Memory Overflow Research Articles

HVCMM: A Hybrid-View Abstractive Model of Automatic Summary Generation for Teaching.

With the continuous development of educational informatization, more and more emerging technologies are applied in teaching activities. These technologies provide massive and multidimensional information for teaching research, but at the same time, the information obtained by teachers and students presents an explosive increase. Extracting the core content of the class record text through text summarization technology to generate concise class minutes can significantly improve the efficiency of teachers and students to obtain information. This article proposes a hybrid-view class minutes automatic generation model (HVCMM). The HVCMM model uses a multilevel encoding strategy to encode the long text of the input class records to avoid memory overflow in the calculation after the long text is input into the single-level encoder. The HVCMM model uses the method of coreference resolution and adds role vectors to solve the problem that the excessive number of participants in the class may lead to confusion about the referential logic. Machine learning algorithms are used to analyze the topic and Section of the sentence to capture structural information. We test the HVCMM model on the Chinese class minutes dataset (CCM) and the augmented multiparty interaction (AMI) dataset, and the results show that the HVCMM model outperforms other baseline models on the ROUGE metric. With the help of the HVCMM model, teachers can improve the efficiency of reflection after class and improve the teaching level. Students can review the key content to strengthen their understanding of what they have learned with the help of the class minutes automatically generated by the model.

Enhancing Linux System Security: A Kernel-Based Approach to Fileless Malware Detection and Mitigation

In the late 20th century, computer viruses emerged as powerful malware that resides permanently in target hosts. For a virus to function, it must load into memory from persistent storage, such as a file on a hard drive. Due to the significant destructive potential of viruses, numerous defense measures have been developed to protect computer systems. Among these, antivirus software is one of the most recognized and widely used. Typically, antivirus solutions rely on static analysis (signature-based) technologies to detect infections in files stored on permanent storage devices, such as hard drives or USB (Universal Serial Bus) flash drives. However, a new breed of malware, fileless malware, has been designed to evade detection and enhance durability. Fileless malware resides solely in the memory of the target hosts, circumventing traditional antivirus software, which cannot access or analyze processes executed directly from memory. This study proposes the Check-on-Execution (CoE) kernel-based approach to detect fileless malware on Linux systems. CoE intervenes by suspending code execution before a program executes code from a process’s writable and executable memory area. To prevent the execution of fileless malware, CoE extracts the code from memory, packages it with an ELF (Executable and Linkable Format) header to create an ELF file, and uses VirusTotal for analysis. Experimental results demonstrate that CoE significantly enhances a Linux system’s ability to defend against fileless malware. Additionally, CoE effectively protects against shell code injection attacks, including buffer and memory overflows, and can handle packed malware. However, it is important to note that this study focuses exclusively on fileless malware, and further research is needed to address other types of malware.

LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five-fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods.

EFACT: An External Function Auto-Completion Tool to strengthen static binary lifting

Static binary lifting is essential in binary rewriting frameworks. Existing tools overlook the impact of External Function Completion (EXFC) in static binary lifting. EXFC recovers the declarations of External Functions (EXFs, functions defined in standard shared libraries) using only the function symbols available. Incorrect EXFC can misinterpret the source binary, or cause memory overflows in static binary translation, which eventually results in program crashes. Notably, existing tools struggle to recover the declarations of mangled EXFs originating from binaries compiled from C++. Moreover, they require time-consuming manual processing to support new libraries.This paper presents EFACT, an External Function Auto-Completion Tool for static binary lifting. Our EXF recovery algorithm better recovers the declarations of mangled EXFs, particularly addressing the template specialization mechanism in C++. EFACT is designed as a lightweight plugin to strengthen other static binary rewriting frameworks in EXFC. Our evaluation shows that EFACT outperforms RetDec and McSema in mangled EXF recovery by 96.4% and 97.3% on SPECrate 2017.Furthermore, we delve deeper into static binary translation and address several cross-ISA EXFC problems. When integrated with McSema, EFACT correctly translates 36.7% more benchmarks from x86-64 to x86-64 and 93.6% more from x86-64 to AArch64 than McSema alone on EEMBC.

