Regression Models and Life-Tables

Journal of the Royal Statistical Society: Series B

Abstract: Foundational paper introducing the Cox proportional-hazards model for time-to-event data with censoring.


Nonparametric Estimation from Incomplete Observations

Journal of the American Statistical Association

Abstract: The Kaplan-Meier nonparametric estimator of the survival function from censored data.


Modeling Survival Data: Extending the Cox Model

Abstract: Standard reference on extending the Cox model, including time-varying covariates and diagnostics.


Power-Law Distributions in Empirical Data

A. Clauset
C. R. Shalizi
M. E. J. Newman

Abstract: The maximum-likelihood-plus-goodness-of-fit procedure for fitting and testing power-law distributions in empirical data.


powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

Abstract: Python implementation of the Clauset-Shalizi-Newman heavy-tail fitting and model comparison framework.


Fitting Heavy Tailed Distributions: The poweRlaw Package

Abstract: R package implementing maximum-likelihood power-law fitting with bootstrap goodness-of-fit.


Queueing Systems, Volume 1: Theory (reviewed)

Abstract: Standard reference on waiting-time distributions and their tails in queueing systems.


On the Theory of Queues with Many Servers

Transactions of the American Mathematical Society

Abstract: Kendall notation and the embedded-Markov-chain analysis of queues.


Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems

D. Terekhov
T. T. Tran
D. G. Down
J. C. Beck

Journal of Artificial Intelligence Research

Abstract: Integrates queueing theory with scheduling to study how queue-discipline choices shape the realized wait distribution.


Automated Scheduling for NASA's Deep Space Network

M. D. Johnston
D. Tran
B. Arroyo
S. Sorensen
P. Tay
J. Carruth
A. Coffman
M. Wallace

Abstract: Description of the deployed DSN scheduling engine and integrated planning workflow.


Delta-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming

T. Claudet
R. Alimo
E. Goh
M. D. Johnston

Abstract: MILP formulation targeting schedule changes rather than full re-solves for the DSN.


Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

Abstract: Framing of DSN allocation as multi-objective optimization under uncertainty.


Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

M. D. Johnston
D. Tran
B. Arroyo
C. Page

Abstract: Mid- and long-range DSN scheduling automation across the planning horizon.


Deep Space Network Scheduling Using Evolutionary Computational Methods

A. Guillaume
S. Lee
Y.-F. Wang
H. Zheng
R. Hovden
S. Chau
Y.-W. Tung
R. J. Terrile

Abstract: Evolutionary-computation approach to DSN scheduling.


Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

E. Goh
H. S. Venkataram
M. Hoffmann
M. D. Johnston
B. Wilson

Abstract: Formulates DSN allocation as a sequential decision problem addressed with deep reinforcement learning.


Integrated Planning and Scheduling for NASA's Deep Space Network - from Forecasting to Real-time

T. M. Hackett
S. G. Bilen
M. D. Johnston

Abstract: Describes the integrated DSN planning workflow spanning forecasting to real-time operations.


Traffic Modeling for Deep Space Network in the Human Exploration Era

D. S. Abraham
B. E. MacNeal
D. P. Heckman

Abstract: Models DSN traffic for the human-exploration era and projects crewed-mission loading.


Expanding the Deep Space Network to Support the Heliophysics System Observatory

Frontiers in Astronomy and Space Sciences

Abstract: Quantifies the gap between projected demand and current DSN supply for a heliophysics science portfolio.


A Dynamical Systems Analysis of the Effects of the Launch Rate Distribution on the Stability of the Orbital Environment

Abstract: Applies inflow-shape (distribution of timing, not total) logic in a space-systems setting; the temporal shape of an inflow determines whether a stock stabilizes or runs away.


Understanding Long-Term Orbital Debris Population Dynamics

Abstract: Stock-and-flow population dynamics of the orbital debris environment.


Industrial Dynamics (and the system-dynamics tradition)

Abstract: Founding statement of system dynamics: system behavior governed by stocks, flows, information feedback, and delay.


Deep Space Network Radio Science and Ground-Based Planetary Radar in the Next Decade

Abstract: Next-decade assessment documenting how DSN time competes across mission classes.


Counterintuitive Behavior of Social Systems

Abstract: Forrester's argument that system behavior is dominated by loop structure and delay, not actor intentions, producing counterintuitive concentration.


Urban Dynamics

Abstract: Forrester's system-dynamics treatment of urban systems; canonical demonstration of stock-flow-delay structure driving counterintuitive behavior.


Bathtub Dynamics: Initial Results of a Systems Thinking Inventory

Abstract: Empirical demonstration that even educated subjects fail to infer accumulation behavior from flows (stock-flow failure), justifying formal modeling of backlog stocks.


Leverage Points: Places to Intervene in a System

The Sustainability Institute / Routledge

Abstract: Ranks twelve system-intervention points from weakest (parameters) to strongest (paradigm), framing where DSN remedies would have the most leverage.


The Precautionary Principle (with Application to the Genetic Modification of Organisms)

N. N. Taleb
R. Read
R. Douady
J. Norman
Y. Bar-Yam

arXiv (Extreme Risk Initiative working paper)

Abstract: Non-naive precautionary principle: confine precaution to the joint case of fat tails and systemic risk; invert the burden of proof onto the party assuming thin tails.


Mathematical Definition, Mapping, and Detection of (Anti)Fragility

Abstract: Formalizes fragility as concave (and antifragility as convex) response to a dose of stressor; the convex load-response signature adopted as the fragility test.


Working with Convex Responses: Antifragility from Finance to Oncology

N. N. Taleb
J. Norman
Y. Bar-Yam
R. Read

Abstract: Develops the convexity (Jensen-gap) characterization of system response to variability; the methodological basis for testing convex load-response curves.


MOCAT-pySSEM: An open-source Python library and user interface for orbital source-sink environment modeling

Abstract: Operationalizes the system-dynamics stock-and-flow method as a reusable simulator: object populations as stocks with launch, decay, and removal flows.


