Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Software-based Virtual Network in the Public Cloud

Today's cloud network consists of sophisticated virtual networks, and a virtual switch is a key element of these networks. Although there is tremendous interest in measuring cloud network performance, little is known about the impact of software-based virtual network on latency. In this paper, we conduct the impact of virtual network on latency in the public cloud based on OpenStack. We measured the throughput of VMs and simultaneously captured their packets on hosts. We analyzed the traces by using well-known metrics, such as throughput and RTT, and investigated the abrupt fluctuation of latency called as ‘the burstiness of latency’. We quantitatively clarify the impact of software-based virtual network on latency. In our public cloud, the latency is approximately 35.2% of RTT and 10% of burstiness mainly contributes to the increased RTT. The total latency was increased by the receiving side regardless of data and ACK paths. Our analysis results, discussions, and implications can not only help cloud researchers and developers design the next generation of software-based virtual network but can also help cloud operators improve the performance of virtual network.
Grid-Free Direction-of-Arrival Estimation with Compressed Sensing and Arbitrary Antenna Arrays

We study the problem of direction of arrival estimation for arbitrary antenna arrays. We formulate it as a continuous line spectral estimation problem and solve it under a sparsity prior without any gridding assumptions. Moreover, we incorporate the array's beampattern in form of the Effective Aperture Distribution Function (EADF), which allows to use arbitrary (synthetic as well as measured) antenna arrays. This generalizes known atomic norm based grid-free DOA estimation methods (that have so far been limited to uniformly spaced arrays) to arbitrary antenna arrays. In addition, our formulation allows to incorporate compressed sensing in form of special linear combinations of the antennas' output ports. We provide conditions for the successful reconstruction of a certain number of targets depending on the amount of compression and the EADF of the antenna array. Our results are applicable to measurement matrices from any sub-Gaussian distribution.
Effects of self-transcendence on neural responses to persuasive messages and health behavior change

Significance People often feel defensive when reminded of their unhealthy behavior. We propose that self-transcendence, encouraged by focusing on others’ well-being, can lessen subsequent focus on threatened aspects of the self and increase receptivity. We show that activating self-transcendent mindsets, either by reflecting on self-transcendent values or making positive wishes for others, increased activity in brain regions chosen for their role in positive valuation and reward. Furthermore, during subsequent health message exposure, self-transcendence increased activity in brain regions tracking message receptivity, which predicted later decreases in sedentary behavior. The current findings support the idea that having a positive, other-focused mindset can be a rewarding experience, and may allow people to see the personal value of potentially threatening yet beneficial health messages. Self-transcendence refers to a shift in mindset from focusing on self-interests to the well-being of others. We offer an integrative neural model of self-transcendence in the context of persuasive messaging by examining the mechanisms of self-transcendence in promoting receptivity to health messages and behavior change. Specifically, we posited that focusing on values and activities that transcend the self can allow people to see that their self-worth is not tied to a specific behavior in question, and in turn become more receptive to subsequent, otherwise threatening health information. To test whether inducing self-transcendent mindsets before message delivery would help overcome defensiveness and increase receptivity, we used two priming tasks, affirmation and compassion, to elicit a transcendent mindset among 220 sedentary adults. As preregistered, those who completed a self-transcendence task before health message exposure, compared with controls, showed greater increases in objectively logged levels of physical activity throughout the following month. In the brain, self-transcendence tasks up-regulated activity in a region of the ventromedial prefrontal cortex, chosen for its role in positive valuation and reward processing. During subsequent health message exposure, self-transcendence priming was associated with increased activity in subregions of the ventromedial prefrontal cortex, implicated in self-related processing and positive valuation, which predicted later decreases in sedentary behavior. The present findings suggest that having a positive self-transcendent mindset can increase behavior change, in part by increasing neural receptivity to health messaging.
The probability of avoiding consecutive patterns in the Mallows distribution

