Se-Young Yun

Femtocells are a promising technology for handling exponentially increasing wireless data traffic. Although extensive attention has been paid to resource control mechanisms, for example, power control and load balancing in femtocell networks, their success largely depends on whether operators and users accept this technology or not. In this paper, we study the economic aspects of femtocell services for the case of monopoly market, and aim to answer questions on operator's revenue, user surplus,

We consider the problem of community detection in the Stochastic Block Model with a finite number $K$ of communities of sizes linearly growing with the network size $n$. This model consists in a random graph such that each pair of vertices is connected independently with probability $p$ within communities and $q$ across communities. One observes a realization of this random graph, and the objective is to reconstruct the communities from this observation. We show that under spectral algorithms, t

The femtocell is an enabling technology to handle exponentially increasing wireless data traffic. Despite extensive attentions paid to resource control, e.g., power control and load balancing in femtocell networks, the success largely depends on whether operators and users accept this technology or not. In this paper, we study the economic aspects of femtocell services with game theoretic models between providers and/or users. We consider three services: users can access only macro BSs (mobile-o

Carrier Sense Multiple Access (CSMA) has been widely used as a medium access control (MAC) scheme in wireless networks mainly due to its simple and totally distributed operations. Recently, it has been reported in the community that even such simple CSMA-type algorithms can achieve optimality in terms of throughput and utility, by smartly controlling its operational parameters such as backoff and holding times. In this survey paper, we summarize the recent research efforts in this area with main

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases.Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes.To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data.In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to

In this paper, we consider networks consisting of a finite number of non-overlapping communities. To extract these communities, the interaction between pairs of nodes may be sampled from a large available data set, which allows a given node pair to be sampled several times. When a node pair is sampled, the observed outcome is a binary random variable, equal to 1 if nodes interact and to 0 otherwise. The outcome is more likely to be positive if nodes belong to the same communities. For a given bu

Recent studies on MAC scheduling have shown that carrier sense multiple access (CSMA) algorithms can be throughput optimal for arbitrary wireless network topology. However, these results are highly sensitive to the underlying assumption on `static' or `fixed' system conditions. For example, if channel conditions are time-varying, it is unclear how each node can adjust its CSMA parameters, so-called backoff and channel holding times, using its local channel information for the desired high perfor

We study contextual multi-armed bandit problems under linear realizability on rewards and uncertainty (or noise) on features. For the case of identical noise on features across actions, we propose an algorithm, coined NLinRel, having O(T⁷/₈(log(dT)+K√d)) regret bound for T rounds, K actions, and d-dimensional feature vectors. Next, for the case of non-identical noise, we observe that popular linear hypotheses including NLinRel are impossible to achieve such sub-linear regret. Instead, under assu

We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)$ between two items in clusters indexed by $i$ and $j$, respectively. The objective is to reconstruct the clusters from the observation of these random labels. Clustering under the

In this paper, we consider sparse networks consisting of a finite number of non-overlapping communities, i.e. disjoint clusters, so that there is higher density within clusters than across clusters. Both the intra- and inter-cluster edge densities vanish when the size of the graph grows large, making the cluster reconstruction problem nosier and hence difficult to solve. We are interested in scenarios where the network size is very large, so that the adjacency matrix of the graph is hard to mani

In wireless cellular networks, indoor coverage is a problem from the perspective of the service provider. A femto Access Point, also known as femto AP, is utilized to solve the problem. Generally, the femto AP is deployed in a home to support indoor users. Since there might be a small number of users in the home, it could be expected that the traffic load of the femto AP is lower than its wireless channel capacity. Though the femto AP could support indoor users, it also causes interference probl

We consider the problem of distributed load balancing in heterogenous parallel server systems, where the service rate achieved by a user at a server depends on both the user and the server. Such heterogeneity typically arises in wireless networks (e.g., servers may represent frequency bands, and the service rate of a user varies across bands). We assume that each server equally shares in time its capacity among users allocated to it. Users initially attach to an arbitrary server, but at random i

Carrier sense multiple access (CSMA), which resolves contentions over wireless networks in a fully distributed fashion, has recently gained a lot of attentions since it has been proved that appropriate control of CSMA parameters guarantees optimality in terms of stability (i.e., scheduling) and system-wide utility (i.e., scheduling and congestion control). Most CSMA-based algorithms rely on the popular Markov chain Monte Carlo technique, which enables one to find optimal CSMA parameters through

CSMA (Carrier Sense Multiple Access), which resolves contentions over wireless networks in a fully distributed fashion, has recently gained a lot of attentions since it has been proved that appropriate control of CSMA parameters guarantees optimality in terms of system-wide utility. Most algorithms rely on the popular MCMC (Markov Chain Monte Carlo) technique, which enables one to find optimal CSMA parameters through iterative loops of simulation-and-update. However, such a simulation-based appr

To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token. However, we observed several shortcomings, including performance degradation caused by a state copying mechanism or numerous exit paths, and sensitivity to exit confidence thresholds. Consequently, we propose a Fast and Robust Early-Exiting (F