Large-scale DSM registration via motion averaging

Abstract. Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typical registration algorithm, since when a large number of observations from these multiple DSMs are considered, it may easily cause memory overflow. Sequential registration algorithms, although can significantly reduce the computation, are especially vulnerable for small overlapped pairs, leading to a large error accumulation. In this work, we propose a novel solution that builds the DSM registration task as a motion averaging problem: pair-wise DSMs are registered to build a scene graph, with edges representing relative poses between DSMs. Specifically, based on the grid structure of the large DSM, the pair-wise registration is performed using a novel nearest neighbor search method. We show that the scene graph can be optimized via an extremely fast motion average algorithm with O(N) complexity (N refers to the number of images). Evaluation of high-resolution satellite-derived DSM demonstrates significant improvement in computation and accuracy.

3D seismic Fault Detection via Contrastive-Reconstruction Representation Learning

Fault detection is a critical step in structural modeling and characterizing reservoirs. 3D seismic fault labeling is almost impossible to obtain, while networks trained on synthetic fault data have limited generalization to real data. We use self-supervised representation learning to enable networks to learn seismic field data features during pre-training, improving generalization. However, widely popular ViT/SwinViT-based methods cannot capture low-level fault features well. CNN-based have limited capability in transferring to downstream tasks. Tackle this, we designed the Tiny Self-Attention and extensively embedded it into the HRNet. It merges the advantages of convolutional and self-attention, allowing for in-depth learning of seismic representation information during the representation learning phase, thus achieving better inter-class separation in downstream tasks. We crafted two proxy tasks. The contrastive task aims to minimize the projected feature distance of overlapping areas from two distinct views. To tackle the issue of memory overflow and training suspension caused by the high spatial and temporal complexity of 3D high-resolution feature matching, we introduced sparse distance matching and an adaptive feature aggregation. In reconstruction task, we designed a masking strategy tailored to the unique attributes of 3D seismic data. It process named “Fault Detection via Contrast-Reconstruction Representation Learning” (FaultCRL). Experimentally, we compared the latest fault detection methods, and representation learning techniques like MAE and SwinUNETR, showcasing the notable advantage of FaultCRL in fault detection tasks. The appendix provides all qualitative results from mainstream geophysical journals regarding The Netherlands-F3 data in recent years, indicating that our approach has reached the current state-of-the-art level.

FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation

Data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability. To address these issues, we introduce FusionFlow, a system that cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing stage of DL training that runs the data augmentation algorithm. FusionFlow orchestrates data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training. In doing so, it effectively mitigates the risk of GPU memory overflow by managing memory allocations of the tasks within the GPU-wide free space. Furthermore, FusionFlow provides a dynamic scheduling strategy for tasks with varying computational demands and reallocates compute resources on the fly to enhance training throughput for both single and multi-GPU DL jobs. Our evaluations show that FusionFlow outperforms existing CPU-based methods by 16--285% in single-machine scenarios and, to achieve similar training speeds, requires 50--60% fewer CPUs compared to utilizing scalable compute resources from external servers.

QZRAM: A Transparent Kernel Memory Compression System Design for Memory-Intensive Applications with QAT Accelerator Integration

In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes to exit without warning, resulting in negative effects on a system’s performance and stability. To mitigate OOM errors, many operating systems implement memory compression (e.g., Linux’s ZRAM) to provide flexible and larger memory space. However, these schemes incur two problems: (1) high-compression algorithms consume significant CPU resources, which inevitably degrades application performance; and (2) compromised compression algorithms with low latency and low compression ratios result in insignificant increases in memory space. In this paper, we propose QZRAM, which achieves a high-compression-ratio algorithm without high computing consumption through the integration of QAT (an ASIC accelerator) into ZRAM. To enhance hardware and software collaboration, a page-based parallel write module is introduced to serve as a more efficient request processing flow. More importantly, a QAT offloading module is introduced to asynchronously offload compression to the QAT accelerator, reducing CPU computing resource consumption and addressing two challenges: long CPU idle time and low usage of the QAT unit. The comprehensive evaluation validates that QZRAM can reduce CPU resources by up to 49.2% for the FIO micro-benchmark, increase memory space (1.66×) compared to ZRAM, and alleviate the memory overflow phenomenon of the Redis benchmark.