We use combinatorial and probabilistic techniques to study growth rates for the probability that a random permutation from the Mallows distribution avoids consecutive patterns. The Mallows distribution is a q‐analogue of the uniform distribution weighting each permutation π by q inv ( π ) , where inv ( π ) is the number of inversions in π and q is a positive, real‐valued parameter. We prove that the growth rate exists for all patterns and all q > 0, and we generalize Goulden and Jackson's cluster method to keep track of the number of inversions in permutations avoiding a given consecutive pattern. Using singularity analysis, we approximate the growth rates for length‐3 patterns, monotone patterns, and non‐overlapping patterns starting with 1, and we compare growth rates between different patterns. We also use Stein's method to show that, under certain assumptions on q and σ, the number of occurrences of a given pattern σ is well approximated by the normal distribution.
A Novel Approach for Image-Guided 131I Therapy of Pancreatic Ductal Adenocarcinoma Using Mesenchymal Stem Cell-Mediated NIS Gene Delivery

The sodium iodide symporter (SLC5A5/NIS) as theranostic gene would allow for non-invasive imaging of functional NIS expression and therapeutic radioiodine application. Genetically engineered mesenchymal stem cells (MSC), based on their tumor-homing abilities, show great promise as tumor-selective NIS gene delivery vehicles for non-thyroidal tumors. As a next step towards clinical application, tumor specificity and efficacy of MSCs were investigated in an advanced genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC). Syngeneic murine MSCs were stably transfected with a NIS-expressing plasmid driven by the CMV-promoter (NIS-MSC). In vivo 123I-scintigraphy and 124I-PET revealed significant perchlorate-sensitive NIS-mediated radioiodide accumulation in PDAC after systemic injection of NIS-MSCs. Active MSC recruitment into the tumor stroma was confirmed using NIS immunohistochemistry (IHC). A therapeutic strategy, consisting of three cycles of systemic MSC-mediated NIS delivery, followed by 131I application, resulted in a significant delay and reduction in tumor growth as compared to controls. Furthermore, IHC analysis of α-SMA and Ki67 revealed differences in the amount and behavior of activated fibroblasts in tumors of mice injected with NIS-MSCs as compared with saline-treated mice. Taken together, MSCs as NIS gene delivery vehicles in this advanced endogenous PDAC mouse model demonstrated high stromal targeting of NIS by selective recruitment of NIS-MSCs after systemic application resulting in an impressive 131I therapeutic effect. Implications: These data expand the prospect of MSC-mediated radioiodine imaging-guided therapy of pancreatic cancer using the sodium iodide symporter as a theranostic gene in a clinical setting.
The Study of Harmonic-Mode Operation of Transfer Electron Devices on Based Graded-Gap Semiconductors

Mathematical model and simulation results of devices with intervalley electron transfer effect based on graded-gap semiconductors are presented in this article. Drift of space-charge waves and the occurrence of self-oscillations in such devices are analyzed. Moreover, this paper aims to show that the use of graded-gap semiconductors makes it possible to obtain the generation in the sub-terahertz range.
Maximum-Entropy Fine-Grained Classification

Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data. Utilizing this notion of small visual diversity, we revisit Maximum-Entropy learning in the context of fine-grained classification, and provide a training routine that maximizes the entropy of the output probability distribution for training convolutional neural networks on FGVC tasks. We provide a theoretical as well as empirical justification of our approach, and achieve state-of-the-art performance across a variety of classification tasks in FGVC, that can potentially be extended to any fine-tuning task. Our method is robust to different hyperparameter values, amount of training data and amount of training label noise and can hence be a valuable tool in many similar problems.
Coordinated Dense Aerial Traffic with Self-Driving Drones

In this paper we present a general, decentralized air traffic control solution using autonomous drones. We challenge some of the most difficult dense traffic situations, namely, crosswalk and package-delivery scenarios, where intelligent collective collision avoidance and motion planning is essential for a jam-free optimal traffic flow. We build up a force-based distributed multi-robot control model using a tunable selection of interaction terms: anisotropic repulsion, behaviour-driven velocity alignment, self-organized queueing and conflict-avoiding self-driving. We optimize the model with evolution in a realistic simulation framework and demonstrate its applicability with 30 autonomous drones in a coordinated outdoor flight within a densely packed virtual arena.
A multi-criteria optimization approach for HDR prostate brachytherapy: I. Pareto surface approximation

