Intraoral scan (IoS) provides high-resolution data on the tooth crown, but does not contain information on the tooth root and thus has limitations in applications requiring 3D models of the whole tooth, e.g., virtual dental simulators. In this paper, we consider a diffusion-based model for root completion from IoS crowns. A key challenge is the lack of ground truth, i.e., the scan data of roots are typically unavailable. To train our model, we instead use the Cone-Beam CT (CBCT) data matched to IoS images, and use its crown as input and root as the pseudo-ground truth. Due to the difference in input data between training (CBCT crown) and inference (IoS crown), there is an issue of domain shift. To address the issue, we take a coarse-to-fine approach: we make a coarse prediction of roots using Coarse Estimator; introduce Perturbed Patch Generator (PPG) which generates patches from coarse points and perturbs them with noise for a robust prediction against the domain shift; and use Transformer denoiser for refined reconstruction. We also propose loss functions designed to facilitate the training of the denoiser with perturbed patches. Experiments show that our method outperforms prior techniques in various benchmark evaluations, demonstrating its robust performance in generating high-quality root data. Our code is available at https://github.com/yhJang94/RootCompletion.git.

Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, PX only provides a flattened 2D image, lacking in a 3D view of the oral structure. In this paper, we propose NeBLa (Neural Beer-Lambert) to estimate 3D oral structures from real-world PX. NeBLa tackles full 3D reconstruction for varying subjects (patients) where each reconstruction is based only on a single panoramic image. We create an intermediate representation called simulated PX (SimPX) from 3D Cone-beam computed tomography (CBCT) data based on the Beer-Lambert law of X-ray rendering and rotational principles of PX imaging. SimPX aims at not only truthfully simulating PX, but also facilitates the reverting process back to 3D data. We propose a novel neural model based on ray tracing which exploits both global and local input features to convert SimPX to 3D output. At inference, a real PX image is translated to a SimPX-style image with semantic regularization, and the translated image is processed by generation module to produce high-quality outputs. Experiments show that NeBLa outperforms prior state-of-the-art in reconstruction tasks both quantitatively and qualitatively. Unlike prior methods, NeBLa does not require any prior information such as the shape of dental arches, nor the matched PX-CBCT dataset for training, which is difficult to obtain in clinical practice. Our code is available at https://github.com/sihwa-park/nebla.

We consider the problem of reducing the learning latency of layerwise federated learning through joint device selection and bandwidth allocation. Specifically, we examine practical scenarios with heterogeneous devices with varying system parameters (e.g., CPU frequency, transmit power, etc.) and energy budgets. We formulate a long-term optimization problem, which is difficult to solve even with perfect channel state information. To address the issue, we employ Lyapunov theory to transform the problem into a series of online optimization problems, each of which can be efficiently solved using an alternating optimization-based method. Simulation results show that our scheduling scheme surpasses baseline schemes not only in terms of reducing the learning latency but also in reducing the energy deficit.

Previous methods to solve the problem of metal artifact reduction (MAR) have mostly focused on 2D MAR, making it challenging to apply to problems with 3-dimensional CT such as CBCT. In this paper, we propose a novel approach for 3D MAR which utilizes two diffusion models to model the metal-free CBCT prior and metal artifact prior. Through dual-domain guidance in the image and projection domains, the 3D connectivity is enhanced in the restored images. Moreover, we propose a memory-efficient technique for an efficient sampling of 3-dimensional data, which reduces the memory usage by orders of magnitude. Experiments show that our method achieves the state-of-the-art performance not only with synthetic data but also with real-world clinical and out-of-distribution data.

We investigate the minimum-delay multicast scheduling problem for millimeter wave (mmWave) networks. Salient characteristics of mmWave links, directionality and reflections, are considered under sectored antenna model. We first consider the model where the signal is received at a single Direction-of-Arrival (DoA) with the highest SNR at each node. We identify the property such that the optimal policy can be recursively partitioned into smaller sizes and propose an iterative method based on graphs which finds the optimal schedule in polynomial time. Next, we extend our model where a node leverages signals received at multiple DoAs through reflections. We introduce the concept of receiving direction diversity (RDD) which states that the availability of multiple receiving directions enables opportunistic reduction of multicast delay. We prove NP-hardness of the problem, and propose approximations with performance bounds and heuristics of reduced complexity. Next, we consider multicast scheduling with rateless codes (RCs) which reduces delay by flexible packet reception. For both cases of coded multicast with and without RDD, we formulate linear programming problems and propose greedy algorithms with nearly optimal performance and reduced complexity. By simulation we show the outperformance of our method over conventional ones, and numerically characterize the gain of RDD and RCs.