Future Deep Space Mission Communications Trends and Their Implications for NASA’s Deep Space Network

Abraham, Douglas S.
Chen, Yijiang
Heckman, David P.
Heid, Leslie
Kwok, Andrew
MacNeal, Bruce E.
Tran, Kristy
Xie, Hua

Abstract: In support of NASA’s Space Communications and Navigation (SCaN) Program Systems Engineering (PSE) Office, the Jet Propulsion Laboratory (JPL) periodically models and analyzes projected future-mission demand on the Deep Space Network (DSN) out to a thirty-year horizon. These efforts culminate in a set of capacity, spectrum, capability, and network loading trends and associated implications that can then be used to inform decisions regarding the DSN’s evolution. This chapter, an expanded version of the paper presented at SpaceOps 2023, describes the findings and recommendations emerging from the latest iteration of these studies [ 1 ]. These are always presented to SCaN for strategic planning and programmatic decisions. On the whole, three key factors appear to be driving how the DSN will need to evolve in the future: (1) unparalleled growth in the number of robotic spacecraft; (2) the emergence of relatively short but tracking-intensive human lunar exploration missions requiring DSN support in the coming decade, followed by an increasing cadence of robotic and then human exploration missions to Mars; and (3) dramatic data rate and associated data volume increases. The growth in robo


Deep Space Network Scheduling Using Quantum Annealing

A. Guillaume
Edwin Y. Goh
M. Johnston
Brian Wilson
Anita Ramanan
Frances Tibble
Brad Lackey

IEEE Transactions on Quantum Engineering

Abstract: The National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN) is responsible for communication and navigation for several NASA and international missions. The DSN comprises three complexes located in Goldstone (California, USA), Cambera (Australia), and Madrid (Spain). This distribution in longitude guarantees a full sky coverage. Each complex has one 70-m and several 34-m antennas. The network routinely serves a few dozen missions. The scheduling of the DSN is complex and involves human interventions as well as automated solutions. In order to increase the level of automation, different computing paradigms have been explored. In this article, we report on the quadratic unconstrained binary optimization (QUBO) formulation of the DSN scheduling, as well as a custom classical solver designed around some of the unique features of this scheduling problem. Thanks to a hybrid framework that extends the size of the problems that can be solved with a quantum annealer, we are able to generate a schedule from the QUBO formulation of the problem for one week's worth of user antenna requests, which represents the time period scheduled during operation. In other words, thi


Deep Space Network Scheduling via Mixed-Integer Linear Programming

Alex Sabol
R. Alimo
F. Kamangar
Ramtin Madani

Abstract: NASA’s Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its’ services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN’s limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm’s performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in


Radio Astronomy with NASA’s Deep Space Network

Abstract: The Deep Space Network (DSN) is the spacecraft tracking and communication infrastructure for NASA’s deep space missions. At three sites, approximately equally separated in (terrestrial) longitude, there are multiple radio antennas outfitted with cryogenic microwave receiving systems both for receiving transmissions from deep space spacecraft and for conducting radio astronomical observations, particularly in the L band (1350 MHz–1800 MHz), X band (8200 MHz–8600 MHz), and K band (18 GHz–27 GHz). In particular, the 70 m antennas at the Canberra and Madrid DSN Complexes are well-equipped to participate in international very long baseline interferometry (VLBI) observations. Over the past five years, there has been an effort to refurbish and modernize equipment such as receiving and signal transport systems for radio astronomical observations. We summarize current capabilities, on-going refurbishment activities, and possible future opportunities.


Enabling a Larger Deep Space Mission Suite: A Deep Space Network Queuing Antenna for Demand Access

M. S. Net
J. Wyatt
R. Castaño
S. Townes
T. Lazio
B. Malphrus
J. Kruth
C. Hart

Abstract: The advent of deep space small spacecraft, as exemplified by the Mars Cubesat One (MarCO), Lunar Trailblazer, Janus, the Escape and Plasma Acceleration and Dynamics Explorers (EscaPADE), and the thirteen Artemis 1 missions, opens the possibility that a much larger number of deep space space-craft may be launched over the next 10 years and beyond. While scientifically exciting, the prospect of a (much) larger mission suite raises significant challenges for the current approach to ground stations and mission operations. We have been investigating an integrated approach for ground stations and missions operations to enable new modes of operation while maintaining the capabilities of the current operational techniques. This integrated approach is built around three core capabilities: (1) A queuing antenna that enables monitoring the status of a much larger number of spacecraft, and allows spacecraft to transmit requests for telemetry with NASA's Deep Space Network (DSN); (2) a flexible scheduling system that expands the current DSN scheduling services to enable allocating time on DSN antennas in near realtime; and (3) a cloud-based ground data system that can be spun up and down accord


Machine learning for deep space network antenna motions detection

Abstract: Highly stable frequency and timing standards are essential for deep-space missions and radio science. At the NASA Deep Space Network (DSN), these standards are distributed through a network of underground fiber cables to support several Goldstone antennas. Independently developed frequency-measuring instruments generate tremendous quantities of data to monitor and validate the antennas’ stringent frequency requirements. In this paper, we propose a lightweight processing tool capable of detecting disturbances on the frequency signal caused by DSN antenna motions. Our training data is sampled from the movement log of the antenna of interest and the generated data from the fiber optic metrology instrument linked to the antenna. We demonstrate that a convolutional neural network (CNN) model can achieve high accuracies on classifying instances of antenna movements and is an effective predictor when used iteratively on longer, variable stretches of metrology data. The simplicity, low training cost, and high accuracies of our model strongly suggest its efficacy in identifying and troubleshooting frequency disturbances caused by the antenna.


Optimal Scheduling of a Constellation of Earth-Imaging Satellites, for Maximal Data Throughput and Efficient Human Management

Sean Augenstein
Alejandra Estanislao
Emmanuel Guere
Sean Blaes

Proceedings of the International Conference on Automated Planning and Scheduling

Abstract: A mixed-integer linear program (MILP) approach to scheduling a large constellation of Earth-imaging satellites is presented. The algorithm optimizes the assignment of imagery collects, image data downlinks, and "health & safety" contacts, generating schedules for all satellites and ground stations in a network. Hardware-driven constraints (e.g., the limited agility of the satellites) and operations-driven constraints (e.g., guaranteeing a minimum contact frequency for each satellite) are both addressed. Of critical importance to the use of this algorithm in real-world operations, it runs fast enough to allow for human operator interaction and repeated rescheduling. This is achieved by a partitioning of the problem into sequential steps for downlink scheduling and image scheduling, with a novel dynamic programming (DP) heuristic providing a stand-in for imaging activity in the MILP when scheduling the downlinks.


A Mixed Integer Linear Programming Model for Resolution of the Antenna-Satellite Scheduling Problem

Lorena Linares
Rafael Vázquez
Federico Perea
J. Galán

IEEE Transactions on Aerospace and Electronic Systems

Abstract: This paper deals with one of the types of “Satellite Range Scheduling” problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous Integer Linear Programming (ILP) model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shortening Model , with only integer variables), we propose a Mixed Integer Linear Programming (MILP) model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shaving Model , which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the Shortening Model, which used intersections between passes to determine the best schedule, the shortening oper


Automatic scheduling of satellite tracking tasks by means of policy-gradient reinforcement learning and transformer-based pointer networks

