Parallel Variant Research Articles

From lexical collation to significant omissions and paraphrases: New evidence for refining relations within the Prick of Conscience Group-IV Manuscripts

Abstract: The Prick of Conscience is known to have survived in 97 manuscripts of the Main Version, 19
 of the Southern Recension, and about 50 short extracts. An initial collation of one lexical item in the 97
 extant copies of the Main Version and subsequent comparison of another 109 items in 54 of these copies
 allow for identifying parallel variant readings throughout the poem’s almost 10,000 lines. Those variants
 often transcend the word level affecting the line, the couplet, or more extensive passages. This paper
 contributes to refining textual relations within the Group-IV family of the work by showing distinct
 variance common to Dublin, Trinity College, 157 (D.4.11) (MV 21), London, Sion College, Arc. L. 40.
 2/E. 25 (MV 49), and Shrewsbury, School, III (Mus. III. 39) (MV 95). Apart from unfolding and expanding
 the extent of the relationship pointed out by Lewis & McIntosh (1982), this research also proves that the
 hitherto unsubclassified London, Lambeth Palace, 492 (MV 48) is another member of the subgroup. To
 illustrate how the proposed subset relates to a version closer to the presumed original and other Group-IV
 witnesses, readings from the following London, British Library manuscripts are also provided for reference:
 Cotton Galba E. IX (MV 27); Harley 4196 (MV 34); Egerton 657 (MV 29); Additional 22283 (MV 40).

A product space reformulation with reduced dimension for splitting algorithms

In this paper we propose a product space reformulation to transform monotone inclusions described by finitely many operators on a Hilbert space into equivalent two-operator problems. Our approach relies on Pierra’s classical reformulation with a different decomposition, which results in a reduction of the dimension of the outcoming product Hilbert space. We discuss the case of not necessarily convex feasibility and best approximation problems. By applying existing splitting methods to the proposed reformulation we obtain new parallel variants of them with a reduction in the number of variables. The convergence of the new algorithms is straightforwardly derived with no further assumptions. The computational advantage is illustrated through some numerical experiments.

Етнонимът „българи“ в ранносредновековните арменски текстове

The standard form Bulghar, with which Bulgarians and the Land of the Bulgarians (Bulgaria) are recorded in medieval Armenian texts, has been established as traditional only since the 11th century. Until then, there were several attested variations, while the oldest form in the earliest Armenian texts was Bulkar, with “k” instead of “gh” (Movses Khorenatsi’s “History of the Armenians”, circa 482 AD), with parallel variants Bolkar and Blkar in the expanded version of the geography Ashkharats’uits’ of Anania Shirakatsi, who lived in the 7th century AD. This characteristic of the earliest Armenian records of the name “Bulgarians” puts an end to the speculation that the form Bulghar in some copies of the history of Movses Khorenatsi proves that his “Bulgarian passages” are a late interpolation making their reliability disputable. On the other hand, the parallel forms Bulkar, Bolkar and Blkar in Ashkharats’uits’ are a valuable indication that different Proto-Bulgarian groups have pronounced the name “Bulgarians” differently. The last leads to the hypothesis of the existence of dialectal differences among the Proto-Bulgarian tribes in the 7th century and even earlier.

Compensated summation and dot product algorithms for floating-point vectors on parallel architectures: Error bounds, implementation and application in the Krylov subspace methods

The aim of the paper is to improve parallel algorithms that obtain higher precision in floating point reduction-type operations while working within the basic floating point type. The compensated parallel variants of summation and dot product operations for floating point vectors are considered (level 1 BLAS operations). The methods are based on the work of Rump, Ogita and Oishi. Parallel implementations in block and pairwise reduction variants are under consideration. Analytical error bounds are obtained for real- and complex-valued vectors that are represented by floating point numbers according to the IEEE 754 (IEC 60559) standard for all variants of parallel algorithms. The algorithms are written in C++ Compute Unified Device Architecture (CUDA) for Graphics Processing Units (GPUs) and their accuracy is tested for different vector sizes and different condition numbers. The suggested compensated variant is compared to the multiple-precision library for GPUs in terms of efficiency. The designed algorithms are tested in Krylov-type matrix-based methods with preconditioners that originate from different challenging computational problems. It is shown that the compensated variant of algorithms allows one to accelerate convergence and obtain more accurate results even when the matrix operations are in base precision.