We jointly consider full-duplex operation and network coding in two-hop relay networks to enhance the throughput of the block transmission of packets over erasure channels. Two coded transmission schemes, termed Fewest Broadcast Packet First (FBPF) and Buffer Contents-based Coded Transmission (BCCT), are proposed, where random linear network coding is employed at the Base Station (BS) and the Relay Station (RS), respectively. Both schemes do not rely on users’ Channel State Information (CSI), buffer status, channel parameters, etc., and hence are practically viable. We derive closed-form upper bounds on the throughput of both schemes. We prove that both schemes achieve the optimal throughput when the BS-to-RS channel is perfect. Through extensive simulations, we demonstrate that both schemes incur substantially higher throughput than the traditional uncoded Automatic Repeat-reQuest (ARQ) scheme and perform close to a general upper bound on the system throughput. Furthermore, even with imperfect Self-Interference Cancellation (SIC) at the full-duplex RS, our schemes are shown to be superior to state-of-the-art coded transmission schemes designed for half-duplex relay networks, given that the impact of imperfect SIC on the BS-to-RS channel quality is not high.

Real-world knowledge graphs (KG) are mostly incomplete. The problem of recovering missing relations, called KG completion, has recently become an active research area. Knowledge graph (KG) embedding, a low-dimensional representation of entities and relations, is the crucial technique for KG completion. Convolutional neural networks in models such as ConvE, SACN, InteractE, and RGCN achieve recent successes. This paper takes a different architectural view and proposes ComDensE which combines relation-aware and common features using dense neural networks. In the relation-aware feature extraction, we attempt to create relational inductive bias by applying an encoding function specific to each relation. In the common feature extraction, we apply the common encoding function to all input embeddings. These encoding functions are implemented using dense layers in ComDensE. ComDensE achieves the state-of-the-art performance in the link prediction in terms of MRR, HIT@1 on FB15k-237 and HIT@1 on WN18RR compared to the previous baseline approaches. We conduct an extensive ablation study to examine the effects of the relation-aware layer and the common layer of the ComDensE. Experimental results illustrate that the combined dense architecture as implemented in ComDensE achieves the best performance.

We consider the minimum-delay multicast scheduling problem for switched beamforming systems. A salient characteristic of mmWave links, reflection, is considered, which enables opportunistic reduction of data dissemination delay. We formulate the problem as a mixed integer nonlinear programming, which is difficult to solve directly. Instead, we decompose the problem into a set of subproblems, by allocating a fixed path to each receiver for data reception. The optimal solution to each subproblem has a contiguous structure, and hence can be computed using a dynamic programming-based approach. We propose an optimal algorithm for the original problem based on the solutions to the subproblems. By simulation we show the outperformance of our algorithm over an optimal multicast scheduling policy without leveraging reflections and a broadcast baseline scheme.

We consider the channel-aware scheduling (CAS) problem for block transmission of packets in two-hop full-duplex relay networks with multiple users. At each time slot, the full-duplex relay station (RS) can fetch a network-coded packet from the macro base station (BS), and schedule a previously received packet for broadcasting to the users over time-varying channels. Our goal is to maximize the broadcast throughput. Since the associated Markov decision programming problem turns out to be intractable as the size of the problem increases, we propose a CAS scheme which is simple to implement and also achieves near-optimal performance. We provide a closed-form expression of the throughput of our scheme when the BS-to-RS channel is perfect, and prove that our scheme is optimal for one-user systems. Finally, numerical results demonstrate that our scheme performs close to an upper bound of the system and outperforms other transmission schemes.

We investigate the minimum-delay multicast problem for millimeter wave (mmWave) networks. Salient characteristics of mmWave links, directionality and reflections, are considered under sectored antenna model. We first consider directionality only, and identify the property such that the optimal policy can be recursively partitioned into smaller sizes. Using such optimal substructure, we propose an iterative method based on graphs which finds the optimal schedule in polynomial time. Next, we extend our model to incorporate reflections. We introduce the concept of path diversity which states that the availability of reflected paths enables opportunistic reduction of multicast delay. We prove NP-hardness of the problem, and propose approximations with performance bounds and heuristics of reduced complexity. By simulation we show the outperformance of our method over conventional ones, and numerically characterize the gain of path diversity in terms of network size.

