Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.
Cross-Frequency Coupling Between Cerebral Blood Flow Velocity and EEG in Ischemic Stroke Patients With Large Vessel Occlusion

Background: Neurovascular coupling enables a rapid adaptation of cerebral blood flow (CBF) to support neuronal activities. Whether this mechanism is compromised during the acute phase after ischemic stroke remains unknown. In this study, we applied a phase-amplitude cross-frequency coupling (PAC) algorithm to investigate multimodal neuro signals including CBF velocity (CBFV), and electroencephalography (EEG). Methods: Acute ischemic stroke patients admitted to the Neurointensive Care Unit, Tiantan Hospital, Capital Medical University (Beijing, China) with continuous monitoring of 8-lead EEG (F3-C3, T3-P3, P3-O1, F4-C4, T4-P4, P4-O2), non-invasive arterial blood pressure (ABP), and bilateral CBFV of the middle cerebral arteries or posterior cerebral arteries were retrospectively analyzed. PAC was calculated between the phase of CBFV in frequency bands (0–0.05 and 0.05–0.15 Hz) and the EEG amplitude in five bands (δ, θ, α, β, γ). The global PAC was calculated as the sum of all PACs across the six EEG channels and five EEG bands for each patient. The hemispherical asymmetry of cross-frequency coupling (CFC) was calculated as the difference between left and right PAC. Results: Sixteen patients (3 males) met our inclusion criteria. Their age was 60.9 ± 7.9 years old. The mean ABP, mean left CBFV, and mean right CBFV were 90.2 ± 31.2 mmHg, 57.3 ± 20.6 cm/s, and 68.4 ± 20.9 cm/s, respectively. The PAC between CBFV and EEG was significantly higher in β and γ bands than in the other three bands. Occipital region (P3-O1 and P4-O2 channels) showed stronger PAC than the other regions. The deceased group tended to have smaller global PAC than the survival group (the area under the receiver operating characteristic curve [AUROC] was 0.81, p = 0.57). The unfavorable outcome group showed smaller global PAC than the favorable group (AUROC = 0.65, p = 0.23). The PAC asymmetry between the two brain hemispheres correlates with the degree of stenosis in stroke patients (p = 0.01). Conclusion: We showed that CBFV interacts with EEG in β and γ bands through a phase-amplitude CFC relationship, with the strongest PAC found in the occipital region and that the degree of hemispherical asymmetry of CFC correlates with the degree of stenosis.
Processor in Non-Volatile Memory (PiNVSM): Towards to Data-centric Computing in Decentralized Environment

The AI problem has no solution in the environment of existing hardware stack and OS architecture. CPU-centric model of computation has a huge number of drawbacks that originate from memory hierarchy and obsolete architecture of the computing core. The concept of mixing memory and logic has been around since 1960s. However, the concept of Processor-In-Memory (PIM) is unable to resolve the critical issues of the CPU-centric computing model because of inevitable replication of von Neumann architecture's drawbacks. The next generation of NVM/SCM memory is able to give the second birth to the data-centric computing paradigm. This paper presents a concept of Processor in Non-Volatile Memory (PiNVSM) architecture. The basis of PiNVSM architecture is the concept of DPU that contains the NVM memory and dedicated PU. All necessary PU's registers can be implemented in the space of NVM memory. NVM memory of DPU is the single space for storing and transformation of data. In the basis of PiNVSM architecture lies the DPU array is able to overcome the limitations as Turing machine model as von Neumann architecture. The DPU array hasn't a centralized computing core. Every data portion has dedicated computing core that excludes the necessity to transfer data to the place of data processing. Every DPU contains data portion that is associated with the set of keywords. Any complex data structure can be split on elementary items that can be stored into independent DPU with dedicated computing core(s). One DPU is able to apply the elementary transformation on one item. But the DPU array is able to make the transformation of complex structure by means of concurrent execution of elementary transformations in different DPUs. The PiNVSM architecture suggests a principally new architecture of the computing core that creates a new opportunity for data self-organization, data and code synthesis.
Affordance Learning for End-to-End Visuomotor Robot Control

Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional latent representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using Franka Panda robot arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies. We also introduce a method for affordance dataset generation, which is easily generalizable to new tasks, objects and environments, and requires no manual pixel labeling.
Manipulation by Feel: Touch-Based Control with Deep Predictive Models

Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google. com/view/deeptactilempc
Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft

Deep Q-Learning has been successfully applied to a wide variety of tasks in the past several years. However, the architecture of the vanilla Deep Q-Network is not suited to deal with partially observable environments such as 3D video games. For this, recurrent layers have been added to the Deep Q-Network in order to allow it to handle past dependencies. We here use Minecraft for its customization advantages and design two very simple missions that can be frames as Partially Observable Markov Decision Process. We compare on these missions the Deep Q-Network and the Deep Recurrent Q-Network in order to see if the latter, which is trickier and longer to train, is always the best architecture when the agent has to deal with partial observability.
Urokinase Plasminogen Activator Overexpression Reverses Established Lung Fibrosis

