolutional Network (FCN), OSM availability for deep learning methods are discussed to reveal potential usage on compensating street view images and automatic road semantic annotation.
Stem-like tumor cells involved in heterogeneous vasculogenesis in breast cancer.

Sorafenib, a small molecule tyrosine kinase inhibitor with antiangiogenic activity, has been used in liver cancer and kidney cancer treatment. However, clinical trials with sorafenib for breast cancer were stopped in phase Ⅲ due to limited efficacy. The existence of heterogeneous vasculatures involving tumor cells, such as vessel-like structures formed by vasculogenic mimicry and mosaic vessels, and their resistance to antiangiogenic therapy are thought to be a possible reason for failure of sorafenib therapy. Nevertheless, the features and mechanism of vasculogenesis by tumor cells remain unclear. In the present study, we found that breast cancer stem-like cells (BCSLCs, ALDH1+ cells) were involved in vasculogenic mimicry and mosaic vessel formation in triple-negative breast cancer tissues. Further, only ALDH1+ BCSLCs sorted from MDA-MB-231 could exhibit the tube formation and angiogenesis ability. Sorafenib could inhibit vascularization from endothelial cells rather than that from ALDH1+ cells. α-SMA was identified as a key molecule in vascular formation of BCSLCs. Mechanistically, HIF-1α enhanced the mRNA and protein levels of α-SMA by binding to the HRE element in the promoter directly and meanwhile increased the BCSLCs population. Interestingly, pigment epithelium-derived factor (PEDF), an endogenous angiogenesis inhibitor, could inhibit both endothelial cell-derived and tumor cell-derived angiogenesis by down-regulating HIF-1α in breast cancer. Our finding clarified the possible reason for the poor outcome of anti-angiogenesis therapy and PEDF may have the therapeutic potential.
Stroke-based Artistic Rendering Agent with Deep Reinforcement Learning

Excellent painters can use only a few strokes to create a fantastic painting, which is a symbol of human intelligence and art. Reversing the simulator to interpret images is also a challenging task of computer vision in recent years. In this paper, we present SARA, a stroke-based artistic rendering agent that combines the neural renderer and deep reinforcement learning (DRL), allowing the machine to learn the ability to deconstruct images using strokes and create amazing visual effects. Our agent is an end-to-end program that converts natural images into paintings. The training process does not require the experience of human painting or stroke tracking data.
Fast Single Image Reflection Suppression via Convex Optimization

Removing undesired reflections from images taken through the glass is of great importance in computer vision. It serves as a means to enhance the image quality for aesthetic purposes as well as to preprocess images in machine learning and pattern recognition applications. We propose a convex model to suppress the reflection from a single input image. Our model implies a partial differential equation with gradient thresholding, which is solved efficiently using Discrete Cosine Transform. Extensive experiments on synthetic and real-world images demonstrate that our approach achieves desirable reflection suppression results and dramatically reduces the execution time compared to the state of the art.
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering

Deep generative models are stochastic neural networks capable of learning the distribution of data so as to generate new samples. Conditional Variatonal Autoencoder (CVAE) is a powerful deep generative model aiming at maximizing the lower bound of training data log-likelihood. In this structure, there exists appropriate regularizer, which makes it applicable for suitably constraining the solution space in solving ill-posed problems and providing high generalization power. Considering the stochastic prediction characteristic in CVAE, depending on the problem at hand, it is desirable to be able to control the uncertainty in CVAE predictions. Therefore, in this paper we analyze the impact of CVAE's condition on the diversity of solutions given by our designed CVAE in 3D shape inverse rendering as a prediction problem. The experimental results using Modelnet10 and Shapenet datasets and comparison with several recent methods show the appropriate performance of our designed CVAE and verify the hypothesis: “The more informative the conditions in terms of object pose are, the less diverse the CVAE predictions are”.