User-specified Target Research Articles

Single-step phase identification and phase locking for coherent beam combination using deep learning

Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations

Survival outcomes are common in comparative effectiveness studies and require unique handling because they are usually incompletely observed due to right-censoring. A ``once for all'' approach for causal inference with survival outcomes constructs pseudo-observations and allows standard methods such as propensity score weighting to proceed as if the outcomes are completely observed. For a general class of model-free causal estimands with survival outcomes on user-specified target populations, we develop corresponding propensity score weighting estimators based on the pseudo-observations and establish their asymptotic properties. In particular, utilizing the functional delta-method and the von Mises expansion, we derive a new closed-form variance of the weighting estimator that takes into account the uncertainty due to both pseudo-observation calculation and propensity score estimation. This allows valid and computationally efficient inference without resampling. We also prove the optimal efficiency property of the overlap weights within the class of balancing weights for survival outcomes. The proposed methods are applicable to both binary and multiple treatments. Extensive simulations are conducted to explore the operating characteristics of the proposed method versus other commonly used alternatives. We apply the proposed method to compare the causal effects of three popular treatment approaches for prostate cancer patients.

Cross-Attention and Seamless Replacement of Latent Prompts for High-Definition Image-Driven Video Editing

Recently, text-driven video editing has received increasing attention due to the surprising success of the text-to-image model in improving video quality. However, video editing based on the text prompt is facing huge challenges in achieving precise and controllable editing. Herein, we propose Latent prompt Image-driven Video Editing (LIVE) with a precise and controllable video editing function. The important innovation of LIVE is to utilize the latent codes from reference images as latent prompts to rapidly enrich visual details. The novel latent prompt mechanism endows two powerful capabilities for LIVE: one is a comprehensively interactive ability between video frame and latent prompt in the spatial and temporal dimensions, achieved by revisiting and enhancing cross-attention, and the other is the efficient expression ability of training continuous input videos and images within the diffusion space by fine-tuning various components such as latent prompts, textual embeddings, and LDM parameters. Therefore, LIVE can efficiently generate various edited videos with visual consistency by seamlessly replacing the objects in each frame with user-specified targets. The high-definition experimental results from real-world videos not only confirmed the effectiveness of LIVE but also demonstrated important potential application prospects of LIVE in image-driven video editing.

Direct Determination of Optimal Real-Space Orbitals for Correlated Electronic Structure of Molecules.

We demonstrate how to determine numerically nearly exact orthonormal orbitals that are optimal for the evaluation of the energy of arbitrary (correlated) states of atoms and molecules by minimization of the energy Lagrangian. Orbitals are expressed in real space using a multiresolution spectral element basis that is refined adaptively to achieve the user-specified target precision while avoiding the ill-conditioning issues that plague AO basis set expansions traditionally used for correlated models of molecular electronic structure. For light atoms, the orbital solver, in conjunction with a variational electronic structure model [selected Configuration Interaction (CI)] provides energies of comparable precision to a state-of-the-art atomic CI solver. The computed electronic energies of atoms and molecules are significantly more accurate than the counterparts obtained with the orbital sets of the same rank expanded in Gaussian AO bases, and can be determined even when linear dependence issues preclude the use of the AO bases. It is feasible to optimize more than 100 fully correlated numerical orbitals on a single computer node, and significant room exists for additional improvement. These findings suggest that real-space orbital representations might be the preferred alternative to AO representations for high-end models of correlated electronic states of molecules and materials.

