Original Object Research Articles

Adaptive practices of working with industrial heritage in a historical settlement

Examples of industrial heritage located in a historical urban environment that have partially or completely lost their original function are considered. An analysis of world and domestic practices in working with similar objects is carried out, the possibility of their adaptation to use in the conditions of the historical settlement of the city of Samara is assessed. Based on the conducted research, a number of techniques have been identified that make it possible to competently adapt industrial facilities and industrial territories to modern use, while revealing their architectural, construction, historical and cultural potential. The above practices of working with industrial heritage objects can be applied when developing a strategy for the renovation of partially used or lost their original function historical objects of industrial architecture in the historical settlement of Samara and in a number of historical settlements with similar characteristics.

A safety realignment framework via subspace-oriented model fusion for large language models

To improve the performance of large language models (LLMs) on specific tasks, task-specific instruction fine-tuning is essential. However, this process can easily compromise the safety of a task-specific model, making it susceptible to obeying malicious instructions and generating harmful content. Current methods against fine-tuning attack usually interfere with the original fine-tuning objectives or require substantial amounts of data to realign the compromised model. To address these two major challenges, we propose reusing the initial aligned model and realigning task-specific model in the safety subspace. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to transfer the safeguard capabilities of an initially aligned model into the current task-specific model. Our approach begins by disentangling all task vectors from the parameters of each task-specific model. We then identify safety-critical regions within these vectors by subspace masking techniques. Finally, we fuse the initial safely aligned LLM with all task vectors based on the identified safety subspace to restore the model’s safety properties. Our experiments confirm that our safety realignment framework satisfies the safety requirements of an independent task-specific model as well as traditional multitask models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on specific tasks while exhibiting higher data efficiency. The code is publicly available at https://github.com/xinykou/safety_realignment.

SCUBA implements a storage format-agnostic API for single-cell data access in R

While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of development of the analysis suites and their underlying data structures. To address this, we created SCUBA (Single-Cell Unified Backend API), an R package that implements a unified data access API for all common R and Python single-cell object formats. SCUBA extends the data access approach from the widely used Seurat package to SingleCellExperiment and anndata objects. SCUBA also implements new data-specific access functions for all supported object types. Performance scales well across all SCUBA-supported formats. In addition to performance, SCUBA offers several advantages over object conversion for the visualization and further analysis of pre-processed single-cell data. First, SCUBA extracts only data required for the operation at hand, leaving the original object unmodified. This process is simpler, less error prone, and less memory intensive than object conversion, which operates on the entire dataset. Second, code written with SCUBA can use any supported object class as input, with simple and consistent syntax across object formats. This allows a single analysis script or package (like our interactive single-cell browser, scExploreR) to work seamlessly with multiple object types, reducing the complexity of the code and improving both readability and reproducibility. Adoption of SCUBA will ultimately improve collaboration and reproducible research in single-cell analysis by lowering the barriers between package ecosystems.

Dual-spherical-wave optical scanning holographic microscopy with resolution enhancement and an extended depth of field

Conventional optical scanning holographic microscopy (OSHM) is realized by using a plane wave and a spherical wave as the illumination. The resolution of the conventional OSHM is limited by the numerical aperture of the spherical wave and cannot exceed the Rayleigh limit. In this paper, the OSHM by using dual spherical waves as the illumination is studied. The model of a Gaussian wave, together with the constraints on the signal strength and the visibility, are considered in the analysis of the dual-spherical-wave (DS) OSHM. For an object located at the symmetry plane of the DS-OSHM, the resolution can slightly exceed the Rayleigh limit, and the depth of field (DoF) is extended significantly. For a transparent object, the noise in the DS-OSHM is less than that of the conventional OSHM, thanks to the highly divergent scanning beam. If the object is off the symmetry plane, the resolution is still enhanced, but the extension of the DoF is limited. In addition, a clear reconstructed image will be observed not only at the original object plane, but also at its mirror plane. To the best of our knowledge, this phenomenon is reported for the first time.

Capgras Syndrome Accompanying Schizophrenia: An Adolescent Case Report

In Capgras syndrome, the patient believes that the original person or objects have been replaced by identical ones (fakes). The syndrome rarely occurs in its pure form. It often accompanies psychotic illness and organic pathology. Capgras syndrome can occur in all age groups and is more common in women. In this article, the case of a 16-year-old female patient who applied to the hospital with complaints of aggression, harming her environment and wanting to have a DNA test will be presented. During her first admission examination, she was found to have Capgras delusions, and she was hospitalized and monitored in a psychiatric clinic. The patient, who was followed up with a diagnosis of early-onset psychotic disorder, was determined to be a case of CS accompanying Schizophrenia. Early-onset Schizophrenia is already a rare condition, and the onset of CS at this age is uncommon. Our case is valuable as it shows that CS, a rare condition, can also be seen in adolescence.

Single‐Shot Non‐Invasive Imaging Through Dynamic Scattering Media Beyond the Memory Effect via Virtual Reference‐Based Correlation Holography

AbstractNon‐invasive wide‐field imaging through dynamic random media is a sought‐after goal with important applications ranging from medical diagnosis to remote sensing. However, some existing methods, such as speckle correlation‐based techniques, are limited in field of view due to the memory effect; while some other methods, such as wavefront shaping and transmission matrix techniques, face considerable challenges when applied in dynamic scenarios because of the complexity involved in modulation and measurement. These limitations significantly impede the effectiveness and applicability of these approaches. Here, the concept of virtual reference light (VRL), which allows for the reconstruction of the original object with just a single‐shot detection of the speckle is proposed. Experimental results demonstrate that the imaging field achieves a 3.8‐fold memory effect range. In the experimental setup, the light source and detector are positioned on one side of the random medium, while the sample is placed on the opposite side, enabling non‐invasive detection. Imaging results with both static and dynamic scattering media are presented to verify the feasibility of the proposed method, offering an effective solution for real‐time target imaging and detection.