Parallelization of the B static traffic assignment algorithm

A widely used technique for predicting traffic flows in individual roads of a road traffic network is the user-equilibrium (UE) traffic assignment (TA). This technique assigns trips from origins to destinations in a road traffic network so that all trips use the cheapest path. The cost of the path, which consists of roads (edges), is the sum of the roads costs. These costs increase with increasing flow in these roads. In this paper, we describe the parallelization of the B algorithm – a relatively new TA algorithm with a fast convergence to a solution. Since the nature of the algorithm and the nature of its fast convergence complicate the parallelization itself, we considered and implemented three parallel variants and tested them on real road traffic networks to investigate their convergence, usability, and speed. The parallelization is intended for a shared memory parallel computing environment. The description of the parallelization along with the performed tests is the main contribution of this paper.

Parallel profiling of antigenicity alteration and immune escape of SARS-CoV-2 Omicron and other variants

SARS-CoV-2 variants have evolved a variety of critical mutations, leading to antigenicity changes and immune escape. The recent emerging SARS-CoV-2 Omicron variant attracted global attention due to its significant resistance to current antibody therapies and vaccines. Here, we profiled the mutations of Omicron and other various circulating SARS-CoV-2 variants in parallel by computational interface analysis and in vitro experimental assays. We identified critical mutations that lead to antigenicity changes and diminished neutralization efficiency of a panel of 14 antibodies due to diverse molecular mechanisms influencing the antigen-antibody interaction. Our study identified that Omicron exhibited extraordinary potency in immune escape compared to the other variants of concern, and explores the application of computational interface analysis in SARS-CoV-2 mutation surveillance and demonstrates its potential for the early identification of concerning variants, providing preliminary guidance for neutralizing antibody therapy.

Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains.

SUMMARYAcidic activation domains are intrinsically disordered regions of the transcription factors that bind coactivators. The intrinsic disorder and low evolutionary conservation of activation domains have made it difficult to identify the sequence features that control activity. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a high-throughput assay in human cell culture. We found that strong activation domain activity requires a balance between the number of acidic residues and aromatic and leucine residues. These findings motivated a predictor of acidic activation domains that scans the human proteome for clusters of aromatic and leucine residues embedded in regions of high acidity. This predictor identifies known activation domains and accurately predicts previously unidentified ones. Our results support a flexible acidic exposure model of activation domains in which the acidic residues solubilize hydrophobic motifs so that they can interact with coactivators. A record of this paper’s transparent peer review process is included in the supplemental information.

Aggregated CDMA Crossbar With Hybrid ARQ for NoCs

Network-on-chips (NoCs) are the dominant interconnection technique in modern system-on-chips (SoCs). The medium access technique used in the physical layer of NoC routers profoundly impacts the performance and footprint of the router. Code division multiple access (CDMA) is a medium access technique widely deployed in various wireless communication systems, and it has recently been proposed as a prominent switching method for NoC routers. In a CDMA crossbar, several processing elements (PEs) can communicate simultaneously over a single communication channel by applying the direct-sequence spread spectrum technique to digital interconnects. However, existing CDMA switches are bit-wise architectures in which a binary data bit is spread and serially communicated on an exclusive digital channel while replicating this configuration to communicate multiple data bits, which increases the crossbar area and wiring density. In this work, we propose aggregated CDMA (ACDMA) to improve the area, throughput, and power consumption of existing CDMA NoC routers. ACDMA exploits the static nature and relative noise immunity of on-chip interconnects to aggregate transmission of multiple data bits into M-ary symbols on a single digital communication channel, which significantly reduces the wiring density and area overhead of the crossbar. Serial and parallel variants of the ACDMA crossbar offering a wide range of area-speed trade-offs are implemented in the ASIC <inline-formula> <tex-math notation="LaTeX">$65~nm$ </tex-math></inline-formula> standard cell technology. The implementation results show that the throughput-per-area (TPA) of the serial and parallel ACDMA crossbars is improved by 96.3&#x0025;, 18.2&#x0025;, and 118.6&#x0025;; and 400&#x0025;, 255.3&#x0025;, and 184.2&#x0025; compared to the serial and parallel counterparts of the Walsh Basis (WB), Standard Basis (SB), and Overloaded CDMA Interconnect (OCI) crossbars, respectively. A 65-node ACDMA NoC router is fully realized and compared to the state-of-the-art CDMA and CONNECT NoC routers under multiple synthetic workloads. Communication reliability of the ACDMA NoC router subject to noise is investigated and a hybrid ARQ approach is proposed to improve the interconnect reliability.