We consider the full-duplex operation with network coding in two-hop relay networks to enhance the throughput for block transmission of packets. We propose a low-complexity transmission scheme, which does not rely on channel state information (CSI), and hence can be easily implemented in practical systems. We derive a closed-form upper bound on the asymptotic throughput of the proposed scheme, and show that the derived upper bound is tighter than a general upper bound on the throughput of any transmission scheme even with perfect CSI. Simulation results show that, the proposed scheme actually performs close to the general upper bound, and in most cases it substantially outperforms the traditional uncoded Automatic Repeat-reQuest scheme which relies heavily on the ACK/NAK feedback for packet retransmission.

Edge computing is a promising approach for provisioning distributed cloud services to Internet of Things (IoT) systems. Many recent studies propose that edge nodes use blockchain for the decentralized management and access control of IoT data. However, due to the massive volume of data and related transactions, edge servers will eventually run out of space to store the full chain. We introduce scalable and lightweight architecture called LiTiChain, a blockchain of blocks with finite lifetime. In LiTiChain, outdated transactions and blocks, that is, the blocks whose lifetimes are expired, can be safely removed from the chain. Two graphs are merged into the structure of LiTiChain: 1) a tree representing the order of expiry of lifetimes and 2) a linear graph representing the order of block creation. We show that this construction not only ensures the connectivity of the chain after block deletions but also helps to maintain the block height of shortened chain. LiTiChain also supports transactions whose lifetime is unknown at the time of creation. It is possible that some expired blocks need to be retained in the chain, in case they are needed to validate remaining blocks, which incurs additional storage costs. A detailed analysis of such overhead in storage costs is presented for stochastic and worst case scenarios. Extensive simulation is performed on actual and synthetic IoT data so as to gain insights on the storage costs under various lifetime distributions. It is demonstrated that LiTiChain provides a simple yet effective solution to scalability problems in storing blockchains for the IoT ecosystems.

Next-generation wireless architectures are expected to enable slices of shared wireless infrastructure, which are customized to specific mobile operators/services. Given infrastructure costs and the stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing among such slices. We study a simple dynamic resource sharing policy, which allocates a “share” of a pool of (distributed) resources to each slice-share constrained proportionally fair (SCPF). We give a characterization of SCPF's performance gains over static slicing and general processor sharing. We show that higher gains are obtained when a slice's spatial load is more “imbalanced” than, and/or “orthogonal” to, the aggregate network load, and that the overall gain across slices is positive. We then address the associated dimensioning problem. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, which characterizes the existence of a feasible share allocation, given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control, which maximizes their carried loads subject to performance requirements. We show that this can be modeled as a “traffic shaping” game with an achievable Nash equilibrium. Under high loads, the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF versus static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and the range of validity of our high-load equilibrium analysis.

Next generation wireless architectures are expected to enable slices of shared wireless infrastructure which are customized to specific mobile operators/services. Given infrastructure costs and the stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing amongst network slices. We study a simple dynamic resource sharing policy which allocates a `share' of a pool of (distributed) resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a characterization of the achievable performance gains over static slicing, showing higher gains when a slice's spatial load is more `imbalanced' than, and/or `orthogonal' to, the aggregate network load. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, addressing the existence of a feasible share allocation given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control which maximizes their carried loads subject to performance requirements. We show this can be modeled as a "traffic shaping" game with an achievable Nash equilibrium. Under high loads the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF vs. static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and range of validity of our high load equilibrium analysis.

We consider the multicast scheduling problem in the heterogeneous network using a half-duplex relay station (RS). Our goal is to minimize the delay of transmitting a block of packets to users over time-varying channels using rateless codes. Due to half-duplex operation, at each time slot, the RS can choose to either multicast a packet to the users, or fetch a packet from the macro base station. We formulate a fluid relaxation for the optimal decision problem, and reveal that the optimal policy has a threshold-based structure so as to exploit the opportunism of multicast channel: the RS should multicast only when the channel quality is sufficiently “high”. We propose an online policy based on the relaxation which does not require the knowledge of channel distribution. When the channel distribution is symmetric across users, we provide a closed-form expression of the asymptotic performance of our policy. For two-user systems, we prove that our scheme is asymptotically optimal. When the users' channels are independent, we derive a performance bound based on water-filling rate allocation which approximates the optimal policy well. Simulation results show that our scheme performs close to theoretical bounds, under correlated as well as independent fading channels.