High dose rate (HDR) brachytherapy planning usually involves an iterative process of refining planning objectives until a clinically acceptable plan is produced. The purpose of this two-part study is to improve current planning practice by designing a novel inverse planning algorithm based on multi-criteria optimization (MCO). In the first part, complete Pareto surfaces were approximated and studied for prostate cases. A Pareto surface approximation algorithm was implemented within the framework of Inverse Planning Simulated Annealing. The Pareto surfaces of 140 prostate cases were approximated with the proposed MCO algorithm. For each case, the Pareto surface was represented by automatically generating 300 Pareto optimal plans, and the clinically acceptable region was identified. Thus, 42 000 Pareto optimal plans were created to characterize Pareto surfaces for all the cases. In addition, the relationship between the clinically acceptable region and four anchor plans was studied. As a result, a set of polynomial regression models was extracted to rapidly predict the clinically acceptable region on the Pareto surface based on anchor plans. Pareto surfaces for HDR brachytherapy prostate cases were well characterized in this study. The proposed regression models may help define the most relevant solution phase space.
Prolonged antimicrobial prophylaxis following cardiac device procedures increases preventable harm: insights from the VA CART program

Abstract Background The rate of cardiovascular implantable electronic device (CIED) infection is increasing coincident with an increase in the number of device procedures. Preprocedural antimicrobial prophylaxis reduces CIED infections; however, there is no evidence that prolonged postprocedural antimicrobials additionally reduce risk. Thus, we sought to quantify the harms associated with this approach. Objective To measure the association between Clostridium difficile infection (CDI), acute kidney injury (AKI) and receipt of prolonged postprocedural antimicrobials. Methods CIED procedures entered into the VA Clinical Assessment Reporting and Tracking Electrophysiology (CART-EP) database during fiscal years 2008–2016 were included. The primary outcome was 90-day incidence of CDI and the secondary outcome was the 7-day incidence of AKI. The primary exposure measure was duration of postprocedural antimicrobial therapy. Associations were measured using Cox-proportional hazards and binomial regression. Results Prolonged postprocedural antimicrobial therapy was identified following 3,331 of 6,497 CIED procedures (51.3%), and the median duration of prophylaxis was 5 days. Prolonged postprocedural antimicrobial use was associated with increased risk of CDI (hazard ratio [HR], 2.90; 95% confidence interval [CI], 1.54–5.46). Of the 27 patients who developed CDI, 11 subsequently died. Postprocedural antimicrobial use with ≥2 antimicrobials was associated with an increased risk of AKI (OR, 4.16; 95% CI, 2.50–6.90). The impact was particularly significant when one of the dual agents prescribed was vancomycin (adjusted OR, 8.41; 95% CI, 5.53–12.79). Conclusions Prolonged antimicrobial prophylaxis following CIED procedures increases preventable harm; this practice should be discouraged in procedural settings such as the cardiac electrophysiology laboratory.
Effects of protein size, thermodynamic stability, and net charge on cotranslational folding on the ribosome

Significance There is increasing interest to understand how proteins fold as they are synthesized on the ribosome. However, we still lack basic knowledge such as how protein size, net charge, and thermodynamic stability impact cotranslational folding. Here, we have studied eight proteins of increasing size and provide a comprehensive picture of how the location in the ribosome exit tunnel where a protein folds correlates with protein size. Moreover, we demonstrate that the force exerted on the nascent chain by protein folding varies linearly with the thermodynamic stability of the folded state, and that the ribosome environment disfavors folding of domains of high net-negative charge. These findings establish important basic facts about cotranslational folding. During the last five decades, studies of protein folding in dilute buffer solutions have produced a rich picture of this complex process. In the cell, however, proteins can start to fold while still attached to the ribosome (cotranslational folding) and it is not yet clear how the ribosome affects the folding of protein domains of different sizes, thermodynamic stabilities, and net charges. Here, by using arrest peptides as force sensors and on-ribosome pulse proteolysis, we provide a comprehensive picture of how the distance from the peptidyl transferase center in the ribosome at which proteins fold correlates with protein size. Moreover, an analysis of a large collection of mutants of the Escherichia coli ribosomal protein S6 shows that the force exerted on the nascent chain by protein folding varies linearly with the thermodynamic stability of the folded state, and that the ribosome environment disfavors folding of domains of high net-negative charge.
The Probability Density Function for Network Coding with Three Input Flows

