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Marcelo Ponce

Publications and source records attributed to Marcelo Ponce.

At least 19 recordsLinked to original sources

A Quantitative Framework for Comparing Classical and Quantum Algorithms for the Traveling Salesman Problem

The Traveling Salesman Problem is a classical NP-hard problem with significant implications in logistics, circuit design, and operations research. This paper presents a comparative study of four approaches to solving the Traveling Salesman Problem: brute-force enumeration, a 2-approximation algorithm using minimum spanning trees, simulated annealing, and the Quantum Approximate Optimization Algorithm. We implement each technique and evaluate them on graphs of varying sizes to analyze performance, solution quality, and scalability. In doing so, we have also developed an open-source framework that allows researchers and practitioners to explore, test and extend these methods.

cs.CC

An Open-Source Framework to Emulate Delay and Disruption Tolerant Networks for International Space Station Communication

Delay and Disruption Tolerant Networks (DTN) are critical for reliable communications in challenged network environments, particularly for space systems where end-to-end connectivity cannot be guaranteed. We present an open-source, full-stack implementation of the Bundle protocol for communicating with the International Space Station (ISS), with complete security features including Bundle Authentication Block (BAB), Payload Integrity Block (PIB), and Payload Confidentiality Block (PCB) using HMAC-SHA256 and AES-256-CBC encryption. The system includes bundle fragmentation and reassembly, priority-based queuing, custody transfer with ACK/NAK mechanisms, and automatic retransmission. Our system also includes a frontend facilitated by a modern responsive web interface. We consider this work highly relevant in the context of computer networking because: i) it demonstrates a full stack, open-source, freely available implementation of this critical and reliable protocol; and ii) it offers an interactive educational and learning framework in the field of computer networks and communications.

cs.NI

Parallel Ray Tracing of Black Hole Images Using the Schwarzschild Metric

Rendering images of black holes by utilizing ray tracing techniques is a common methodology employed in many aspects of scientific and astrophysical visualizations. Similarly, general ray tracing techniques are widely used in areas related to computer graphics. In this work we describe the implementation of a parallel open-source program that can ray trace images in the presence of a black hole geometry. We do this by combining a couple of different techniques usually present in parallel scientific computing, such as, mathematical approximations, utilization of scientific libraries, shared-memory and distributed-memory parallelism.

cs.DC

Integrating Captive Portal Technology into Computer Science Education: A Modular, Hands-On Approach to Infrastructure

In this paper, we present an educational project aimed to introduce students to the technology behind Captive Portals infrastructures. For doing this, we developed a series of modules to emphasize each of the different aspects and features of this technology. The project is based on an open source implementation which is widely used in many computer network courses, making it well-suited and very appealing for instructors and practitioners in this field.

cs.CY

Parallelizing a 1-Dim Nagel-Schreckenberg Traffic Model

The Nagel-Schreckenberg model is a stochastic one-dimensional traffic model. In this assignment, we guide students through the process of implementing a shared-memory parallel and reproducible version of an existing serial code that implements this model, and to analyze its scaling behavior. One of the key elements in this traffic model is the presence of randomness, without which it would lack realistic phenomena such as traffic jams. Its implementation thus requires techniques associated with Monte Carlo simulations and pseudo-random number generation (PRNG). PRNGs are notoriously tricky to deal with in parallel when combined with the requirement of reproducibility. This assignment was created for the graduate course PHY1610 Scientific Computing for Physicists at the University of Toronto, which had its origin in the training program of the SciNet HPC Consortium, and is also very suitable for other scientific disciplines. Several variations of the assignment have been used over the years.

cs.DC

Cybersecurity Training for Users of Remote Computing

End users of remote computing systems are frequently not aware of basic ways in which they could enhance protection against cyber-threats and attacks. In this paper, we discuss specific techniques to help and train users to improve cybersecurity when using such systems. To explain the rationale behind these techniques, we go into some depth explaining possible threats in the context of using remote, shared computing resources. Although some of the details of these prescriptions and recommendations apply to specific use cases when connecting to remote servers, such as a supercomputer, cluster, or Linux workstation, the main concepts and ideas can be applied to a wider spectrum of cases.

