3,266 research outputs found
Addiction in context
The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use
Decentralized and collaborative machine learning framework for IoT
Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.Axencia Galega de Innovación | Ref. 25/IN606D/2021/2612348Agencia Estatal de Investigación | Ref. PID2020-113795RB-C3
Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment
Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’.
Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy.
The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy.
The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools
Innovator - Spring 2023
03 - The President’s Column
06 - Time Machine: Fabric of the Heart
10 - The Nexus: News at Jefferson
20 - Student to #Philebrity: Influenced by Jefferson
24 - The Light Fantastic
30 - We Are One Jefferson
34 - Question & Innovate
36 - Reimagine
40 - Ram Roundup
44 - Class Notes
56 - In Memory
58 - Trivi
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential
use in large-scale industrial applications. Existing federated learning works
mainly focus on model homogeneous settings. However, practical federated
learning typically faces the heterogeneity of data distributions, model
architectures, network environments, and hardware devices among participant
clients. Heterogeneous Federated Learning (HFL) is much more challenging, and
corresponding solutions are diverse and complex. Therefore, a systematic survey
on this topic about the research challenges and state-of-the-art is essential.
In this survey, we firstly summarize the various research challenges in HFL
from five aspects: statistical heterogeneity, model heterogeneity,
communication heterogeneity, device heterogeneity, and additional challenges.
In addition, recent advances in HFL are reviewed and a new taxonomy of existing
HFL methods is proposed with an in-depth analysis of their pros and cons. We
classify existing methods from three different levels according to the HFL
procedure: data-level, model-level, and server-level. Finally, several critical
and promising future research directions in HFL are discussed, which may
facilitate further developments in this field. A periodically updated
collection on HFL is available at https://github.com/marswhu/HFL_Survey.Comment: 42 pages, 11 figures, and 4 table
Threshold Encrypted Mempools: Limitations and Considerations
Encrypted mempools are a class of solutions aimed at preventing or reducing
negative externalities of MEV extraction using cryptographic privacy. Mempool
encryption aims to hide information related to pending transactions until a
block including the transactions is committed, targeting the prevention of
frontrunning and similar behaviour. Among the various methods of encryption,
threshold schemes are particularly interesting for the design of MEV mitigation
mechanisms, as their distributed nature and minimal hardware requirements
harmonize with a broader goal of decentralization.
This work looks beyond the formal and technical cryptographic aspects of
threshold encryption schemes to focus on the market and incentive implications
of implementing encrypted mempools as MEV mitigation techniques. In particular,
this paper argues that the deployment of such protocols without proper
consideration and understanding of market impact invites several undesired
outcomes, with the ultimate goal of stimulating further analysis of this class
of solutions outside of pure cryptograhic considerations. Included in the paper
is an overview of a series of problems, various candidate solutions in the form
of mempool encryption techniques with a focus on threshold encryption,
potential drawbacks to these solutions, and Osmosis as a case study. The paper
targets a broad audience and remains agnostic to blockchain design where
possible while drawing from mostly financial examples
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
The most essential component of every Distributed Ledger Technology (DLT) is
the Consensus Algorithm (CA), which enables users to reach a consensus in a
decentralized and distributed manner. Numerous CA exist, but their viability
for particular applications varies, making their trade-offs a crucial factor to
consider when implementing DLT in a specific field. This article provided a
comprehensive analysis of the various consensus algorithms used in distributed
ledger technologies (DLT) and blockchain networks. We cover an extensive array
of thirty consensus algorithms. Eleven attributes including hardware
requirements, pre-trust level, tolerance level, and more, were used to generate
a series of comparison tables evaluating these consensus algorithms. In
addition, we discuss DLT classifications, the categories of certain consensus
algorithms, and provide examples of authentication-focused and
data-storage-focused DLTs. In addition, we analyze the pros and cons of
particular consensus algorithms, such as Nominated Proof of Stake (NPoS),
Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the
applicability of these consensus algorithms to various Cyber Physical System
(CPS) use cases, including supply chain management, intelligent transportation
systems, and smart healthcare.Comment: 50 pages, 20 figure
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity
With distributed machine learning being a prominent technique for large-scale
machine learning tasks, communication complexity has become a major bottleneck
for speeding up training and scaling up machine numbers. In this paper, we
propose a new technique named Common randOm REconstruction(CORE), which can be
used to compress the information transmitted between machines in order to
reduce communication complexity without other strict conditions. Especially,
our technique CORE projects the vector-valued information to a low-dimensional
one through common random vectors and reconstructs the information with the
same random noises after communication. We apply CORE to two distributed tasks,
respectively convex optimization on linear models and generic non-convex
optimization, and design new distributed algorithms, which achieve provably
lower communication complexities. For example, we show for linear models
CORE-based algorithm can encode the gradient vector to -bits
(against ), with the convergence rate not worse, preceding the
existing results
Design and Real-World Evaluation of Dependable Wireless Cyber-Physical Systems
The ongoing effort for an efficient, sustainable, and automated interaction between humans, machines, and our environment will make cyber-physical systems (CPS) an integral part of the industry and our daily lives. At their core, CPS integrate computing elements, communication networks, and physical processes that are monitored and controlled through sensors and actuators. New and innovative applications become possible by extending or replacing static and expensive cable-based communication infrastructures with wireless technology. The flexibility of wireless CPS is a key enabler for many envisioned scenarios, such as intelligent factories, smart farming, personalized healthcare systems, autonomous search and rescue, and smart cities.