Low-Latency and Minor-Error Architecture for Parallel Computing XY-like Functions with High-Precision Floating-Point Inputs

This paper proposes a novel architecture for the computation of XY-like functions based on the QH CORDIC (Quadruple-Step-Ahead Hyperbolic Coordinate Rotation Digital Computer) methodology. The proposed architecture converts direct computing of function XY to logarithm, multiplication, and exponent operations. The QH CORDIC methodology is a parallel variant of the traditional CORDIC algorithm. Traditional CORDIC suffers from long latency and large area, while the QH CORDIC has much lower latency. The computation of functions lnx and ex is accomplished with the QH CORDIC. To solve the problem of the limited range of convergence of the QH CORDIC, this paper employs two specific techniques to enlarge the range of convergence for functions lnx and ex, making it possible to deal with high-precision floating-point inputs. Hardware modeling of function XY using the QH CORDIC is plotted in this paper. Under the TSMC 65 nm standard cell library, this paper designs and synthesizes a reference circuit. The ASIC implementation results show that the proposed architecture has 30 more orders of magnitude of maximum relative error and average relative error than the state-of-the-art. On top of that, the proposed architecture is also superior to the state-of-the-art in terms of latency, word length and energy efficiency (power × latency × period /efficient bits).

Cross–testing Symbolic and Connectionist Machine Learning Approaches in Specialized Acute Appendicitis Databases

Purpose: In the world of learning with medical supervised machine approaches, we often face a lack of dataset objects suitable for training a classifier. Two of the most common reasons are the lack of funds to perform all of the required tests and dataset gathering, or simply the condition is too rare to collect a suitable number of cases. In this paper we present the results of a very rare opportunity to test and train classifiers on three acute appendicitis datasets with almost identical structures, but from different sources and of different sizes.&#x0D; Methods: We performed a parallel variant of cross–testing of two types of classifiers (decision trees [DT] and artificial neural networks [ANN]). Our variant of cross–testing focuses on training the classifiers on one dataset and testing all of the remaining available datasets, including the one derived from the same source, as the training set. To compare the three acute abdominal pain databases of different sizes and origins, we selected 18 parameters from patient medical histories, clinical examinations, and the decision attribute/diagnosis, acute appendicitis.&#x0D; Results: The comparison of results was based on overall accuracy, sensitivity, specificity, and balance.&#x0D; Conclusion: The results we obtained were quite surprising, and factors such as dataset size and theoretical power of the methods did not prove to be as important as first expected.

UPIC–M: Efficient and Scalable Preparation of Clonal Single Mutant Libraries for High-Throughput Protein Biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate-limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal–Mutant (uPIC–M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC–M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC–M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

A Sort-Seq Approach to the Development of Single Fluorescent Protein Biosensors.

