Representation Of Structure Research Articles

Assessing Forest Resources with Terrestrial and Backpack LiDAR: A Case Study on Leaf-On and Leaf-Off Conditions in Gari Mountain, Hongcheon, Republic of Korea

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management.

Deep Learning-Assisted Design of Novel Donor-Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency.

Designing donor (D) and acceptor (A) structures and discovering promising D-A combinations can effectively improve organic photovoltaic (OPV) device performance. However, to obtain excellent power conversion efficiency (PCE), the trial-and-error structural design in the infinite chemical space is time-consuming and costly. Herein, a deep learning (DL)-assisted design framework for OPV materials is proposed. To effectively digitally represent the D and A structures, a structure representation method, polymer fingerprints, is developed, and a database of OPV materials is constructed. By applying an end-to-end graph neural network modeling method, high-precision DL models for predicting OPV performance are established. After combining the existing structures, ≈0.6 million virtual D-A combinations are generated. Then, the OPV performance of these candidate combinations is predicted by the well-trained models, and numbers of novel D-A combinations with high efficiency are identified. Experimental validations confirm that the prediction accuracy is greater than 93% and one of the screened combinations (i.e., D18:BTP-S11) exhibits an efficiency above 19.3% in single-junction organic solar cells. Finally, based on the structural gene analysis, the design rules to guide experimental explorations are suggested. The developed DL-assisted approach can accelerate the design of D-A combinations with ultrahigh efficiency and bring property breakthroughs for OPV devices.

Ensemble Kalman filter with precision localization

AbstractWe propose a novel localization strategy for the ensemble Kalman filter procedure based on sparse precision matrices, which we denote precision localization. We assume the latent state vector to come from a Gaussian Markov random field and use the iterative proportional scaling algorithm to estimate the associated sparse precision matrix. Precision localization is compared against a standard covariance localization technique in two simulation studies; one with a Gauss-linear model and one based on the Lorenz 96 model. We evaluate the results by their prediction accuracy and to what degree the generated ensembles give a realistic representation of the exact filtering distributions. In the Gauss-linear example we also compare our results with the Kalman filter solution. Here we see that both precision and covariance localization produce reasonably good results in terms of prediction accuracy, but that precision localization provides the best representation of the correlation structure in the filtering distribution. For the Lorenz model we cannot compare with the Kalman filter solution, but precision localization seems to provide the most accurate predictions and the most realistic uncertainty representation.

When abstract becomes concrete, naturalistic encoding of concepts in the brain.

Language is acquired and processed in complex and dynamic naturalistic contexts, involving the simultaneous processing of connected speech, faces, bodies, objects, etc. How words and their associated concepts are encoded in the brain during real-world processing is still unknown. Here, the representational structure of concrete and abstract concepts was investigated during movie watching to address the extent to which brain responses dynamically change depending on visual context. First, across contexts, concrete and abstract concepts are shown to encode different experience-based information in separable sets of brain regions. However, these differences are reduced when multimodal context is considered. Specifically, the response profile of abstract words becomes more concrete-like when these are processed in visual scenes highly related to their meaning. Conversely, when the visual context is unrelated to a given concrete word, the activation pattern resembles more that of abstract conceptual processing. These results suggest that while concepts generally encode habitual experiences, the underlying neurobiological organisation is not fixed but depends dynamically on available contextual information.

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981–2018 over the CONUS at daily temporal resolution. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.

HIGH-RESOLUTION MICRO-COMPUTED TOMOGRAPHY AND SCANNING ELECTRON MICROSCOPY FOR MORPHOLOGICAL ANALYSIS OF TYPE II DENS INVAGINATUS IN A MAXIL-LARY LATERAL INCISOR: A CASE REPORT

This case report aims to present micro CT and SEM analysis of type II dens invaginatus extracted for orthodontic reasons three years after the eruption. An industrial micro CT was used to double scan a freshly extracted left lateral incisor from the upper jaw with a peg shape and type II invagination, according to Ohlers. The first scan captured the entire tooth with a resolution of twelve micrometers, while the second scan focused on the coronal part of the tooth with a magnification of seven micrometers. This allowed for a detailed view of the overall anatomy and a reliable representation of the tooth's structure. After the scanning procedure, the tooth was sectioned, and SEM-EDS was performed on the invagination to obtain further details. Segmented image analysis provides a new detailed overview of the structural and morphological characteristics that can be observed in this type of malformation. This anatomical presentation, and SEM analysis can bring new helpful insights for practitioners and students to navigate their treatment strategies.