Abstract: Low-Earth-orbit (LEO) mega-constellations turn satellite–ground scheduling into a large-scale, real-time combinatorial problem. This work presents a neural-combinatorial framework that couples policy-gradient reinforcement learning with a transformer-based pointer network. The overall solution is implemented as a modular pipeline composed of distinct components for preprocessing, training, scheduling, and emergency handling, wherein the learning component consists of a single policy-gradient agent. The policy operates on ephemeris-derived contact graphs where candidates are encoded by priority, start time, satellite ID, and ground-station ID; feasibility is enforced through masking and a reward that captures buffer, non-overlap, and cool-down constraints. In simulations on OneWeb ( $$\approx 650$$ ≈ 650 sats) and Starlink ( $$\approx 3500$$ ≈ 3500 sats), the method reduces the mean number of missed satellites by up to 54.3% versus a tuned greedy search and by up to 29.5% versus dynamic programming (DP) over 6–48 h horizons, at the cost of modestly higher idle time (4.1–16.1%). End-to-end runtime is competitive at scale: for a 3500-satellite, 48 h test the policy schedules in 366.2


Multi-objective approaches to ground station scheduling for optimization of communication with satellites

Gašper Petelin
Margarita Antoniou
Gregor Papa

Abstract: AbstractThe ground station scheduling problem is a complex scheduling problem involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining a set of trade-off solutions. In contrast to some conventional methods, that aggregate the objectives into one weighted-sum objective function, multi-objective evolutionary algorithms manage to find a set of solutions in the Pareto-optimal front. Selecting one algorithm, however, for a specific problem adds additional challenge. In this paper the ground station scheduling problem was solved through six different evolutionary multi-objective algorithms, the NSGA-II, NSGA-III, SPEA2, GDE3, IBEA, and MOEA/D. The goal is to test their efficacy and performance to a number of benchmark static instances of the ground scheduling problem. Benchmark instances are of different sizes, allowing further testing of the behavior of the algorithms to different dimensionality of the problem. The solutions are compared to the recent solutions of a weighted-sum approach solved by the GA. The results show that all multi-objective algorithms manage to find


Battery-Aware Contact Plan Design for LEO Satellite Constellations: The Ulloriaq Case Study

Juan A. Fraire
Gilles Nies
Carsten Gerstacker
Holger Hermanns
Kristian Bay
Morten Bisgaard

IEEE Transactions on Green Communications and Networking

Abstract: Power demands of communication technologies between LEO small-satellites are difficult to counterbalance by solar infeed and on-board battery storage, due to size and weight limitations. This makes the problem of battery-powered inter-satellite communication a very difficult one. Its management requires a profound understanding as well as techniques for a proper extrapolation of the electric power budget as part of the inter-satellite and satellite-to-ground communication design. We discuss how the construction of contact plans in delay tolerant networking can profit from a sophisticated model of the on-board battery behavior. This model accounts for both nonlinearities in battery behavior as well as stochastic fluctuations in charge, so as to control the risk of battery depletion. We take an hypothetical Ulloriaq constellation based on the GOM X-4 satellites from GomSpace as a reference for our studies.


Optimizing the Communication Capacity of a Ground Station Network

Journal of Aerospace Technology and Management

Abstract: Small satellites are growing in use for educational, scientific, and commercial purposes, usually in Low Earth Orbit (LEO) flights, given their lower costs and associated risks, as well as smaller lead times for assembling and testing. However, the typically short periods of LEO passes bring the need to find ways of optimizing the communication between the ground and space segments. In that direction, several projects have relied on ground station networks to increase the total time of contact with the satellites. In this type of arrangement, the stations agree in monitoring one or more satellites in such a way that, as the spacecraft exits one station’s field of view, another station assumes its tracking, extending the total communication time and compensating the short passes. This type of solution, while very efficient in terms of costs, on the other hand demands a good synchronization procedure, so that all constraints present in its operations are taken into account and the network can operate effectively. This paper aims at describing a model implemented for orchestrating ground station networks that optimizes the communication capacity of the ground network, while taking int


Multiclass multiserver queueing system in the Halfin–Whitt heavy traffic regime: asymptotics of the stationary distribution

Abstract: We consider a heterogeneous queueing system consisting of one large pool of O(r) identical servers, where r→∞ is the scaling parameter. The arriving customers belong to one of several classes which determines the service times in the distributional sense. The system is heavily loaded in the Halfin–Whitt sense, namely the nominal utilization is $1-a/\sqrt{r}$ where a>0 is the spare capacity parameter. Our goal is to obtain bounds on the steady state performance metrics such as the number of customers waiting in the queue Qr(∞). While there is a rich literature on deriving process level (transient) scaling limits for such systems, the results for steady state are primarily limited to the single class case.This paper is the first one to address the case of heterogeneity in the steady state regime. Moreover, our results hold for any service policy which does not admit server idling when there are customers waiting in the queue. We assume that the interarrival and service times have exponential distribution, and that customers of each class may abandon while waiting in the queue at a certain rate (which may be zero). We obtain upper bounds of the form $O(\sqrt{r})$ on both Qr(∞) and the


Feedback Queueing Models for Time-Shared Systems

Abstract: Time-shared processing systems (e.g. communication or computer systems) are studied by considering priority disciplines operating in a stochastic queueing environment. Results are obtained for the average time spent in the system, conditioned on the length of required service (e.g. message lenght or number of computations). No chage is made for swap time, and the results hold only for Markov assumptions for the arrival and service processes. Two distinct feedback models with a single quantum-controlled service are considered. The first is a round-robin (RR) system in which the service facility processes each customer for a maximum of q sec. If the customer's service is completed during this quantum, he leaves the system; otherwise he returns to the end of the queue to await another quantum of service. The second is a feedback (FB N ) system with N queues in which a new arrival joins the tail of the first queue. The server gives service to a customer from the n th queue only if all lower numbered queues are empty. When taken from the n th queue, a customer is given q sec of service. If this completes his processing requirement he leaves the system; otherwise he joins the tail of the


Scheduling a multi class queue with many exponential servers: asymptotic optimality in heavy traffic

Abstract: We consider the problem of scheduling a queueing system in which many statistically identical servers cater to several classes of impatient customers. Service times and impatience clocks are exponential while arrival processes are renewal. Our cost is an expected cumulative discounted function, linear or nonlinear, of appropriately normalized performance measures. As a special case, the cost per unit time can be a function of the number of customers waiting to be served in each class, the number actually being served, the abandonment rate, the delay experienced by customers, the number of idling servers, as well as certain combinations thereof. We study the system in an asymptotic heavy-traffic regime where the number of servers n and the offered load r are simultaneously scaled up and carefully balanced: n\approx r+\beta \sqrtr for some scalar \beta. This yields an operation that enjoys the benefits of both heavy traffic (high server utilization) and light traffic (high service levels.)


