Heuristic for Lowering Electricity Costs for Routing in Optical Data Center Networks

Optical data centers consume a large quantity of energy and the cost of that energy has a significant contribution to the operational cost in data centers. The amount of electricity consumption in data centers and their related costs are increasing day by day. Data centers are geographically distributed all around the continents and the growing numbers of data replicas have made it possible to find more cost effective network routing. Besides flat-rate prices, today, there are companies which offers real-time pricing. In order to address the energy consumption cost problem, we propose an energy efficient routing scheme to find least cost path to the replicas based on real-time pricing model called energy price aware routing (EPAR). Our research considers anycast data transmission model to find the suitable replica as well as the fixed window traffic allocation model for demand request to reduce the energy consumption cost of data center networks

FPGA Deployment of LFADS for Real-time Neuroscience Experiments

Large-scale recordings of neural activity are providing new opportunities to study neural population dynamics. A powerful method for analyzing such high-dimensional measurements is to deploy an algorithm to learn the low-dimensional latent dynamics. LFADS (Latent Factor Analysis via Dynamical Systems) is a deep learning method for inferring latent dynamics from high-dimensional neural spiking data recorded simultaneously in single trials. This method has shown a remarkable performance in modeling complex brain signals with an average inference latency in milliseconds. As our capacity of simultaneously recording many neurons is increasing exponentially, it is becoming crucial to build capacity for deploying low-latency inference of the computing algorithms. To improve the real-time processing ability of LFADS, we introduce an efficient implementation of the LFADS models onto Field Programmable Gate Arrays (FPGA). Our implementation shows an inference latency of 41.97 $\mu$s for processing the data in a single trial on a Xilinx U55C.Comment: 6 pages, 8 figure

Graph Neural Network-based Tracking as a Service

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.Comment: 7 pages, 4 figures, Proceeding of Connected the Dots Workshop (CTD 2023

Association of Zinc, Copper and Magnesium with bone mineral density in Iranian postmenopausal women - a case control study

Background: The risk of inadequate nutrition such as trace elements and vitamin deficiencies is considerable in postmenopausal women. The aim of this study was to compare trace elements (Zinc, Copper and Magnesium) concentration in nail, urine and serum among osteoporotic postmenopausal women with control group in Iran.Methods: Forty eight postmenopausal women aged 36-60 years, were recruited, consisting 30 osteoporotic patients and 18 healthy controls. Blood, nail and urine concentration of Zinc (Zn), copper (Cu) and magnesium (Mg) were determined using Inductively Coupled Plasma -Atomic Emission Spectrometry (ICP-AES) method. Their Bone Mineral Density was measured by Dual X-ray Absorption (DEXA) method.Results: The urine level of trace elements had significant difference between osteoporotic groups and controls (p < 0.001). Moreover Mg level significantly differed in serum between two groups (p < 0.04). There was no statistically significant difference in trace minerals in nail beyond groups.Conclusion: Our findings indicate that Urine Zn level could be considerable an appropriate marker for bone absorption, usage of Zn supplements in postmenopausal women may result a beneficial reduction in osteoporotic risk. © 2014 Razmandeh et al.; licensee BioMed Central Ltd

Arsenic-Associated Oxidative Stress, Inflammation, and Immune Disruption in Human Placenta and Cord Blood