Resumptive pronouns and code-switched A-bar dependencies: investigating the effects of optimization strategies in Egyptian Arabic/English bilinguals

In this paper we investigate bilinguals' sensitivity to two structures that display overlapping word orders across their two languages but are argued to have different derivational properties in their formation. We focus on filler-gap dependencies with and without resumptive pronouns in Egyptian Arabic, a language argued to have grammatical resumptive pronouns base generated at the tail end of nominal A-bar dependencies, and English, a language argued to have intrusive resumptive pronouns inserted post-syntactically due to illicit movement operations, such as in syntactic islands. Using experimental data from code-switched filler-gap dependencies, we argue that when given conflicting requirements of structural well-formedness, this population of bilinguals converge on a single structural representation across their two languages, resulting in a one-to-one mapping between derivational properties and surface form rather than maintaining two distinct representations resulting in a many-to-one mapping. To explain why bilinguals may have chosen to converge onto a unified structure rather than maintaining two distinct representations, we highlight that such one-on-one mapping is part of an arsenal of optimization strategies observable in the grammars of various bilingual populations in which bilinguals capitalize on the structural overlaps already present between their two languages. For the purpose of this paper, such optimization results in a structure that is ultimately common to both English and Egyptian Arabic, for this population of bilinguals.

Accuracy and Precision of Earthquake Location Programs: Insights from a Synthetic Controlled Experiment

Abstract Earthquake location programs employ diverse approaches to address the challenges posed by incomplete knowledge and simplified representation of complex Earth structures. Assessing their reliability in location and uncertainty characterization remains challenging as benchmark datasets with known event locations are rare, and usually confined to particular sources, such as quarry blasts. This study evaluates eight earthquake location methods (GrowClust, HypoDD, Hypoinverse, HypoSVI, NonLinLoc, NonLinLoc_SSST, VELEST, and XCORLOC) through a controlled synthetic computational experiment on 1000 clustered earthquakes based on the setting of the 2019 Ridgecrest, California, earthquake sequence. We construct a travel-time dataset using the fast-marching method and a 3D velocity model extracted from the Community Velocity Model, supplemented with a von Karman perturbation to represent small-scale heterogeneity, and including elevation effects. Picking errors, phase availability, and outliers are introduced to mimic difficulties encountered in seismic network monitoring. We compare the location results from eight programs applied to the same travel-time dataset and 1D velocity structure against the ground-truth locations. For this aftershock sequence, our results reveal the superior accuracy and precision of differential time-based location methods compared to single-event location methods. The results validate the significance of compensating for deviations from assumed 1D velocity structure either by path or site correction modeling or by cancellation of unmodeled structure using differential arrival times. We also evaluate the uncertainty quantification of each program and find that most of the programs underestimate the errors.

Disentangled Representation Learning.

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controlability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, and data mining. In this article, we comprehensively investigate DRL from various aspects including motivations, definitions, methodologies, evaluations, applications, and model designs. We first present two well-recognized definitions, i.e., Intuitive Definition and Group Theory Definition for disentangled representation learning. We further categorize the methodologies for DRL into four groups from the following perspectives, the model type, representation structure, supervision signal, and independence assumption. We also analyze principles to design different DRL models that may benefit different tasks in practical applications. Finally, we point out challenges in DRL as well as potential research directions deserving future investigations. We believe this work may provide insights for promoting the DRL research in the community.

Process-Based Evaluation of Intraseasonal Oceanic Kelvin Waves in the Pacific Ocean in CMIP6 Models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models. Significance Statement Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.