Multi-Attention Infused Integrated Facial Attribute Editing Model: Enhancing the Robustness of Facial Attribute Manipulation

Facial attribute editing refers to the task of modifying facial images by altering specific target facial attributes. Existing approaches typically rely on the combination of generative adversarial networks and encoder–decoder architectures to tackle this problem. However, current methods may exhibit limited accuracy when dealing with certain attributes. The primary objective of this research is to enhance facial image modification based on user-specified target facial attributes, such as hair color, beard removal, or gender transformation. During the editing process, it is crucial to selectively modify only the regions relevant to the target attributes while preserving the details of other unrelated facial attributes. This ensures that the editing results appear more natural and realistic. This study introduces a novel approach called MAGAN (Combining GRU Structure and Additive Attention with AGU—Adaptive Gated Units). Moreover, a discriminative attention mechanism is introduced to automatically identify key regions in the input images that are relevant to facial attributes. This mechanism concentrates attention on these regions, enhancing the model’s ability to accurately capture and analyze subtle facial attribute features. The method incorporates external attention within the convolutional layers of the encoder–decoder architecture, facilitating the modeling of linear complexity across image regions and implicitly considering correlations among all data samples. By employing discriminative attention in the discriminator, the model achieves more precise attribute editing. To evaluate the effectiveness of MAGAN, experiments were conducted on the CelebA dataset. The average precision of facial attribute generation in images edited by our model stands at 91.83%. PSNR and SSIM for reconstructed images are 32.52 and 0.957, respectively. In comparison with existing methodologies (AttGAN, STGAN, MUGAN), noteworthy enhancements have been achieved in the domain of facial attribute manipulation.

Generalized Stratified Sampling for Efficient Reliability Assessment of Structures against Natural Hazards

Performance-based engineering for natural hazards facilitates the design and appraisal of structures with rigorous evaluation of their uncertain structural behavior under potentially extreme stochastic loads expressed in terms of failure probabilities against stated criteria. As a result, efficient stochastic simulation schemes are central to computational frameworks that aim to estimate failure probabilities associated with multiple limit states using limited sample sets. In this work, a generalized stratified sampling scheme is proposed in which two phases of sampling are involved: the first is devoted to the generation of strata-wise samples and the estimation of strata probabilities, whereas the second phase aims at the estimation of strata-wise failure probabilities. Phase-I sampling enables the selection of a generalized stratification variable (i.e., not necessarily belonging to the input set of random variables) for which the probability distribution is not known a priori. To improve the efficiency, Markov Chain Monte Carlo Phase-I sampling is proposed when Monte Carlo simulation is deemed infeasible, and optimal Phase-II sampling is implemented based on user-specified target coefficients of variation for the limit states of interest. The expressions for these coefficients are derived with due regard to the sample correlations induced by the Markov chains and the uncertainty in the estimated strata probabilities. The proposed stochastic simulation scheme reaps the benefits of near-optimal stratified sampling for a broader choice of stratification variables in high-dimensional reliability problems with a mechanism to approximately control the accuracy of the estimators of multiple failure probabilities. The practicality and efficiency of the scheme are demonstrated using two examples involving the estimation of failure probabilities associated with highly nonlinear responses induced by wind and seismic excitations.

Fuzzy Logic Controller for Temperature Control

This paper describes the use of MATLAB in the design and implementation of a temperature control system for agricultural applications based on fuzzy logic. The system uses two inputs—the user-specified target temperature and the temperature sensed from the agricultural field—to produce two outputs for positive and negative pulse width modulation (P-PWM-C and N-PWM-C). With membership functions defined for the input and output ranges, the fuzzy logic controller is designed using the MATLAB Fuzzy Logic Designer. Ten guidelines are created to regulate the temperature control procedure. The relationship between the inputs and outputs is shown in the 3D surface viewer, which sheds light on the fuzzy logic control approach.

Computational design of CRISPR guide RNAs to enable strain-specific control of microbial consortia