Asymptotic exponentiality of the tail of the waiting-time distribution in a <i>Ph/Ph/C</i> queue

Abstract: It is shown that, in a multiserver queue with interarrival and service-time distributions of phase type (PH/PH/c), the waiting-time distribution W(x) has an asymptotically exponential tail, i.e., 1 – W(x) ∽ Ke–ckx. The parameter k is the unique positive number satisfying T*(ck) S*(–k) = 1, where T*(s) and S*(s) are the Laplace–Stieltjes transforms of the interarrival and the service-time distributions. It is also shown that the queue-length distribution has an asymptotically geometric tail with the rate of decay η = T*(ck). The proofs of these results are based on the matrix-geometric form of the state probabilities of the system in the steady state.The equation for k shows interesting relations between single- and multiserver queues in the rates of decay of the tails of the waiting-time and the queue-length distributions.The parameters k and η can be easily computed by solving an algebraic equation. The multiplicative constant K is not so easy to compute. In order to obtain its numerical value we have to solve the balance equations or estimate it from simulation.


Delay-Optimal Scheduling for Heavy-Tailed and Light-Tailed Flows via Reinforcement Learning

Mian Guo
Q. Guan
Weiqi Chen
Fei Ji
Zhiping Peng

International Conference on Conceptual Structures

Abstract: We consider a delay-optimal scheduling problem in a queueing system with a mix of heavy-tailed and light-tailed flows. A light-tailed flow requires more stringent quality of services (QoSs) than a heavy-tailed flow. However, the arrival process of a heavy-tailed flow is far more bursty than that of a light-tailed flow. In addition, flows having a similar tail distribution also require distinct QoSs. This is a NP-hard problem in general. We propose a scheduling scheme that consists of two separate and parallel algorithms, including dynamic-weight-earliest-deadline-first (DWEDF) and reinforcement learning (RL), called DWEDF- RL, to address it. Specifically, we provide delay-bound-based fairness to flows having similar tail distributions in intra-queue buffering process with DWEDF. Inter-queue scheduling process further maximizes the QoS provisioning efficiency by dynamically prioritizing light-tailed flows according to network environments and QoS requirements via reinforcement learning. The effectiveness of the proposal in QoS provisioning has been demonstrated through simulation results.


A Multiserver Queueing System with Impatient Customers

Abstract: Many real-world situations involve queueing systems in which customers wait for service for a limited time only and leave the system if service has not begun within that time. This paper considers a multiserver queueing system with impatient customers, where the customers arrive according to a Poisson process and the service requirements have a general distribution. A simple and insightful solution is presented for the loss probability. The solution is exact for exponential services and is an excellent heuristic for general service times.


Fluid Limits for Processor-Sharing Queues with Impatience

H. Christian Gromoll
Philippe Robert
Bert Zwart

Abstract: We investigate a processor-sharing queue with renewal arrivals and generally distributed service times. Impatient jobs may abandon the queue or renege before completing service. The random time representing a job's patience has a general distribution and may be dependent on its initial service time requirement. A scaling procedure that gives rise to a fluid model with nontrivial yet tractable steady state behavior is presented. This fluid model captures many essential features of the underlying stochastic model, and it is used to analyze the impact of impatience in processor-sharing queues.


Maximizing Service Reward for Queues with Deadlines

Abstract: In this paper we consider a real time queuing system with rewards and deadlines. We assume that packet processing time is known upon arrival, as is the case in communication networks. This assumption allows us to demonstrate that the well known Earliest-Deadline-First policy performance can be improved. We then propose a scheduling policy that provides excellent results for packets with rewards and deadlines. We prove that the policy is optimal under deterministic service time and binomial reward distribution. In the more general case we prove that the policy processes the maximal number of packets while collecting rewards higher than the expected reward. We present simulation results that show its high performance in more generic cases compared to the most commonly used scheduling policies.


On the self-similar nature of Ethernet traffic

W.E. Leland
Murad S. Taqqu
Walter Willinger
Daniel V. Wilson

ACM SIGCOMM Computer Communication Review

Abstract: We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, and analysis of high-speed, cell-based networks. Intuitively, the critical characteristic of this self-similar traffic is that there is no natural length of a "burst": at every time scale ranging from a few milliseconds to minutes and hours, similar-looking traffic bursts are evident; we find that aggregating streams of such traffic typically intensifies the self-similarity ("burstiness") instead of smoothing it.Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a discussion of the underlying mathematical and statistical properties of self-similarity and their relationship with actual network behavior. We also consider some implications for congestion control in high-bandwidth networks and present traffic models based on self-similar stochastic processes that are simple, accurate, and realistic for aggreg


Delay Bounds in Communication Networks With Heavy-Tailed and Self-Similar Traffic

Jörg Liebeherr
Almut Burchard
Florin Ciucu


Loss Analysis for Networks based on Heavy-Tailed and Self-Similar Traffic

Abstract: Abstract Many businesses on the computer network appear heavy-tailed self-similarity (long range dependency), which means network traffic exists burst. Various service source with burst characteristic which show self-similar have a significant effect on transmission performance, network traffic control strategy and network performance indicators such as loss. Loss is an important QoS parameter at the network node, which need to be considered and controlled in all types of traffic, but there is no paper study loss analysis based on heavy-tailed self-similarity. Thus, only analyze and evaluate loss under self-similar traffic can reduce the adverse effects which caused by traffic self-similarity and optimize network performance. We adopt stochastic network calculus approach to bewrite loss lssue. Based on this, we present the loss analysis in the case of a single node network using heavy-tailed service curve and heavy-tailed self-similar arrival curve, and loss analysis under cross traffic, multi-node networks with concatenation. And we also get the relational graph between the loss and the arrival rate, service rate.


Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory

Abstract: Abstract Good estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses.


A Conditional Approach for Multivariate Extreme Values (with Discussion)

Journal of the Royal Statistical Society Series B (Statistical Methodology)

Abstract: Summary Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d-dimensional random variable. Existing approaches are based on limiting arguments in which all components of the variable become large at the same rate. This limit approach is inappropriate when the extreme values of all the variables are unlikely to occur together or when interest is in regions of the support of the joint distribution where only a subset of components is extreme. In practice this restricts existing methods to applications where d is typically 2 or 3. Under an assumption about the asymptotic form of the joint distribution of a d-dimensional random variable conditional on its having an extreme component, we develop an entirely new semiparametric approach which overcomes these existing restrictions and can be applied to problems of any dimension. We demonstrate the performance of our approach and its advantages over existing methods by using theoretical examples and simulation studies. The approach is used to analyse air pollution data and reveals complex extremal dependence behaviour that is consistent with scie


Multivariate generalized Pareto distributions

Abstract: Statistical inference for extremes has been a subject of intensive research over the past couple of decades. One approach is based on modelling exceedances of a random variable over a high threshold with the generalized Pareto (GP) distribution. This has proved to be an important way to apply extreme value theory in practice and is widely used. We introduce a multivariate analogue of the GP distribution and show that it is characterized by each of following two properties: first, exceedances asymptotically have a multivariate GP distribution if and only if maxima asymptotically are extreme value distributed; and second, the multivariate GP distribution is the only one which is preserved under change of exceedance levels. We also discuss a bivariate example and lower-dimensional marginal distributions.


