arXiv subjects

Steven Wong

Publications and source records attributed to Steven Wong.

4 recordsLinked to original sources

arXiv2026

Reconfiguring Subgraphs with Extra Resources

The subgraph reconfiguration problem asks whether one subgraph can be transformed into another via a sequence of local changes while maintaining a specified graph property. In this work, we focus on the setting where the subgraph is specified by its set of edges. Our contributions in this paper are twofold. First, motivated by the contrast that path reconfiguration is $\textsf{NP}$-hard while tree reconfiguration is solvable in linear time, we prove two generalizations: (1) for any fixed $k$ at least one, reconfiguring connected graphs with pathwidth at most $k$ is $\textsf{NP}$-hard, and (2) for any fixed $k$ at least two, reconfiguring graphs with pathwidth at most $k$ is also $\textsf{NP}$-hard. En route to proving (2), we show a general hardness result that applies to a range of minor-closed graph classes, which we use to show planar graph reconfiguration is also $\textsf{NP}$-hard. Second, given our negative results, we extend the problem to a resource-focused setting, asking how much additional buffer space is needed to turn a non-reconfigurable instance into a reconfigurable one. We show that $\Omega(n)$ extra buffer space is needed for planar graphs and graphs with bounded pathwidth and treewidth, while $O(1)$ extra buffer space is sufficient for cactus graphs in a restricted setting.

cs.DM↗

arXiv2024

A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain

There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on crowd-sourced user data gathered from IoT devices. However, this raises security and privacy issues in practice. Some researchers propose to use federated learning to partially overcome privacy problems, but there still remain security concerns, e.g., single-point failure and malicious attacks. In this paper, we propose a framework named DFLoc to achieve precise 3D localization tasks while considering the following two security concerns. Particularly, we design a specialized blockchain to decentralize the framework by distributing the tasks such as model distribution and aggregation which are handled by a central server to all clients in most previous works, to address the issue of the single-point failure for a reliable and accurate indoor localization system. Moreover, we introduce an updated model verification mechanism within the blockchain to alleviate the concern of malicious node attacks. Experimental results substantiate the framework's capacity to deliver accurate 3D location predictions and its superior resistance to the impacts of single-point failure and malicious attacks when compared to conventional centralized federated learning systems.

cs.CR↗

arXiv2021

Traffic Forecasting using Vehicle-to-Vehicle Communication

We take the first step in using vehicle-to-vehicle (V2V) communication to provide real-time on-board traffic predictions. In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning. Specifically, we train recurrent neural networks to improve the predictions given by first principle models. Our approach is able to predict the velocity of individual vehicles up to a minute into the future with improved accuracy over first principle-based baselines. We conduct a comprehensive study to evaluate different methods of integrating first principle models with deep learning techniques. The source code for our models is available at https://github.com/Rose-STL-Lab/V2V-traffic-forecast .

cs.LG↗

arXiv2013

The Effect of Cancellation in Neutrinoless Double Beta Decay

In light of recent experimental results, we carefully analyze the effects of interference in neutrinoless double beta decay, when more than one mechanism is operative. We assume a complete cancellation is at work for $^{136}\rm{Xe}$, and find its implications on the half-life of other isotopes, such as $^{76}\rm{Ge}$. For definiteness, we consider the role of light and heavy sterile neutrinos. In this case, the effective Majorana mass parameter can be redefined to take into account all contributions and its value gets suppressed. Hence, larger values of neutrino masses are required for the same half-life. The canonical light neutrino contribution can not saturate the present limits of half-lives or the positive claim of observation of neutrinoless double beta decay, once the stringent bounds from cosmology are taken into account. For the case of cancellation, where all the sterile neutrinos are heavy, the tension between the results from neutrinoless double beta decay and cosmology becomes more severe. We show that the inclusion of light sterile neutrinos in this set up can resolve this issue. Using the recent results from GERDA, we derive upper limits on the active-sterile mixing angles and compare it with the case of no cancellation. The required values of the mixing angles become larger, if a cancellation is at work. A direct test of destructive interference in $^{136}\rm{Xe}$ is provided by the observation of this process in other isotopes and we study in detail the correlation between their half-lives. Finally, we discuss the model realizations which can accommodate light and heavy sterile neutrinos and the cancellation. We show that sterile neutrinos of few hundred MeV or GeV mass range, coming from an Extended seesaw framework or a further extension, can satisfy the required cancellation.

hep-ph↗