Microbes naturally coexist in complex, multistrain communities. However, extracting individual microbes from and specifically manipulating the composition of these consortia remain challenging. The sequence-specific nature of CRISPR guide RNAs can be leveraged to accurately differentiate microorganisms and facilitate the creation of tools that can achieve these tasks. We developed a computational program, ssCRISPR, which designs strain-specific CRISPR guide RNA sequences with user-specified target strains, protected strains, and guide RNA properties. We experimentally verify the accuracy of the strain specificity predictions in both Escherichia coli and Pseudomonas spp. and show that up to three nucleotide mismatches are often required to ensure perfect specificity. To demonstrate the functionality of ssCRISPR, we apply computationally designed CRISPR-Cas9 guide RNAs to two applications: the purification of specific microbes through one- and two-plasmid transformation workflows and the targeted removal of specific microbes using DNA-loaded liposomes. For strain purification, we utilize gRNAs designed to target and kill all microbes in a consortium except the specific microbe to be isolated. For strain elimination, we utilize gRNAs designed to target only the unwanted microbe while protecting all other strains in the community. ssCRISPR will be of use in diverse microbiota engineering applications.

A machine learning approach for ball milling of alumina ceramics

In this work, machine learning approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally collected by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between processing parameters (inputs) and quality responses (outputs) are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing user-specified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired final powders with quality responses quite similar to the target responses.

Development and Evaluation of a Digital MPF5-Moisture Meter

An important advantage for applying the right amount of water to the fields is provided by the effective irrigation management techniques based on soil moisture monitoring. With a calibrated and exact resolution of 0.03m3/m3 and 36 mV, the developed MPF5 moisture meter monitors soil moisture from a depth of 10 cm to 80 cm using a set of sensors, transducers, capacitors, resistors, and variable micro-switch. The response monitoring system compares the soil moisture to the user-specified target values and generates an alert if the soil moisture falls below the required level for particular crops. There were many similarities between the instrument's output and that of other automated soil moisture meters like the REOTEMP-MM17 moisture meter and the PR2 capacitance moisture meter. The device works very well for both mineral and organic soils to monitor soil moisture for reliable irrigation scheduling

Target-Mass Grasping of Entangled Food Using Pre-Grasping & Post-Grasping

Food packing industries typically use seasonal ingredients with immense variety that factory workers manually pack. For small pieces of food picked by volume or weight that tend to get entangled, stick or clump together, it is difficult to predict how intertwined they are from a visual examination, making it a challenge to grasp the requisite target mass accurately. Workers rely on a combination of weighing scales and a sequence of complex maneuvers to separate out the food and reach the target mass. This makes automation of the process a non-trivial affair. In this study, we propose methods that combines 1) pre-grasping to reduce the degree of the entanglement, 2) post-grasping to adjust the grasped mass using a novel gripper mechanism to carefully discard excess food when the grasped amount is larger than the target mass, and 3) selecting the grasping point to grasp an amount likely to be reasonably higher than target grasping mass with confidence. We evaluate the methods on a variety of foods that entangle, stick and clump, each of which has a different size, shape, and material properties such as volumetric mass density. We show significant improvement in grasp accuracy of user-specified target masses using our proposed methods.

Scale-Resolving Simulations of a Statistically Targeted Forcing Method for Synthetic Turbulence Generation in a Freestream Turbulence

Abstract Computational fluid dynamics results for synthetic turbulence generation of freestream turbulence by a proposed statistically targeted forcing (STF) method are presented. The STF method has been previously documented for homogeneous isotropic and anisotropic turbulence and formulated to introduce a fluctuating velocity field with a distribution of first and second moments that match a user-specified target mean velocity and Reynolds stress tensor, by incorporating deterministic time-dependent forcing terms into the momentum equation for the resolved flow. This study extends its applicability to generation of freestream turbulence in scale-resolving simulations in which flow is spatially developing. The method provides flexibility in regions where synthetic turbulence needs to be generated or damped, for use in engineering level scale-resolving simulations such as large eddy simulation (LES), and hybrid Reynolds-averaged Navier–Stokes (RANS)-LES. The objective of this study is to evaluate the performance of the proposed STF method in simulations that incorporate monotonically integrated LES (MILES), Smagorinsky (SMAG) LES subgrid stress model, and dynamic hybrid RANS-LES (DHRL) model in reproducing inflow freestream turbulent flow, and the capability of each model to reproduce proper energy decay characteristics downstream of forcing. Results are inter-rogated and compared to target statistical velocity and turbulent stress distributions for inflow turbulence and evaluated in terms of energy spectra. Analysis of the influence of STF model parameters, mesh resolution, and LES subgrid stress model on the results is investigated. Results show that the STF method can successfully reproduce desired statistical distributions in a nearly isotropic freestream turbulent flow.