We consider the energy efficiency of collecting sparse data in wireless sensor networks using compressive sensing (CS). We use a sparse random matrix as the sensing matrix, which we call Sparse Random Sampling (SRS). In SRS, only a randomly selected subset of nodes, called the source nodes, are required to report data to the sink. Given the source nodes, we intend to construct a data gathering tree such that (1) it is rooted at the sink and spans every source node and (2) the minimum residual energy of the tree nodes after the data collection is maximized. We first show that this problem is NP-complete and then develop a polynomial time algorithm to approximately solve the problem. We greedily construct a sequence of data gathering trees over multiple rounds and propose a polynomial-time algorithm to collect linearly combined measurements at each round. We show that the proposed algorithm is provably near-optimal. Simulation and experimental results show that the proposed algorithm excels not only in increasing the minimum residual energy, but also in extending the network lifetime.

We consider the multicast scheduling problem for the block transmission of packets in a heterogeneous network using a half-duplex relay station (RS). The RS uses random linear coding to efficiently transmit packets over time-varying multicast channels. Our goal is to minimize the average decoding delay. Because of the half-duplex operation, at each time slot, the RS must decide to either: (1) fetch a new packet for encoding from the base station or (2) multicast a coded packet to wireless users. Thus, optimal scheduling hinges on exploiting multicast opportunities while persistently supplying the encoder (at the RS) with new packets. We formulate an associated fluid control problem and show that the optimal policy incorporates opportunism across multicast channels, i.e., the RS performs a multicast transmission only if the collection of channel conditions is favorable; otherwise, it performs a fetch. Based on the fluid policy, we propose an online algorithm. We prove that our algorithm asymptotically incurs no more than 4/3 and 2 times the optimal delay, for two-user and arbitrary number of user system, respectively. Simulation results show that, in fact, our algorithm's performance is very close to theoretical bounds.

In this paper, we study a downlink band allocation game and algorithms in heterogeneous networks consisting of eNodeB (eNB) and femtocells (FC). If eNB and FC act as selfish players which compete to maximize their own utility, the resulting Nash equilibria (NE) may show poor performance due to interference. We propose an algorithm to avoid such equilibria. Specifically, we impose high prices on certain bands, which discourages FC from allocating such bands to its users. The case of 2-player 2-band is analyzed, under which our proposed scheme excels the case of full competition. Simulation results show that our algorithm prevents the players from ending up in inefficient equilibrium points, and also improves performance and fairness.

Network coding has been shown to improve various performance metrics in computer networks. However, the use of network coding, especially random linear network coding, incurs serious time delay in the decoding process and thus it is imperative to use a network coding implementation that has low decoding latency characteristics, e.g. a parallelized implementation. In this paper, we investigate the problem of parallelizing Pipeline network coding, a variant of random linear coding recently developed in order to alleviate the problems of random linear coding. We propose a novel massively parallelized decoding algorithm leveraging General Purpose Graphics Processing Unit (GPGPU) and show its performance enhancement by up to 100% compared with previous GPGPU-based parallel algorithms via experiments on real systems.

In this paper, we study a data aggregation problem in wireless sensor networks. We propose a Compressive Sensing (CS) based strategy which is able to reduce energy consumption and data collection latency. We adopt a random sensing matrix with entries drawn i.i.d. according to strictly sub-Gaussian distributions. Such a matrix have property such that a fraction of its entries are equal to zero with high probability. This enables us to collect data from only a fraction of the network without affecting data recovery, which helps reduce communication overheads. Linear networks and planar networks are considered. We compare the energy consumption and latency performance of our strategy with those of Compressive Data Gathering (CDG) scheme. Analytical and simulation results show that our scheme can reduce up to 44% and 67% of the energy consumption for linear and planar networks respectively, when the number of nodes is large. A significant improvement on the latency performance is observed as well.

In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted$\ell_{1}$-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozeki's inequality and shifting inequality in order to bound the errors in the associated weighted$\ell_{1}$-minimization. Our result offers generalized performance bounds on recovery capturing known support information. Improved sufficient conditions for recovery are derived based on our results, even for the cases where the accuracy of prior support information is arbitrarily low.

We consider the problem of reducing delays in block transmissions of packets over multicast erasure channels in heterogeneous networks using relay stations. The macro base station performs random linear network coding over a block of packets which are relayed to the relay station which broadcasts the packets to the users. We propose a fluid approximation to our problem, and obtain the optimal solution for the fluid model when the users' channels are homogeneous. For the general case we propose an approximate algorithm which is simple to implement. We observe that it is crucial to explore the trade-off between the opportunity in the users' channels and moving packets out of the system. Simulation results show that our scheme achieves a decoding delay which is close to a theoretical lower bound.