Abstract Introduction Impaired plasminogen activation (PA) is causally related to the development of lung fibrosis. Prior studies demonstrate that enhanced PA in the lung limits the severity of scarring following injury and in vitro studies indicate that PA promotes matrix degradation and fibroblast apoptosis. These findings led us to hypothesize that increased PA in an in vivo model would enhance the resolution of established lung fibrosis in conjunction with increased myofibroblast apoptosis. Methods Transgenic C57BL/6 mice with doxycycline inducible lung-specific urokinase plasminogen activator (uPA) expression or littermate controls were treated (day 0) with bleomycin or saline. Doxycycline was initiated on days 1, 9, 14, or 21. Lung fibrosis, stiffness, apoptosis, epithelial barrier integrity, and inflammation were assessed. Results Protection from fibrosis with uPA upregulation from day 1 through day 28 was associated with reduced parenchymal stiffness as determined by atomic force microscopy. Initiation of uPA expression beginning in the late inflammatory or the early fibrotic phase reduced stiffness and fibrosis at day 28. Induction of uPA activity in mice with established fibrosis decreased lung collagen and lung stiffness while increasing myofibroblast apoptosis. Upregulation of uPA did not alter lung inflammation but was associated with improved epithelial cell homeostasis. Conclusion Restoring intrapulmonary PA activity diminishes lung fibrogenesis and enhances the resolution of established lung fibrosis. This PA-mediated resolution is associated with increased myofibroblast apoptosis and improved epithelial cell homeostasis. These studies support the potential capacity of the lung to resolve existing scar in murine models.
Joint Inference of Kinematic and Force Trajectories with Visuo-Tactile Sensing

To perform complex tasks, robots must be able to interact with and manipulate their surroundings. One of the key challenges in accomplishing this is robust state estimation during physical interactions, where the state involves not only the robot and the object being manipulated, but also the state of the contact itself. In this work, within the context of planar pushing, we extend previous inference-based approaches to state estimation in several ways. We estimate the robot, object, and the contact state on multiple manipulation platforms configured with a vision-based articulated model tracker, and either a biomimetic tactile sensor or a force-torque sensor. We show how to fuse raw measurements from the tracker and tactile sensors to jointly estimate the trajectory of the kinematic states and the forces in the system via probabilistic inference on factor graphs, in both batch and incremental settings. We perform several benchmarks with our framework and show how performance is affected by incorporating various geometric and physics based constraints, occluding vision sensors, or injecting noise in tactile sensors. We also compare with prior work on multiple datasets and demonstrate that our approach can effectively optimize over multi-modal sensor data and reduce uncertainty to find better state estimates.
CopyCAN: An Error-Handling Protocol based Intrusion Detection System for Controller Area Network

In the last years, the automotive industry has incorporated more and more electronic components in vehicles, leading to complex on-board networks of Electronic Control Units (ECUs) that communicate with each other to control all vehicle functions, making it safer and easier to drive. This communication often relies on Controller Area Network (CAN), a bus communication protocol that defines a standard for real-time reliable and efficient transmission. However, CAN does not provide any security measure against cyber attacks. In particular, it lacks message authentication, leading to the possibility of transmitting spoofed CAN messages for malicious purposes. Nowadays, Intrusion Detection Systems (IDSs) detect such attacks by identifying inconsistencies in the stream of information allegedly transmitted by a single ECU, hence assuming the existence of a second malicious node generating these messages. However, attackers can bypass this defense technique by disconnecting from the network the ECU of which they want to spoof the messages, therefore removing the authentic source of information. To contrast this attack, we present CopyCAN, an Intrusion Detection System (IDS) that detects whether a node has been disconnected by monitoring the traffic and deriving the error counters of ECUs on CAN. Through this process, it flags subsequent spoofed messages as attacks and reacts accordingly even if there is no inconsistency in the stream of information. Our system, differently from many previous works, does not require any modification to the protocol or to already installed ECUs. Instead, it only requires the installation of a monitoring unit to the existing network, making it easily deployable in current systems and compliant with required CAN standards.
Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks

Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.
Dynamic Demand Prediction for Expanding Electric Vehicle Sharing Systems: A Graph Sequence Learning Approach

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe. During their fast expansion, one fundamental determinant for success is the capability of dynamically predicting the demand of stations as the entire system is evolving continuously. There are several challenges in this dynamic demand prediction problem. Firstly, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and support for decision making. Secondly, for the stations to be deployed, there is no historical record or additional mobility data available to help the prediction of their demand. Finally, the impact of deploying/closing stations to the remaining stations in the system can be very complex. To address these challenges, in this paper we propose a novel dynamic demand prediction approach based on graph sequence learning, which is able to model the dynamics during the system expansion and predict demand accordingly. We use a local temporal encoding process to handle the available historical data at individual stations, and a dynamic spatial encoding process to take correlations between stations into account with graph convolutional neural networks. The encoded features are fed to a multi-scale prediction network, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach on real-world data collected from a major EV sharing platform in Shanghai for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the rapidly expanding EV sharing system.
Carboxylesterase-Cleavable Biotinylated Nanoparticle for Tumor-Dual Targeted Imaging

