1,155 research outputs found
Cooperative Online Learning: Keeping your Neighbors Updated
We study an asynchronous online learning setting with a network of agents. At
each time step, some of the agents are activated, requested to make a
prediction, and pay the corresponding loss. The loss function is then revealed
to these agents and also to their neighbors in the network. Our results
characterize how much knowing the network structure affects the regret as a
function of the model of agent activations. When activations are stochastic,
the optimal regret (up to constant factors) is shown to be of order
, where is the horizon and is the independence
number of the network. We prove that the upper bound is achieved even when
agents have no information about the network structure. When activations are
adversarial the situation changes dramatically: if agents ignore the network
structure, a lower bound on the regret can be proven, showing that
learning is impossible. However, when agents can choose to ignore some of their
neighbors based on the knowledge of the network structure, we prove a
sublinear regret bound, where is the clique-covering number of the network
Coarse Correlation in Extensive-Form Games
Coarse correlation models strategic interactions of rational agents
complemented by a correlation device, that is a mediator that can recommend
behavior but not enforce it. Despite being a classical concept in the theory of
normal-form games for more than forty years, not much is known about the merits
of coarse correlation in extensive-form settings. In this paper, we consider
two instantiations of the idea of coarse correlation in extensive-form games:
normal-form coarse-correlated equilibrium (NFCCE), already defined in the
literature, and extensive-form coarse-correlated equilibrium (EFCCE), which we
introduce for the first time. We show that EFCCE is a subset of NFCCE and a
superset of the related extensive-form correlated equilibrium. We also show
that, in two-player extensive-form games, social-welfare-maximizing EFCCEs and
NFCEEs are bilinear saddle points, and give new efficient algorithms for the
special case of games with no chance moves. In our experiments, our proposed
algorithm for NFCCE is two to four orders of magnitude faster than the prior
state of the art
DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles
Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows
charging an electric vehicle while driving thanks to a dedicated road
infrastructure. DWPT's capabilities in automatically establishing charging
sessions and billing without users' intervention make it prone to cybersecurity
attacks. Hence, security is essential in preventing fraud, impersonation, and
user tracking. To this aim, researchers proposed different solutions for
authenticating users. However, recent advancements in quantum computing
jeopardize classical public key cryptography, making currently existing
solutions in DWPT authentication nonviable. To avoid the resource burden
imposed by technology upgrades, it is essential to develop
post-quantum-resistant solutions. In this paper, we propose DynamiQS, the first
post-quantum secure authentication protocol for dynamic wireless charging.
DynamiQS is privacy-preserving and secure against attacks on the DWPT. We
leverage an Identity-Based Encryption with Lattices in the Ring Learning With
Error framework. Furthermore, we show the possibility of using DynamiQS in a
real environment, leveraging the results of cryptographic computation on real
constrained devices and simulations. DynamiQS reaches a total time cost of
around 281 ms, which is practicable in dynamic charging settings (car and
charging infrastructure)
Progettazione di un sistema di acquisizione dati in motoveicoli, finalizzato allo sviluppo di una centralina elettronica per la gestione degli indicatori di direzione
Questa tesi si inserisce in un progetto piĂč ampio che persegue la finalitĂ di sviluppare una centralina elettronica, a basso costo, per la gestione degli indicatori di direzione dei motoveicoli. In particolare si vuole ottenere un sistema elettronico che non richieda lâintervento umano, ma disattivi le frecce automaticamente. Si pone quindi il problema di scegliere delle grandezze fisiche, che, variando durante una manovra cambio di direzione eseguita col motoveicolo, indichino il momento in cui tale manovra possa considerarsi terminata. Per il raggiungimento del suddetto scopo si presentano in questa tesi i principali passi per la progettazione di un sistema di acquisizione dati per i motoveicoli, finalizzato a comprendere quali siano queste grandezze maggiormente soggette a variazione durante una curva. Dopo unâanalisi dello stato dellâarte e una rassegna sui principali sensori utilizzabili sul motoveicolo, si arriva a delineare uno schema elettrico di massima. In seguito si procede allo sviluppo di un prototipo che si basa su di un sensore magnetico, quindi lâidea Ăš quella di riconoscere il termine della manovra cambio di direzione, mediante la variazione del campo magnetico terrestre rilevato dal sensore. Il mercato del motoveicolo impone vincoli stringenti in termini di prezzo per lâelettronica a bordo, perciĂČ sarĂ utilizzato un sensore di Hall a basso costo. Lâobiettivo che ci si propone con il prototipo Ăš quello di recuperare informazioni sul comportamento del sensore durante una manovra cambio di direzione e di verificare che riesca effettivamente a riconoscere tali manovre
Stable allometric trajectories in picea abies (L.) karst. trees along an elevational gradient
