Miracle-dz/UniFAR-FTUs huggingface dataset:
A combined timing/spectral study of IRAS 13224-3809 using XMM–Newton data

We present the results from an X-ray variability study of IRAS 13224-3809. This is probably the best source for X-ray reverberation studies since it is X-ray bright, extremely variable, and it has been extensively observed with XMM-Newton. We used all the archival XMM-Newton data from the three EPIC cameras (to increase the signal-to-noise) and, given the many observations of the source, we were able to compute the time-lags spectra in three different flux levels/periods. We fitted the time-lags and energy spectra, simultaneously, using a new X-ray reverberation code which computes the time dependent reflection spectra of the disc as a response to an X-ray flash from a point source located on the axis of the black-hole (BH) accretion disc (lamp-post geometry). To the best of our knowledge, this is the first time for an AGN that both time-lags and energy spectra are fitted by a model simultaneously in different flux periods. The model fits in the case when the BH is rapidly rotating are significantly better than the model fits in the case of a Schwarzschild BH. This result strongly favours the hypothesis of a rotating central BH in this source. We also detect significant variations in the height of the X-ray corona. The X-ray height appears to increase from 3-5 gravitational radii when the X-ray luminosity is of the order of 1.5-3 percent of the Eddington limit, up to 10 gravitational radii, when the luminosity doubles.
Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping

We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC)-based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model based. However, in practice, detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input-output data measured from the unknown system to predict the future trajectories and calculate the optimal control policy. We showcase that the DeePC algorithm can effectively attenuate interarea oscillations even in the presence of measurement noise, communication delays, nonlinear loads, and uncertain load fluctuations. We investigate the performance under different matrix structures as data-driven predictors. Furthermore, we derive a novel Min-Max DeePC algorithm to be applied independently in multiple VSC-HVDC stations to mitigate interarea oscillations, which enables decentralized and robust optimal wide-area control. Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification. We illustrate our results with high-fidelity, nonlinear, and noisy simulations of a four-area test system.
Data Assimilation and Online Optimization With Performance Guarantees

This article considers a class of real-time stochastic optimization problems dependent on an unknown probability distribution. In the considered scenario, data are streaming frequently while trying to reach a decision. Thus, we aim to devise a procedure that incorporates samples (data) of the distribution sequentially and adjusts decisions accordingly. We approach this problem in a distributionally robust optimization framework and propose a novel Online Data Assimilation Algorithm (OnDA Algorithm) for this purpose. This algorithm guarantees out-of-sample performance of decisions with high probability, and gradually improves the quality of the decisions by incorporating the streaming data. We show that the OnDA Algorithm converges under a sufficiently slow data streaming rate, and provide a criteria for its termination after certain number of data have been collected. Simulations illustrate the results.
Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed

Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper, we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning-from-demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.
Methanol formation chemistry with revised reactions scheme

The aim of the presented work is to analyze the impact of experimentally evaluated reactions of hydrogen abstraction on surfaces of interstellar grains on the chemical evolution of methanol and its precursors on grains and in the gas phase under conditions of cold dark cloud and during the collapse of the translucent cloud into the dark cloud. Analysis of simulation results shows that those reactions are highly efficient destruction channels for HCO and H2CO on grain surfaces, and significantly impact the abundances of almost all molecules participating in the formation of CH3OH. Next, in models with those reactions maximum abundances of methanol in gas and on grain surface decrease by more than 2-3 orders of magnitude in comparison to models without surface abstraction reactions of hydrogen. Finally, we study the impact of binding energies of CH2OH and CH3O radicals on methanol chemistry.
Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations-from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover , quantisation of weights positively affects the network training by decreasing overfitting.
Mean propagation velocity of multiphoton wave-packet states with nonzero Lorentz-invariant mass