Motivated by the growing importance of single fluorescent protein biosensors (SFPBs) in biological research and the difficulty in rationally engineering these tools, we sought to increase the rate at which SFPB designs can be optimized. SFPBs generally consist of three components: a circularly permuted fluorescent protein, a ligand-binding domain, and linkers connecting the two domains. In the absence of predictive methods for biosensor engineering, most designs combining these three components will fail to produce allosteric coupling between ligand binding and fluorescence emission. While methods to construct diverse libraries with variation in the site of GFP insertion and linker sequences have been developed, the remaining bottleneck is the ability to test these libraries for functional biosensors. We address this challenge by applying a massively parallel assay termed "sort-seq," which combines binned fluorescence-activated cell sorting, next-generation sequencing, and maximum likelihood estimation to quantify the brightness and dynamic range for many biosensor variants in parallel. We applied this method to two common biosensor optimization tasks: the choice of insertion site and optimization of linker sequences. The sort-seq assay applied to a maltose-binding protein domain-insertion library not only identified previously described high-dynamic-range variants but also discovered new functional insertion sites with diverse properties. A sort-seq assay performed on a pyruvate biosensor linker library expressed in mammalian cell culture identified linker variants with substantially improved dynamic range. Machine learning models trained on the resulting data can predict dynamic range from linker sequences. This high-throughput approach will accelerate the design and optimization of SFPBs, expanding the biosensor toolbox.

A parallel worklist algorithm and its exploration heuristics for static modular analyses

One way to speed up static programme analysis is to make use of today’s multi-core CPUs by parallelising the analysis. Existing work on parallel analysis usually targets traditional data-flow analyses for static, first-order languages such as C. Less attention has been given so far to the parallelisation of more general analyses that can also target dynamic, higher-order languages such as JavaScript. These are significantly more challenging to parallelise, as dependencies between analysis results are only discovered during the analysis itself. State-of-the-art parallel analyses for such languages are therefore usually limited, both in their applicability and performance gains.In this work, we propose the parallelisation of modular analyses. Modular analyses compute different parts of the analysis in isolation of one another, and therefore offer inherent opportunities for parallelisation that have not been explored so far. In addition, they can be used to develop a general class of analysers for dynamic, higher-order languages. We present a parallel variant of the worklist algorithm that is used to drive such modular analyses. To further speed up its convergence, we show how this algorithm can exploit the monotonicity of the analysis. Existing modular analyses can be parallelised without additional effort by instead employing this parallel worklist algorithm. We demonstrate this for ModF, an inter-procedural modular analysis, and for ModConc, an inter-process modular analysis. For ModConc, we reveal an additional opportunity to exploit even more parallelism in the analysis: analyses of individual ModConc components can themselves be parallel, resulting in a doubly-parallel exploration. Finally, we present several heuristics for the exploration order of the analysis and discuss how they can impact its performance.The parallel worklist algorithm and the exploration heuristics are implemented for and integrated into MAF, a framework for modular programme analysis. On a set of Scheme benchmarks for ModF, we observe speedups between 3× and 8× when using 4 workers, and speedups between 8× and 32× when using 16 workers, with a maximum speedup of 333× using 128 workers. For ModConc, we achieve a maximum speedup of 37× with 32 workers. We observe that on a ModF analysis, among 11 exploration heuristics, the heuristics prioritising either components with smaller environments or with less dependencies result in consistent speedups that can reach 20× those of a random exploration strategy. We find a clear correlation between the mean number of dependencies in a programme and the speedup obtained by this heuristic.

Using Multiplexed Genome Editing to Distinguish between Loss- and Gain-of-Function Variants in CARD11

Genetic variants in CARD11 contribute to several diseases caused by dysregulation of the adaptive immune system. Dominant-negative, loss-of-function and gain-of-function variants in CARD11 variants lead to primary immunodeficiency disease. However, primary immunodeficiency disease caused by CARD11 gain-of-function variants often progresses to B-cell malignancy. The clinical course and treatment options depend on the type of CARD11 mutation. Unfortunately lymphocyte immunophenotyping and traditional proliferation assays do not distinguish the variant effect or predict the likelihood of malignancy. To address this clinical challenge, we coupled multiplexed genome editing to a cell growth assay that is read out by DNA sequencing in order to assess functional effects of thousands of CARD11 variants in parallel. By co-delivering CRISPR/Cas9 ribonucleoprotein complexes with libraries of single-stranded oligonucleotide repair templates, we generated lymphoma cell populations containing all possible single-nucleotide variants (~2400 different protein coding changes) in the N-terminal 140 amino acids of CARD11. Due a requirement for CARD11 in these lymphoma cells, those with dominant-negative and loss-of-function CARD11 variants grow more slowly in the absence of treatment, whereas those with gain-of-function variants grow faster in the presence B cell receptor pathway inhibitors. By tracking the relative abundance of each variant in the population by next generation sequencing over multiple conditions, we determined the functional effect of each. Finally, we evaluated previously un-described dominant-negative, loss-of-function and gain-of-function variants during differentiation of primary human B cells and NF-kB signaling. The results of our experiments demonstrate the utility of multiplexed functional assays for determining variant effect in proteins where distinguishing between dominant-negative, loss- and gain-of-function effects are required to guide diagnoses and treatment. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Analysis of equivalence of parallel variants in discrete mathematics tests

Different studentswere given questions in 10 variants. It was tested if all parallel variants of test questions were equivalently difficult.

Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning

With the increasing of the data size and the development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and also makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates at all iterations. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both strongly convex and non-strongly convex problems, while existing asynchronous algorithms (e.g., KroMagnon and ASAGA) only have convergence guarantees for strongly convex problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical experimental results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++.

Anytime and Efficient Multi-agent Coordination for Disaster Response

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (text {CFLA}2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in text {CFLA}2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. text {CFLA}2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

Asynchronous effects

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation’s implementation needs to be executed, and interrupting a running computation with the operation’s result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus, called λ æ . We demonstrate the flexibility of λ æ using examples ranging from a multi-party web application, to preemptive multi-threading, to remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of λ æ ’s type safety proofs in Agda, and a prototype implementation of λ æ in OCaml.

Efficient Asynchronous Semi-Stochastic Block Coordinate Descent Methods for Large-Scale SVD

Eigenvector computation such as Singular Value Decomposition (SVD) is one of the most fundamental problems in machine learning, optimization and numerical linear algebra. In recent years, many stochastic variance reduction algorithms and randomized coordinate descent algorithms have been developed to efficiently solve the leading eigenvalue problem. By taking full advantage of both variance reduction and randomized coordinate descent techniques, this paper proposes a novel Semi-stochastic Block Coordinate Descent algorithm (SBCD-SVD), which is more suitable than existing algorithms for large-scale leading eigenvalue problems of SVD, and can obtain linear convergence. Unlike existing stochastic variance reduction and randomized coordinate descent methods, our algorithm inherits their advantages. Moreover, we propose a new Asynchronous parallel Semi-stochastic Block Coordinate Descent algorithm (ASBCD-SVD) and one new Asynchronous parallel Sparse approximated Variance Reduction algorithm (ASVR-SVD) for large-scale dense and sparse datasets, respectively. Finally, we prove that both dense and sparse asynchronous parallel variants can converge linearly. Extensive experimental results show that our algorithms attain high parallel speedup and achieve almost the same performance with significantly shorter time, and thus they can be widely used in various practice applications.

Multiplex Single-Molecule Kinetics of Nanopore-Coupled Polymerases.

DNA polymerases have revolutionized the biotechnology field due to their ability to precisely replicate stored genetic information. Screening variants of these enzymes for specific properties gives the opportunity to identify polymerases with different features. We have previously developed a single-molecule DNA sequencing platform by coupling a DNA polymerase to an α-hemolysin pore on a nanopore array. Here, we use this approach to demonstrate a single-molecule method that enables rapid screening of polymerase variants in a multiplex manner. In this approach, barcoded DNA strands are complexed with polymerase variants and serve as templates for nanopore sequencing. Nanopore sequencing of the barcoded DNA reveals both the barcode identity and kinetic properties of the polymerase variant associated with the cognate barcode, allowing for multiplexed investigation of many polymerase variants in parallel on a single nanopore array. Further, we develop a robust classification algorithm that discriminates kinetic characteristics of the different polymerase mutants. As a proof of concept, we demonstrate the utility of our approach by screening a library of ∼100 polymerases to identify variants for potential applications of biotechnological interest. We anticipate our screening method to be broadly useful for applications that require polymerases with altered physical properties.