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Abstract Background The hemodynamic status of newborns with intracranial arteriovenous shunts (AVSs) may be extremely complex. Mini-invasive hemodynamic monitoring through innovative techniques such as Near-Infrared Spectroscopy (NIRS) and Pressure Recording Analytical Method (PRAM) may help in understanding hemodynamics in newborns with AVSs. Levosimendan is a calcium sensitizer and inodilator, and it is known to improve ventricular function, but its use in newborns is limited. In our cases, we evaluated the effect of levosimendan on hemodynamics through NIRS and PRAM. Case presentation Herein, we report the cases of two neonates with intracranial arteriovenous shunts, in whom we used levosimendan to manage cardiac failure refractory to conventional treatment. Levosimendan was used at a dosage of 0.1 mcg/kg/min for 72 h. Combined use of NIRS and PRAM helped in real-time monitoring of hemodynamic effects; in particular, levosimendan determined significant improvement in myocardium contractility as well as a reduction of heart rate. Conclusion In two neonatal cases of AVSs, levosimendan led to an overall hemodynamic stabilization, documented by the combination of NIRS and PRAM. Our results suggest introducing levosimendan as a second-line treatment in cases of severe cardiac dysfunction due to AVSs without improvement using standard treatment strategies. Future prospective and larger studies are highly warranted.

Background: The hemodynamic status of newborns with intracranial arteriovenous shunts (AVSs) may be extremely complex. Mini-invasive hemodynamic monitoring through innovative techniques such as Near-Infrared Spectroscopy (NIRS) and Pressure Recording Analytical Method (PRAM) may help in understanding hemodynamics in newborns with AVSs. Levosimendan is a calcium sensitizer and inodilator, and it is known to improve ventricular function, but its use in newborns is limited. In our cases, we evaluated the effect of levosimendan on hemodynamics through NIRS and PRAM. Case presentation: Herein, we report the cases of two neonates with intracranial arteriovenous shunts, in whom we used levosimendan to manage cardiac failure refractory to conventional treatment. Levosimendan was used at a dosage of 0.1 mcg/kg/min for 72 h. Combined use of NIRS and PRAM helped in real-time monitoring of hemodynamic effects; in particular, levosimendan determined significant improvement in myocardium contractility as well as a reduction of heart rate. Conclusion: In two neonatal cases of AVSs, levosimendan led to an overall hemodynamic stabilization, documented by the combination of NIRS and PRAM. Our results suggest introducing levosimendan as a second-line treatment in cases of severe cardiac dysfunction due to AVSs without improvement using standard treatment strategies. Future prospective and larger studies are highly warranted. [ABSTRACT FROM AUTHOR]

We present the first parallel algorithms that decide strong and branching bisimilarity in linear time. More precisely, if a transition system has n states, m transitions and | A c t | action labels, we introduce an algorithm that decides strong bisimilarity in O (n + | A c t |) time on max (n , m) processors and an algorithm that decides branching bisimilarity in O (n + | A c t |) time using up to max (n 2 , m , | A c t | n) processors. [ABSTRACT FROM AUTHOR]

A metric tree embedding of expected stretch α ≥ 1 maps a weighted n-node graph G = (V, E, ω) to a weighted tree T = (VT, ET , ωT) with V ⊑ VT such that, for all v,w ∈ V, dist(v, w, G) ≤ dist(v, w, T), and E[dist(v, w, T)] ≤ α dist(v, w, G). Such embeddings are highly useful for designing fast approximation algorithms as many hard problems are easy to solve on tree instances. However, to date, the best parallel polylog n)-depth algorithm that achieves an asymptotically optimal expected stretch of α ∈ O(log n) requires Ω (n2) work and a metric as input. In this article, we show how to achieve the same guarantees using polylog n depth and Õ(m1+ϵ) work, where m = |E| and ϵ > 0 is an arbitrarily small constant. Moreover, one may further reduce the work to Õ(m + n1+ε) at the expense of increasing the expected stretch to O(ε−1 log n). Our main tool in deriving these parallel algorithms is an algebraic characterization of a generalization of the classic Moore-Bellman-Ford algorithm. We consider this framework, which subsumes a variety of previous "Moore-Bellman-Ford-like" algorithms, to be of independent interest and discuss it in depth. In our tree embedding algorithm, we leverage it to provide efficient query access to an approximate metric that allows sampling the tree using polylog n depth and Õ(m) work. We illustrate the generality and versatility of our techniques by various examples and a number of additional results. Specifically, we (1) improve the state of the art for determining metric tree embeddings in the Congest model, (2) determine a (1 + εˆ)-approximate metric regarding the distances in a graph G in polylogarithmic depth and Õ(n(m+n1 + ε )) work, and (3) improve upon the state of the art regarding the k-median and the buy-at-bulk network design problems. [ABSTRACT FROM AUTHOR]

Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps. We give efficient, fast, and practicable parallel and distributed algorithms for building data structures that support sampling single items (alias tables, compressed data structures). This also yields a simplified and more space-efficient sequential algorithm for alias table construction. Our approaches to sampling k out of n items with/without replacement and to subset (Poisson) sampling are output-sensitive, i.e., the sampling algorithms use work linear in the number of different samples. This is also interesting in the sequential case. Weighted random permutation can be done by sorting appropriate random deviates. We show that this is possible with linear work. Finally, we give a communication-efficient, highly scalable approach to (weighted and unweighted) reservoir sampling. This algorithm is based on a fully distributed model of streaming algorithms that might be of independent interest. Experiments for alias tables and sampling with replacement show near linear speedups using up to 158 threads of shared-memory machines. An experimental evaluation of distributed weighted reservoir sampling on up to 5,120 cores also shows good speedups. [ABSTRACT FROM AUTHOR]

Herein we report the case of a neonate with a prenatally diagnosed large pulmonary arteriovenous malformation, managed with minimally invasive hemodynamic monitoring in our Neonatal Intensive Care Unit. The combination of Near-Infrared Spectroscopy and Pressure Recording Analytical Method could guide neonatal management of critical cases of vascular anomalies: immediate data are offered to clinicians, from which therapeutic decisions such as timing of surgical resection are made to achieve a positive outcome. We also systemically collected and summarized information on patients' characteristics of previous cases reported in literature to data, and we compared them to our case. [ABSTRACT FROM AUTHOR]

With the emergence of the Internet of things (IoT), embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare, home automation and mainly Industry 4.0. These Embedded IoT devices are mostly battery-driven. It has been analyzed that usage of Dynamic Random-Access Memory (DRAM) centered core memory is considered the most significant source of high energy utility in Embedded IoT devices. For achieving the low power consumption in these devices, Non-volatile memory (NVM) devices such as Parameter Random Access Memory (PRAM) and Spin-Transfer Torque Magnetic Random- Access Memory (STT-RAM) are becoming popular among main memory alternatives in embedded IoT devices because of their features such as high thickness, byte addressability, high scalability and low power intake. Additionally, Non-volatile Random-Access Memory (NVRAM) is widely adopted to save the data in the embedded IoT devices. NVM, flash memories have a limited lifetime, so it is mandatory to adopt intelligent optimization in managing the NVRAM-based embedded devices using an intelligent controller while considering the endurance issue. To address this challenge, the paper proposes a powerful, lightweight machine learning-based workload-adaptive write schemes of the NVRAM, which can increase the lifetime and reduce the energy consumption of the processors. The proposed system consists of three phases like Workload Characterization, Intelligent Compression and Memory Allocators. These phases are used for distributing the write-cycles to NVRAM, following the energy-time consumption and number of data bytes. The extensive experimentations are carried out using the IoMT (Internet of Medical things) benchmark in which the different endurance factors such as application delay, energy and write-time factors were evaluated and compared with the different existing algorithms. [ABSTRACT FROM AUTHOR]

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a Parallel Random Access Machine (PRAM). These algorithms maintain a spanning forest by merging and compressing trees, which requires pointer-chasing operations that increase memory access latency and are limited to shared-memory systems. Many of these PRAM algorithms are also very complicated to implement. Another popular method is "leader-contraction" where the challenge is to select a constant fraction of leaders that are adjacent to a constant fraction of non-leaders with high probability, but this can require adding more edges than were in the original graph. Instead we investigate label propagation because it is deterministic, easy to implement, and does not rely on pointer-chasing. Label propagation exchanges representative labels within a component using simple graph traversal, but it is inherently difficult to complete in a sublinear number of steps. We are able to overcome the problems with label propagation for graph connectivity. We introduce a surprisingly simple framework for deterministic, undirected graph connectivity using label propagation that is easily adaptable to many computational models. It achieves logarithmic convergence independently of the number of processors and without increasing the edge count. We employ a novel method of propagating directed edges in alternating direction while performing minimum reduction on vertex labels. We present new algorithms in PRAM, Stream, and MapReduce. Given a simple, undirected graph G = (V , E) with n = | V | vertices, m = | E | edges, our approach takes O(m) work each step, but we can only prove logarithmic convergence on a path graph. It was conjectured by Liu and Tarjan (2019) to take O (log n) steps or possibly O (log 2 n) steps. Our experiments on a range of difficult graphs also suggest logarithmic convergence. We leave the proof of convergence as an open problem. [ABSTRACT FROM AUTHOR]

The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.

The ergonomics of transport is a topic widely described in the literature. One of the fields of ergonomics that researchers are engaged in is vibrometry (both laser and accelerometry) of travel and its translation into NVH (Noise, Vibration and Harshness). However, so far, the influence of baby carriage movement on the generated vibrations has not been described in more detail. The topic seems to be particularly important considering occurrence of vibrations with significant amplitudes, whose frequency range can have a direct bearing on the resonance frequencies of the child's internal organs. The article presents the results of research consisting in the measurement of vibrations to which an infant, lying in two different types of prams, may be exposed when being transported on different surfaces. The author's measurement system, based on accelerometry, was used for the research. The obtained weighted RMS acceleration values not only exceeded human comfort level in all cases (according to ISO standard) but several times were in the range of the highest discomfort (>2 m/s2). Furthermore, the observed vibration frequency range (≈0 ÷ 32 Hz) coincided with the frequencies of free vibration of organs and parts of the child's body. [ABSTRACT FROM AUTHOR]