Bayesian analysis of extreme events with threshold estimation

Cibele N Behrens
Hedibert F. Lopes
Dani Gamerman

Abstract: The aim of this paper is to analyse extremal events using generalized Pareto distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters on the basis of data beyond it, discarding all the information available below the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold included. Prior distributions for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov chain Monte Carlo methods. Simulations are carried out in order to analyse the performance of our proposed model under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financial market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling


Extreme tail risk estimation with the generalized Pareto distribution under the peaks-over-threshold framework

Communications in Statistics - Theory and Methods


Fitting phase–type scale mixtures to heavy–tailed data and distributions

Abstract: We consider the fitting of heavy tailed data and distributions with a special attention to distributions with a non–standard shape in the “body” of the distribution. To this end we consider a dense class of heavy tailed distributions introduced in Bladt et al. (Scand. Actuar. J., 573–591 2015), employing an EM algorithm for the maximum likelihood estimation of its parameters. We present methods for fitting to observed data, histograms, censored data, as well as to theoretical distributions. Numerical examples are provided with simulated data and a benchmark reinsurance dataset. Empirical examples show that the methods will in most cases adequately fit both body and tail simultaneously.


Managing the Downside of Active and Passive Strategies—<i>Part 1: Convexity and Fragilities</i>

Abstract: In this article, the author addresses the question of how to manage a large (or small) portfolio in low interest rate conditions while equity markets bear significant drawdown risk. More generally, he explains how to build an antifragile portfolio that can weather the most extreme market scenarios without affecting long-term performance. He also discusses how active strategies systematically create or increase existing market instabilities. By analyzing in depth market behavior during past speculative bubbles and credit crises, the author aims to address these issues. In this first part of a two-part series, the author describes as faithfully as possible the major mechanisms at stake, avoiding the trap of mapping the complexity of financial markets into a single mathematical model, which would necessarily be wrong at some point. Starting from Minsky’s financial instability hypothesis, he tries to disentangle the complex relation between dynamics and randomness, including the presence of fat tails. TOPICS:Mutual fund performance, passive strategies, portfolio theory Key Findings • Financial crises are, in their vast majority, the result of internal instabilities


Regression to the Tail: Why the Olympics Blow Up

Bent Flyvbjerg
Alexander Budzier
Daniel Lunn

Abstract: The Olympic Games are the largest, highest-profile, and most expensive megaevent hosted by cities and nations. Average sports-related costs of hosting are $12.0 billion. Non-sports-related costs are typically several times that. Every Olympics since 1960 has run over budget, at an average of 172 percent in real terms, the highest overrun on record for any type of megaproject. The paper tests theoretical statistical distributions against empirical data for the costs of the Games, in order to explain the cost risks faced by host cities and nations. It is documented, for the first time, that cost and cost overrun for the Games follow a power-law distribution. Olympic costs are subject to infinite mean and variance, with dire consequences for predictability and planning. We name this phenomenon "regression to the tail": it is only a matter of time until a new extreme event occurs, with an overrun larger than the largest so far, and thus more disruptive and less plannable. The generative mechanism for the Olympic power law is identified as strong convexity prompted by six causal drivers: irreversibility, fixed deadlines, the Blank Check Syndrome, tight coupling, long planning horizons,


System dynamics perspectives and modeling opportunities for research in operations management

John D. Sterman
Rogelio Oliva
Kevin Linderman
Elliot Bendoly

Abstract: It is an exciting time to work in operations management. Advances in theory and methods, including behavioral operations, dynamic modeling, experimental methods, and field studies provide new insights into challenging operational contexts. Yet the world of operations continues to change rapidly, creating new and difficult challenges for scholars. Increasingly, operations management requires theory, models and empirical methods to address the cross-functional, interdisciplinary character of modern operational systems and the complex nonlinear dynamics these systems generate. The OM research community has a long tradition of dynamic modeling, going back at least to the pioneering work of Forrester (1958) and Holt et al. (1960). These innovators recognized that even core processes in organizations, such as production and supply chain management, involve critical feedbacks with other organizational functions and with other organizations and actors including customers, suppliers, workers, competitors, financial markets, and others. They recognized that these interactions and feedbacks often involve significant time delays, nonlinearities, information distortions, and behavioral response


Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

Rachel Croson
Karen Donohue
Elena Katok
John D. Sterman

Abstract: The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on‐hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providi


Competing risks in epidemiology: possibilities and pitfalls

Per Kragh Andersen
Ronald B. Geskus
Théo de Witte
Hein Putter

Abstract: BACKGROUND: In studies of all-cause mortality, the fundamental epidemiological concepts of rate and risk are connected through a well-defined one-to-one relation. An important consequence of this relation is that regression models such as the proportional hazards model that are defined through the hazard (the rate) immediately dictate how the covariates relate to the survival function (the risk). METHODS: This introductory paper reviews the concepts of rate and risk and their one-to-one relation in all-cause mortality studies and introduces the analogous concepts of rate and risk in the context of competing risks, the cause-specific hazard and the cause-specific cumulative incidence function. RESULTS: The key feature of competing risks is that the one-to-one correspondence between cause-specific hazard and cumulative incidence, between rate and risk, is lost. This fact has two important implications. First, the naïve Kaplan-Meier that takes the competing events as censored observations, is biased. Secondly, the way in which covariates are associated with the cause-specific hazards may not coincide with the way these covariates are associated with the cumulative incidence. An exampl


Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare

Abstract: Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. A unique feature of survival data is that typically not all patients experience the event (eg, death) by the end of the observation period, so the actual survival times for some patients are unknown. This phenomenon, referred to as censoring, must be accounted for in the analysis to allow for valid inferences. Moreover, survival times are usually skewed, limiting the usefulness of analysis methods that assume a normal data distribution. As part of the ongoing series in Anesthesia & Analgesia, this tutorial reviews statistical methods for the appropriate analysis of time-to-event data, including nonparametric and semiparametric methods-specifically the Kaplan-Meier estimator, log-rank test, and Cox proportional hazards model. These methods are by far the most commonly used techniques for such data in medical literature. Illustrative examples from studies published in Anesthesia & Analgesia demonstrate how these techniques are used in practice. Full parametric models and models to deal with special circ


Survival Analysis, Kaplan-Meier Curves, and Cox Regression: Basic Concepts

Indian Journal of Psychological Medicine

Abstract: Survival analysis is used to analyze data from patients who are followed for different periods of time and in whom the outcome of interest, a dichotomous event, may or may not have occurred at the time the study is halted; data from all patients are used in the analysis, including data from patients who dropped out, regardless of the duration of follow-up. This article discusses basic concepts in survival analysis, explains technical terms such as censoring, and provides reasons why ordinary methods of analysis cannot be applied to such data. The Kaplan-Meier survival curve is described, as is the Cox proportional hazards regression and the hazard ratio. Supplementary information includes a data file, graphs with explanations, and additional discussions; these are provided to enhance the reader’s experience and understanding.