Now many researches have been studying the optimization between throughput/energy consuming and delay with a couple of independent input flows in network coding (NC). However, the implementation of network coding (NC) requires a thorough investigation of its impact on relevant queueing systems which little work focuses on. In this paper, the scenario with three independent Poisson input flows based on our previous work about the pdf with two independent Poisson input flows. , and one output flow is considered. The asynchronous NC-based strategy is that a new arrival evicts a head packet holding in its queue when waiting for another packet from the other flow to encode. The three flows are divided into two parts for getting the probability density function (pdf) of the output flow. The pdf for the output flow which contains both coded and uncoded packets is derived. Besides, the statistic characteristics of this strategy are analyzed. These results are verified by numerical simulations.
Subjective Assessment of Different Locomotion Techniques in Virtual Reality Environments

This paper presents the results of a subjective study aimed at assessing different locomotion (may also be referred to as navigation) techniques in virtual reality environments. We implemented a simple virtual environment in which participants were asked to assess the following four techniques using the HTC Vive platform: teleportation, controller movement, controller movement with tunneling vision, and human joystick. A total of 29 participants took part in the study and rated perceived level of immersion, physical discomfort, and overall Quality of Experience for all tested locomotion techniques. Moreover, participants indicated whether they needed a break after a specific test due to physical discomfort. The results indicate that teleportation yields overall highest QoE and lowest discomfort.
Cover Song Identification Using Song-to-Song Cross-Similarity Matrix with Convolutional Neural Network

In this paper, we propose a cover song identification algorithm using a convolutional neural network (CNN). We first train the CNN model to classify any non-/cover relationship, by feeding a cross-similarity matrix that is generated from a pair of songs as an input. Our main idea is to use the CNN output–the cover-probabilities of one song to all other candidate songs–as a new representation vector for measuring the distance between songs. Based on this, the present algorithm searches cover songs by applying several ranking methods: 1. sorting without using the representation vectors; 2. the cosine distance between the representation vectors; and 3. the correlation between the vectors. In our experiment, the proposed algorithm significantly outperformed the algorithms used in recent studies, by achieving a mean average precision (MAP) of 93.18% in a dataset consisting of 3,300 cover-pairs and 496,200 non-cover-pairs.
Comprehensive Transcriptomic and Genomic Profiling of Subtypes of Follicular Variant of Papillary Thyroid Carcinoma.

BACKGROUND Among subtypes of follicular variant of papillary thyroid carcinoma (FVPTC), encapsulated FVPTC (EFVPTC) shows more indolent behavior than infiltrative FVPTC (IFVPTC). In particular, noninvasive EFVPTC, now designated as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), tends to have an excellent prognosis. However, it remains unclear whether the molecular pathogenesis or signature of the various forms of FVPTC is different. By massively parallel sequencing analysis, this study comprehensively characterized the transcriptional and mutational landscape of FVPTC and established correlations with phenotypic subtypes. METHODS This study included 48 FVPTCs: 17 NIFTPs, 13 invasive EFVPTCs (I-EFVPTCs), and 18 IFVPTCs. For comparison, 55 classical papillary thyroid carcinomas (cPTCs) harboring a BRAFV600E mutation, six follicular adenomas (FAs), and 15 minimally invasive follicular thyroid carcinomas (miFTCs) with RAS mutations were also included. RESULTS In NIFTP, the BRAFV600E mutation was not found, but RAS and other alterations were present in 64.7% and 17.6% of cases, respectively. However, in I-EFVPTC and IFVPTC, the proportions of BRAFV600E mutation (38.5% and 38.9%, respectively) and of RAS mutations (38.5% and 38.9%, respectively) or other alterations (15.4% and 16.7%, respectively) were similar. On a molecular level, RAS-mutated FVPTCs were all RAS-like except for one IFVPTC case. Transcriptomic profiles of NIFTP, I-EFVPTC, and FA/miFTC were comparable, although the profile of RAS-mutated IFVPTC was altered to activate molecular pathways involved in cell adhesion and invasion. Interestingly, 80% of BRAFV600E-mutated I-EFVPTCs were also classified as RAS-like, whereas all BRAFV600E-mutated IFVPTCs were BRAF-like and indistinguishable from cPTC. Molecular pathways associated with cell adhesion and invasion were also differentially activated in BRAFV600E-mutated IFVPTC. CONCLUSIONS Molecular profiles of NIFTP and I-EFVPTC may be shared with FA/miFTC, while IFVPTC seems to be associated with a similar profile as cPTC. Activation of cell adhesion and invasion pathways may play a key role in the development of invasive phenotypes of FVPTC.
Insecticide Resistance and Its Intensity in Populations of Malaria Vectors in Colombia