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.

Evaluation of a Statistically Targeted Forcing Method for Synthetic Turbulence Generation in Scale-Resolving Simulations

Abstract Computational fluid dynamics (CFD) results are presented for synthetic turbulence generation by a proposed statistically targeted forcing (STF) method. The new method seeks to introduce a fluctuating velocity field with a distribution of first and second moments that approximate a user-specified target mean velocity and Reynolds stress tensor, by incorporating deterministic time-dependent forcing terms into the momentum equation for the resolved flow. The STF method is formulated to extend the applicability of previously documented methods and provide flexibility in regions where synthetic turbulence needs to be generated or sustained, for use in engineering level large eddy and hybrid large eddy/Reynolds-averaged Navier–Stokes CFD simulations. The objective of this study is to evaluate the performance of the proposed STF method in large-eddy simulation (LES) simulations of isotropic and anisotropic homogeneous turbulent flow test cases. Results are interrogated and compared to target statistical velocity and turbulent stress distributions and evaluated in terms of energy spectra. Analysis of the influence of STF model parameters, mesh resolution, and LES subgrid stress model on the results is investigated. Results show that the new method can successfully reproduce desired statistical distributions in a homogeneous turbulent flow.

Construction of \xe0 la carte QconCAT protein standards for multiplexed quantification of user-specified target proteins

BackgroundQconCATs are quantitative concatamers for proteomic applications that yield stoichiometric quantities of sets of stable isotope-labelled internal standards. However, changing a QconCAT design, for example, to replace poorly performing peptide standards has been a protracted process.ResultsWe report a new approach to the assembly and construction of QconCATs, based on synthetic biology precepts of biobricks, making use of loop assembly to construct larger entities from individual biobricks. The basic building block (a Qbrick) is a segment of DNA that encodes two or more quantification peptides for a single protein, readily held in a repository as a library resource. These Qbricks are then assembled in a one tube ligation reaction that enforces the order of assembly, to yield short QconCATs that are useable for small quantification products. However, the DNA context of the short construct also allows a second cycle of loop assembly such that five different short QconCATs can be assembled into a longer QconCAT in a second, single tube ligation. From a library of Qbricks, a bespoke QconCAT can be assembled quickly and efficiently in a form suitable for expression and labelling in vivo or in vitro.ConclusionsWe refer to this approach as the ALACAT strategy as it permits à la carte design of quantification standards. ALACAT methodology is a major gain in flexibility of QconCAT implementation as it supports rapid editing and improvement of QconCATs and permits, for example, substitution of one peptide by another.

An editable learner model for text recommendation for language learning

Abstract Extracurricular reading is important for learning foreign languages. Text recommendation systems typically classify users and documents into levels, and then match users with documents at the same level. Although this approach can be effective, it has two significant shortcomings. First, the levels assume a standard order of language acquisition and cannot be personalized to the users’ learning patterns. Second, recommendation decisions are not transparent because the leveling algorithms can be difficult for users to interpret. We propose a novel method for text recommendation that addresses these two issues. To enhance personalization, an open, editable learner model estimates user knowledge of each word in the foreign language. The documents are ranked by new-word density (NWD) – that is, the percentage of words that are new to the user in the document. The system then recommends documents according to a user-specified target NWD. This design offers complete transparency as users can scrutinize recommendations by reviewing the NWD estimation of the learner model. This article describes an implementation of this method in a mobile app for learners of Chinese as a foreign language. Evaluation results show that users were able to manipulate the learner model and NWD parameters to adjust the difficulty of the recommended documents. In a survey, users reported satisfaction with both the concept and implementation of this text recommendation method.

