970 research outputs found
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Many deep learning applications benefit from using large models with billions
of parameters. Training these models is notoriously expensive due to the need
for specialized HPC clusters. In this work, we consider alternative setups for
training large models: using cheap "preemptible" instances or pooling existing
resources from multiple regions. We analyze the performance of existing
model-parallel algorithms in these conditions and find configurations where
training larger models becomes less communication-intensive. Based on these
findings, we propose SWARM parallelism, a model-parallel training algorithm
designed for poorly connected, heterogeneous and unreliable devices. SWARM
creates temporary randomized pipelines between nodes that are rebalanced in
case of failure. We empirically validate our findings and compare SWARM
parallelism with existing large-scale training approaches. Finally, we combine
our insights with compression strategies to train a large Transformer language
model with 1B shared parameters (approximately 13B before sharing) on
preemptible T4 GPUs with less than 200Mb/s network.Comment: Accepted to International Conference on Machine Learning (ICML) 2023.
25 pages, 8 figure
Real-time spatial-intertemporal dynamic pricing for balancing supply and demand in a ride-hailing network: near-optimal policies and the value of dynamic pricing
Motivated by the growth of ride-hailing services in urban areas, we study a (tactical) real-time spatial–intertemporal dynamic pricing problem where a firm uses a pool of homogeneous servers (e.g., a fleet of taxis) to serve price-sensitive customers (i.e., a rider requesting a trip from an origin to a destination) within a finite horizon (e.g., a day). We consider a revenue maximization problem in a model that captures the stochastic and nonstationary nature of demands, and the nonnegligible travel time from one location to another location. We first show that the relative revenue loss of any static pricing policy is at least in the order of n−1/2 in a large system regime where the demand arrival rate and the number of servers scale linearly with n, which highlights the limitation of static pricing control. We also propose a static pricing control with a matching performance (up to a multiplicative logarithmic term). Next, we develop a novel state-dependent dynamic pricing control with a reduced relative revenue loss of order n−2/3. The key idea is to dynamically adjust the prices in a way that reduces the impact of past “errors” on the balance of future distributions of servers and customers across the network. Our extensive numerical studies using both a synthetic data set and a real data set from the New York City Taxi and Limousine Commission, confirm our theoretical findings and highlight the benefit of dynamic pricing over static pricing, especially when dealing with nonstationary demands. Interestingly, we also observe that the revenue improvement under our proposed policy primarily comes from an increase in the number of customers served instead of from an increase in the average prices compared with the static pricing policy. This suggests that dynamic pricing can be potentially used to simultaneously increase both revenue and the number of customers served (i.e., service level). Finally, as an extension, we discuss how to generalize the proposed policy to a setting where the firm can also actively relocate some of the available servers to different locations in the network in addition to implementing dynamic pricing
Causal Message Passing: A Method for Experiments with Unknown and General Network Interference
Randomized experiments are a powerful methodology for data-driven evaluation
of decisions or interventions. Yet, their validity may be undermined by network
interference. This occurs when the treatment of one unit impacts not only its
outcome but also that of connected units, biasing traditional treatment effect
estimations. Our study introduces a new framework to accommodate complex and
unknown network interference, moving beyond specialized models in the existing
literature. Our framework, which we term causal message-passing, is grounded in
a high-dimensional approximate message passing methodology and is specifically
tailored to experimental design settings with prevalent network interference.
Utilizing causal message-passing, we present a practical algorithm for
estimating the total treatment effect and demonstrate its efficacy in four
numerical scenarios, each with its unique interference structure
Application of knowledge management principles to support maintenance strategies in healthcare organisations
Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and
resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled
hands of the staff and those who manage the infrastructure that supports the daily operations of the
service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector
is undergoing rapid changes, driven by rising costs, technological advancements, changing patient
expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare
challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare
delivery requires the integration of various practices that enhance the efficiency and effectiveness of
healthcare infrastructural assets. One critical area that requires attention is the management of
healthcare facilities.
Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services,
as shortcomings in the provision of facilities management (FM) services in hospitals may have much more
drastic negative effects than in any other general forms of buildings. An essential element in healthcare
FM is linked to the relationship between action and knowledge. With a full sense of understanding of
infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users
and to ensure the functionality of the structure and processes.