Insecticide resistance in malaria vectors threatens malaria prevention and control efforts. In Colombia the three primary vectors, Anopheles darlingi, An. nuneztovari s.l. , and An. albimanus, have reported insecticide resistance to pyrethroids, organophosphates, carbamates, and DDT; however, the insecticide resistance monitoring is not continuous, and the data on the prevalence of resistance is scarce and geographically limited. We describe the resistance levels and intensity of previously detected resistant populations among primary malaria vectors from the most endemic malaria areas in Colombia. The study was carried out in 10 localities of five states in Colombia. Bioassays were carried out following the methodology of CDC Bottle Bioassay using the discriminating concentration and in order to quantify the intensity the specimens were exposed to 2, 5, and 10X discriminating concentrations. Five insecticides were tested: deltamethrin, lambda-cyhalothrin, alpha-cypermethrin, permethrin, and DDT. The results provide evidence of low resistance intensity and resistance highly localized to pyrethroids and DDT in key malaria vectors in Colombia. This may not pose a threat to malaria control yet but frequent monitoring is needed to follow the evolution of insecticide resistance.
Shortened Taper Duration after Implementation of a Standardized Protocol for Iatrogenic Benzodiazepine and Opioid Withdrawal in Pediatric Patients: Results of a Cohort Study

Introduction: Methadone and lorazepam prescribing discrepancies for the use of iatrogenic withdrawal were observed among providers. A standardized pharmacist-managed methadone and lorazepam taper protocol was implemented at a pediatric tertiary care facility with the aim to reduce the length of taper for patients with iatrogenic withdrawal. Methods: A multidisciplinary team of nurses, pharmacists, and physicians reviewed the current literature, then developed and implemented a standardized withdrawal taper protocol. Outcomes were compared with a retrospective control group using past prescribing practices. The primary endpoint was the length of methadone and/or lorazepam taper. Secondary endpoints included evaluation for significant differences between the control and standardized protocol groups regarding additional breakthrough withdrawal medications, pediatric intensive care unit (PICU) and hospital length of stay. We also evaluated provider satisfaction with the protocol. Results: The standardized protocol group included 25 patients who received methadone and/or lorazepam taper. A retrospective control group contained 24 patients. Median methadone taper length before protocol implementation was 9.5 days with an interquartile range (IQR) of 5.5–14.5 days; after protocol implementation, it was 6.0 (IQR, 3.0–9.0) days (P = 0.0145). Median lorazepam taper length before protocol implementation was 13.0 (IQR, 8.0–18.0) days; after protocol implementation, it was 6.0 (4.0–7.0) days (P = 0.0006). A statistical difference between PICU length of stay, hospital length of stay, or the number of additional medications for breakthrough withdrawal was not found. Conclusions: The use of a standardized withdrawal protocol resulted in shorter taper duration for both the methadone and lorazepam groups. There was no difference in PICU or hospital length of stay.