The concept of Lorenz invariant mass and mean propagation velocity have been investigated in detail for various multiphoton wave-packet states of light. Based on photodetection theory and straightforward kinematics, we presented a physically reasonable and at the same time rigorous proof that mean propagation velocity is consistent with the Lorentz-invariant mass concept for an arbitrary multiphoton wave-packet state. We argued that mean propagation velocity is less than the speed of light constant in vacuum and is governed by geometric properties of state's amplitude in wave-vector space for arbitrary wave-packet states. To classify states with different fixed values of Lorentz-invariant mass, we introduced a specific set of modes that allow us to describe the wave-packet state in its rest frame.
Maat

The malware analysis and detection research community relies on the online platform VirusTotal to label Android apps based on the scan results of around 60 antiviral scanners. Unfortunately, there are no standards on how to best interpret the scan results acquired from VirusTotal, which leads to the utilization of different threshold-based labeling strategies (e.g., if 10 or more scanners deem an app malicious, it is considered malicious). While some of the utilized thresholds may be able to accurately approximate the ground truths of apps, the fact that VirusTotal changes the set and versions of the scanners it uses makes such thresholds unsustainable over time. We implemented a method, Maat, that tackles these issues of standardization and sustainability by automatically generating a Machine Learning (ML)-based labeling scheme, which outperforms threshold-based labeling strategies. Using the VirusTotal scan reports of 53K Android apps that span 1 year, we evaluated the applicability of Maat’s Machine Learning (ML)-based labeling strategies by comparing their performance against threshold-based strategies. We found that such ML-based strategies (a) can accurately and consistently label apps based on their VirusTotal scan reports, and (b) contribute to training ML-based detection methods that are more effective at classifying out-of-sample apps than their threshold-based counterparts.
Review on biophysical modelling and simulation studies for transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a technique for noninvasively stimulating a brain area for therapeutic, rehabilitation treatments and neuroscience research. Despite our understanding of the physical principles and experimental developments pertaining to TMS, it is difficult to identify the exact brain target as the generated electric field exhibits a non-uniform distribution owing to the complicated and subject-dependent brain anatomy and the lack of biomarkers that can quantify the effects of TMS in most cortical areas. Computational dosimetry has progressed significantly and enables TMS assessment by computation of the induced electric field (the primary physical agent known to activate the brain neurons) in a digital representation of the human head. In this review, TMS dosimetry studies are summarised, clarifying the importance of the anatomical and human biophysical parameters and computational methods. This review shows that there is a high consensus on the importance of a detailed cortical folding representation and an accurate modelling of the surrounding cerebrospinal fluid. Recent studies have also enabled the prediction of individually optimised stimulation based on magnetic resonance imaging of the patient/subject and have attempted to understand the temporal effects of TMS at the cellular level by incorporating neural modelling. These efforts, together with the fast deployment of personalised TMS computations, will permit the adoption of TMS dosimetry as a standard procedure in medical applications.
Wrinkles, folds, and ripplocations: Unusual deformation structures of confined elastic sheets at nonzero temperatures

We study the deformation of a fluctuating crystalline sheet confined between two flat rigid walls as a simple model for layered solids where bonds among atoms {\it within} the same layer are much stronger than those {\it between} layers. When subjected to sufficiently high loads in an appropriate geometry, these solids deform and fail in unconventional ways. Recent experiments suggest that configurations named {\it ripplocations}, where a layer folds backwards over itself, are involved. These structures are distinct and separated by large free energy barriers from smooth {\it ripples} of the atomic layers that are always present at any non-zero temperature. We use Monte Carlo simulation in combination with an umbrella sampling technique to obtain conditions under which such structures form and study their specific experimental signatures.
Revealing the two-electron cusp in the ground states of He and 
H2
 via quasifree double photoionization

This work studies quasifree double photoionization using reaction microscopy and companion $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ calculations. The quasifree mechanism is confirmed for H${}_{2}$ and introduced as a probe for the electron-electron cusp density in the ground state of a two-electron target.
Extracorporeal Membrane Oxygenation Resource Planning in the Setting of Pandemic Respiratory Illness