The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical
environment in which it operates and that improving the environment can result in direct benefits in
operational performance. The goal of healthcare FM is to support the achievement of organisational
mission and goals by designing and managing space and infrastructural assets in the best combination of
suitability, efficiency, and cost. In operational terms, performance refers to how well a building
contributes to fulfilling its intended functions.
Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality
healthcare provision while positively impacting overall patient experiences. In this regard, incorporating
knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring
sustainable healthcare provision and enhancement of patient experiences. Organisations implementing
KM principles are better positioned to navigate the constantly evolving business ecosystem easily.
Furthermore, KM is vital in processes and service improvement, strategic decision-making, and
organisational adaptation and renewal.
In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable
healthcare delivery. Knowledge management assumes that organisations that manage their
organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in
improving processes and services, strategic decision-making, and adapting and renewing an organisation.
The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most
people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include
patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists
for the success of such approaches at knowledge bottlenecks, such as infection prevention and control,
working safely, compliances, automated systems and reminders, and recall based on best practices. The
ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of
the most significant challenges for those responsible for KM. However, despite the potential benefits,
applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can
be effectively applied in this context, and few studies have explored the potential challenges and
opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare
delivery.
This study explores applying KM principles to support maintenance strategies in healthcare organisations.
The study also explores the challenges and opportunities, for healthcare organisations and FM
practitioners, in operationalising a framework which draws the interconnectedness between healthcare.
The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses
the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector.
The study also examines the challenges that healthcare FM face in managing knowledge and how the
application of KM principles can help to overcome these challenges. The study then explores the different
KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes,
reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals.
Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered.
In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented
and discussed.
The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges.
In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve
the research aim. As part of the research, a number of objectives will be evaluated, including appraising
the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM,
evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural
differences impact knowledge sharing and performance in healthcare FM organisations.
This study uses a combination of qualitative methods, such as meetings, observations, document analysis
(internal and external), and semi-structured interviews, to discover the subjective experiences of
healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate
and facilitating KM development in the delivery and practice of healthcare FM.
The study describes the research methodology using the theoretical concept of the "research onion". The
qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England.
Findings from the research study revealed that while the concept of KM has grown significantly in recent
years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM
directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten
operatives). These seven groups were purposively selected as the target population because they play a
crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all
participants based on their pre-determined availability. Out of the 50-target population, only 25 were
successfully interviewed to the point of saturation. Data collected from the interview were coded and
analysed using NVivo to identify themes and patterns related to KM in healthcare FM.
The study is divided into eight major sections. First, it discusses literature findings regarding healthcare
FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the
research establishes the study's methodology, introducing the five research objectives, questions and
hypothesis. The chapter introduces the literature on methodology elements, including philosophical views
and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a
conclusion and recommendation summarise the research objectives and suggest further research.
Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare
FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging
knowledge to improve patient care and organisational effectiveness
Metaverse. Old urban issues in new virtual cities
Recent years have seen the arise of some early attempts to build virtual cities,
utopias or affective dystopias in an embodied Internet, which in some respects appear to
be the ultimate expression of the neoliberal city paradigma (even if virtual). Although
there is an extensive disciplinary literature on the relationship between planning and
virtual or augmented reality linked mainly to the gaming industry, this often avoids design
and value issues. The observation of some of these early experiences - Decentraland,
Minecraft, Liberland Metaverse, to name a few - poses important questions and problems
that are gradually becoming inescapable for designers and urban planners, and allows
us to make some partial considerations on the risks and potentialities of these early virtual
cities
INSAM Journal of Contemporary Music, Art and Technology 10 (I/2023)
Having in mind the foundational idea not only of our Journal but also the INSAM Institute itself, the main theme of this issue is titled “Technological Aspects of Contemporary Artistic and Scientific Research”. This theme was recognized as important, timely, and necessary by a number of authors coming from various disciplines.
The (Inter)Views section brings us three diverse pieces; the issue is opened by Aida Adžović’s interview with the legendary Slovene act Laibach regarding their performance of the Wir sing das Volk project at the Sarajevo National Theater on May 9, 2023. Following this, Marija Mitrović prepared an interview with media artist Leon Eckard, concerning this artist’s views on contemporary art and the interaction between technology and human sensitivity. An essay by Alexander Liebermann on the early 20th-century composer Erwin Schulhoff, whose search for a unique personal voice could be encouraging in any given period, closes this rubric.