cs.CR

covid19.analytics: An R Package to Obtain, Analyze and Visualize Data from the Coronavirus Disease Pandemic

With the emergence of a new pandemic worldwide, a novel strategy to approach it has emerged. Several initiatives under the umbrella of "open science" are contributing to tackle this unprecedented situation. In particular, the "R Language and Environment for Statistical Computing" offers an excellent tool and ecosystem for approaches focusing on open science and reproducible results. Hence it is not surprising that with the onset of the pandemic, a large number of R packages and resources were made available for researches working in the pandemic. In this paper, we present an R package that allows users to access and analyze worldwide data from resources publicly available. We will introduce the covid19.analytics package, focusing in its capabilities and presenting a particular study case where we describe how to deploy the "COVID19.ANALYTICS Dashboard Explorer".

cs.CY

Deploying a Top-100 Supercomputer for Large Parallel Workloads: the Niagara Supercomputer

Niagara is currently the fastest supercomputer accessible to academics in Canada. It was deployed at the beginning of 2018 and has been serving the research community ever since. This homogeneous 60,000-core cluster, owned by the University of Toronto and operated by SciNet, was intended to enable large parallel jobs and has a measured performance of 3.02 petaflops, debuting at #53 in the June 2018 TOP500 list. It was designed to optimize throughput of a range of scientific codes running at scale, energy efficiency, and network and storage performance and capacity. It replaced two systems that SciNet operated for over 8 years, the Tightly Coupled System (TCS) and the General Purpose Cluster (GPC). In this paper we describe the transition process from these two systems, the procurement and deployment processes, as well as the unique features that make Niagara a one-of-a-kind machine in Canada.

cs.DC

Software-Enhanced Teaching and Visualization Capabilities of an Ultra-High-Resolution Video Wall

This paper presents a modular approach to enhance the capabilities and features of a visualization and teaching room using software. This approach was applied to a room with a large, high resolution (7680$\times$4320 pixels), tiled screen of 13 $\times$ 7.5 feet as its main display, and with a variety of audio and video inputs, connected over a network. Many of the techniques described are possible because of a software-enhanced setup, utilizing existing hardware and a collection of mostly open-source tools, allowing to perform collaborative, high-resolution visualizations as well as broadcasting and recording workshops and lectures. The software approach is flexible and allows one to add functionality without changing the hardware.

cs.GR

RACS: Rapid Analysis of ChIP-Seq data for contig based genomes

Background: Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-Seq) is a widely used technique to investigate the function of chromatin-related proteins in a genome-wide manner. ChIP-Seq generates large quantities of data which can be difficult to process and analyse, particularly for organisms with contig based genomes. Contig-based genomes often have poor annotations for cis-elements, for example enhancers, that are important for gene expression. Poorly annotated genomes make a comprehensive analysis of ChIP-Seq data difficult and as such standardized analysis pipelines are lacking. Methods: We report a computational pipeline that utilizes traditional High-Performance Computing techniques and open source tools for processing and analysing data obtained from ChIP-Seq. We applied our computational pipeline "Rapid Analysis of ChIP-Seq data" (RACS) to ChIP-Seq data that was generated in the model organism Tetrahymena thermophila, an example of an organism with a genome that is available in contigs. Results: To test the performance and efficiency of RACs, we performed control ChIP-Seq experiments allowing us to rapidly eliminate false positives when analyzing our previously published data set. Our pipeline segregates the found read accumulations between genic and intergenic regions and is highly efficient for rapid downstream analyses. Conclusions: Altogether, the computational pipeline presented in this report is an efficient and highly reliable tool to analyze genome-wide ChIP-Seq data generated in model organisms with contig-based genomes. RACS is an open source computational pipeline available to download from: https://bitbucket.org/mjponce/racs --or-- https://gitrepos.scinet.utoronto.ca/public/?a=summary&p=RACS

q-bio.GN

Bridging the Educational Gap between Emerging and Established Scientific Computing Disciplines