Planar Coil Optimization in a Magnetically Shielded Cylinder

Hybrid magnetic shields with both active field generating components and high-permeability magnetic shielding are increasingly needed for various technologies and experiments that require precision-controlled magnetic field environments. However, the fields generated by the active components interact with the passive magnetic shield, distorting the desired field profiles. Consequently, optimization of the active components needed to generate user-specified target fields must include coupling to the high-permeability passive components. Here, we consider the optimization of planar active systems, on which an arbitrary static current flows, coupled to a closed high-permeability cylindrical shield. We modify the Green's function for the magnetic vector potential to match boundary conditions on the shield's interior surface, enabling us to construct an inverse optimization problem to design planar coils that generate user-specified magnetic fields inside high-permeability shields. We validate our methodology by designing two biplanar hybrid active-passive systems, which generate a constant transverse field, $\mathbf{B}=\stackrel{^}{\mathbf{x}}$, and a linear field gradient, $\mathbf{B}=(\ensuremath{-}x\stackrel{^}{\mathbf{x}}\ensuremath{-}y\stackrel{^}{\mathbf{y}}+2z\stackrel{^}{\mathbf{z}})$, respectively. For both systems, the inverse-optimized magnetic field profiles agree well with forward numerical simulations. Our design methodology is accurate and flexible, facilitating the miniaturization of high-performance hybrid magnetic field generating technologies with strict design constraints and spatial limitations.

Template-Free Try-On Image Synthesis via Semantic-Guided Optimization.

The virtual try-on task is so attractive that it has drawn considerable attention in the field of computer vision. However, presenting the 3-D physical characteristic (e.g., pleat and shadow) based on a 2-D image is very challenging. Although there have been several previous studies on 2-D-based virtual try-on work, most: 1) required user-specified target poses that are not user-friendly and may not be the best for the target clothing and 2) failed to address some problematic cases, including facial details, clothing wrinkles, and body occlusions. To address these two challenges, in this article, we propose an innovative template-free try-on image synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose according to the user-specified in-shop clothing. Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose. The qualitative and quantitative experiments both indicate that the proposed TF-TIS outperforms the state-of-the-art methods, especially for difficult cases.

Idleness-Aware Dynamic Power Mode Selection on the i.MX 7ULP IoT Edge Processor

Power management is a crucial concern in micro-controller platforms for the Internet of Things (IoT) edge. Many applications present a variable and difficult to predict workload profile, usually driven by external inputs. The dynamic tuning of power consumption to the application requirements is indeed a viable approach to save energy. In this paper, we propose the implementation of a power management strategy for a novel low-cost low-power heterogeneous dual-core SoC for IoT edge fabricated in 28 nm FD-SOI technology. Ss with more complex power management policies implemented on high-end application processors, we propose a power management strategy where the power mode is dynamically selected to ensure user-specified target idleness. We demonstrate that the dynamic power mode selection introduced by our power manager allows achieving more than 43% power consumption reduction with respect to static worst-case power mode selection, without any significant penalty in the performance of a running application.

Lookup Table-Based Fast Reliability-Aware Sample Preparation Using Digital Microfluidic Biochips

Reliability of the prepared fluidic samples is a major concern for automated sample preparation using microfluidic biochips, where induced errors in the resultant concentration values severely affect the assay outcome. However, the existing design automation techniques have not thoroughly considered the reliability model to reduce the induced concentration errors during sample preparation. This article proposes a fast reliability-aware sample preparation (RASP) method for determining the optimized sequence of mixing steps (mixing process) with the enhanced reliability. In RASP, a probabilistic concentration prediction model is proposed for analyzing the reliability of a given mixing process. Based on this probabilistic model, a lookup table construction algorithm along with the table query method is proposed to obtain the optimized mixing process. The simulation results show that for any user-specified target concentration, RASP can effectively determine the optimized mixing process, which generates the droplets with target concentration within the error tolerance of 0.1%. Compared with the state-of-the-art sample preparation algorithm, RASP improves the reliability-related accuracy by 91.4% on average via 2048 testcases.