Approximation algorithms for vehicle-aided periodic data collection from mobile sensors with obstacle avoidance in WSNs

Data collection from the sensors in time is an integral part of many applications of wireless sensor networks (WSNs). Vehicles, referred to as Mobile Sinks (SNKs), may be used to collect data from the sensors by visiting them. Since the sensors have limited memory, the sensed data needs to be collected by the SNKs within a predefined time interval to avoid memory overflow. Periodic data collection by SNKs becomes even more challenging when the sensors are mobile. Moreover, the presence of obstacles in the area makes the path planning for SNKs even more complicated. In this paper, an optimization problem, referred to as Minimum Mobile Sink Aided Periodic Data Collection (MinSnkDC) problem, is formulated, where the objective is to determine the minimum number of SNKs that collect data for every time period from the mobile sensors in the WSN, while avoiding collision with the obstacles in the area. The problem is proved to be NP-complete. Two constant factor approximation algorithms, namely MinSnkDC and Modified MinSnkDC (M-MinSnkDC), are proposed to solve the problem. From the simulation results, it is evident that M-MinSnkDC can produce a better solution compared to the existing obstacle-aware SNK-based data collection algorithms, while using a small number of SNKs.

Determination of Characteristics of Associative Storage Devices in Radio Telemetry Systems with Data Compression

In the radio telemetry systems of spacecraft, various data compression methods are used for data processing. When using any compression methods, the data obtained as a result of compression is formed randomly, and transmission over radio communication channels should be carried out evenly over time. This leads to the need to use special buffer storage devices. In addition, existing spacecraft radio telemetry systems require grouping of compressed data streams by certain characteristics. This leads to the need to sort compressed data by streams. Therefore, it is advisable to use associative buffer storage devices in such systems. This article is devoted to the analysis of the processes of formation of output streams of compressed data generated at the output of an associative storage device (ASD). Since the output stream of compressed data is random, queue theory and probability theory are used for analysis. At the same time, associative memory is represented as a queue system. Writing and reading in an ASD can be interpreted as servicing orders in a queue system. The purpose of the analysis is to determine the characteristics of an associative storage device (ASD). Such characteristics are the queue length M{N} in the ASD, the deviation of the queue length D{N} in the ASD and the probability pn of a given volume n of compressed data in the ASD (including the probability of emptying and the probability of memory overflow). The results obtained are of great practical importance, since they can be used to select the amount of memory of an associative storage device (ASD) when designing compression devices for telemetry systems of spacecraft.

Fast Real-Time Video Object Segmentation with a Tangled Memory Network

In this article, we present a fast real-time tangled memory network that segments the objects effectively and efficiently for semi-supervised video object segmentation (VOS). We propose a tangled reference encoder and a memory bank organization mechanism based on a state estimator to fully utilize the mask features and alleviate memory overhead and computational burden brought by the unlimited memory bank used in many memory-based methods. First, the tangled memory network exploits the mask features that uncover abundant object information like edges and contours but are not fully explored in existing methods. Specifically, a tangled two-stream reference encoder is designed to extract and fuse the features from both RGB frames and the predicted masks. Second, to indicate the quality of the predicted mask and feedback the online prediction state for organizing the memory bank, we devise a target state estimator to learn the IoU score between the predicted mask and ground truth. Moreover, to accelerate the forward process and avoid memory overflow, we use a memory bank of fixed size to store historical features by designing a new efficient memory bank organization mechanism based on the mask state score provided by the state estimator. We conduct comprehensive experiments on the public benchmarks DAVIS and YouTube-VOS, demonstrating that our method obtains competitive results while running at high speed (66 FPS on the DAVIS16-val set).

Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

A solution technique for capacitated two-level hierarchical time minimization transportation problem

As a generalization of two-level hierarchical time minimization transportation problem (2HTMTP), capacitated two-level hierarchical time minimization transportation problem (C2HTMTP) is a vital issue due to the route shipping capacity finiteness in reality, and no research report on C2HTMTP is available owing to the intractability. Only 4 approaches with defects are available in the literature to resolve 2HTMTP, and they cannot be applied directly to C2HTMTP although their generalized versions can. In this paper, by creating mathematical model along with auxiliary models and constructing network with lower and upper arc capacities, C2HTMTP is transformed into a collection of searching feasible flow in the constructed network, and consequently 4 iteration algorithms, with two respectively derived from other two by applying binary search, are developed as C2HTMTP’s solution method basing feasible flow algorithm. It is proved that the 4 iteration algorithms find C2HTMTP’s optimum solution in a polynomial computation time. Because of sufficiently exploiting C2HTMTP’s intrinsic network flow structure, the 4 iteration algorithms hold the merits such as no memory overflow, easier implementation on computer, higher computation efficiency, and easier extension to the capacitated multilevel hierarchical time minimization transportation problem with level exceeding two, and successfully overcome the deficiencies of extant solving approaches to 2HTMTP. Computational experiments validate that as compared to extant best solving method for 2HTMTP, the 4 iteration algorithms are robust and efficient C2HTMTP’s solution method able to serve as a powerful tool to solve other related complex optimization problems, where in terms of computation time, when applied to solve 2HTMTP, three algorithms vastly outperform (even the worst performing one rivals) extant best solving approach to 2HTMTP, especially when applied to solve C2HTMTP, two fast descent algorithms are rivals ranking first and second (one performs better for small and medium scale instances, another performs better for large scale instances), and significantly outperform the ranking third algorithm with rival as extant best 2HTMTP’s solving algorithm’s generalized version and great superiority to the ranking last algorithm.