Singularity-free dark energy star in Rastall gravity

The notion of dark energy presents a potential avenue to avoid the gravitational collapse of compact objects, leading to singularities. The present article is devoted primarily to study a novel singularity-free relativistic solution of the Einstein field equations for dark energy stars in the framework of the Rastall theory of gravity. To analyse our model, we consider the Low-Mass X-ray Binary (LMXB) 4U 1608-52 having a mass of 1.74M⊙ and a radius of 9.3 Km (Güver et al., 2010) as a possible dark energy star candidate. We begin with the dark energy equation of state, where the density of dark energy is proportional to the isotropic perfect fluid distribution through a coupling parameter α. We compute the induced metric and extrinsic curvature tensors at the stellar hyper-surface to evaluate the necessary unknown constants appearing in the model. We conduct a thorough and comprehensive analysis to demonstrate the dependence of the physical behaviour of the model on the Rastall parameter (ξ), and interestingly, we obtain a possible phase transition from the dark to baryonic profile, which is sensitive to both α and ξ. We also compute the percentage of dark energy present in the model by varying ξ. However, for a fixed value of ξ, the percentage variation of dark energy with the mass of a star shows that the percentage of dark energy depends on the mass and radius of a star. The causality and energy conditions are well met in the present model, which describes its physical acceptability. The stability of the stellar configuration is established through the study of stability analysis. The graphical nature of the physical parameters and the present theoretical study show that our proposed model is non-singular and a viable representation of a stable realistic stellar structure in the presence of dark and baryonic matter simultaneously.

LIA: Latent Image Animator.

Previous animation techniques mainly focus on leveraging explicit structure representations (e.g., meshes or keypoints) for transferring motion from driving videos to source images. However, such methods are challenged with large appearance variations between source and driving data, as well as require complex additional modules to respectively model appearance and motion. Towards addressing these issues, we introduce the Latent Image Animator (LIA), streamlined to animate high-resolution images. LIA is designed as a simple autoencoder that does not rely on explicit representations. Motion transfer in the pixel space is modeled as linear navigation of motion codes in the latent space. Specifically such navigation is represented as an orthogonal motion dictionary learned in a self-supervised manner based on proposed Linear Motion Decomposition (LMD). Extensive experimental results demonstrate that LIA outperforms state-of-the-art on VoxCeleb, TaichiHD, and TED-talk datasets with respect to video quality and spatio-temporal consistency. In addition LIA is well equipped for zero-shot high-resolution image animation. Code, models, and demo video are available at https://github.com/wyhsirius/LIA.

Challenging Power and Redefining Progress: Arundhati Roy’s Critique of Neoliberalism, Militarization, and Environmental Injustice

Abstract: This study examines how Arundhati Roy critically articulates power dynamics in her groundbreaking writings The Greater Common Good and Power Politics: The Reincarnation of Rumpelstiltskin, offering a thorough analysis of neoliberalism, militarization, and environmental injustice. By dissecting modern development theories, Roy reveals structural oppressive mechanisms that disproportionately affect ecological systems and vulnerable groups. The study shows the complex relationships between economic exploitation, environmental degradation, and social marginalization by critically examining the ways in which corporate interests and state-sponsored policies work together to sustain structural violence. The research examines the rhetorical construction of development via a postcolonial feminist perspective, exposing the systematic commodification of natural resources and the dispossession of indigenous population by neoliberal ideology. Roy’s philosophical intervention highlights grassroots resistance as a transformative political approach and centers the realities of marginalized people, challenging prevailing narratives. Large- scale infrastructure projects, like the Sardar Sarovar Dam, are symbolic representations of modern power structures that put economic expansion ahead of the wellbeing of people and the environment, as the analysis shows. The ethical implications of development theories are critically examined in this study, which adds to larger academic conversations about social resistance, political economy, and environmental justice. Roy’s writing are regarded as an important intellectual critique that reveals the normalized violence present in international capitalism systems and provides substitute ideas of progress, sovereignty, and ecological interdependence.

A plug-and-play knowledge-enhanced module for medical reports generation

Medical reports generation from patient–doctor conversations aims to capture the salient contents from dialogues in the form of medical reports, to assist the healthcare archiving and follow-ups. Unlike general summarization tasks, summaries in specialized domains, such as medical report generation, require stronger support from domain-specific knowledge to enhance the internal logic specific to their field. However, existing methods either model the context sequentially without incorporating knowledge graphs, resulting in unclear representation of domain-specific logical structures, or introduce the expert medical knowledge using homogeneous graphs, which fail to effectively fuse information from different semantic spaces. To address this, we propose to incorporate the knowledge into the step of context modeling by introducing a plug-and-play heterogeneous graph encoder. Furthermore, a fact-aware module is introduced to help our model in integrating key context information filtered by external knowledge during decoding. Compared with previous works, our method makes full use of knowledge, and the proposed modules demonstrate easy adaption on existing frameworks. Experimental results on two public medical dialogue summarization datasets indicate that our method significantly outperforms a range of baselines while being smaller and capable of achieving the state-of-the-art performance11Code is available at https://github.com/salvatoreferragamo/Med-HGE..