The ergonomics of transport is a topic widely described in the literature. One of the fields of ergonomics that researchers are engaged in is vibrometry (both laser and accelerometry) of travel and its translation into NVH (Noise, Vibration and Harshness). However, so far, the influence of baby carriage movement on the generated vibrations has not been described in more detail. The topic seems to be particularly important considering occurrence of vibrations with significant amplitudes, whose frequency range can have a direct bearing on the resonance frequencies of the child’s internal organs. The article presents the results of research consisting in the measurement of vibrations to which an infant, lying in two different types of prams, may be exposed when being transported on different surfaces. The author’s measurement system, based on accelerometry, was used for the research. The obtained weighted RMS acceleration values not only exceeded human comfort level in all cases (according to ISO standard) but several times were in the range of the highest discomfort (>2 m/s2). Furthermore, the observed vibration frequency range (≈0 ÷ 32 Hz) coincided with the frequencies of free vibration of organs and parts of the child’s body.

We present optimal algorithms for sorting on parallel CREW and EREW versions of the pointer machine model. Intuitively, one can view our methods as being based on a parallel mergesort using linked lists rather than arrays (the usual parallel data structure). We also show how to exploit the “locality” of our approach to solve the set expression evaluation problem, a problem with applications to database querying and logic-programming in O(log n) time using O(n) processors. Interestingly, this is an asymptotic improvement over what seems possible using previous techniques. [ABSTRACT FROM AUTHOR]

We exploit the connections between measurement error and data perturbation for disclosure limitation in the context of small area estimation. Our starting point is the model in Ybarra and Lohr (2008), where some of the covariates (all continuous) are measured with error. Using a fully Bayesian approach, we extend the aforementioned model including continuous and categorical auxiliary variables, both possibily perturbed by disclosure limitation methods, with masking distributions fixed according to the assumed protection mechanism. In order to investigate the feasibility of the proposed method, we conduct a simulation study exploring the effect of different post-randomization scenarios on the small area model.

Parallel machines have become more widely used. Unfortunately parallel programming technologies have advanced at a much slower pace except for regular programs. For irregular programs, this advancement is inhibited by high synchronization costs, non-loop parallelism, non-array data structures, recursively expressed parallelism and parallelism that is too fine-grained to be exploitable. We present ICE, a new parallel programming language that is easy-to-program, since: (i) ICE is a synchronous, lock-step language so there is no need for programmer-specified synchronization; (ii) for a PRAM algorithm its ICE program amounts to directly transcribing it; and (iii) the PRAM algorithmic theory offers unique wealth of parallel algorithms and techniques. We propose ICE to be a part of an ecosystem consisting of the XMT architecture, the PRAM algorithmic model, and ICE itself, that together deliver on the twin goal of easy programming and efficient parallelization of irregular programs. The XMT architecture, developed at UMD, can exploit fine-grained parallelism in irregular programs. We have built the ICE compiler which translates the ICE language into the multithreaded XMTC language; the significance of this is that multi-threading is a feature shared by practically all current scalable parallel programming languages thus providing a method to compile ICE code. As one indication of ease of programming, we observed a reduction in code size in 11 out of 16 benchmarks as compared to hand-optimized XMTC. For these programs, the average reduction in number of lines of code was 35.5 percent. The remaining 5 benchmarks had almost the same code size for both ICE and hand-optimized XMTC. Our main result is perhaps surprising: The run-time was comparable to XMTC with a 0.53 percent average gain for ICE across all benchmarks. [ABSTRACT FROM AUTHOR]

Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC (Lempel-Ziv-Compress)-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have this advantage, since they are compressed by the Lempel-Ziv sliding window technique. [ABSTRACT FROM AUTHOR]

Owing to the increasing interest in the commercialization of phase-change memory (PCM) devices, a number of TCAD models have been developed for their simulation. These models formulate the melting, amorphization and crystallization of phase-change materials as well as their extreme conductivity dependence on both electric field and temperature into a set of self-consistently-solved thermoelectric and phase-field partial-differential equations. However, demonstrations of the ability of such models to match actual experimental results are rare. In addition, such PCM devices also require a so-called selector device – such as an Ovonic Threshold Switching (OTS) device – in series for proper memory operation. However, monolithic simulation of both the PCM and OTS selector device in a single simulation is largely absent from the literature, despite its potential value for material- and design-space explorations. It is the goal of this work to first characterize a PCM device in isolation against experimental data, then to demonstrate the qualitative behavior of a simulated OTS device in isolation and finally to perform a single monolithic simulation of the PCM + OTS device within the confines of a commercially available TCAD solver: GTS Framework. • Phase-change memory (PCM) devices are important devices of commercial interest. • PCM models are rarely validated against experiment. • In this work a PCM model is validated against experimental pulse data. • PCM devices also require "selector" devices in series which is often ignored. • Here we consider a monolithic simulation of PCM+OTS selector device. [ABSTRACT FROM AUTHOR]