Extracorporeal Membrane Oxygenation Resource Planning in the Setting of Pandemic Respiratory Illness Raghu Seethala and Steven P. Keller Division of Emergency Critical Care Medicine, Department of Emergency Medicine, and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts; and Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts
Suppression of LPS-Induced Inflammation by Chalcone Flavokawain A through Activation of Nrf2/ARE-Mediated Antioxidant Genes and Inhibition of ROS/NFκB Signaling Pathways in Primary Splenocytes

Oxidative stress is an important contributing factor for inflammation. Piper methysticum, also known as Kava-kava, is a shrub whose root extract has been consumed as a drink by the pacific islanders for a long time. Flavokawain A (FKA) is a novel chalcone derived from the kava plant that is known to have medicinal properties. This study was aimed at demonstrating the antioxidant molecular mechanisms mediated by FKA on lipopolysaccharide- (LPS-) induced inflammation in BALB/c mouse-derived primary splenocytes. In vitro data show that the nontoxic concentrations of FKA (2-30 μM) significantly suppressed the proinflammatory cytokine (TNF-α, IL-1β, and IL-6) release but induced the secretion of interleukin-10 (IL-10), an anti-inflammatory cytokine. It was also shown that FKA pretreatment significantly downregulated the LPS-induced ROS production and blocked the activation of the NFκB (p65) pathway leading to the significant suppression of iNOS, COX-2, TNF-α, and IL-1β protein expressions. Notably, FKA favored the nuclear translocation of Nrf2 leading to the downstream expression of antioxidant proteins HO-1, NQO-1, and γ-GCLC via the Nrf2/ARE signaling pathway signifying the FKA's potent antioxidant mechanism in these cells. Supporting the in vitro data, the ex vivo data obtained from primary splenocytes derived from the FKA-preadministered BALB/c mice (orally) show that FKA significantly suppressed the proinflammatory cytokine (TNF-α, IL-1β, and IL-6) secretion in control-, LPS-, or Concanavalin A- (Con A-) stimulated cells. A significant decrease in the ratios of pro- and anti-inflammatory cytokines (IL-6/IL-10; TNF-α/IL-10) showed that FKA possesses strong anti-inflammatory properties. Furthermore, BALB/c mice induced with experimental pancreatitis using cholecystokinin- (CCK-) 8 showed decreased serum lipase levels due to FKA pretreatment. We conclude that with its potent antioxidant and anti-inflammatory properties, chalcone flavokawain A could be a novel therapeutic agent in the treatment of inflammation-associated diseases.
Ultrasound-Guided Selective Nerve Root Block versus Fluoroscopy-Guided Interlaminar Epidural Block versus Fluoroscopy-Guided Transforaminal Epidural Block for the Treatment of Radicular Pain in the Lower Cervical Spine: A Retrospective Comparative Study

Background Recently, ultrasound- (US-) guided selective nerve root block (SNRB) has been reported to have similar effects compared to fluoroscopy- (FL-) guided cervical epidural steroid injection (CESI). There is no published study comparing the therapeutic efficacy and safety of interlaminar- (IL-) CESI and transforaminal- (TF-) CESI with US-guided SNRB. Our retrospective study aimed to compare the mid-term effects and advantages of the US-guided SNRB, FL-guided IL-CESI, and TF-CESI for radicular pain in the lower cervical spine through assessment of pain relief and functional improvement. Methods Patients with radicular pain in the lower cervical spine who received guided SNRB (n = 44) or FL-guided IL (n = 41) or TF-CESI (n = 37) were included in this retrospective study. All procedures were performed using a FL or US. The complication frequencies during the procedures, adverse event, treatment effects, and functional improvement were compared at 1, 3, and 6 months after the last injection. Results Both the Neck Disability Index (NDI) and Verbal Numeric Scale (VNS) scores showed improvements at 1, 3, and 6 months after the last injection in all groups, with no significant differences between groups (P < 0.05). Furthermore, the treatment success rate at all time points was not significantly different between groups. Logistic regression analysis revealed that the injection method (US- or FL-guided), cause, sex, age, number of injections, and pain duration were not independent predictors of treatment success. Blood was aspirated before injection in 7% (n = 3), 14% (n = 6), and 0% patients in the FL-guided IL, TF, and US-guided groups, respectively. In 2 patients of FL-guided IL and 7 of FL-guided TF group, intravascular contrast spread was noted during injection. Conclusions Our results suggest that, compared with FL-guided IL and TF-CESI, US-guided SNRB has a low intravascular injection rate; it is unlikely that serious complications will occur. Also, US-guided SNRB requires a shorter administration duration while providing similar pain relief and functional improvements. Therefore, for the treatment of patients with lower cervical radicular pain, US-guided SNRB should be considered as a prior epidural steroid injection.
qPCR multiplex detection of microRNA and messenger RNA in a single reaction