In this paper we describe our experience in developing curriculum courses aimed at graduate students in emerging computational fields, including biology and medical science. We focus primarily on computational data analysis and statistical analysis, while at the same time teaching students best practices in coding and software development. Our approach combines a theoretical background and practical applications of concepts. The outcomes and feedback we have obtained so far have revealed several issues: students in these particular areas lack instruction like this although they would tremendously benefit from it; we have detected several weaknesses in the formation of students, in particular in the statistical foundations but also in analytical thinking skills. We present here the tools, techniques and methodology we employ while teaching and developing this type of courses. We also show several outcomes from this initiative, including potential pathways for fruitful multi-disciplinary collaborations.

cs.CY

Trends in Demand, Growth, and Breadth in Scientific Computing Training Delivered by a High-Performance Computing Center

We analyze the changes in the training and educational efforts of the SciNet HPC Consortium, a Canadian academic High Performance Computing center, in the areas of Scientific Computing and High-Performance Computing, over the last six years. Initially, SciNet offered isolated training events on how to use HPC systems and write parallel code, but the training program now consists of a broad range of workshops and courses that users can take toward certificates in scientific computing, data science, or high-performance computing. Using data on enrollment, attendence, and certificate numbers from SciNet's education website, used by almost 1800 users so far, we extract trends on the growth, demand, and breadth of SciNet's training program. Among the results are a steady overall growth, a sharp and steady increase in the demand for data science training, and a wider participation of 'non-traditional' computing disciplines, which has motivated an increasingly broad spectrum of training offerings. Of interest is also that many of the training initiatives have evolved into courses that can be taken as part of the graduate curriculum at the University of Toronto.

cs.CY

The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila

Background: The chromatin remodelers of the SWI/SNF family are critical transcriptional regulators. Recognition of lysine acetylation through a bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes, this function is attributed to SNF2/Brg1. Results: Using affinity purification coupled to mass spectrometry (AP-MS) we identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila. SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt, two proteins with potential chromatin interacting domains and four proteins without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits localize exclusively to the transcriptionally active macronucleus (MAC) during growth and development, consistent with a role in transcription. While Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it to be a subunit of several additional protein complexes, including putative SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on histones correlated with active transcription. Consistent with our AP-MS and histone array data suggesting a role in regulation of gene expression, ChIP-Seq analysis of Ibd1 indicated that it primarily binds near promoters and within gene bodies of highly expressed genes during growth. Conclusions: Our results suggest that through recognizing specific histones marks, Ibd1 targets active chromatin regions of highly expressed genes in Tetrahymena where it subsequently might coordinate the recruitment of several chromatin remodeling complexes to regulate the transcriptional landscape of vegetatively growing Tetrahymena cells.

q-bio.GN

Scientific Computing, High-Performance Computing and Data Science in Higher Education

We present an overview of current academic curricula for Scientific Computing, High-Performance Computing and Data Science. After a survey of current academic and non-academic programs across the globe, we focus on Canadian programs and specifically on the education program of the SciNet HPC Consortium, using its detailed enrollment and course statistics for the past four to five years. Not only do these data display a steady and rapid increase in the demand for research-computing instruction, they also show a clear shift from traditional (high performance) computing to data-oriented methods. It is argued that this growing demand warrants specialized research computing degrees. The possible curricula of such degrees are described next, taking existing programs as an example, and adding SciNet's experiences of student desires as well as trends in advanced research computing.

cs.CY

The Influence of Neutrinos on r-Process Nucleosynthesis in the Ejecta of Black Hole-Neutron Star Mergers