Virtual airways heatmaps to optimize point of entry location in lung biopsy planning systems.

We present a virtual model to optimize point of entry (POE) in lung biopsy planning systems. Our model allows to compute the quality of a biopsy sample taken from potential POE, taking into account the margin of error that arises from discrepancies between the orientation in the planning simulation and the actual orientation during the operation. Additionally, the study examines the impact of the characteristics of the lesion. The quality of the biopsy is given by a heatmap projected onto the skeleton of a patient-specific model of airways. The skeleton provides a 3D representation of airways structure, while the heatmap intensity represents the potential amount of tissue that it could be extracted from each POE. This amount of tissue is determined by the intersection of the lesion with a cone that represents the uncertainty area in the introduction of biopsy instruments. The cone, lesion, and skeleton are modelled as graphical objects that define a 3D scene of the intervention. We have simulated different settings of the intervention scene from a single anatomy extracted from a CT scan and two lesions with regular and irregular shapes. The different scenarios are simulated by systematic rotation of each lesion placed at different distances from airways. Analysis of the heatmaps for the different settings shows a strong impact of lesion orientation for irregular shape and the distance for both shapes. The proposed heatmaps help to visually assess the optimal POE and identify whether multiple optimal POEs exist in different zones of the bronchi. They also allow us to model the maximum allowable error in navigation systems and study which variables have the greatest influence on the success of the operation. Additionally, they help determine at what point this influence could potentially jeopardize the operation.

Advancing Credit Risk Management: The Adoption of Probabilistic Graphical Models in Banking

Assessment and management of credit risk at banks is a critical factor that ensures the stability and profitability of these institutions. Existing traditional statistical approaches that worked in the past are already proving to be incapable of coping with this new environment and the complexities intertwined within modern financial markets. Modern methodologies like probabilistic graphical models (PGMs) provide sophisticated methods for modeling these complex relationships, which integrate graph theory with probability theory. In this paper, we will explore Credit Risk and its main components, the mathematical foundation behind credit risk assessment, and the modeling techniques of these components. It compares traditional statistical models (eg, logistic regression and Monte Carlo simulations) with advanced Probabilistic Graphical models (PGMs). The paper highlights selecting the latter based on its capacity for more accurate representation of complex dependencies and uncertainties. PGMs that are covered here include Bayesian and Markov Networks, specifically for their structural representation of joint probability distributions, conditional independence as well as efficient inference. PGMs stand out for an improved model of non-linear interactions, and they allow the incorporation of uncertainty in a natural way, dynamic updating and systematic risk segmentation. This includes using Expectation-Maximization and Gradient-Based Optimization — bringing machine learning and modern computational methods to PGMs. It exemplifies the practical use of PGMs in credit risk management with examples ranging from default probability prediction to portfolio risk assessment and real-time risk monitoring. In conclusion, this paper points to the future promise of PGMs in credit risk management through continuing advancements in computation facilitated by embedding within an ML/AI framework. Reimagining This transformation will revolutionize how financial institutions measure and predict risks.

Methods for refining the depth map obtained from depth sensors

Depth maps are essential in applications such as robotics, augmented reality, autonomous vehicles, and medical imaging, providing critical spatial information. However, depth maps from sensors like time-of-flight (ToF) and structured light systems often suffer from low resolution, noise, and missing data. Addressing these challenges, this study presents an innovative method to refine depth maps by integrating high-resolution color images. The proposed approach employs both hard- and soft-decision pixel assignment strategies to adaptively enhance depth map quality. The hard-decision model simplifies edge classification, while the soft-decision model, integrated within a Markov Random Field framework, improves edge consistency and reduces noise. By analyzing discrepancies between edges in depth maps and color images, the method effectively mitigates artifacts such as texturecopying and blurred edges, ensuring better alignment between the datasets. Key innovations include the use of the Canny edge detection operator to identify and categorize edge inconsistencies and anisotropic affinity calculations for precise structural representation. The soft-decision model introduces advanced noise reduction techniques, improving depth map resolution and preserving edge details better than traditional methods. Experimental validation on Middlebury benchmark datasets demonstrates that the proposed method outperforms existing techniques in reducing Mean Absolute Difference values, especially in high-upscaling scenarios. Visual comparisons highlight its ability to suppress artifacts and enhance edge sharpness, confirming its effectiveness across various conditions. This approach holds significant potential for applications requiring high-quality depth maps, including robotics, augmented reality, autonomous systems, and medical imaging. By addressing critical limitations of current methods, the study offers a robust, versatile solution for depth map refinement, with opportunities for real-time optimization in dynamic environments.

