Miracle-dz/UniFAR-FTUs huggingface dataset:
Alarmin HMGB1 and Soluble RAGE as New Tools to Evaluate the Risk Stratification in Patients With the Antiphospholipid Syndrome

Antiphospholipid antibody syndrome (APS) is a systemic autoimmune disease characterized by arterial and/or venous thrombosis, pregnancy morbidity in the presence of circulating “anti-phospholipid antibodies” (aPL). One of the main target antigens of aPL is β2-glycoprotein I (β2-GPI). APS may occur as a primary syndrome or associated with Systemic Lupus Erythematosus (SLE). High Mobility Group Box 1 (HMGB1) is a nuclear non-histone protein which is secreted from different type of cells during activation and/or cell death and may act as a proinflammatory mediator through ligation to its receptors, including RAGE. There is accumulating evidence that HMGB1 contributes to the pathogenesis of inflammatory and autoimmune diseases, especially SLE. In a previous study we demonstrated increased serum levels of HMGB1 in both primary and secondary APS patients. In this work we analyzed: (i) in vitro whether anti-β2-GPI antibodies from APS patients may induce both a HMGB1 cellular relocation by activation of its putative receptor RAGE in platelets and monocytes and, (ii) ex vivo, serum levels of HMGB1/soluble RAGE (sRAGE) in APS patients and their possible correlation with clinical manifestations. Platelets and monocytes from healthy donors were incubated with affinity purified anti-β2-GPI antibodies. HMGB1 and RAGE expression were analyzed by Western Blot. Sera from 60 consecutive APS patients (primary or secondary), diagnosed according to the Sydney Classification Criteria, were enrolled. As a control, 30 matched healthy subjects were studied. Serum levels of HMGB1 and sRAGE were analyzed by Western Blot. In vitro results showed that anti-β2-GPI antibodies were able to induce RAGE activation and HMGB1 cellular relocation in both monocytes and platelets. HMGB1 and sRAGE serum levels were significantly increased in APS patients in comparison with healthy subjects (p<0.0001). Interestingly, APS patients with spontaneous recurrent abortion showed significantly higher levels of sRAGE; moreover, in APS patients a direct correlation between serum levels of HMGB1 and disease duration was detected. Our observations suggest that anti-β2-GPI antibodies may trigger RAGE activation and HMGB1 cellular relocation during APS. Monitoring these molecules serum levels may represent an useful tool to evaluate the pathogenesis and risk stratification of clinical manifestations in APS.
Correlation of Space and Time of Fire Incidence leading to Fire Risk Forecasting Model

Fire safety and prevention has always been a subject of concern in the Philippines, as fire incidents are a common cause of economic and ecological troubles every year. The aim of this study was to identify significant relationship of fire incidents in terms of location and time to build a model of fire risk forecasting in the capital city of Manila. A total of 3,506 fire incidents were recorded by the Bureau of Fire Protection (BFP) during the six-year period from 2011 to 2016. The overall record are 3506 fire incidents included in the study which analyzed using the Pearson’s Chi-square statistical technique. The summer months February to May showed the utmost number of fire, and that most fire incidents occurred from 9 AM to 11 AM and from 6 PM to 10 PM. The highest number of fire incidents recorded happened in the densely-populated districts of Sampaloc and Tondo. The study shows that there are no significant relationship of location (space) to time and to day however in terms of location to month and year show highly significant. The findings of this study can prove useful to the BFP office in Manila, in that they need to look at recurring trends in fire incidents in terms of location and month to create programs aimed towards fire prevention awareness. This research can be extended to include other spatial factors such as weather conditions and geographical coordinates in the assessment of fire incidents.
Neural Underpinnings of Financial Decision Bias in Older Adults: Putative Theoretical Models and a Way to Reconcile Them

Department of Psychology, Auburn University, Auburn, AL, United States, Human Neuroscience Institute, Cornell University, Ithaca, NY, United States, Department of Human Development, Cornell University, Ithaca, NY, United States, Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, United States, Magnetic Resonance Imaging Facility, Cornell University, Ithaca, NY, United States, Center for Research on Applied Gerontology, University of Alabama at Birmingham, Birmingham, AL, United States, Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States, Center for Neuroscience, Auburn University, Auburn, AL, United States, 9 Alabama Advanced Imaging Consortium, Birmingham, AL, United States, Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
Combining Model-Based  $Q$ -Learning With Structural Knowledge Transfer for Robot Skill Learning