Near-infrared (NIR) nanoprobes with fluorescence “Turn-On” property are advantageous in cancer diagnosis but, to the best of our knowledge, “smart” nanoprobe that simultaneously targets both biotin receptor and carboxylesterase (CES) for HepG2 tumor-dual targeted imaging has not been reported. Methods: Using CBT-Cys click condensation reaction, we rationally designed a “smart” NIR fluorescence probe H2N-Cys(StBu)-Lys(Biotin)-Ser(Cy5.5)-CBT (NIR-CBT) and used it to facilely prepare the fluorescence-quenched nanoparticle NIR-CBT-NP. Results: In vitro results indicated that, after NIR-CBT-NP was incubated with CES for 6 h, its fluorescence was turned “On” by 69 folds. Cell experiments verified that NIR-CBT-NP was uptaken by HepG2 cells via biotin receptor-assisted endocytosis and its fluorescence was turned “On” by intracellular CES hydrolysis. Moreover, NIR-CBT-NP was successfully applied to image both biotin receptor- and CES-overexpressing HepG2 tumors. Conclusion: Fluorescence-quenched nanoparticle NIR-CBT-NP was facilely prepared to actively target biotin receptor-overexpressing HepG2 cancer cells and turn the fluorescence “On” by intracellular CES hydrolysis for tumor-dual targeted imaging. We anticipate that our fluorescence “Turn-On” nanoparticle could be applied for liver cancer diagnosis in clinic in the near future.
How Presenters Perceive and React to Audience Flow Prediction In-situ

The degree and quality of instructor-student interactions are crucial for students' engagement, retention, and learning outcomes. However, such interactions are limited in live online lectures, where instructors no longer have access to important cues such as raised hands or facial expressions at the time of teaching. As a result, instructors cannot fully understand students' learning progresses. This paper presents an explorative study investigating how presenters perceive and react to audience flow prediction when giving live-stream lectures, which has not been examined yet. The study was conducted with an experimental system that can predict audience's psychological states (e.g., anxiety, flow, boredom) through real-time facial expression analysis, and can provide aggregated views illustrating the flow experience of the whole group. Through evaluation with 8 online lectures (N_instructors=8, N_learners=21), we found such real-time flow prediction and visualization can provide value to presenters. This paper contributes a set of useful findings regarding their perception and reaction of such flow prediction, as well as lessons learned in the study, which can be inspirational for building future AI-powered system to assist people in delivering live online presentations.
Analysis of the use of smart cards on the urban railway

The article analyzes the patterns of use of railway stations in the Moscow region. The basis for the analysis is the data of smart cards on the entrances and exits of passengers. The technical tool is time series similarity analysis. As a result, the work identifies the main patterns of user behavior on the use of railway stations (railway transport). The results of the work were used in the design of new lines of urban railways. Obviously, the use patterns reflect the current state of the transport system and the urban environment. Accordingly, the recorded changes in usage patterns can serve as indicators and metrics for changes in the urban environment.
Let's Encrypt: An Automated Certificate Authority to Encrypt the Entire Web

Let's Encrypt is a free, open, and automated HTTPS certificate authority (CA) created to advance HTTPS adoption to the entire Web. Since its launch in late 2015, Let's Encrypt has grown to become the world's largest HTTPS CA, accounting for more currently valid certificates than all other browser-trusted CAs combined. By January 2019, it had issued over 538~million certificates for 223~million domain names. We describe how we built Let's Encrypt, including the architecture of the CA software system (Boulder) and the structure of the organization that operates it (ISRG), and we discuss lessons learned from the experience. We also describe the design of ACME, the IETF-standard protocol we created to automate CA--server interactions and certificate issuance, and survey the diverse ecosystem of ACME clients, including Certbot, a software agent we created to automate HTTPS deployment. Finally, we measure Let's Encrypt's impact on the Web and the CA ecosystem. We hope that the success of Let's Encrypt can provide a model for further enhancements to the Web PKI and for future Internet security infrastructure.
Ancient Introgression between Two Ape Malaria Parasite Species

Abstract The Laverania clade comprises the human malaria parasite Plasmodium falciparum as well as at least seven additional parasite species that infect wild African apes. A recent analysis of Laverania genome sequences (Otto TD, et al. 2018. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat Microbiol. 3: 687–697) reported three instances of interspecies gene transfer, one of which had previously been described. Generating gene sequences from additional ape parasites and re-examining sequencing reads generated in the Otto et al. study, we identified one of the newly described gene transfers as an assembly artifact of sequences derived from a sample coinfected by two parasite species. The second gene transfer between ancestors of two divergent chimpanzee parasite lineages was confirmed, but involved a much larger number of genes than originally described, many of which encode exported proteins that remodel, or bind to, erythrocytes. Because successful hybridization between Laverania species is very rare, it will be important to determine to what extent these gene transfers have shaped their host interactions.