Brussonol and komaroviquinone as inhibitors of the SARS-CoV-2 Omicron BA.2 variant spike protein: A molecular docking, molecular dynamics, and quantum biochemistry approach

Since late 2019, humanity has faced the challenges posed by the COVID-19 pandemic, caused by the SARS-CoV-2 virus. The continuous evolution of SARS-CoV-2 has led to the emergence of multiple Variants of Concern (VOCs) and Variants of Interest (VOIs), posing significant risks to global health. SARS-CoV-2 infects host cells via the angiotensin-converting enzyme 2 (ACE2) receptors, facilitated by the spike (S) protein. Icetexane diterpenes, including brussonol and komaroviquinone, exhibit notable anti-inflammatory, antibacterial, antiviral, antiproliferative, and anticancer properties. Recent research has explored their potential as inhibitors of the SARS-CoV-2 3Clpro protease, showing promising efficacy comparable to Nirmatrelvir. This study investigates brussonol and komaroviquinone as potential inhibitors of the SARS-CoV-2 Omicron BA.2 variant spike protein using molecular docking, molecular dynamics simulations, and quantum biochemistry approaches. The stability and interaction energies of brussonol, komaroviquinone, and mefloquine with the SARS-CoV-2 Omicron BA.2 variant spike protein were evaluated. RMSD analysis demonstrated that komaroviquinone and mefloquine maintain more stable binding poses with the spike protein compared to various NAGs and glycans. Electrostatic potential maps revealed significant interactions with ASN603, a critical residue for ligand binding efficacy. Furthermore, this study addresses a gap in current research, as no studies were found that simulate the trimer of the SARS-CoV-2 BA.2 variant spike protein. Most existing studies focus on the monomer and often exclude the NAGs and glycans. This research underscores the importance of maintaining the NAGs and glycans in the trimer simulations, providing a more accurate representation of the protein's structure and its interactions with ligands. The findings indicate that both komaroviquinone and brussonol exhibit higher binding affinities compared to mefloquine. This study provides valuable insights into the molecular interactions of these compounds, highlighting their potential for further development as antiviral agents against SARS-CoV-2.

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, several approaches have been developed that make use of information determined from the 3D structure for a variety of downstream tasks. However, methods for predicting binding affinity that do consider protein structure generally do not take full advantage of such 3D structural protein information, often using such information only to define nearest-neighbor graphs based on inter-residue or inter-atomic distances. Here, we present a joint architecture that we call CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity) that makes use of an SE(3)-equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations, and we further augment these representations by incorporating an attention-based mechanism by which individual residues in a protein can attend to atoms in a ligand and vice-versa to improve interpretability. In this manner, we show that using equivariant graph neural networks in our architecture enables CASTER-DTA to approach and exceed state-of-the-art performance in predicting drug-target affinity without the inclusion of external information, such as protein language model embeddings. We do so on the Davis and KIBA datasets, common benchmarks for predicting drug-target affinity. We also discuss future steps to further improve performance.

Automatic code generation based on Abstract Syntax-based encoding. Application on malware detection code generation based on MITRE ATT&CK techniques

In the last decade, the area of code generation based on natural language was one of the most studied machine learning topics. The paper addresses the problem of code generation from natural language, by generating a syntax-error-free generator model, which creates an Syntax-based model, later translated into code, for generating the structure of the code. Two approaches are comparatively investigated for generating the structure of the program. The first approach generates code templates in the form of an Abstract Syntax Tree, while the second generates the code in the form of an Abstract Syntax Graph, a new introduced concept which reduces the initial redundancy of Abstract Syntax Trees and uses it as a new way to generate code. The proposed methodology is tested on two literature data sets and on malware detection code generation based on a real data set containing MITRE ATT&CK techniques. The results outperform the state-of-the-art Abstract Syntax Tree approaches by 2.46% and the plain text-based approaches with more than 12.5%, highlighting that the proposed methodology learns better the structural representation than other literature approaches.