Learning skills autonomously is a particularly important ability for an autonomous robot. A promising approach is reinforcement learning (RL) where agents learn policy through interaction with its environment. One problem of RL algorithm is how to tradeoff the exploration and exploitation. Moreover, multiple tasks also make a great challenge to robot learning. In this paper, to enhance the performance of RL, a novel learning framework integrating RL with knowledge transfer is proposed. Three basic components are included: 1) probability policy reuse; 2) dynamic model learning; and 3) model-based <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning. In this framework, the prelearned skills are leveraged for policy reuse and dynamic learning. In model-based <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning, the Gaussian process regression is used to approximate the <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-value function so as to suit for robot control. The prior knowledge retrieved from knowledge transfer is integrated into the model-based <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>-learning to reduce the needed learning time. Finally, a human-robot handover experiment is performed to evaluate the learning performance of this learning framework. Experiment results show that fewer exploration is needed to obtain a high expected reward, due to the prior knowledge obtained from knowledge transfer.
On Safe Robot Navigation Among Humans as Dynamic Obstacles in Unknown Indoor Environments

In this paper, we rigorously test two conjectures in mobile robot navigation among dynamic obstacles in unknown environments: i) a planner for static obstacles, if executed at a fast update rate (i.e., fast replanning), might be quite effective in dealing with dynamic obstacles, and ii) existing implemented planners have been effective in humans environments (with humans being dynamic obstacles) primarily because humans themselves avoid the robot and if this were not the case, robot will run into collisions with humans much more frequently. The core planning approach used is a Global path planner combined with a local Dynamic Window planner with repeated re-planning (GDW). We compare two planners within this framework: i) all obstacles are treated as static (GDW-S) and ii) predicted trajectories of dynamic obstacles are used to avoid future collisions within a given planning horizon time (GDW-D). The effect of humans avoiding robot (and other humans) is simulated via a simple local potential field based approach. We indicate such environments by a suffix +R (repulsion) for the corresponding planner. Hence there are four categories that we tested: GDW-S, GDW-D, GDW-S+R and GDW-D+R in different environments of varying complexity. The performance metrics used were the percentage of successful runs without collisions and total number of collisions. The results indicate that i) GDW-D planner outperforms GDW-S planner, i.e., conjecture 1 is false, and ii) humans avoiding robots does result in more successful runs, i.e., conjecture ii) is true. Furthermore, we've implemented both GDW-S and GDW-D planners on a real system and report experimental results for single obstacle case.
Extending Operational Limit of IPMSM in Signal-Injection Sensorless Control by Manipulation of Convergence Point

This paper analyzes the convergence of signal-injection sensorless control (SISC) especially for heavily saturated interior permanent-magnet synchronous motors (IPMSMs). In the analysis, it is revealed that the harmonic inductance and the operating current variation are critical factors in determining the operational limit of SISC. The clear boundary of SISC for an IPMSM can be obtained through the proposed convergence analysis. Based on this analysis, a control algorithm is proposed to extend the operational limit of SISC. With the proposed method, the torque capability under SISC can be maximized. Simulations and experiments are performed to verify the proposed method.
Model Predictive Control of a Switched Reluctance Machine for Guaranteed Overload Torque

Electric machines can be overloaded for short periods of time due to their inherently large thermal capacitance. The state-of-the-art overload strategies are often based on static characteristics of a machine. The real-time thermal condition is not considered in such strategies, leaving overload potential partially unused. This paper presents a model predictive overload strategy for a water-cooled automotive switched reluctance machine. The maximum allowed torque is predicted based on a real-time hot-spot temperature estimation, ensuring that the thermal capacitance of the machine is fully exploited. Due to the predictive nature of the algorithm, the peak torque production is inherently guaranteed over the prediction horizon. The proposed strategy is validated on a test bench. The proposed overload algorithm can be applied to other machine types as well.
Variational Bayes method for matrix factorization to two sparse factorized matrices

Matrix factorization is the problem of factorizing a given observed matrix into two matrices, and this can be applied to various fields in information science. We use variational Bayes method for the study of this problem. In the past study of variational Bayes analysis, multivariate Gaussian prior is used for low-rank matrices for convenience of analysis. In this article, we make another assumption for sparse matrix problem: the observation matrix is the multiplication of two sparse matrices, whose priors are exponential distributions for describing sparsity and non-negativity. Under this assumption, we obtain the analytical expression of factorized matrices with several approximations. We also conduct numerical experiment to observe the property of factorized matrices via our analysis.
Matching-Based Task Offloading for Vehicular Edge Computing