The Main theme section contains seven scientific articles. In the first one, Filipa Magalhães, Inês Filipe, Mariana Silva and Henrique Carvalho explore the process and details of technological and artistic challenges of reviving the music theater work FE...DE...RI...CO... (1987) by Constança Capdeville. The second article, written by Milan Milojković, is dedicated to the analysis of historical composer Vojislav Vučković and his ChatGPT-generated doppelganger and opera. The fictional narrative woven around the actual historical figure served as an example of the current possibilities of AI in the domain of musicological work. In the next paper, Luís Arandas, Miguel Carvalhais and Mick Grierson expand on their work on the film Irreplaceable Biography, which was created via language-guided generative models in audiovisual production. Thomas Moore focuses on the Belgium-based Nadar Ensemble and discusses the ways in which the performers of the ensemble understand the concept of the integrated concert and distinguish themselves from it, specifying the broadening of performers’ competencies and responsibilities. In her paper, Dana Papachristou contributes to the discussion on the politics of connectivity based on the examination of three projects: the online project Xenakis Networked Performance Marathon 2022, 2023Eleusis Mystery 91_Magnetic Dance in Elefsina European Capital of Culture, and Spaces of Reflection offline PirateBox network in the 10th Berlin Biennale. The penultimate article in the section is written by Kenrick Ho and presents us with the author’s composition Flou for solo violin through the prism of the relationship between (historically present) algorithmic processes, the composer, and the performer. Finally, Rijad Kaniža adds to the critical discourse on the reshaping of the musical experience via technology and the understanding of said technology using the example of musique concrète.
In the final Review section, Bakir Memišević gives an overview of the 13th International Symposium “Music in Society” that was held in Sarajevo in December 2022
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Queues, Planes and Games: Algorithms for Scheduling Passengers, and Decision Making in Stackelberg Games
In this dissertation, I present three theoretical results with real-world applications related to scheduling and distributionally-robust games, important fields in discrete optimization, and computer science.
The first chapter provides simple, technology-free interventions to manage elevator queues in high-rise buildings when passenger demand far exceeds the capacity of the elevator system. The problem was motivated by the need to manage passengers safely in light of reduced elevator capacities during the COVID-19 pandemic. We use mathematical modeling, epidemiological expertise, and simulation to design and evaluate our algorithmic solutions. The key idea is to explicitly or implicitly group passengers that are going to the same floor into the same elevator as much as possible, substantiated theoretically using a technique from queuing theory known as stability analysis. This chapter is joint work with Charles Branas, Adam Elmachtoub, Clifford Stein, and Yeqing Zhou, directly in collaboration with the New York City Mayor’s Office of the Chief Technology Officer and the Department of Citywide Administrative Services.
The second chapter proposes new algorithms for recomputing passenger itineraries for airlines during major disruptions when carefully planned schedules are thrown into disarray. An airline network is a massive temporal graph, often with tight regulatory and operational constraints. When disruptions propagate through an airline network, the objective is to \textit{recover} within a given time frame from a disruption, meaning we replan schedules affected by the disruption such that the new schedules have to match the originally planned schedules after the time frame. We aim to solve the large-scale airline recovery problem with quick, user-independent, consistent, and near-optimal algorithms. We provide new algorithms to solve the passenger recovery problem, given recovered flight and crew solutions. We build a preprocessing step and construct an Integer Program as well as a network-based approach based on solving multiple-label shortest path problems. Experiments show the tractability of our proposed algorithms on airline data sets with heavy flight disruptions. This chapter is joint work with Clifford Stein, stemming from an internship and collaboration with the Machine Learning team (Artificial Intelligence organization) of GE Global Research, Niskayuna, New York.
The third chapter is about computing distributionally-robust strategies for a popular game theory model called Stackelberg games, where one player, called the leader, is able to commit to a strategy first, assuming the other player(s), called follower(s) would best respond to the strategy. In many of the real-world applications of Stackelberg games, parameters such as payoffs of the follower(s) are not known with certainty. Distributionally-robust optimization allows a distribution over possible model parameters, where this distribution comes from a set of possible distributions. The goal for the leader is to maximize their expected utility with respect to the worst-case distribution from the set. We initiate the study of distributionally-robust models for Stackelberg games, show that a distributionally-robust Stackelberg equilibrium always exists across a wide array of uncertainty models, and provide tractable algorithms for some general settings with experimental results. This chapter is joint work with Christian Kroer
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