387 research outputs found
Planetary Hinterlands:Extraction, Abandonment and Care
This open access book considers the concept of the hinterland as a crucial tool for understanding the global and planetary present as a time defined by the lasting legacies of colonialism, increasing labor precarity under late capitalist regimes, and looming climate disasters. Traditionally seen to serve a (colonial) port or market town, the hinterland here becomes a lens to attend to the times and spaces shaped and experienced across the received categories of the urban, rural, wilderness or nature. In straddling these categories, the concept of the hinterland foregrounds the human and more-than-human lively processes and forms of care that go on even in sites defined by capitalist extraction and political abandonment. Bringing together scholars from the humanities and social sciences, the book rethinks hinterland materialities, affectivities, and ecologies across places and cultural imaginations, Global North and South, urban and rural, and land and water
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
DM: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
Current approaches to multi-agent cooperation rely heavily on centralized
mechanisms or explicit communication protocols to ensure convergence. This
paper studies the problem of distributed multi-agent learning without resorting
to centralized components or explicit communication. It examines the use of
distribution matching to facilitate the coordination of independent agents. In
the proposed scheme, each agent independently minimizes the distribution
mismatch to the corresponding component of a target visitation distribution.
The theoretical analysis shows that under certain conditions, each agent
minimizing its individual distribution mismatch allows the convergence to the
joint policy that generated the target distribution. Further, if the target
distribution is from a joint policy that optimizes a cooperative task, the
optimal policy for a combination of this task reward and the distribution
matching reward is the same joint policy. This insight is used to formulate a
practical algorithm (DM), in which each individual agent matches a target
distribution derived from concurrently sampled trajectories from a joint expert
policy. Experimental validation on the StarCraft domain shows that combining
(1) a task reward, and (2) a distribution matching reward for expert
demonstrations for the same task, allows agents to outperform a naive
distributed baseline. Additional experiments probe the conditions under which
expert demonstrations need to be sampled to obtain the learning benefits
The Public Performance Of Sanctions In Insolvency Cases: The Dark, Humiliating, And Ridiculous Side Of The Law Of Debt In The Italian Experience. A Historical Overview Of Shaming Practices
This study provides a diachronic comparative overview of how the law of debt has been applied by certain institutions in Italy. Specifically, it offers historical and comparative insights into the public performance of sanctions for insolvency through shaming and customary practices in Roman Imperial Law, in the Middle Ages, and in later periods.
The first part of the essay focuses on the Roman bonorum cessio culo nudo super lapidem and on the medieval customary institution called pietra della vergogna (stone of shame), which originates from the Roman model.
The second part of the essay analyzes the social function of the zecca and the pittima Veneziana during the Republic of Venice, and of the practice of lu soldate a castighe (no translation is possible).
The author uses a functionalist approach to apply some arguments and concepts from the current context to this historical analysis of ancient institutions that we would now consider ridiculous.
The article shows that the customary norms that play a crucial regulatory role in online interactions today can also be applied to the public square in the past. One of these tools is shaming. As is the case in contemporary online settings, in the public square in historic periods, shaming practices were used to enforce the rules of civility in a given community. Such practices can be seen as virtuous when they are intended for use as a tool to pursue positive change in forces entrenched in the culture, and thus to address social wrongs considered outside the reach of the law, or to address human rights abuses
Hybrid Cognition for Target Tracking in Cognitive Radar Networks
This work investigates online learning techniques for a cognitive radar
network utilizing feedback from a central coordinator. The available spectrum
is divided into channels, and each radar node must transmit in one channel per
time step. The network attempts to optimize radar tracking accuracy by learning
the optimal channel selection for spectrum sharing and radar performance. We
define optimal selection for such a network in relation to the radar
observation quality obtainable in a given channel. This is a difficult problem
since the network must seek the optimal assignment from nodes to channels,
rather than just seek the best overall channel. Since the presence of primary
users appears as interference, the approach also improves spectrum sharing
performance. In other words, maximizing radar performance also minimizes
interference to primary users. Each node is able to learn the quality of
several available channels through repeated sensing. We define hybrid cognition
as the condition where both the independent radar nodes as well as the central
coordinator are modeled as cognitive agents, with restrictions on the amount of
information that can be exchanged between the radars and the coordinator.
Importantly, each part of the network acts as an online learner, observing the
environment to inform future actions. We show that in interference-limited
spectrum, where the signal-to-interference-plus-noise ratio varies by channel
and over time for a target with fixed radar cross section, a cognitive radar
network is able to use information from the central coordinator in order to
reduce the amount of time necessary to learn the optimal channel selection. We
also show that even limited use of a central coordinator can eliminate
collisions, which occur when two nodes select the same channel.Comment: 34 pages, single-column, 10 figure
Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling
Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.
Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating.
In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023
Human Rights at the Intersections
At a time when states are increasingly hostile to the international rights regime, human rights activists have turned to non-state and sub-state actors to begin the implementation of human rights law. This complicates the conventional analysis of relationships between local actors, global norms, and cosmopolitanism. The contributions in this open access collection examine the “lived realities of human rights” and critically engage with debates on gender, sexuality, localism and cosmopolitanism, weaving insights from multiple disciplines into a broader call for interdisciplinary scholarship informed by practice. Overall, the contributors argue that the power of human rights depends on their ability to be continuously broadened and re-imagined in locales around the world. It is only on this basis that human rights can remain relevant and be effectively used to push local, national and international institutions to put in place structural reforms that advance equity and pluralism in these perilous times. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com
Cooperative Thresholded Lasso for Sparse Linear Bandit
We present a novel approach to address the multi-agent sparse contextual
linear bandit problem, in which the feature vectors have a high dimension
whereas the reward function depends on only a limited set of features -
precisely . Furthermore, the learning follows under
information-sharing constraints. The proposed method employs Lasso regression
for dimension reduction, allowing each agent to independently estimate an
approximate set of main dimensions and share that information with others
depending on the network's structure. The information is then aggregated
through a specific process and shared with all agents. Each agent then resolves
the problem with ridge regression focusing solely on the extracted dimensions.
We represent algorithms for both a star-shaped network and a peer-to-peer
network. The approaches effectively reduce communication costs while ensuring
minimal cumulative regret per agent. Theoretically, we show that our proposed
methods have a regret bound of order
with high probability, where is the time horizon. To our best knowledge, it
is the first algorithm that tackles row-wise distributed data in sparse linear
bandits, achieving comparable performance compared to the state-of-the-art
single and multi-agent methods. Besides, it is widely applicable to
high-dimensional multi-agent problems where efficient feature extraction is
critical for minimizing regret. To validate the effectiveness of our approach,
we present experimental results on both synthetic and real-world datasets
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