Vehicular edge computing has emerged as a promising technology to accommodate the tremendous demand for data storage and computational resources in vehicular networks. By processing the massive workload tasks in the proximity of vehicles, the quality of service can be guaranteed. However, how to determine the task offloading strategy under various constraints of resource and delay is still an open issue. In this paper, we study the task offloading problem from a matching perspective and aim to optimize the total network delay. The task offloading delay model is derived based on three different velocity models, i.e., a constant velocity model, vehicle-following model, and traveling-time statistical model. Next, we propose a pricing-based one-to-one matching algorithm and pricing-based one-to-many matching algorithms for the task offloading. The proposed algorithm is validated based on three different simulation scenarios, i.e., straight road, the urban road with the traffic light, and crooked road, which are extracted from the realistic road topologies in Beijing and Guangdong, China. The simulation results confirm that significant delay decreasing can be achieved by the proposed algorithm.
A robust and singular-free analytical solution for the hand-eye calibration of a novel surgical robot

For the hand-eye calibration of the robot-assisted minimally invasive surgery with an endoscopic camera, the closed-form solutions should be considered primarily by taking the time occupancy and security of the surgery into account. Although all the existing analytical solutions are widely used, at least one of them (i.e., the analytical solution based on Lie Group theory) may be singular. According to the polar decomposition of a matrix, the singularity of the Lie Group-based solution is firstly pointed out. For avoiding the above singularity, a singular-free analytical solution based on singular value decomposition is put forward. In addition, simulation and real-data experiments are conducted for the proposed and another two classical solutions of the hand-eye calibration, which can demonstrate the feasibility and accuracy of the proposed closed-form solution.
Internet of Things (IOT)-Based Mobile Application for Monitoring of Automated Aquaponics System

Traditional farming and fishkeeping have enabled human populations to produce foods, but as communities grow though, less and less agricultural and farm land is available. In addition, due to climate change, producing crops progressively is a difficult thing to do. Aquaponics address these concerns. Aquaponics is the integration of aquaculture and hydroponics in one system. The purpose of this study is to create an automated aquaponics system using Nile Tilapia and Romaine Lettuce with access and control of pH level and temperature through Internet of Things (IoT). Intel Edison is used as the microprocessor which continuously sends information of the aquaponics’ status and adjusts them if the parameters falls below their optimal levels. The system can be monitored with the use of an Internet Protocol (IP) camera. Weekly comparison of the growth of the plant and fish in automated aquaponics, and traditional fishkeeping and hydroponics are done and growth in the automated aquapponics is significantly greater than its traditional counterpart.
Surrogate Decision Tree Visualization

With the growing interest towards the application of Machine Learning techniques to many application domains, the need for transparent and interpretable ML is getting stronger. Visualizations methods can help model developers understand and refine ML models by making the logic of a given model visible and interactive. In this paper we describe a visual analytics tool we developed to support developers and domain experts (with little to no expertise in ML) in understanding the logic of a ML model without having access to the internal structure of the model (i.e., a model-agnostic method). The method is based on the creation of a “surrogate” decision tree which simulates the behavior of the black-box model of interest and presents readable rules to the end-users. We evaluate the effectiveness of the method with a preliminary user study and IUI Workshops’19, March 20, 2019, Los Angeles, USA Copyright © 2019 for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. analysis of the level of fidelity the surrogate decision tree can reach with respect to the original model.
Modeling Deep Context in Spatial and Temporal Domain

Title of dissertation: MODELING DEEP CONTEXT IN SPATIAL AND TEMPORAL DOMAIN Xiyang Dai, Doctor of Philosophy, 2018 Dissertation directed by: Professor Larry S. Davis Institute for Advanced Computer Studies Department of Computer Science Context has been one of the most important aspects in computer vision researches because it provides useful guidance to solve variant tasks in both spatial and temporal domain. As the recent rise of deep learning methods, deep networks have shown impressive performances on many computer vision tasks. Model deep context explicitly and implicitly in deep networks can further boost the effectiveness and efficiency of deep models. In spatial domain, implicitly model context can be useful to learn discriminative texture representations. We present an effective deep fusion architecture to capture both the second order and first older statistics of texture features; Meanwhile, explicitly model context can also be important to challenging task such as fine-grained classification. We then present a deep multi-task network that explicitly captures geometry constraints by simultaneously conducting fine-grained classification and key-point localization. In temporal domain, explicitly model context can be crucial to activity recognition and localization. We present a temporal context network to explicitly capture relative context around a proposal, which samples two temporal scales pair-wisely for precise temporal localization of human activities; Meanwhile, implicitly model context can lead to better network architecture for video applications. We then present a temporal aggregation network that learns a deep hierarchical representation for capturing temporal consistency. Finally, we conduct research on jointly modeling context in both spatial and temporal domain for human action understanding, which requires to predict where, when and what a human action happens in a crowd scene. We present a decoupled framework that has dedicated branches for spatial localization and temporal recognition. Contexts in spatial and temporal branches are modeled explicitly and fused together later to generate final predictions. MODELING DEEP CONTEXT IN SPATIAL AND TEMPORAL DOMAIN
A Novel Step-Up Single Source Multilevel Inverter: Topology, Operating Principle, and Modulation