High dependability, efficiency, and adaptivity requirements complement the demand for wireless and low-cost solutions in such applications. For instance, industrial and medical systems should work reliably and predictably with performance guarantees, even if parts of the system fail. Because emerging CPS will feature mobile and battery-driven devices that can execute various tasks, the systems must also quickly adapt to frequently changing conditions. Moreover, as applications become ever more sophisticated, featuring compact embedded devices that are deployed densely and at scale, efficient designs are indispensable to achieve desired operational lifetimes and satisfy high bandwidth demands.
Meeting these partly conflicting requirements, however, is challenging due to imperfections of wireless communication and resource constraints along several dimensions, for example, computing, memory, and power constraints of the devices. More precisely, frequent and correlated message losses paired with very limited bandwidth and varying delays for the message exchange significantly complicate the control design. In addition, since communication ranges are limited, messages must be relayed over multiple hops to cover larger distances, such as an entire factory. Although the resulting mesh networks are more robust against interference, efficient communication is a major challenge as wireless imperfections get amplified, and significant coordination effort is needed, especially if the networks are dynamic.
CPS combine various research disciplines, which are often investigated in isolation, ignoring their complex interaction. However, to address this interaction and build trust in the proposed solutions, evaluating CPS using real physical systems and wireless networks paired with formal guarantees of a system’s end-to-end behavior is necessary. Existing works that take this step can only satisfy a few of the abovementioned requirements. Most notably, multi-hop communication has only been used to control slow physical processes while providing no guarantees. One of the reasons is that the current communication protocols are not suited for dynamic multi-hop networks.
This thesis closes the gap between existing works and the diverse needs of emerging wireless CPS. The contributions address different research directions and are split into two parts. In the first part, we specifically address the shortcomings of existing communication protocols and make the following contributions to provide a solid networking foundation:
• We present Mixer, a communication primitive for the reliable many-to-all message exchange in dynamic wireless multi-hop networks. Mixer runs on resource-constrained low-power embedded devices and combines synchronous transmissions and network coding for a highly scalable and topology-agnostic message exchange. As a result, it supports mobile nodes and can serve any possible traffic patterns, for example, to efficiently realize distributed control, as required by emerging CPS applications.
• We present Butler, a lightweight and distributed synchronization mechanism with formally guaranteed correctness properties to improve the dependability of synchronous transmissions-based protocols. These protocols require precise time synchronization provided by a specific node. Upon failure of this node, the entire network cannot communicate. Butler removes this single point of failure by quickly synchronizing all nodes in the network without affecting the protocols’ performance.
In the second part, we focus on the challenges of integrating communication and various control concepts using classical time-triggered and modern event-based approaches. Based on the design, implementation, and evaluation of the proposed solutions using real systems and networks, we make the following contributions, which in many ways push the boundaries of previous approaches:
• We are the first to demonstrate and evaluate fast feedback control over low-power wireless multi-hop networks. Essential for this achievement is a novel co-design and integration of communication and control. Our wireless embedded platform tames the imperfections impairing control, for example, message loss and varying delays, and considers the resulting key properties in the control design. Furthermore, the careful orchestration of control and communication tasks enables real-time operation and makes our system amenable to an end-to-end analysis. Due to this, we can provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics.
• We propose control-guided communication, a novel co-design for distributed self-triggered control over wireless multi-hop networks. Self-triggered control can save energy by transmitting data only when needed. However, there are no solutions that bring those savings to multi-hop networks and that can reallocate freed-up resources, for example, to other agents. Our control system informs the communication system of its transmission demands ahead of time so that communication resources can be allocated accordingly. Thus, we can transfer the energy savings from the control to the communication side and achieve an end-to-end benefit.
• We present a novel co-design of distributed control and wireless communication that resolves overload situations in which the communication demand exceeds the available bandwidth. As systems scale up, featuring more agents and higher bandwidth demands, the available bandwidth will be quickly exceeded, resulting in overload. While event-triggered control and self-triggered control approaches reduce the communication demand on average, they cannot prevent that potentially all agents want to communicate simultaneously. We address this limitation by dynamically allocating the available bandwidth to the agents with the highest need. Thus, we can formally prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.:Abstract
Acknowledgements
List of Abbreviations
List of Figures
List of Tables
1 Introduction
1.1 Motivation
1.2 Application Requirements
1.3 Challenges
1.4 State of the Art
1.5 Contributions and Road Map
2 Mixer: Efficient Many-to-All Broadcast in Dynamic Wireless Mesh Networks
2.1 Introduction
2.2 Overview
2.3 Design
2.4 Implementation
2.5 Evaluation
2.6 Discussion
2.7 Related Work
3 Butler: Increasing the Availability of Low-Power Wireless Communication Protocols
3.1 Introduction
3.2 Motivation and Background
3.3 Design
3.4 Analysis
3.5 Implementation
3.6 Evaluation
3.7 Related Work
4 Feedback Control Goes Wireless: Guaranteed Stability over Low-Power Multi-Hop Networks
4.1 Introduction
4.2 Related Work
4.3 Problem Setting and Approach
4.4 Wireless Embedded System Design
4.5 Control Design and Analysis
4.6 Experimental Evaluation
4.A Control Details
5 Control-Guided Communication: Efficient Resource Arbitration and Allocation in Multi-Hop Wireless Control Systems
5.1 Introduction
5.2 Problem Setting
5.3 Co-Design Approach
5.4 Wireless Communication System Design
5.5 Self-Triggered Control Design
5.6 Experimental Evaluation
6 Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
6.1 Introduction
6.2 Problem and Related Work
6.3 Overview of Co-Design Approach
6.4 Predictive Triggering and Control System
6.5 Adaptive Communication System
6.6 Integration and Stability Analysis
6.7 Testbed Experiments
6.A Proof of Theorem 4
6.B Usage of the Network Bandwidth for Control
7 Conclusion and Outlook
7.1 Contributions
7.2 Future Directions
Bibliography
List of Publication
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