The effect of temperature on tree phenology and growth has gained particular attention in relation to climate change. While a number of reports indicate that warming can extend the length of the growing season and enhance tree growth rates, it is still debated whether temperature also affects biomass partitioning. Addressing the question of whether trees grown at different elevations invest similarly in various organs, we established four sites along an elevational gradient (320 to 595 m a.s.l.) in managed Norway spruce (Picea abies (L.) Karts) stands regenerating after clearcuts in central Norway. There, differences in temperature, bud break, tree growth, and allometric scaling were measured in small spruce trees (up to 3 m height). The results showed that bud break and shoot growth are affected by temperature, as lower sites completed the bud break process 5 days earlier than the higher sites did. There was some evidence indicating that the summer drought of 2018 affected tree growth during the season, and the implications of this are discussed. The allometric scaling coefficients did not change for the crown volume (slope value range 2.66â2.84), crown radius (0.77â0.89), and tree diameter (0.89â0.96) against tree height. A slight difference was found in the scaling coefficients of crown length against tree height (slope value range 1.04â1.12), but this did not affect the general scaling of the crown volume with tree height. Our results showed that different local environmental conditions affect both the growth rate and phenology in Norway spruce trees but, on the contrary, that the biomass partitioning among different parts of the tree remains essentially unchanged. This demonstrates that the allometric approach is an important tool for unraveling true vs. apparent plant plasticity, which in turn is an essential awareness for predicting plant responses to environmental changes.publishedVersio
Dynamic Pricing with Finitely Many Unknown Valuations
Motivated by posted price auctions where buyers are grouped in an unknown
number of latent types characterized by their private values for the good on
sale, we investigate revenue maximization in stochastic dynamic pricing when
the distribution of buyers' private values is supported on an unknown set of
points in [0,1] of unknown cardinality . This setting can be viewed as an
instance of a stochastic -armed bandit problem where the location of the
arms (the unknown valuations) must be learned as well. In the
distribution-free case, we prove that our setting is just as hard as -armed
stochastic bandits: no algorithm can achieve a regret significantly better than
, (where T is the time horizon); we present an efficient algorithm
matching this lower bound up to logarithmic factors. In the
distribution-dependent case, we show that for all our setting is strictly
harder than -armed stochastic bandits by proving that it is impossible to
obtain regret bounds that grow logarithmically in time or slower. On the other
hand, when a lower bound on the smallest drop in the demand curve is
known, we prove an upper bound on the regret of order . This is a significant improvement on previously known
regret bounds for discontinuous demand curves, that are at best of order
. When in the distribution-dependent case, the
hardness of our setting reduces to that of a stochastic -armed bandit: we
prove that an upper bound of order (up to factors)
on the regret can be achieved with no information on the demand curve. Finally,
we show a upper bound on the regret for the setting in which the
buyers' decisions are nonstochastic, and the regret is measured with respect to
the best between two fixed valuations one of which is known to the seller
Evidence and knowledge use in a meta-policy: results from an Italian case study
The analysis of the case study was conducted by means of qualitative content analysis of documents and interviews of 14 stakeholders involved in the policy making processes. In-depth interviews to five policy makers were carried out, and the emerging elements were compared with the results of the content analysis of the policy documents.
On the Minimax Regret for Online Learning with Feedback Graphs
In this work, we improve on the upper and lower bounds for the regret of
online learning with strongly observable undirected feedback graphs. The best
known upper bound for this problem is , where is the number of actions, is the independence
number of the graph, and is the time horizon. The factor is
known to be necessary when (the experts case). On the other hand,
when (the bandits case), the minimax rate is known to be
, and a lower bound is known to hold for any . Our improved upper bound
holds for any
and matches the lower bounds for bandits and experts, while
interpolating intermediate cases. To prove this result, we use FTRL with
-Tsallis entropy for a carefully chosen value of that
varies with . The analysis of this algorithm requires a new bound on
the variance term in the regret. We also show how to extend our techniques to
time-varying graphs, without requiring prior knowledge of their independence
numbers. Our upper bound is complemented by an improved
lower bound for all
, whose analysis relies on a novel reduction to multitask learning.
This shows that a logarithmic factor is necessary as soon as
QEVSEC: Quick Electric Vehicle SEcure Charging via Dynamic Wireless Power Transfer
Dynamic Wireless Power Transfer (DWPT) can be used for on-demand recharging
of Electric Vehicles (EV) while driving. However, DWPT raises numerous security
and privacy concerns. Recently, researchers demonstrated that DWPT systems are
vulnerable to adversarial attacks. In an EV charging scenario, an attacker can
prevent the authorized customer from charging, obtain a free charge by billing
a victim user and track a target vehicle. State-of-the-art authentication
schemes relying on centralized solutions are either vulnerable to various
attacks or have high computational complexity, making them unsuitable for a
dynamic scenario. In this paper, we propose Quick Electric Vehicle SEcure
Charging (QEVSEC), a novel, secure, and efficient authentication protocol for
the dynamic charging of EVs. Our idea for QEVSEC originates from multiple
vulnerabilities we found in the state-of-the-art protocol that allows tracking
of user activity and is susceptible to replay attacks. Based on these
observations, the proposed protocol solves these issues and achieves lower
computational complexity by using only primitive cryptographic operations in a
very short message exchange. QEVSEC provides scalability and a reduced cost in
each iteration, thus lowering the impact on the power needed from the grid.Comment: 6 pages, conferenc
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