Instance Segmentation Based on Improved Self-Adaptive Normalization.

The single batch normalization (BN) method is commonly used in the instance segmentation algorithms. The batch size is concerned with some drawbacks. A too small sample batch size leads to a sharp drop in accuracy, but a too large batch may result in the memory overflow of graphic processing units (GPU). These problems make BN not feasible to some instance segmentation tasks with inappropriate batch sizes. The self-adaptive normalization (SN) method, with an adaptive weight loss layer, shows good performance in instance segmentation algorithms, such as the YOLACT. However, the parameter averaging mechanism in the SN method is prone to problems in the weight learning and assignment process. In response to such a problem, the paper proposes to replace the single BN with an adaptive weight loss layer in SN models, based on which a weight learning method is developed. The proposed method increases the input feature expression ability of the subsequent layers. By building a Pytorch deep learning framework, the proposed method is validated in the MS-COCO data set and Autonomous Driving Cityscapes data set. The experimental results prove that the proposed method is effective in processing samples independent from the batch size. The stable accuracy for all kinds of target segmentation is achieved, and the overall loss value is significantly reduced at the same time. The convergence speed of the network is also improved.

An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF

This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite trajectory, sunlight, among others. The amount of computation effort derived from the use of large high-resolution images is addressed by data-intensive computing techniques that assume an underlying cluster architecture. As a start, satellite data from the region of study are automatically downloaded; then, data from different sensors are corrected and merged to obtain an orthomosaic; finally, the orthomosaic is split into user-defined segments to fill in missing data, and filled segments are assembled to produce an orthomosaic with a reduced amount of missing data. As a proof of concept, the proposed data-intensive approach was implemented to study the concentration of chlorophyll at the Mexican oceans by merging data from MODIS-TERRA, MODIS-AQUA, VIIRS-SNPP, and VIIRS-JPSS-1 sensors. The results revealed that the proposed approach produces results that are similar to state-of-the-art approaches to estimate chlorophyll concentration but avoid memory overflow with large images. Visual and statistical comparison of the resulting images revealed that the proposed approach provides a more accurate estimation of chlorophyll concentration when compared to the mean of pixels method alone.

Fast continuous streaming sort in big streaming data environment under fixed-size single storage.

Big streaming data environment concerns a complicated scenario where data to be processed continuously flow into a processing unit and certainly cause a memory overflow problem. This obstructs the adaptation of deploying all existing classic sorting algorithms because the data to be sorted must be entirely stored inside the fixed-size storage including the space in internal and external storage devices. Generally, it is always assumed that the size of each data chunk is not larger than the size of storage (M) but in fact the size of the entire stream (n) is usually much larger than M. In this paper, a new fast continuous streaming sorting is proposed to cope with the constraint of storage overflow. The algorithm was tested with various real data sets consisting of 10,000 to 17,000,000 numbers and different storage sizes ranging from 0.01n to 0.50n. It was found that the feasible lower bound of storage size is 0.35n with 100% sorting accuracy. The sorting time outperforms bubble sort, quick sort, insertion sort, and merge sort when data size is greater than 1,000,000 numbers. Remarkably, the sorting time of the proposed algorithm is 1,452 times less than the sorting time of external merge sort and 28.1767 times less than the sorting time of streaming data sort. The time complexity of proposed algorithm is O(n) while the space complexity is O(M).