Reverse Transcription-Quantitative PCR (RT-qPCR) is one of the standards for analytical measurement of different RNA species in biological models. However, current Reverse Transcription (RT) based priming strategies are unable to synthesize differing RNAs and ncRNAs especially miRNAs, within a single tube. We present a new methodology, referred to as RNAmp, that measures in parallel miRNA and mRNA expression. We demonstrate this in various cell lines, then evaluate clinical utility by quantifying several miRNAs and mRNA simultaneously in sera. PCR efficiency in RNAmp was estimated between 1.8 and 1.9 which is comparable to standard miRNA and random primer RT approaches. Furthermore, when using RNAmp to detect selected mRNA and miRNAs, the quantification cycle (Cq) was several cycles lower. This low volume single-tube duplex protocol reduces technical variation and reagent usage and is suitable for uniform analysis of single or multiple miRNAs and/or mRNAs within a single qPCR reaction.
Teleoperation of Collaborative Robot for Remote Dementia Care in Home Environments

As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator’s hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.
Role of aggregation conditions and presence of small heat shock proteins on abeta structure, stability and toxicity

Role of Aggregation Conditions and Presence of Small Heat Shock Proteins on Aβ Structure, Stability and Toxicity. (May 2005) Sung Mun Lee, B.S., Korea University; M.S., Seoul National University Co-Chairs of Advisory Committee: Dr. Theresa A. Good Dr. Daniel F. Shantz Alzheimer’s disease (AD) is a neurodegenerative disorder that is one of such diseases associated with protein aggregation. Aβ is the main protein component of senile plaques in AD, and is neurotoxic when aggregated. In particular, soluble oligomeric forms of Aβ are closely related to neurotoxicity. In this dissertation, we examine the differences in Aβ aggregation intermediates, and final structures formed when only a simple modification in Aβ aggregation conditions is made, the presence or absence of mixing during aggregation. We show that intermediates in the aggregation pathway show significantly different structural rearrangements. The protein stabilities of Αβ species show that spherical aggregates corresponding to the most toxic Αβ species change their structure the most rapidly in denaturant, and that in general, increased toxicity correlated with decreased aggregate stability. In Alzheimer’s disease, even delaying Aβ aggregation onset or slowing its progression might be therapeutically useful, as disease onset is late in life. Small heat
Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising

Real-Time Bidding (RTB) display advertising is a method for purchasing display advertising inventory in auctions that occur within milliseconds. The performance of RTB campaigns is generally measured with a series of Key Performance Indicators (KPIs) - measurements used to ensure that the campaign is cost-effective and that it is purchasing valuable inventory. While an RTB campaign should ideally meet all KPIs, simultaneous improvement tends to be very challenging, as an improvement to any one KPI risks a detrimental effect toward the others. Here we present an approach to simultaneously controlling multiple KPIs with a PID-based feedback-control system. This method generates a control score for each KPI, based on both the output of a PID controller module and a metric that quantifies the importance of each KPI for internal business needs. On regular intervals, this algorithm - Sequential Control - will choose the KPI with the greatest overall need for improvement. In this way, our algorithm is able to continually seek the greatest marginal improvements to its current state. Multiple methods of control can be associated with each KPI, and can be triggered either simultaneously or chosen stochastically, in order to avoid local optima. In both offline ad bidding simulations and testing on live traffic, our methods proved to be effective in simultaneously controlling multiple KPIs, and bringing them toward their respective goals.