OS-level virtualization (e.g., Linux containers) has become a cornerstone of modern cloud systems. While it offers the illusion of isolated kernels for processes, these processes share the same underlying kernel, raising critical concerns around security, fault isolation, and the inability to customize kernels. Existing solutions address the issues by employing virtual machines that isolate kernels. However, these approaches incur significant performance overhead.

Read-Copy-Update (RCU) is a critical synchronization mechanism for concurrent data structures, enabling efficient deferred memory reclamation. However, implementing and using RCU correctly is challenging due to its inherent concurrency complexities. While previous work verified RCU, they either relied on unrealistic assumptions of sequentially consistent (SC) memory model or lacked three key features of general-purpose RCU libraries: modular specification, switchable critical sections, and concurrent writer support. We present the first formal verification of a general-purpose RCU in realistic relaxed memory consistency (RMC), addressing the challenges posed by these features. To achieve modular specification that encompasses relaxed behaviors, we extend existing SC specifications to account for explicit synchronization. To support switchable critical sections, which require read-after-write (RAW) synchronization, we introduce a reasoning principle for RAW-synchronizing SC fences . Using this principle, we also present the first formal verification of Peterson's mutex in RMC. To support concurrent writers performing partially ordered writes, we avoid assuming a total order of links and instead formulate invariants based on per-node incoming link histories. Our proofs are mechanized in the iRC11 relaxed memory separation logic, built upon Iris, in Rocq.

We report the first formal verification of a lock-free list, skiplist, and a skiplist-based priority queue against a strong specification in relaxed memory consistency (RMC). RMC allows relaxed behaviors in which memory accesses may be reordered with other operations, posing two significant challenges for the verification of lock-free traversals. (1) Specification challenge : formulating a specification that is flexible enough to capture relaxed behaviors, yet simple enough to be easily understood and used. We address this challenge by proposing the per-key linearizable history specification that enforces a total order of operations for each key that respects causality, rather than a total order of all operations. (2) Verification challenge : devising verification techniques for reasoning about the reachability of edges for traversing threads, which can read stale edges due to relaxed behaviors. We address this challenge by introducing the shadowed-by relation that formalizes the notion of outdated edges. This relation enables us to establish a total order of edges and thus their associated operations for each key, required to satisfy the strong specification. All our proofs are mechanized on the iRC11 relaxed memory separation logic, built on the Iris framework in Rocq.

Linearizability is the de facto standard for correctness of concurrent objects–it essentially says that all the object’s operations behave as if they were atomic. There have been a number of recent advances in developing increasingly strong linearizability specifications for relaxed memory consistency (RMC), but scalable proof methods for these specifications do not exist due to the challenges arising from out-of-order executions (requiring event reordering) and selected synchronization (requiring tracking of view transfers). We propose a proof recipe for the linearizable history specifications by Dang et al . in the Iris-based iRC11 concurrent separation logic in Coq. Key to our proof recipe is the notion of object modification order (OMO) , which generalizes the modification order of the C11 memory model to an object-local setting. Using OMO we minimize the conditions that need to be proved for event reordering. To enable proof reuse for concurrent libraries that are built on top of others, OMO provides the novel notion of a commit-with relation that connects the linearization points of the lower and upper libraries. Using our recipe, we verify the linearizability of the Michael–Scott queue, the elimination stack, and Folly’s MPMC queue in RMC for the first time; and verify stronger specifications of a spinlock and atomic reference counting in RMC than prior work.

Formal verification is an effective method to address the challenge of designing correct and efficient concurrent data structures. But verification efforts often ignore memory reclamation , which involves nontrivial synchronization between concurrent accesses and reclamation. When incorrectly implemented, it may lead to critical safety errors such as use-after-free and the ABA problem. Semi-automatic safe memory reclamation schemes such as hazard pointers and RCU encapsulate the complexity of manual memory management in modular interfaces. However, this modularity has not been carried over to formal verification. We propose modular specifications of hazard pointers and RCU, and formally verify realistic implementations of them in concurrent separation logic. Specifically, we design abstract predicates for hazard pointers that capture the meaning of validating the protection of nodes, and those for RCU that support optimistic traversal to possibly retired nodes. We demonstrate that the specifications indeed facilitate modular verification in three criteria: compositional verification, general applicability, and easy integration. In doing so, we present the first formal verification of Harris’s list, the Harris-Michael list, the Chase-Lev deque, and RDCSS with reclamation. We report the Coq mechanization of all our results in the Iris separation logic framework.

Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide pathology images using a set of novel completed and statistical local binary pattern (CSLBP) descriptors. First, the technique divides the whole slide image (WSI) into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are extracted from salient image tiles to characterize different Gleason patterns. Finally, the CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient slides with different Gleason scores such as 6, 7, or ≥ 8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved superior performances over state-of-the-art texture descriptors and baseline methods including deep learning models for prostate cancer Gleason grading.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 block error rate within 1 ms transmission period. Since the current wireless standard designed to maximize the coding gain by transmitting capacity achieving long code-block is not relevant for this purpose, entirely new transmission strategy is required. In this paper, we propose a new approach to transmit short packet information, called sparse vector coding (SVC). Key idea behind the proposed method is to transmit the control channel information after the sparse vector transformation. By mapping the transmit information into the position of nonzero elements and then transmitting it after the random spreading, we obtain underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations on realistic channel setting and decoder performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced physical downlink control channel (PDCCH) scheme.

Fifth generation wireless standards require much lower latency than what current wireless systems can guarantee. The main challenge in fulfilling these requirements is the development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. Since the available training resources are limited by the packet size, reliable channel and interference covariance estimation with reduced training overhead are crucial to any system using short data packets. In this paper, we propose an efficient receiver that exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. In the proposed method, the receive filter (i.e., the sample covariance matrix) is estimated using the received samples from the data transmission without using an interference training period. A channel estimation algorithm to use the most reliable data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed receiver algorithms enhance the reception quality of the short packet transmission.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, the 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10-5packet error rate within 1-ms transmission period. Since the current wireless transmission scheme, which is designed to maximize the coding gain by transmitting the capacity achieving long codeblock, is not relevant for this purpose, and a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). The key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme.

Cell-less communication is one of a promising technology of 5G wireless communications for providing a user-centric approach that goes beyond the regional cell of the conventional cellular system. In order to provide user-centric services, location information of the mobile station is required. In this paper, we propose a new localization technique to support the cell-less communication. The proposed scheme consists of two parts. First, estimating the signal parameters, angle of arrival (AOA) and time of arrival (TOA) through the maximum likelihood estimation. After the signal parameter estimation, the localization of the mobile station is performed using the estimated AOA-TOA information. Simulation results demonstrate that the proposed method achieves substantial performance in estimating the location of the mobile station.

Motivation: To better predict and analyze gene associations with the collection of phenotypes organized in a phenotype ontology, it is crucial to effectively model the hierarchical structure among the phenotypes in the ontology and leverage the sparse known associations with additional training information. In this paper, we first introduce Dual Label Propagation (DLP) to impose consistent associations with the entire phenotype paths in predicting phenotype-gene associations in Human Phenotype Ontology (HPO). DLP is then used as the base model in a transfer learning framework (tlDLP) to incorporate functional annotations in Gene Ontology (GO). By simultaneously reconstructing GO term-gene associations and HPO phenotype-gene associations for all the genes in a protein-protein interaction network, tlDLP benefits from the enriched training associations indirectly through relation with GO terms. Results: In the experiments to predict the associations between human genes and phenotypes in HPO based on human protein-protein interaction network, both DLP and tlDLP improved the prediction of gene associations with phenotype paths in HPO in cross-validation and the prediction of the most recent associations added after the snapshot of the training data. Moreover, the transfer learning through GO term-gene associations significantly improved association predictions for the phenotypes with no more specific known associations by a large margin. Examples are also shown to demonstrate how phenotype paths in phenotype ontology and transfer learning with gene ontology can improve the predictions. Availability and Implementation: Source code is available at http://compbio.cs.umn.edu/onto phenome . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Multiuser multiple-input multiple-output (MU-MIMO) transmission techniques have been popularly used to improve the spectral efficiency and user experience. However, due to the coarse knowledge of channel state information at the transmitter, the quality of transmit precoding to control multiuser interference is degraded, and hence, co-scheduled user equipment may suffer from large residual multiuser interference. In this paper, we propose a new channel estimation technique employing reliable soft symbols to improve the channel estimation and subsequent detection quality of MU-MIMO systems. To this end, we pick reliable data tones from both desired and interfering users and then use them as pilots to re-estimate the channel. In order to jointly estimate the channel and data symbols, we employ the expectation maximization algorithm, where the channel estimation and data decoding are performed iteratively. From numerical experiments in realistic MU-MIMO scenarios, we show that the proposed method achieves substantial performance gain in channel estimation and detection quality over conventional channel estimation approaches.

5G wireless standards require a much lower latency than what current wireless systems can guarantee. The main challenge to fulfill this requirement is the capability to support short packet transmission, in contrast to most of the current standards which use a long data packet structure. In this paper, we propose an efficient receiver technique that exploits information obtained during the data transmission period to improve the reception quality of the short packet transmission. Two key ingredients of the proposed method are 1) estimation of the receiver filter using the received samples in the data transmission period, not in the interference training period, and 2) soft decision- directed channel estimation that uses the data symbols for re-estimation of the channels. Numerical results confirm the effectiveness of the proposed receiver algorithms.

Multiuser multiple-input multiple-output (MU-MIMO) transmission technique based on orthogonal frequency division multiplexing (OFDM) system has been received great deal of attention in recent years due to its potential higher spectral efficiency. However, because of the accuracy of channel state information, co-scheduled mobile users may suffer large residual multiuser interference in MU-MIMO system. In this paper, we propose a new channel estimation technique using expectation and maximization (EM) algorithm with reliable soft symbols for the MU-MIMO systems in LTE- Advanced. In the proposed scheme, we choose reliable data tones from both desired and interfering signals and improves the channel estimation quality using iterative process between channel estimation and data detection in frequency domain. We show that the proposed method achieves substantial performance gain over conventional channel estimation approaches.

MOTIVATION: Identification of altered pathways that are clinically relevant across human cancers is a key challenge in cancer genomics. Precise identification and understanding of these altered pathways may provide novel insights into patient stratification, therapeutic strategies and the development of new drugs. However, a challenge remains in accurately identifying pathways altered by somatic mutations across human cancers, due to the diverse mutation spectrum. We developed an innovative approach to integrate somatic mutation data with gene networks and pathways, in order to identify pathways altered by somatic mutations across cancers. RESULTS: We applied our approach to The Cancer Genome Atlas (TCGA) dataset of somatic mutations in 4790 cancer patients with 19 different types of tumors. Our analysis identified cancer-type-specific altered pathways enriched with known cancer-relevant genes and targets of currently available drugs. To investigate the clinical significance of these altered pathways, we performed consensus clustering for patient stratification using member genes in the altered pathways coupled with gene expression datasets from 4870 patients from TCGA, and multiple independent cohorts confirmed that the altered pathways could be used to stratify patients into subgroups with significantly different clinical outcomes. Of particular significance, certain patient subpopulations with poor prognosis were identified because they had specific altered pathways for which there are available targeted therapies. These findings could be used to tailor and intensify therapy in these patients, for whom current therapy is suboptimal. AVAILABILITY AND IMPLEMENTATION: The code is available at: http://www.taehyunlab.org CONTACT: [email protected] or [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiuser multiple-input multiple-output (MU-MIMO) transmission technique based on orthogonal frequency division multiplexing (OFDM) system has been received great deal of attention in recent years due to its potential higher spectral efficiency. However, because of the coarse knowledge of channel state information at the transmitter (CSIT), co-scheduled mobile users may suffer large residual multiuser interference in MU-MIMO system. In this paper, we propose a new channel estimation technique using virtual pilot signals for the MU-MIMO OFDM systems. In a nutshell, we choose reliable data tones from both desired and interfering signals as virtual pilot signal and improves the channel estimation quality using iterative detection and decoding (IDD) scheme. We show that the proposed method achieves substantial performance gain over conventional approaches employing single user detection or multiuser detection.

Multiclass problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various methods, including all-pairs (APs), one-versus-all (OVA), and error correcting output code (ECOC), have been studied, to decompose multiclass problems into binary problems. However, little study has been made to optimally aggregate binary problems to determine a final answer to the multiclass problem. In this paper we present a convex optimization method for an optimal aggregation of binary classifiers to estimate class membership probabilities in multiclass problems. We model the class membership probability as a softmax function which takes a conic combination of discrepancies induced by individual binary classifiers, as an input. With this model, we formulate the regularized maximum likelihood estimation as a convex optimization problem, which is solved by the primal-dual interior point method. Connections of our method to large margin classifiers are presented, showing that the large margin formulation can be considered as a limiting case of our convex formulation. In the experiments on human disease classification, we demonstrate that our method outperforms existing aggregation methods as well as direct methods, in terms of the classification accuracy and F-score.

Recent results show that non-parametric linear receiver equipped with multiple receive antennas is an effective solution for ad hoc network under the imperfect channel state information at receiver (CSIR). In this paper, we propose an interference aware node activation strategy that efficiently controls the data transmission and back off based on the measured interference power. Our numerical results show that if optimization over the channel estimation threshold is provided, then the effective data rate of the proposed scheme is higher than that of the conventional channel estimation even under the system delay occurred.

Multi-label learning refers to methods for learning a set of functions that assigns a set of relevant labels to each instance. One of popular approaches to multi-label learning is label ranking, where a set of ranking functions are learned to order all the labels such that relevant labels are ranked higher than irrelevant ones. Rank-SVM is a representative method for label ranking where ranking loss is minimized in the framework of max margin. However, the dual form in Rank-SVM involves a quadratic programming which is generally solved in cubic time in the size of training data. The primal form is appealing for the development of online learning but involves a nonsmooth convex loss function. In this paper we present a method for online multi-label learning where we minimize the primal form using the accelerated nonsmooth stochastic gradient descent which has been recently developed to extend Nesterov's smoothing method to the stochastic setting. Numerical experiments on several large-scale datasets demonstrate the computational efficiency and fast convergence of our proposed method, compared to existing methods including subgradient-based algorithms.

Recent works on ad hoc network study have shown that achievable throughput can be made to scale linearly with the number of receive antennas even if the transmitter has only a single antenna. In this paper, we propose a non-parametric linear minimum mean square error (MMSE) receiver for achieving further gain in performance when the channel state information at receiver (CSIR) of interferers is imperfect. The key feature to make our approach effective is to exploit the autocorrelation of the received signal. In fact, by incorporating the desired channel information on top of the observations including interference and noise only, the proposed method achieves large fraction of the optimal MMSE transmission capacity without transmission rate loss. Simulation results on the realistic ad hoc network system show that the proposed non-parametric linear MMSE receiver brings substantial performance gain over existing multiple receive antenna algorithms.

Multiclass classification problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. We present a convex optimization-based method for aggregating results of binary classifiers in an optimal way to estimate class membership probabilities. We model the class membership probability as a softmax function whose input argument is a conic combination of discrepancies induced by individual binary classifiers. With this model, we formulate the £ι -regularized maximum likelihood estimation as a convex optimization that is solved by geometric programming. Numerical experiments on several UCI datasets demonstrate the high performance of our method, compared to existing methods.

Multiclass classification problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. Various methods have been developed to aggregate binary classifiers, including voting heuristics, loss-based decoding, and probabilistic decoding methods, but a little work on the optimal aggregation has been done. In this paper we present a Bayesian method for optimally aggregating binary classifiers where class membership probabilities are determined by predictive probabilities. We model the class membership probability as a softmax function whose input argument is a linear combination of discrepancies between code words and probability estimates obtained by the binary classifiers. We consider a lower bound on the softmax function, which is represented as a product of logistic sigmoids, and we formulate the problem of learning aggregation weights as a variational logistic regression. Predictive probabilities computed by variational logistic regression yield the class membership probabilities. We stress two notable advantages over existing methods in the viewpoint of complexity and over fitting. Numerical experiments on several datasets confirm its useful behavior.

In this paper we present a method for extracting a speech signal of target speaker from noisy convolutive mixtures of target speech and an interference source, when training utterances of the target speaker are available. We incorporate a statistical latent variable model into blind source separation (BSS), where we make use of spectral bases learned from the training utterances of the target speaker to identify which source corresponds to the target speaker. Combined with any existing BSS methods, our post-processing (which is the main contribution) consists of two steps: (1) channel selection where we identify the source corresponding to the target speaker; (2) enhancement where we further suppress the remaining interference. Numerical experiments confirm that our method substantially improves the separation quality of existing BSS methods and successfully restores the target speaker's speech.

In this paper we present a probabilistic method for source separation in the case where each source has a certain unknown temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can be easily computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. Numerical experiments confirm the useful behavior of our method, compared to existing source separation methods.

Gaussian process (GP) model is a flexible nonparametric Bayesian method that is widely used in regression and classification. In this paper we present a probabilistic method where we solve voice activity detection (VAD) and speech enhancement in a single framework of GP regression, modeling clean speech by a GP smoother. Optimized hyperparameters in GP models lead us to a novel VAD method since learned length-scale parameters in covariance functions are much different between voiced and unvoiced frames. Clean speech is estimated by posterior means in GP models. Numerical experiments confirm the validity of our method.

In this paper we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by sequential expectation maximization, incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.