BACKGROUND: Arsenic (As) exposure during pregnancy induces oxidative stress and increases the risk of fetal loss and low birth weight. OBJECTIVES: In this study we aimed to elucidate the effects of As exposure on immune markers in the placenta and cord blood, and the involvement of oxidative stress. METHODS: Pregnant women were enrolled around gestational week (GW) 8 in our longitudinal, population-based, mother-child cohort in Matlab, an area in rural Bangladesh with large variations in As concentrations in well water. Women (n = 130) delivering at local clinics were included in the present study. We collected maternal urine twice during pregnancy (GW8 and GW30) for measurements of As, and placenta and cord blood at delivery for assessment of immune and inflammatory markers. Placental markers were measured by immunohistochemistry, and cord blood cytokines by multiplex cytokine assay. RESULTS: In multivariable adjusted models, maternal urinary As (U-As) exposure both at GW8 and at GW30 was significantly positively associated with placental markers of 8-oxoguanine (8-oxoG) and interleukin-1β (IL-1β); U-As at GW8, with tumor necrosis factor-α (TNFα) and interferon-γ (IFNγ); and U-As at GW30, with leptin; U-As at GW8 was inversely associated with CD3+ T cells in the placenta. Cord blood cytokines (IL-1β, IL-8, IFNγ, TNFα) showed a U-shaped association with U-As at GW30. Placental 8-oxoG was significantly positively associated with placental proinflammatory cytokines. Multivariable adjusted analyses suggested that enhanced placental cytokine expression (TNFα and IFNγ) was primarily influenced by oxidative stress, whereas leptin expression appeared to be mostly mediated by As, and IL-1β appeared to be influenced by both oxidative stress and As. CONCLUSION: As exposure during pregnancy appeared to enhance placental inflammatory responses (in part by increasing oxidative stress), reduce placental T cells, and alter cord blood cytokines. These findings suggest that effects of As on immune function may contribute to impaired fetal and infant health

Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.Comment: Contribution to Snowmass 202

Measurement of the cross-section and charge asymmetry of WW bosons produced in proton-proton collisions at s=8\sqrt{s}=8 TeV with the ATLAS detector

This paper presents measurements of the $W^+ \rightarrow \mu^+\nu$ and $W^- \rightarrow \mu^-\nu$ cross-sections and the associated charge asymmetry as a function of the absolute pseudorapidity of the decay muon. The data were collected in proton--proton collisions at a centre-of-mass energy of 8 TeV with the ATLAS experiment at the LHC and correspond to a total integrated luminosity of 20.2~\mbox{fb^{-1}}. The precision of the cross-section measurements varies between 0.8% to 1.5% as a function of the pseudorapidity, excluding the 1.9% uncertainty on the integrated luminosity. The charge asymmetry is measured with an uncertainty between 0.002 and 0.003. The results are compared with predictions based on next-to-next-to-leading-order calculations with various parton distribution functions and have the sensitivity to discriminate between them.Comment: 38 pages in total, author list starting page 22, 5 figures, 4 tables, submitted to EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2017-13

Search for chargino-neutralino production with mass splittings near the electroweak scale in three-lepton final states in √s=13 TeV pp collisions with the ATLAS detector

A search for supersymmetry through the pair production of electroweakinos with mass splittings near the electroweak scale and decaying via on-shell W and Z bosons is presented for a three-lepton final state. The analyzed proton-proton collision data taken at a center-of-mass energy of √s=13  TeV were collected between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 139  fb−1. A search, emulating the recursive jigsaw reconstruction technique with easily reproducible laboratory-frame variables, is performed. The two excesses observed in the 2015–2016 data recursive jigsaw analysis in the low-mass three-lepton phase space are reproduced. Results with the full data set are in agreement with the Standard Model expectations. They are interpreted to set exclusion limits at the 95% confidence level on simplified models of chargino-neutralino pair production for masses up to 345 GeV

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2 mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38 mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5 mm weighted RMS values for the East, North, and Up components, respectively

Regional integration of long-term national dense GNSS network solutions

The EUREF Permanent Network Densification is a collaborative effort of 26 European GNSS analysis centers providing series of daily or weekly station position estimates of dense national and regional GNSS networks, in order to combine them into one homogenized set of station positions and velocities. During the combination, the station meta-data, including station names, DOMES numbers, and position offset definitions were carefully homogenized, position outliers were efficiently eliminated, and the results were cross-checked for any remaining inconsistencies. The results cover the period from March 1999 to January 2017 (GPS week 1000-1933) and include 31 networks with positions and velocities for 3192 stations, well covering Europe. The positions and velocities are expressed in ITRF2014 and ETRF2014 reference frames based on the Minimum Constraint approach using a selected set of ITRF2014 reference stations. The position alignment with the ITRF2014 is at the level of 1.5, 1.2, and 3.2\ua0mm RMS for the East, North, Up components, respectively, while the velocity RMS values are 0.17, 0.14, and 0.38\ua0mm/year for the East, North, and Up components, respectively. The high quality of the combined solution is also reflected by the 1.1, 1.1, and 3.5\ua0mm weighted RMS values for the East, North, and Up components, respectively