This paper considers the design of multiuser opportunistic packet schedulers for users sharing a time-varying wireless channel from performance and robustness points of view. For a simplified model falling in the classical Markov decision process framework, we numerically compute and characterize mean-delay-optimal scheduling policies. The computed policies exhibit radial sum-rate monotonicity: As users' queues grow linearly, the scheduler allocates service in a manner that deemphasizes the balancing of unequal queues in favor of maximizing current system throughput (being opportunistic). This is in sharp contrast to previously proposed throughput-optimal policies, e.g., Exp rule and MaxWeight (with any positive exponent of queue length). In order to meet performance and robustness objectives, we propose a new class of policies, called the Log rule, that are radial sum-rate monotone (RSM) and provably throughput-optimal. In fact, it can also be shown that an RSM policy minimizes the asymptotic probability of sum-queue overflow. We use extensive simulations to explore various possible design objectives for opportunistic schedulers. When users see heterogenous channels, we find that emphasizing queue balancing, e.g., Exp rule and MaxWeight, may excessively compromise the overall delay. Finally, we discuss approaches to implement the proposed policies for scheduling and resource allocation in OFDMA-based multichannel systems.

This paper considers the design of opportunistic packet schedulers for users sharing a time-varying wireless channel from the performance and the robustness points of view. Firstly, for a simplified model falling in the classical Markov decision process framework where arrival and channel statistics are known, we numerically compute and evaluate the characteristics of mean-delay-optimal scheduling policies. The computed policies exhibit radial sum-rate monotonicity (RSM), i.e., when users' queues grow linearly (i.e. scaled up by a constant), the scheduler allocates service in a manner that de-emphasizes the balancing of unequal queues in favor of maximizing current system throughput (being opportunistic). This is in sharp contrast to previously proposed policies, e.g., MaxWeight and Exp rule. The latter, however, are throughput-optimal, in that without knowledge of arrival/channel statistics they achieve stability if at all feasible. To meet performance and robustness objectives, secondly, we propose a new class of policies, called the Log rule, that are radial sum-rate monotone and provably throughput optimal. Our simulations for realistic wireless channels confirm the superiority of the Log rule which achieves up to 80% reduction in mean packet delays. However, recent asymptotic analysis showed that Exp rule is optimal in terms of minimizing the asymptotic probability of max-queue overflow. In turn, in a companion paper we have shown that an RSM policy minimizes the asymptotic probability of sum-queue overflow. Finally, we use extensive simulations to explore the various possible design objectives for opportunistic schedulers. When users see heterogenous channels, we find that minimizing the worst asymptotic exponent across users may excessively compromise the overall delay. Our simulations show that only if perfectly tuned to the load will the Exp rule achieve low homogenous tails across users. Otherwise the Log rule achieves a 20-75% reduction in the 99thpercentile for most, if not all, the users. We conclude that for wireless environments, where precise resource allocation is virtually impossible, the Log rule may be more desirable for its robust and graceful degradation to unpredicted changes.

In this paper, we propose a stochastic geometric model to study the energy burdens seen in a large scale hierarchical sensor network. The network makes use of aggregation nodes, for compression, filtering, and/or data fusion of locally sensed data. Aggregation nodes (AGNs) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network, it may have the deleterious effect of concentrating loads on paths between AGNs and the sinks—such inhomogeneities in the energy burden may in turn lead to nodes with depleted energy reserves. To remedy this problem, we consider how one might achieve a more balanced energy burden across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the lifetime for the system. We show that the scale of aggregation and degree of spreading can be optimized. Additionally, if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast, if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.

In this paper we investigate the use of proactive multipath routing to achieve energy efficient operation of ad hoc wireless networks. The focus is on optimizing trade-offs between the energy cost of spreading traffic and the improved spatial balance of energy burdens. We first propose a simple scheme for multipath routing based on node proximity. Then combining stochastic geometric and queuing models we develop a continuum model for such networks, permitting consideration of different types of designs, i.e., with and without energy replenishing and storage capabilities. We propose a parameterized family of energy balancing strategies for grids and approximate the spatial distributions of energy burdens based on their associated second order statistics. Our analysis and simulations show the fundamental importance of the tradeoff explored in this paper, and how its optimization depends on the relative values of the energy reserves/storage, replenishing rates, and network load characteristics. Simulation results show that proactive multipath routing decreases the probability of energy depletion by orders of magnitude versus that of shortest path routing scheme when the initial energy reserve is high.

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-typically related to proximity-and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio.