Editing Operations Research Articles

High throughput edit distance computation on FPGA-based accelerators using HLS

Edit distance is a computational grand challenge problem to quantify the minimum number of editing operations required to modify one string of characters to the other, finding many applications of natural language processing. In recent years, relevant and increasing interest has also emerged from deoxyribonucleic acid (DNA) applications, like Next Generation Sequencing and DNA storage technologies. Both applications share two crucial features: i) the information is coded into the four bases of DNA and ii) the level of operational noise is still high causing errors in the data, requiring inclusion in the workflow of the computation of algorithms such as the edit distance for finding similarities between sequences. To boost this computation many solutions are available in the literature. Among them, the FPGAs are largely used since the data domain of those applications is strings of 4 characters represented as two-bit values, inconveniently fitting the basic data types of ordinary CPUs and GPUs, with additional benefits of providing a high level of parallelism and low processing latency. This contribution presents a computing- and energy-efficient design implementing the edit distance algorithm combining metaprogramming and High-Level Synthesis. We also assess the performance of our design targeting recent FPGA-based accelerators. Our solution uses nearly 90% of FPGA basic-block hardware resources achieving about 90% of computing efficiency delivering a maximum throughput of 16.8 TCUPS and an energy efficiency of 46 Mpair/Joule, enabling the use of FPGAs as a new class of accelerators for High Performance Computing in DNA applications.

String editing under pattern constraints

We introduce the novel Nearest Pattern Constrained String (NPCS) problem of finding a minimum set Q of character mutation, insertion, and deletion edit operations sufficient to modify a string x to contain all contiguous substrings in a pattern set P and no contiguous substrings in a forbidden pattern set F. Letting Σ be the alphabet of allowed characters, and letting η and ϒ be the longest string length and sum of all string lengths in P∪F, respectively, we show that NPCS is fixed-parameter tractable in |P| with time complexity O(2|P|⋅ϒ⋅|Σ|⋅(|P|+η)(|x|+1)). Additionally, we consider a generalization of the NPCS problem in which we allow for constraints based on the membership of substrings in regular languages. In particular, we introduce a problem we denote String Editing under Substring in Language Constraints (StrEdit-SILC), where provided a wildcard-free string x∈Σ⁎, a finite set of regular languages R={L1,L2,…}, and a regular language LF, the objective is to find a minimum cost set of mutation, insertion, and deletion edit operations Q that suffice to convert the input string x into a string x′∈Σ⁎, where no substring has membership in LF, and ∀Li∈R, there exists a substring in Li. Here, letting Ψ and ϖ be the sum of all regular expression lengths and longest regular expression length for languages in R∪{LF}, respectively, and letting Cmid∈N be the maximum cost of an edit operation, we show that StrEdit-SILC is fixed-parameter tractable with respect to Ψ, having time complexity O(2Ψ⋅|x|⋅(ϖ⋅|Σ|+Cmid)). However, we also show that StrEdit-SILC is MAX-SNP-hard and otherwise difficult to approximate under stringent constraints.

Highly efficient CRISPR-mediated genome editing through microfluidic droplet cell mechanoporation

Clustered regularly interspaced short palindromic repeats (CRISPR)-based editing tools have transformed the landscape of genome editing. However, the absence of a robust and safe CRISPR delivery method continues to limit its potential for therapeutic applications. Despite the emergence of various methodologies aimed at addressing this challenge, issues regarding efficiency and editing operations persist. We introduce a microfluidic gene delivery system, called droplet cell pincher (DCP), designed for highly efficient and safe genome editing. This approach combines droplet microfluidics with cell mechanoporation, enabling encapsulation and controlled passage of cells and CRISPR systems through a microscale constriction. Discontinuities created in cell and nuclear membranes upon passage facilitate the rapid CRISPR-system internalization into the nucleus. We demonstrate the successful delivery of various macromolecules, including mRNAs (~98%) and plasmid DNAs (~91%), using this platform, underscoring the versatility of the DCP and leveraging it to achieve successful genome engineering through CRISPR–Cas9 delivery. Our platform outperforms electroporation, the current state-of-the-art method, in three key areas: single knockouts (~6.5-fold), double knockouts (~3.8-fold), and knock-ins (~3.8-fold). These results highlight the potential of our platform as a next-generation tool for CRISPR engineering, with implications for clinical and biological cell-based research.

Splitting Plane Graphs to Outerplanarity

Vertex splitting replaces a vertex by two copies and partitions its incident edges amongst the copies. This problem has been studied as a graph editing operation to achieve desired properties with as few splits as possible, most often planarity, for which the problem is NP-hard.Here we study how to minimize the number of splits to turn a plane graph into an outerplane one. We tackle this problem by establishing a direct connection between splitting a plane graph to outerplanarity, finding a connected face cover, and finding a feedback vertex set in its dual. We prove NP-completeness for plane biconnected graphs, while we show that a polynomial-time algorithm exists for maximal planar graphs. Additionally, we show upper and lower bounds for certain families of maximal planar graphs. Finally, we provide a SAT formulation for the problem, and evaluate it on a small benchmark.

Retrosynthesis prediction with an iterative string editing model

Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K.

Interactive Invigoration: Volumetric Modeling of Trees with Strands

Generating realistic models of trees and plants is a complex problem because of the vast variety of shapes trees can form. Procedural modeling algorithms are popular for defining branching structures and steadily increasing their expressive power by considering more biological findings. Most existing methods focus on defining the branching structure of trees based on skeletal graphs, while the surface mesh of branches is most commonly defined as simple cylinders. One critical open problem is defining and controlling the complex details observed in real trees. This paper aims to advance tree modeling by proposing a strand-based volumetric representation for tree models. Strands are fixed-size volumetric pipes that define the branching structure. By leveraging strands, our approach captures the lateral development of trees. We combine the strands with a novel branch development formulation that allows us to locally inject vigor and reshape the tree model. Moreover, we define a set of editing operators for tree primary and lateral development that enables users to interactively generate complex tree models with unprecedented detail with minimal effort.

The Widespread Ethical Disagreement of CRISPR-Cas9

In the early 21st century, gene therapy, specifically CRISPR-Cas9, was developed as a viable option to prevent heritable diseases and treat genetic disorders in somatic and germline cells. However, the advancement of this technology has led to misuse, as genetic “engineers” aim to alter the DNA of future generations to conform to an idealistic set of standards. The abuse of technology, coupled with the ability to reconstruct an individual’s genetic makeup, leads to the societal question about the best way to regulate this software. This paper addresses three key factors in preventing misuse while maximizing medical benefits: the public’s role, physician-patient relationships, and diversity in healthcare. This paper analyzes how the public should engage with national organizations and participate in formal discussions to express their views regarding CRISPR-Cas9 governance; without explicit expression of their views, their beliefs will likely be undermined by professionals. Furthermore, this paper emphasizes transparency and communication within physician-patient relationships: physicians must offer the comprehensive health benefits and drawbacks of gene editing to ensure patients considering gene editing operations are aware of its implications. Additionally, this paper asserts the need to bridge the socioeconomic gap within healthcare to extend access to gene editing to a larger group and also gain deeper insight into CRISPR-Cas9’s long-term impacts on people of various backgrounds. This paper states that the resolution of these three components is essential to reach a societal consensus of the appropriate regulations on gene editing, thereby halting the temporary ban on the technology.

Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

CharacterMixer: Rig‐Aware Interpolation of 3D Characters

AbstractWe present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when applied to rigged characters: they either ignore the rig (making interpolated characters no longer posable) or use a fixed rig and mesh topology. To handle different mesh topologies, CharacterMixer uses a signed distance field (SDF) representation of character shapes, with one SDF per bone. To handle different skeleton topologies, it computes a hierarchical correspondence between source and target character skeletons and interpolates the SDFs of corresponding bones. This correspondence also allows the creation of a single “unified skeleton” for posing and animating interpolated characters. We show that CharacterMixer produces qualitatively better interpolation results than two state‐of‐the‐art methods while preserving a rig throughout interpolation. Project page: https://seanxzhan.github.io/projects/CharacterMixer.

Analysing the use of OpenAerialMap images for OpenStreetMap edits

ABSTRACT OpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

Progressive Text-to-Image Diffusion with Soft Latent Direction

In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges. This paper introduces an innovative progressive synthesis and editing operation that systematically incorporates entities into the target image, ensuring their adherence to spatial and relational constraints at each sequential step. Our key insight stems from the observation that while a pre-trained text-to-image diffusion model adeptly handles one or two entities, it often falters when dealing with a greater number. To address this limitation, we propose harnessing the capabilities of a Large Language Model (LLM) to decompose intricate and protracted text descriptions into coherent directives adhering to stringent formats. To facilitate the execution of directives involving distinct semantic operations—namely insertion, editing, and erasing—we formulate the Stimulus, Response, and Fusion (SRF) framework. Within this framework, latent regions are gently stimulated in alignment with each operation, followed by the fusion of the responsive latent components to achieve cohesive entity manipulation. Our proposed framework yields notable advancements in object synthesis, particularly when confronted with intricate and lengthy textual inputs. Consequently, it establishes a new benchmark for text-to-image generation tasks, further elevating the field's performance standards.

Machine Translation in Algeria: State of Use

The field of translation has experienced a remarkable technological leap in recent years. All over the world, translators are using software, electronic dictionaries, and turning their computers into real workstations. This paper aims to explore the machine-human translator or machine translation-bio translation relationship in Algeria, by attempting to answer the following questions: are Algerian translators limited to using electronic dictionaries, or do they use the most developed software and CAT tools? And do they carry out pre- (post)editing operations? To answer these questions, we will conduct a questionnaire targeting Algerian translators, official and/or independent, on social networks. The questionnaire provides important information on the types of software/tools used by Algerian machine translators, the degree of integration of technology in their daily work, and the problems they encounter. The results show that respondents are aware of the role technology can play in the field of translation; they go hand in hand with the latest inventions and new usages. These results will help to improve the use of technology, and consequently the quality of translations, in Algeria.

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

NeuroEditor: a tool to edit and visualize neuronal morphologies.

The digital extraction of detailed neuronal morphologies from microscopy data is an essential step in the study of neurons. Ever since Cajal's work, the acquisition and analysis of neuron anatomy has yielded invaluable insight into the nervous system, which has led to our present understanding of many structural and functional aspects of the brain and the nervous system, well beyond the anatomical perspective. Obtaining detailed anatomical data, though, is not a simple task. Despite recent progress, acquiring neuron details still involves using labor-intensive, error prone methods that facilitate the introduction of inaccuracies and mistakes. In consequence, getting reliable morphological tracings usually needs the completion of post-processing steps that require user intervention to ensure the extracted data accuracy. Within this framework, this paper presents NeuroEditor, a new software tool for visualization, editing and correction of previously reconstructed neuronal tracings. This tool has been developed specifically for alleviating the burden associated with the acquisition of detailed morphologies. NeuroEditor offers a set of algorithms that can automatically detect the presence of potential errors in tracings. The tool facilitates users to explore an error with a simple mouse click so that it can be corrected manually or, where applicable, automatically. In some cases, this tool can also propose a set of actions to automatically correct a particular type of error. Additionally, this tool allows users to visualize and compare the original and modified tracings, also providing a 3D mesh that approximates the neuronal membrane. The approximation of this mesh is computed and recomputed on-the-fly, reflecting any instantaneous changes during the tracing process. Moreover, NeuroEditor can be easily extended by users, who can program their own algorithms in Python and run them within the tool. Last, this paper includes an example showing how users can easily define a customized workflow by applying a sequence of editing operations. The edited morphology can then be stored, together with the corresponding 3D mesh that approximates the neuronal membrane.

Multi-grained contextual code representation learning for commit message generation

Commit messages, precisely describing the code changes for each commit in natural language, makes it possible for developers and succeeding reviewers to understand the code changes without digging into implementation details. However, the semantic and structural gap between code and natural language poses a significant challenge for commit message generation. Several researchers have proposed automated techniques to generate commit messages. Nevertheless, the information about the code is not sufficiently exploited. In this paper, we propose multi-grained contextual code representation learning for commit message generation (COMU). We extract multi-grained information from the changed code at the line and AST levels (i.e., Code_Diff and AST_Diff). In Code_Diff, we construct global contextual semantic information about the changed code, and mark whether a line of code has changed with three different tokens. In AST_Diff, we extract the code structure from source code changes and combine the extracted structure with four types of editing operations to explicitly focus on the detailed information of the changed part. In addition, we build the experimental datasets, since there is still no publicly sufficient dataset for this task. The release of this dataset would contribute to advancing research in this field. We perform an extensive experiment to evaluate the effectiveness of COMU. The experimental evaluation and human study show that our model outperforms the baseline model.

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models.

During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions. This leads to a significant waste of computation, especially for minor editing operations. In this work, we present Spatially Sparse Inference (SSI), a general-purpose technique that selectively performs computation for edited regions and accelerates various generative models, including both conditional GANs and diffusion models. Our key observation is that users prone to gradually edit the input image. This motivates us to cache and reuse the feature maps of the original image. Given an edited image, we sparsely apply the convolutional filters to the edited regions while reusing the cached features for the unedited areas. Based on our algorithm, we further propose Sparse Incremental Generative Engine (SIGE) to convert the computation reduction to latency reduction on off-the-shelf hardware. With about 1%-area edits, SIGE accelerates DDPM by 3.0× on NVIDIA RTX 3090 and 4.6× on Apple M1 Pro GPU, Stable Diffusion by 7.2× on 3090, and GauGAN by 5.6× on 3090 and 5.2× on M1 Pro GPU. layers and apply it to Stable Diffusion. Additionally, we offer support for Apple M1 Pro GPU and include more results to substantiate the efficacy of our method.

An Improved Algorithm for The k -Dyck Edit Distance Problem

A Dyck sequence is a sequence of opening and closing parentheses (of various types) that is balanced. The Dyck edit distance of a given sequence of parentheses S is the smallest number of edit operations (insertions, deletions, and substitutions) needed to transform S into a Dyck sequence. We consider the threshold Dyck edit distance problem, where the input is a sequence of parentheses S and a positive integer k , and the goal is to compute the Dyck edit distance of S only if the distance is at most k , and otherwise report that the distance is larger than k . Backurs and Onak [PODS’16] showed that the threshold Dyck edit distance problem can be solved in O ( n + k 16 ) time. In this work, we design new algorithms for the threshold Dyck edit distance problem which costs O ( n + k 4.544184 ) time with high probability or O ( n + k 4.853059 ) deterministically. Our algorithms combine several new structural properties of the Dyck edit distance problem, a refined algorithm for fast (min , +) matrix product, and a careful modification of ideas used in Valiant’s parsing algorithm.

Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation

AbstractNeural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant challenge. This issue has obstructed NeRF's wider adoption across various applications. To tackle the problem of efficiently editing neural implicit fields, we introduce Neural Impostor, a hybrid representation incorporating an explicit tetrahedral mesh alongside a multigrid implicit field designated for each tetrahedron within the explicit mesh. Our framework bridges the explicit shape manipulation and the geometric editing of implicit fields by utilizing multigrid barycentric coordinate encoding, thus offering a pragmatic solution to deform, composite, and generate neural implicit fields while maintaining a complex volumetric appearance. Furthermore, we propose a comprehensive pipeline for editing neural implicit fields based on a set of explicit geometric editing operations. We show the robustness and adaptability of our system through diverse examples and experiments, including the editing of both synthetic objects and real captured data. Finally, we demonstrate the authoring process of a hybrid synthetic‐captured object utilizing a variety of editing operations, underlining the transformative potential of Neural Impostor in the field of 3D content creation and manipulation.

SELF-EdiT: Structure-constrained molecular optimisation using SELFIES editing transformer

Structure-constrained molecular optimisation aims to improve the target pharmacological properties of input molecules through small perturbations of the molecular structures. Previous studies have exploited various optimisation techniques to satisfy the requirements of structure-constrained molecular optimisation tasks. However, several studies have encountered difficulties in producing property-improved and synthetically feasible molecules. To achieve both property improvement and synthetic feasibility of molecules, we proposed a molecular structure editing model called SELF-EdiT that uses self-referencing embedded strings (SELFIES) and Levenshtein transformer models. The SELF-EdiT generates new molecules that resemble the seed molecule by iteratively applying fragment-based deletion-and-insertion operations to SELFIES. The SELF-EdiT exploits a grammar-based SELFIES tokenization method and the Levenshtein transformer model to efficiently learn deletion-and-insertion operations for editing SELFIES. Our results demonstrated that SELF-EdiT outperformed existing structure-constrained molecular optimisation models by a considerable margin of success and total scores on the two benchmark datasets. Furthermore, we confirmed that the proposed model could improve the pharmacological properties without large perturbations of the molecular structures through edit-path analysis. Moreover, our fragment-based approach significantly relieved the SELFIES collapse problem compared to the existing SELFIES-based model. SELF-EdiT is the first attempt to apply editing operations to the SELFIES to design an effective editing-based optimisation, which can be helpful for fellow researchers planning to utilise the SELFIES.

UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image

Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual changes and cannot easily keep specific details of the edited portion. In this paper we make the observation that image-generation models can be converted to image-editing models simply by fine-tuning them on a single image. We also show that initializing the stochastic sampler with a noised version of the base image before the sampling and interpolating relevant details from the base image after sampling further increase the quality of the edit operation. Combining these observations, we propose UniTune, a novel image editing method. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high fidelity to the input image. UniTune does not require additional inputs, like masks or sketches, and can perform multiple edits on the same image without retraining. We test our method using the Imagen model in a range of different use cases. We demonstrate that it is broadly applicable and can perform a surprisingly wide range of expressive editing operations, including those requiring significant visual changes that were previously impossible.