During the merger of a black hole and a neutron star, baryonic mass can become unbound from the system. Because the ejected material is extremely neutron-rich, the r-process rapidly synthesizes heavy nuclides as the material expands and cools. In this work, we map general relativistic models of black hole-neutron star (BHNS) mergers into a Newtonian smoothed particle hydrodynamics (SPH) code and follow the evolution of the thermodynamics and morphology of the ejecta until the outflows become homologous. We investigate how the subsequent evolution depends on our mapping procedure and find that the results are robust. Using thermodynamic histories from the SPH particles, we then calculate the expected nucleosynthesis in these outflows while varying the level of neutrino irradiation coming from the postmerger accretion disk. We find that the ejected material robustly produces r-process nucleosynthesis even for unrealistically high neutrino luminosities, due to the rapid velocities of the outflow. Nonetheless, we find that neutrinos can have an impact on the detailed pat- tern of the r-process nucleosynthesis. Electron neutrinos are captured by neutrons to produce protons while neutron capture is occurring. The produced protons rapidly form low mass seed nuclei for the r-process. These low mass seeds are eventually incorporated into the first r-process peak at A~78, producing mainly Ge and Se. We consider the mechanism of this process in detail and discuss if it can impact galactic chemical evolution of the first peak r-process nuclei.

astro-ph.HE

Electromagnetic outflows in a class of scalar-tensor theories: binary neutron star coalescence

As we showed in previous work, the dynamics and gravitational emission of binary neutron star systems in certain scalar-tensor theories can differ significantly from that expected from General Relativity in the coalescing stage. In this work we examine whether the characteristics of the electromagnetic counterparts to these binaries -- driven by magnetosphere interactions prior to the merger event -- can provide an independent way to test gravity in the most strongly dynamical stages of binary mergers. We find that the electromagnetic flux emitted by binaries in these scalar-tensor theories can show deviations from the GR prediction in particular cases. These differences are quite subtle, thus requiring delicate measurements to differentiate between GR and the type of scalar-tensor theories considered in this work using electromagnetic observations alone. However, if coupled with a gravitational-wave detection, electromagnetic measurements might provide a way to increase the confidence with which GR will be confirmed (or ruled out) by gravitational observations.

gr-qc

Interaction of misaligned magnetospheres in the coalescence of binary neutron stars

We study the dependence of the electromagnetic luminosity --produced by interactions of force-free magnetospheres-- on dipole inclinations in binary neutron star systems. We show that this interaction extracts kinetic energy from the system and powers a Poynting flux with a strong dependence on the dipole orientations. This dependence can be linked to the reconnection and redistribution of magnetic field as the stars interact. Although the details of the Poynting luminosity are very much dependent on the orientation, all the cases considered here nevertheless radiate a large Poynting flux. This robust emission suggests that the pre-merger stage of binary neutron star systems can yield interesting electromagnetic counterparts to gravitational wave events.

gr-qc

Projected Constraints on Scalarization with Gravitational Waves from Neutron Star Binaries

Certain scalar-tensor theories have the property of endowing stars with scalar hair, sourced either by the star's own compactness (spontaneous scalarization) or, for binary systems, by the companion's scalar hair (induced scalarization) or by the orbital binding energy (dynamical scalarization). Scalarized stars in binaries present different conservative dynamics than in General Relativity, and can also excite a scalar mode in the metric perturbation that carries away dipolar radiation. As a result, the binary orbit shrinks faster than predicted in General Relativity, modifying the rate of decay of the orbital period. In spite of this, scalar-tensor theories can pass existing binary pulsar tests, because observed pulsars may not be compact enough or sufficiently orbitally bound to activate scalarization. Gravitational waves emitted during the last stages of compact binary inspirals are thus ideal probes of scalarization effects. For the standard projected sensitivity of advanced LIGO, we here show that, if neutron stars are sufficiently compact to enter the detector's sensitivity band already scalarized, then gravitational waves could place constraints at least comparable to binary pulsars. If the stars dynamically scalarize while inspiraling in band, then constraints are still possible provided the scalarization occurs sufficiently early in the inspiral, roughly below an orbital frequency of 50Hz. In performing these studies, we derive an easy-to-calculate data analysis measure, an integrated phase difference between a General Relativistic and a modified signal, that maps directly to the Bayes factor so as to determine whether a modified gravity effect is detectable. Finally, we find that custom-made templates are equally effective as model-independent, parameterized post-Einsteinian waveforms at detecting such modified gravity effects at realistic signal-to-noise ratios.

gr-qc