Status of NASA's deep space optical communication technology demonstration

Abhijit Biswas
Meera Srinivasan
Ryan Rogalin
Sabino Piazzolla
John Liu
Brian Schratz
Andre Wong
Erik Alerstam

Abstract: With NASA funding, the Deep Space Optical Communication (DSOC) Project at JPL is planning a system level technology demonstration of optical communications from deep space. A 22 cm diameter flight laser transceiver (FLT) is being developed for space flight. The FLT will be designed to transmit an average laser power of 4W at 1550 nm and receive a weak 1064 nm laser signal (> 100 femtowatts). Use of the Hale telescope at Palomar Mountain, CA, retrofitted with a photon-counting receiver to detect the downlink from space, is planned. The Optical Communication Telescope Laboratory (OCTL) at Table Mountain, CA will transmit a 1064 nm laser beacon to serve as a pointing reference for the FLT and support low-rate uplink data-rates. The DSOC FLT is part of the baseline payload for the Psyche mission spacecraft recently selected for flight by NASA, providing link demonstration opportunities during the mission cruise phase. Link demonstration opportunities at distances of approximately 0.1 to 2 astronomical units (AU) are expected. The DSOC system is being designed to support downlink data-rates of 0.2 to > 200 Mb/s and uplink data rates of approximately 1.6 kb/s. A status update of DSOC Pro


The Deep Space Optical Communications project ground laser receiver

Meera Srinivasan
Erik Alerstam
Emma E. Wollman
Ryan Rogalin
Seán M. Meenehan
Matthew D. Shaw
Angel E. Velasco
Abhijit Biswas

Abstract: The Deep Space Optical Communication (DSOC) project will conduct its technology demonstration concurrently with NASA’s Psyche mission, which hosts the DSOC flight transceiver (FLT) on its spacecraft. The DSOC Ground Laser Receiver (GLR) has been developed by the Jet Propulsion Laboratory and installed at the Palomar Observatory 5m Hale telescope in order to receive the optical downlink signal from the FLT, and is capable of processing discrete downlink data rates from 56 kbps to 265 Mbps over the course of the mission spanning an approximate range of 0.06 to 2.7 AU. In this paper we review the architecture of the completed GLR and its subsystems: (i) the GLR Optics Assembly (GLROA) that acquires the downlink signal and couples it to (ii) the GLR Detector Assembly (GDA) that features a superconducting nanowire single photon counting detector (SNSPD) array, (iii) the GLR Signal Processing Assembly (GSPA) that demodulates and decodes the pulse-position-modulated downlink waveform, and (iv) the GLR Monitor and Control software that is used to interface with the Hale telescope and operate the entire system. We discuss GLR operations in response to planned DSOC downlink activities, and p


Laser Communications Relay Demonstration (LCRD) update and the path towards optical relay operations

David Israël
Bernard L. Edwards
John W. Staren

Abstract: This paper provides a concept for an evolution of NASA's optical communications near Earth relay architecture. NASA's Laser Communications Relay Demonstration (LCRD), a joint project between NASA's Goddard Space Flight Center (GSFC), the Jet Propulsion Laboratory - California Institute of Technology (JPL), and the Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). LCRD will provide a minimum of two years of high data rate optical communications service experiments in geosynchronous orbit (GEO), following launch in 2019. This paper will provide an update of the LCRD mission status and planned capabilities and experiments, followed by a discussion of the path from LCRD to operational network capabilities.


Survey on Free Space Optical Communication: A Communication Theory Perspective

Abstract: Optical wireless communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, infrared (IR), and ultraviolet (UV) bands. In this survey, we focus on outdoor terrestrial OWC links which operate in near IR band. These are widely referred to as free space optical (FSO) communication in the literature. FSO systems are used for high rate communication between two fixed points over distances up to several kilometers. In comparison to radio-frequency (RF) counterparts, FSO links have a very high optical bandwidth available, allowing much higher data rates. They are appealing for a wide range of applications such as metropolitan area network (MAN) extension, local area network (LAN)-to-LAN connectivity, fiber back-up, backhaul for wireless cellular networks, disaster recovery, high definition TV and medical image/video transmission, wireless video surveillance/monitoring, and quantum key distribution among others. Despite the major advantages of FSO technology and variety of its application areas, its widespread use has been hampered by its rather disappointing link reliability particularly in long ranges due to atmospheric tu


Background of Arraying in the Deep Space Network

Antenna Arraying Techniques in the Deep Space Network


Deep Space Station 15: Uranus—The First 34‐Meter High‐Efficiency Antenna

Large Antennas of the Deep Space Network


Deep Space Station 11: Pioneer—The First Large Deep Space Network Cassegrain Antenna


64 Metre to 70 Metre Antenna Extension NASA Deep Space Network Tidbinbilla, Australian Capital Territory


Large Antenna Mechanical Noise Calibration (LANC) System for the NASA Deep Space Network (DSN)

IAF Space Communications and Navigation Symposium


The Gini Index and Measures of Inequality

Abstract: The Gini index is a summary statistic that measures how equitably a resource is distributed in a population; income is a primary example. In addition to a self-contained presentation of the Gini index, we give two equivalent ways to interpret this summary statistic: first in terms of the percentile level of the person who earns the average dollar, and second in terms of how the lower of two randomly chosen incomes compares, on average, to mean income.


The Small-Sample Bias of the Gini Coefficient: Results and Implications for Empirical Research

Abstract: The Gini coefficient is a downward-biased measure of inequality in small populations when income is generated by one of three common distributions. The paper discusses the sources of bias and argues that this property is far more general. This has implications for (i) the comparison of inequality among subsamples, some of which may be small, and (ii) the use of the Gini in measuring firm size inequality in markets with a small number of firms. The small-sample bias has often led to misperceptions about trends in industry concentration. A small-sample adjustment results in a reduced bias, which can no longer be signed. This remaining bias rises with the dispersion and falls with increasing skewness of the distribution. Finally, an empirical example illustrates the importance of using the adjusted Gini. In this example it is shown that, controlling for market characteristics, larger shipping cartels include a set of firms that is stochastically identical (in terms of relative size) to those of smaller shipping cartels.


Conindex: Estimation of Concentration Indices

Owen O’Donnell
Stephen O’Neill
Tom Van Ourti
Brendan Walsh

The Stata Journal Promoting communications on statistics and Stata

Abstract: Concentration indices are frequently used to measure inequality in one variable over the distribution of another. Most commonly, they are applied to the measurement of socioeconomic-related inequality in health. We introduce the user-written command conindex, which provides point estimates and standard errors of a range of concentration indices. The command also graphs concentration curves (and Lorenz curves) and performs statistical inference for the comparison of inequality between groups. We offer an accessible introduction to the various concentration indices that have been proposed to suit different measurement scales and ethical responses to inequality. We also demonstrate the command's capabilities and syntax by analyzing wealth-related inequality in health and health care in Cambodia.