This paper presents a novel step-up dc to ac converter with only one power supply. These types of converters are suitable for renewable and sustainable energy applications with low input dc sources. The proposed topology has the ability of self-voltage balancing and does not apply end side H-bridge to produce a bipolar staircase waveform. Consequently, switching losses and voltage stress of semiconductor components are reduced to a great extent. A small dc voltage source can be used to achieve a high voltage high quality ac waveform through switching the precharged capacitors in series and in parallel. Circuit configuration and its operation principle, capacitors’ charging process, thermal model, capacitances, and losses calculations are discussed in details. Moreover, the comparison of the proposed circuit with the other single source multilevel converters shows that the proposed topology reduces the number of circuit elements. Finally, a laboratory nine-level prototype is built to verify the theoretical analyses and feasibility of the proposed topology. The experimental results show that the converter efficiency at 1 KW output power is 92.75%.
Loss Minimization Control Strategy for Linear Induction Machine in Urban Transit Considering Normal Force

Linear induction machine (LIM) in urban transit suffers greatly from poor efficiency due to the large air-gap length and the end-effects, including both transversal edge- and longitudinal end-effects caused by the cut-open magnetic circuit and different width between primary and secondary. Besides, the unique normal force existing in LIM that could be as high as four times of the thrust would create undesired additional resistance force and power loss, bending of the guide way, and tire wear. Addressing these issues, this paper proposes a novel loss minimization control (LMC) strategy for LIM to reduce the steady-state loss and normal force simultaneously. First, an improved loss model of LIM is proposed on the basis of the analysis of LIM copper and core loss. Second, the LIM normal force including both attractive and repulsive components is modeled in the same manner as the loss model. Third, a novel loss minimization cost function is set up, based on which the optimal solution is obtained through both analytical and numerical approaches, and thus an improved LMC strategy is carried out to achieve the minimization of steady-state loss and normal force at the same time. The proposed method is comprehensively investigated on one 3-kW arc induction machine (one prototype for actual LIM), and its effectiveness is fully validated through both simulation and experimental results.
Adaptive Motor Control for Human-like Spatial-temporal Adaptation

Human arms can produce stable and variable compliant joint motions to achieve tasks in various spatial tasks and temporal scales. To emulate such motions we propose an adaptive motor controller (AMC) allowing for spatial-temporal adaptation of human-like motor control. The AMC is a biomimetic controller consisting of online force and impedance (i.e., stiffness and damping) adaptation to different tasks and unknown arm dynamics. As a result, the AMC can produce more accurate and stable human-like reaching and tracking behaviors, compared to conventional controllers. Moreover, the reproduced spatial-temporal adaptation is comparable to that found in the experiments of human motor control. The proposed AMC may pave a novel and simple way forward to understanding and solving inverse dynamics and variable impedance control in robotics and biomechanics.
On quotient metrics and decoding problems

Linear codes and lattices arising from such codes via Construction A are in a one-to-one correspondence. In [1], Campello et al. showed that solving the closest vector problem with respect to the lp norm on the lattice corresponding to a q-ary code C is equivalent to finding the closest codeword of C with respect to the p-Lee metric. Here these results are extended to the most general situation we can think of.
A Systematic Analysis and Exploration of the Satellite Image Enhancement Techniques

Satellite Images plays a vital role for the applications in various fields of education, agriculture, astrology, geosciences, land cover feature extraction, remote sensing, and other information systems. During the process of image acquisition/Capturing from the outer space with the satellites, most of image quality degraded due to presence of various factors like presence of huge distance between the earth surface & satellite, noise value and different illumination at different time durations. These captured raw images do not possess higher brightness value. So, these images can't be considered for the direct use in any real time application. To make these images usable, there is need to enhance the image quality. There is the availability of various conventional and computational intelligence based concepts that are used by different authors to improve the image intensity, contrast and hence usability. In this paper, we have presented the systematic analysis and exploration of the satellite image enhancement techniques. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the different satellite image enhancement methods, and commonly used evaluation parameters in the existing literature work with a motive to assist researchers for the easy selection of suitable satellite image enhancement method.
Examples of Formal Proofs about Data Compression

Because of the increasing complexity of mathematical proofs, there is a growing interest in formalization using proof-assistants. In this paper, we explain new formal proofs of standard lemmas in data compression (Jensen’s and Kraft’s inequalities) as well as concrete applications (to the analysis of compression methods and Shannon-Fano codes). We explain in particular how one turns the paper proof into formal terms and the relation between the informal proof and the formal one. These formalizations come as an extension to an existing formal library for information theory and error-correcting codes.