Exclusivity Constraints Research Articles

An efficient backtracking heuristic for the resource allocation problem with compatibility and exclusivity constraints

An efficient backtracking heuristic for the resource allocation problem with compatibility and exclusivity constraints

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data.

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Sometimes it is better to know less: How known words influence referent selection and retention in 18- to 24-month-old children

Young children are surprisingly good word learners. Despite their relative lack of world knowledge and limited vocabularies, they consistently map novel words to novel referents and, at later ages, show retention of these new word–referent pairs. Prior work has implicated the use of mutual exclusivity constraints and novelty biases, which require that children use knowledge of well-known words to disambiguate uncertain naming situations. The current study, however, presents evidence that weaker vocabulary knowledge during the initial exposure to a new word may be better for retention of new mappings. Children aged 18–24 months selected referents for novel words in the context of foil stimuli that varied in their lexical strength and novelty: well-known items (e.g., shoe), just-learned weakly known items (e.g., wif), and completely novel items. Referent selection performance was significantly reduced on trials with weakly known foil items. Surprisingly, however, children subsequently showed above-chance retention for novel words mapped in the context of weakly known competitors compared with those mapped with strongly known competitors or with completely novel competitors. We discuss implications for our understanding of word learning constraints and how children use known words and novelty during word learning.

Energy‐efficient resource allocation and RRH association in multitier 5G H‐CRANs

AbstractThe rapid growth of connected devices and multimedia applications intensify the needs of high data rate and energy‐efficient networks. Multitier heterogeneous cloud radio access networks have been presented as a capable candidate for fifth‐generation networks for much higher data rate and energy efficiency (EE) than fourth generation. Therefore, in this paper, we consider the EE maximization in the uplink of a single‐carrier frequency division multiple access–based multitier heterogeneous cloud radio access network. We formulate a joint subchannel assignment, power allocation, and remote radio head association problem for EE maximization. Single‐carrier frequency division multiple access introduces subchannel exclusivity and subchannel adjacency constraints. These constraints, respectively, ensure the allocation of a subchannel to a single user at a time and the allocation of multiple subchannels to a user only if they are adjacent. In addition, we put constraints on each user's minimum data rate and maximum transmit power. Due to the simultaneous consideration of these four constraints, the solution of the formulated problem becomes harder. Therefore, a three‐step algorithm is developed, which solves this problem in an iterative manner. Simulation results verify that our proposed algorithm significantly improve the EE of the network.

Trajectories as Topics: Multi-Object Tracking by Topic Discovery.

This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet process mixture model. The tracking problem is cast as a topic-discovery task, where the video sequence is treated analogously to a document. It addresses tracking issues such as object exclusivity constraints as well as tracking management without the need for heuristic thresholds. Variation of object appearance is modeled as the dynamics of word co-occurrence and handled by updating the cluster parameters across the sequence in the dynamical clustering procedure. We develop two kinds of visual representation based on super-pixel and deformable part model and integrate them into the model of automatic topic discovery for tracking rigid and non-rigid objects, respectively. In experiments on public data sets, we demonstrate the effectiveness of the proposed algorithm.

Joint RRH-Association, Sub-Channel Assignment and Power Allocation in Multi-Tier 5G C-Rans

Multi-tier cloud-radio access networks (C-RANs) have been suggested as a promising network model in fifth generation wireless networks to provide high data rate, high spectral efficiency, and high energy efficiency at low cost. However, to achieve the potential benefits of multi-tier C-RANs, it is important to design efficient resource allocation algorithms provided that the data rate of users is guaranteed. Therefore, in this paper, we considered the joint optimization of remote-radio-heads (RRH) association, sub-channel assignment, and power allocation for network sum-rate maximization in single-carrier frequency division multiple access (SC-FDMA)-based multi-tier C-RAN. Due to the exclusivity and adjacency constraints of SC-FDMA, the resource allocation problem becomes harder to solve, thus, the optimal solution is difficult for reasonably sized network. Therefore, we propose an iterative algorithm that solves this non-linear mixed-integer problem in two steps wherein the first step, power allocation and subchannel assignment are carried out, while the second step of the proposed algorithm is concerned with the RRH-association. The iterative algorithm converges to efficient solution. The simulation results verify the effectiveness of our proposed algorithm.

Automatic Topic Discovery for Multi-Object Tracking

This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet Process Mixture Model (DPMM). The tracking problem is cast as a topic-discovery task where the video sequence is treated analogously to a document. This formulation addresses tracking issues such as object exclusivity constraints as well as cannot-link constraints which are integrated without the need for heuristic thresholds. The video is temporally segmented into epochs to model the dynamics of word (superpixel) co-occurrences and to model the temporal damping effect. In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm.

Centroid estimation in discrete high-dimensional spaces with applications in biology

Maximum likelihood estimators and other direct optimization-based estimators dominated statistical estimation and prediction for decades. Yet, the principled foundations supporting their dominance do not apply to the discrete high-dimensional inference problems of the 21st century. As it is well known, statistical decision theory shows that maximum likelihood and related estimators use data only to identify the single most probable solution. Accordingly, unless this one solution so dominates the immense ensemble of all solutions that its probability is near one, there is no principled reason to expect such an estimator to be representative of the posterior-weighted ensemble of solutions, and thus represent inferences drawn from the data. We employ statistical decision theory to find more representative estimators, centroid estimators, in a general high-dimensional discrete setting by using a family of loss functions with penalties that increase with the number of differences in components. We show that centroid estimates are obtained by maximizing the marginal probabilities of the solution components for unconstrained ensembles and for an important class of problems, including sequence alignment and the prediction of RNA secondary structure, whose ensembles contain exclusivity constraints. Three genomics examples are described that show that these estimators substantially improve predictions of ground-truth reference sets.

Timing analysis for full-custom circuits using symbolic DC formulations

Successful analysis of high-speed integrated circuits requires accurate delay computation. A number of delay models have been developed; however, none can claim to be truly robust in the face of large channel-connected regions (CCRs) with input "exclusivity" constraints. A good circuit-level delay model should: 1) consider input exclusivity constraints; 2) handle a wide range of circuit structures; and 3) have a robust underlying framework that can be applied independent of the actual device model. We present a symbolic timing analysis tool that aims to address these three goals. It uses algebraic decision diagrams (ADDs) to estimate delay within a CCR as a function of its inputs while easily handling Boolean input constraints. It starts with a simple linear resistor model for transistors and from there apply various heuristics to improve the delay estimation without altering the symbolic algorithms. It analyzes delay with simple series-parallel reduction when possible and use symbolic matrix techniques to handle more complex circuit structures. The effectiveness of our approach is demonstrated on circuits from industry used in the Alpha 21264 and 21364 instead of the usual International Symposium on Circuits and Systems (ISCAS) or Microelectronics Center of North Carolina (MCNC) benchmarks. Our delay estimates are within 10% of simulation program with integrated circuits emphasis (SPICE) for over 90% of the circuits we simulated. This difference can translate into significant savings in manpower by avoiding the need to verify many unrealizable worst case conditions with other, more costly, simulation techniques

On a stochastic sequencing and scheduling problem

We present a framework for solving multistage pure 0–1 programs for a widely used sequencing and scheduling problem with uncertainty in the objective function coefficients, the constraint matrix and the right-hand side. The problem has the following form: given a set of operations to be executed along a time horizon, find a schedule to minimize a function included by the expected operations cost over the scenarios under consideration, subject to a set of constraints. Typical elements are: limited availability of the resources, multiperiod operations, subsets of operations with exclusivity and implicative constraints, precedence relationships in the execution of the operations, etc. The stochasticity is in the resources’ consumption by the operations, their availability and the operations cost along the time horizon. A multistage scenario analysis with complete recourse is used. Given the high dimensions of the problem and its combinatorial nature, it is not realistic to obtain the optimal solution for the problem. Instead, we present the so-called Fix-and-Relax Coordination algorithmic framework to exploit the characteristics of the non-anticipativity constraints for each scenario group in the stochastic model. This exploitation basically consists of selectively exploring the nodes of the branching trees in which the branch-and-bound tree is decomposed while the non-anticipativity constraints are relaxed. The algorithm is specifically designed for coordinating and reinforcing the node pruning, and the branching node and variable selection at each branching tree, such that the non-anticipativity constraints are satisfied. Some computational experience is reported.

Symbolic failure analysis of complex CMOS circuits due to excessive leakage current and charge sharing

As process geometries shrink, leakage currents and charge sharing are becoming increasingly critical problems, especially in full-custom circuit designs. Excessive leakage or charge sharing may cause functional failure at some or all operating conditions. Traditional circuit-analysis techniques may be used to verify if leakage currents are within allowable limits so as not to cause functional failures; however, unless the analysis takes into account specific input constraints for the circuit, the results may be overly pessimistic. Similar limitations exist for charge sharing. In this paper, we approach this verification problem symbolically using algebraic decision diagrams (ADDs). Using ADDs allows us to efficiently analyze leakage and charge sharing within a channel-connected region (CCR) as a function of its inputs. Exclusivity constraints are easily included in the analysis, thus allowing for more accurate (and less pessimistic) results. Our approach is general and can be applied to any arbitrary circuit structure, including a mesh. The effectiveness of our approach is demonstrated on circuits from industry used in the Alpha 21264 and 21364 instead of the usual international symposium on circuits and systems or Microelectronics Center of North Carolina benchmarks. We show that such an analysis can lead to up to a 90% difference in worst-case voltage drop. This difference can translate into significant savings in manpower by avoiding the need to verify many unrealizable worst-case conditions with other, more costly, simulation techniques.

S3 sets. An extension of the Beale-Tomlin special ordered sets

In this work an extension of the Beale-Tomlin special ordered sets is introduced that has proved to be efficient for solving certain types of open shop scheduling problems. Besides their usual characteristics, exclusivity constraints in the jobs are allowed, more general than tree-like precedence structures are considered, and semi-active schedules that cannot be labeled as non-optimal solutions may occur. The problem is formulated as a large-scale 0–1 model. Computational experience on some real-life problems is reported.