Income inequality measures: Figure 1

Journal of Epidemiology & Community Health

Abstract: The Gini coefficient has been the most popular method for operationalising income inequality in the public health literature. However, a number of alternative methods exist, and they offer researchers the means to develop a more nuanced understanding of the distribution of income. Income inequality measures such as the generalised entropy index and the Atkinson index offer the ability to examine the effects of inequalities in different areas of the income spectrum, enabling more meaningful quantitative assessments of qualitatively different inequalities. This glossary provides a conceptual introduction to these and other income inequality measures.


On the Tail of the Waiting Time in a Markov-Modulated M/G/1 Queue

Abstract: We show that the “exponential decay parameter” of the waiting time in a Markov-modulated M/G/1 queue is no larger than that of the corresponding M/G/1 queue with “averaged” parameters, and we give a necessary and sufficient condition for equality. We also explore the effect of speeding up the modulation process. A key tool is a Markov-modulated fluid model.


Optimal scheduling of jobs with a DHR tail in the M/G/1 queue

Abstract: We consider the mean delay optimization in the M/G/1 queue for jobs with a service time distribution that has a tail with decreasing hazard rate (DHR). If the DHR property is valid for the whole distribution, then it is known that the Foreground-Background (FB) discipline, which gives priority to th


Modeling human activity in the spirit of Barabasi’s queueing systems

Abstract: Barabasi has shown that the priority-based scheduling rules in single-stage queuing systems (QS) generate fat tail behavior for the task waiting time distributions (WTD). These fat tails are induced by the waiting times of very low priority tasks that stay unserved almost forever as the task priority indices are "frozen in time" (i.e., a task priority is assigned once for all to each incoming task). Here, we study the new dynamic behavior expected when the priority of each incoming task is time-dependent (i.e., "aging mechanisms" are allowed). For two classes of models, namely a population-type model with an age structure and a QS with deadlines assigned to the incoming tasks, which is operated under the "earliest-deadline-first" policy, we are able to extract analytically some relevant characteristics of the task waiting time distribution. As the aging mechanism ultimately assigns high priority to any long waiting tasks, fat tails in the WTD cannot find their origin in the scheduling rule alone, thus showing a fundamental difference between our approach and Barabasi's class of models.


Modeling bursts and heavy tails in human dynamics

Alexei Vázquez
J. G. Oliveira
Zoltán Dezső
K.-I. Goh
Imre Kondor
Albert-Ĺaszló Barabási

Abstract: The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queuing models that capture human activity. The first model assumes tha


Diffusion approximations for open Jackson networks with reneging

Abstract: We consider generalized Jackson networks with reneging in which the customer patience times follow a general distribution that unifies the patience time without scaling adopted by Ward and Glynn (Queueing Syst 50:371–400, 2005 ) and the patience time with hazard rate scaling and unbounded support adopted by Reed and Ward (Math Oper Res 33:606–644, 2008 ). The diffusion approximations for both the queue length process and the abandonment-count process are established under the conventional heavy traffic limit regime. In light of the recent work by Dai and He (Math Oper Res 35:347–362, 2010 ), the diffusion approximations are obtained by the following four steps: first, establishing the stochastic boundedness for the queue length process and the virtual waiting time process; second, obtaining the $$C$$ -tightness and fluid limits for the queue length process and the abandonment-count process; then third, building an asymptotic relationship between the abandonment-count process and the queue length process in terms of the customer patience time. Finally, the fourth step is to get the diffusion approximations by invoking the continuous mapping theorem.


Heavy traffic analysis for single-server SRPT and LRPT queues via EDF diffusion limits

Abstract: Extending the results of Kruk (Queueing theory and network applications. QTNA 2019. Lecture notes in computer science, vol 11688. Springer, Cham, pp 263–275, 2019), we derive heavy traffic limit theorems for a single server, single customer class queue in which the server uses the Shortest Remaining Processing Time (SRPT) policy from heavy traffic limits for the corresponding Earliest Deadline First queueing systems. Our analysis allows for correlated customer inter-arrival and service times and heavy-tailed inter-arrival and service time distributions, as long as the corresponding stochastic primitive processes converge weakly to continuous limits under heavy traffic scaling. Our approach yields simple, concise justifications and new insights for SRPT heavy traffic limit theorems of Gromoll et al. (Stoch Syst 1(1):1–16, 2011). Corresponding results for the longest remaining processing time policy are also provided.


Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox-based Sampling

Buddhika Nettasinghe
Vikram Krishnamurthy

ACM Transactions on Knowledge Discovery from Data

Abstract: This article considers the problem of estimating a power-law degree distribution of an undirected network using sampled data. Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e.g., linear regression on double-logarithmic axes and maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution. As a solution, we present a novel maximum likelihood estimation approach that exploits the friendship paradox to sample more efficiently from the tail of the degree distribution. We analytically show that the proposed method results in a smaller bias, variance and a Cramèr–Rao lower bound compared to the vanilla maximum likelihood estimate obtained with uniformly sampled nodes (which is the most commonly used method in literature). Detailed numerical and empirical results are presented to illustrate the performance of the proposed method under different conditions and how it compares with alternative methods. We also show that the proposed method and its desirable properties (i.e., smaller bias, var


Modified Lomax model: a heavy-tailed distribution for fitting large-scale real-world complex networks

Swarup Chattopadhyay
Tanujit Chakraborty
K. Ghosh
A. Das

Abstract: Real-world networks are generally claimed to be scale-free. This means that the degree distributions follow the classical power-law, at least asymptotically. However, closer observation shows that the classical power-law distribution is often inadequate to meet the data characteristics due to the existence of an identifiable nonlinearity in the entire degree distribution in the log-log scale. The present paper proposes a new variant of the popular heavy-tailed Lomax distribution which we named as the modified Lomax (MLM) distribution that can efficiently capture the crucial aspect of heavy-tailed behavior of the entire degree distribution of real-world complex networks. The proposed MLM model, derived from a hierarchical family of Lomax distributions, can efficiently fit the entire degree distribution of real-world networks without removing lower degree nodes, as opposed to the classical power-law-based fitting. The MLM distribution belongs to the maximum domain of attraction of the Frechet distribution and is right tail equivalent to Pareto distribution. Various statistical properties including characteristics of the maximum likelihood estimates and asymptotic distributions have a


Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

Patricia Guyot
A. E. Ades
Mario JNM Ouwens
Nicky J. Welton

Abstract: BACKGROUND: The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. METHODS: We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. RESULTS: The validation exercise established there w


Introducing Survival and Event History Analysis

Abstract: The Fundamentals of Survival and Event History Analysis Introduction: What Is Survival and Event History Analysis? Key Concepts and Terminology Censoring and Truncation Mathematical Expression and Relation of Basic Statistical Functions How Do the Survivor, Density and Hazard Function Relate? Why Use Survival and Event History Analysis? Overview of Survival and Event History Models Exercises Using R and Other Computer Programs for Survival and Event History Analysis Introduction: Computer Programs for Survival and Event History Analysis Conducting Serious Data Analysis: Life Lessons Why Use R? Downloading R on Your Personal Computer Add-On Packages Running R Determining and Setting your Working Directory Help and Documentation Importing Data Into R Working With Data: Opening and Accessing Variables from a Data Frame Saving Output as File, Workspace and History and Quitting R Exercises Your First Session: Using the Survival Package and Exploring Data Via Descriptive Statistics and Graphs Your First Session Using the 'Survival' Package In F Loading and Examining the Survival Package and Rcmdrplugin.Survival Plug-In Opening and Examining Data The Surv Object: Packaging the 'Survival V


A Systems Dynamic Approach in Aerospace and Defense Systems

Rai Singh Bindra
Ralph C. Tillinghast
Mo Mansouri

Abstract: System Dynamics (SD) is a powerful tool for analyzing and mitigating the cascading effects of complex interactions in systems. This paper explores the application of SD in managing risks associated with delays in component delivery within military aircraft development projects within the aerospace and defense realm. By leveraging insights from causal loop diagrams and stock-and-flow models, the study highlights the critical feedback loops and delays that exacerbate project disruptions. The results demonstrate key intervention points for mitigating cascading failures, reducing risks, and optimizing system performance. Real-world examples, including the Boeing 787 supply chain issues and the Joint Strike Fighter program delays, are used to illustrate the importance of addressing these challenges. These findings show the importance of risk management strategies and supply chain resilience in the aerospace and defense sectors. The study further contributes to the body of knowledge by integrating systems thinking principles into practical applications for managing large-scale, high-stakes projects. In response to reviewer feedback, the paper (i) augments the bibliography with recent, po


Large-aperture ground terminal for high data rate free-space laser communications

A. Arianpour
Janak Carey
J. Gardiner
Mike Serrano
A. Sharpe
Tony Truscott
A. Doan
Cameryn Yow

Abstract: High-data rate satellites capable of communicating with ground-based terminals circumvent the cost and effort required to physically lay networks of communication fiber in rural and metropolitan areas. However, the alternative of free-space laser communication has its own challenges. First, collimated beams incur dynamic pointing and wavefront errors when propagated through atmosphere. Additionally, due to the unknown tilt of the atmosphere during open loop transmissions, an uplink system with a single uplink assembly would suffer from low irradiance at the space terminal requiring the use of multiple independent uplink assemblies. Here we describe a bi-directional ground terminal comprised of four independent uplink telescopes with communication and beacon channels and a downlink telescope with integrated Adaptive Optics (AO) tracking schemes that maximize throughput for single mode fiber coupling. A 1μrad pointing error at 3.3σ CDF was achieved for simulated disturbances under atmospheric conditions with a fried parameter of approximately 7 cm, a Greenwood frequency of nearly 270 Hz, and a measured mechanical jitter of a gimbaled assembly with an 82cm aperture telescope. Open loo


L2D2

Deepak Vasisht
Jayanth Shenoy
Ranveer Chandra

Abstract: Large constellations of Low Earth Orbit satellites promise to provide near real-time high-resolution Earth imagery. Yet, getting this large amount of data back to Earth is challenging because of their low orbits and fast motion through space. Centralized architectures with few multi-million dollar ground stations incur large hourlevel data download latency and are hard to scale. We propose a geographically distributed ground station design, L2D2, that uses low-cost commodity hardware to offer low latency robust downlink. L2D2 is the first system to use a hybrid ground station model, where only a subset of ground stations are uplink-capable. We design new algorithms for scheduling and rate adaptation that enable low latency and high robustness despite the limitations of the receive-only ground stations. We evaluate L2D2 through a combination of trace-driven simulations and real-world satelliteground station measurements. Our results demonstrate that L2D2's geographically distributed design can reduce data downlink latency from 90 minutes to 21 minutes.


Ground station scheduling optimization for a model of a real-world problem instance

Abstract: Effective scheduling of communication windows between orbiting spacecraft and ground stations is a crucial component of efficiently using spacecraft resources. In all but the most trivial cases, this forces the operator to choose a subset of the potentially available access windows such that they can achieve the best possible usage of their hardware and other resources. This is a complex problem not normally solvable analytically, and as a result the standard approach is to apply heuristic algorithms which take an initial guess at a solution and improve upon it in order to increase its quality. Various such algorithms exist, with some being in common practice for this particular problem. This thesis covers the application of several of the most commonly-used algorithms on a problem instance. Additionally, a real-world problem instance is used, and the resultant practical constraints are addressed when applying the heuristics and fine-tuning them for this application.


Multi-Ground Station and Multi-Satellite Resource Scheduling Based on Improved Genetic Algorithm

Jieyu Yang
Yanning Guo
Zhen Shao
Guangfu Ma
Qing Deng

2024 IEEE 14th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)


The impact of reneging in processor sharing queues

H. Christian Gromoll
Philippe Robert
Bert Zwart
Richard Bakker

ACM SIGMETRICS Performance Evaluation Review

Abstract: We investigate an overloaded processor sharing queue with renewal arrivals and generally distributed service times. Impatient customers may abandon the queue, or renege, before completing service. The random time representing a customer's patience has a general distribution and may be dependent on his initial service time requirement.We propose a scaling procedure that gives rise to a fluid model, with nontrivial yet tractable steady state behavior. This fluid model captures many essential features of the underlying stochastic model, and we use it to analyze the impact of impatience in processor sharing queues. We show that this impact can be substantial compared with FCFS, and we propose a simple admission control policy to overcome these negative impacts.


Gradient boosting for extreme quantile regression

Jasper Velthoen
Clément Dombry
Juan‐Juan Cai
Sebastian Engelke

Abstract: Abstract Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the tail region are too scarce. Extreme value theory is used for extrapolation beyond the range of observed values and estimation of conditional extreme quantiles. Based on the peaks-over-threshold approach, the conditional distribution above a high threshold is approximated by a generalized Pareto distribution with covariate dependent parameters. We propose a gradient boosting procedure to estimate a conditional generalized Pareto distribution by minimizing its deviance. Cross-validation is used for the choice of tuning parameters such as the number of trees and the tree depths. We discuss diagnostic plots such as variable importance and partial dependence plots, which help to interpret the fitted models. In simulation studies we show that our gradient boosting procedure outperforms classical methods from quantile regression and extreme value theory, especially for high-dimensional predictor spaces and complex parameter response surfaces. An application to statistical post-processing of weather for