Switched system optimal control approach for drug administration in cancer chemotherapy

This paper considers a drug administration problem in cancer chemotherapy. Our main aim is to choose a drug delivery rate such that the final tumour population is minimized. For this purpose, the drug administration problem is modeled as an optimal control problem of switched systems (OCPSS) with state-dependent switching. Since most of existing numerical algorithms are designed for the OCPSS with time-dependent switching, these algorithms can not be directly used to solve the OCPSS when the subsystems switch under state-dependent and not time-dependent. To tackle this issue, the OCPSS is formulated as a non-convex nonlinear parameter selection problem (NNPSP) with simple bounds on the decision variables by using the relaxation technique, the control parameterization method, and the penalty function idea. To obtain a global optimal solution of this NNPSP, a penalty function-based continuous filled function (PFCFF) algorithm is proposed based on a novel flattening function (NFF), a novel continuous filled function (NCFF), and an adaptive strategy to obtain the initial points. Compared with the existing continuous filled function (CFF) algorithms, the PFCFF algorithm has the following four advantages: a lot of local optimal solutions can be eliminated by using the NFF, which implies that it easier to find the global optimal solution; no parameters need to be adjusted in the NCFF; there exist no the exponential and logarithmic terms in the NCFF, which can effectively avoid memory overflow during the process of numerical computing; and an adaptive strategy to generate the initial points is proposed for obtaining the local minimum of the NCFF. Finally, numerical results show that the PFCFF algorithm has good performance and can obtain a stable and robust performance when considering the small noise perturbations in initial system state.

Control of external devices based on sEMG signals

This topic studies how to use the sEMG signal portable external equipment to collect the surface EMG signal using the EMG instrument. In the signal data processing part, the causes of signal interference are analyzed from the hardware perspective, and the main frequency bands of its occurrence are recorded. In the code part, the data is processed through an analog filter. The communication mode uses TCP/IP protocol to achieve cross system interprocess communication. In the actual test process, there is unavoidable slight delay. At the same time, the voltage changes brought by the real-time adjustment mode of CPU and other computer hardware will also affect the accuracy of surface EMG signals. At the same time, the signal acquisition rate of the EMG is much higher than the transmission rate, so real-time transmission will lead to memory overflow. Therefore, this paper uses the fixed threshold algorithm as the control decision in the outgoing process, and tests the integral mean value of six different data sets and the grasping speed and reaction force of the gripper. It is finally found that when the data set has 20 elements, the delay is not large and the data is not easy to be blocked due to good real-time performance.

Distributed Tip Decomposition on Large Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs. It is a popular research topic with wide applications in document clustering, spam group detection, and analysis of affiliation networks. With the explosive growth of the bipartite graph data scale in these scenarios, it is necessary to use distributed methods to realize its effective storage. For this reason, this paper studies the problem of the tip decomposition on a bipartite graph in the distributed environment for the first time. Firstly, a new relay-based communication mode is proposed to realize effective message transmission when the given bipartite graph is decomposed in a distributed environment. Secondly, the Distributed Butterfly Counting (DBC) algorithm and the Distributed Tip Decomposition (DTD) algorithm are designed. In particular, a controllable parallel vertex activation strategy is proposed to solve the problem of memory overflow when DBC decomposes large-scale bipartite graphs. Finally, the message pruning strategy based on vertex priority and message validity pruning strategy are introduced to further improve the efficiency of the algorithm by reducing redundant communication and computing overhead. The experiment is deployed on the high-performance distributed computing platform of the National Supercomputing Center. The effectiveness and efficiency of the proposed algorithms are verified by experiments on several real datasets.

SpecHap: a diploid phasing algorithm based on spectral graph theory.

Haplotype phasing plays an important role in understanding the genetic data of diploid eukaryotic organisms. Different sequencing technologies (such as next-generation sequencing or third-generation sequencing) produce various genetic data that require haplotype assembly. Although multiple diploid haplotype phasing algorithms exist, only a few will work equally well across all sequencing technologies. In this work, we propose SpecHap, a novel haplotype assembly tool that leverages spectral graph theory. On both in silico and whole-genome sequencing datasets, SpecHap consumed less memory and required less CPU time, yet achieved comparable accuracy with state-of-art methods across all the test instances, which comprises sequencing data from next-generation sequencing, linked-reads, high-throughput chromosome conformation capture, PacBio single-molecule real-time, and Oxford Nanopore long-reads. Furthermore, SpecHap successfully phased an individual Ambystoma mexicanum, a species with gigantic diploid genomes, within 6 CPU hours and 945MB peak memory usage, while other tools failed to yield results either due to memory overflow (40GB) or time limit exceeded (5 days). Our results demonstrated that SpecHap is scalable, efficient, and accurate for diploid phasing across many sequencing platforms.