Shape and topology optimization method with generalized topological derivatives

This paper introduces a novel method for shape and topology optimization based on a generalized approach for evaluating topological derivatives, which are essential for the integration of shape and topology optimization. Traditionally, evaluating these derivatives presents significant mathematical challenges due to the discontinuity introduced by the insertion of a hole within the domain of interest. To overcome this issue, the study employs Helmholtz-type partial differential equations (PDEs) to construct a filtered objective functional. This approach ensures differentiability across the material and void phases and continuity over the fixed design domain while maintaining the same evaluation value as the original objective functional. By considering differentiability, continuity conditions, and the relationship between shape and topological derivatives during asymptotic analysis, generalized topological derivatives are obtained through established mathematical procedures. These topological derivatives exhibit a direct correlation with the PDE solutions and demonstrate satisfactory smoothness, thereby facilitating refined shapes in optimization strategies. Furthermore, an effective shape update algorithm is proposed, which directly integrates topological derivatives into structural optimization problems, simplifying their implementation and improving efficiency. Finally, the efficacy of the proposed methodology is demonstrated through its application to various optimal design problems, including stiffness maximization, compliant mechanisms, and eigenfrequency maximization. Verification results further highlight its potential to enhance existing methods for addressing more practical and complex optimization challenges.

Being Relevant

Abstract: In her first column, new NATS President Alexis Davis-Hazell articulates a vision for the relevance of NATS in the 21st century. She reflects on the original objectives of NATS’ charter members and explores questions that help form an organizational outlook reaching into the year 2044. Davis-Hazell introduces a philosophy of retention involving representation of multiple cultural and professional identities, generations, and musical genres within chapter, district, and region leadership. She challenges current members to nominate more independent teachers for leadership positions and to support the investment in technological advancements for the association that will move us toward fulfilling NATS’ vision.

Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization

This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning. A new design problem is formulated where the topology optimization hyperparameters are defined as design variables and the problem is solved by surrogate optimization. The new design problem is nested, such that a topology optimization problem is solved as an inner problem. To encourage the identification of high-performing solutions while limiting the computational resource requirements, the outer objective function is defined as the original objective combined with penalization for intermediate densities and deviations from the prescribed material consumption. The contribution is demonstrated on density-based topology optimization with various hyperparameters and objectives, including compliance minimization, compliant mechanism design, and buckling load factor maximization. Consistent performance is observed across all tested examples. For a simple two hyperparameter case, the new framework is shown to reduce amount of times a topology optimization algorithm is executed by 90% without notably sacrificing the objective compared to a rigorous manual grid search.

Trust region based chaotic search for solving multi‐objective optimization problems

AbstractA numerical optimization technique used to address nonlinear programming problems is the trust region (TR) method. TR uses a quadratic model, which may represent the function adequately, to create a neighbourhood around the current best solution as a trust region in each step, rather than searching for the original function's objective solution. This allows the method to determine the next local optimum. The TR technique has been utilized by numerous researchers to tackle multi‐objective optimization problems (MOOPs). But there is not any publication that discusses the issue of applying a chaotic search (CS) with the TR algorithm for solving multi‐objective (MO) problems. From this motivation, the main contribution of this study is to introduce trust‐region (TR) technique based on chaotic search (CS) for solving MOOPs. First, the reference point interactive approach is used to convert MOOP to a single objective optimization problem (SOOP). The search space is then randomly initialized with a set of initial points. Second, in order to supply locations on the Pareto boundary, the TR method solves the SOOP. Finally, all points on the Pareto frontier are obtained using CS. A range of MO benchmark problems have demonstrated the efficiency of the proposed algorithm (TR based CS) in generating Pareto optimum sets for MOOPs. Furthermore, a demonstration of the suggested algorithm's ability to resolve real‐world applications is provided through a practical implementation of the algorithm to improve an abrasive water‐jet machining process (AWJM).

Majorisation‐minimisation algorithm for optimal state discrimination in quantum communications

AbstractDesigning optimal measurement operators for quantum state discrimination (QSD) is an important problem in quantum communications and cryptography applications. Prior works have demonstrated that optimal quantum measurement operators can be obtained by solving a convex semidefinite program (SDP). However, solving the SDP can represent a high computational burden for many real‐time quantum communication systems. To address this issue, a majorisation‐minimisation (MM)‐based algorithm, called Quantum Majorisation‐Minimisation (QMM) is proposed for solving the QSD problem. In QMM, the authors reparametrise the original objective, then tightly upper‐bound it at any given iterate, and obtain the next iterate as a closed‐form solution to the upper‐bound minimisation problem. Our numerical simulations demonstrate that the proposed QMM algorithm significantly outperforms the state‐of‐the‐art SDP algorithm in terms of speed, while maintaining comparable performance for solving QSD problems in quantum communication applications.

Mechanoimmune-Driven Backpack Sustains Dendritic Cell Maturation for Synergistic Tumor Radiotherapy.

Cell backpacks present significant potential in both therapeutic and diagnostic applications, making it essential to further explore their interactions with host cells. Current evidence indicates that backpacks can induce sustained immune responses. Our original objective was to incorporate a model antigen into the backpacks to promote dendritic cell maturation and facilitate antigen presentation, thereby inducing immune responses. However, we unexpectedly discovered that both antigen-loaded backpacks and empty backpacks demonstrated comparable abilities to induce dendritic cell maturation, resulting in nearly identical potency in T-cell proliferation. Our mechanistic studies suggest that the attachment of backpacks induces mechanical forces on dendritic cells via opening the PIEZO1 mechanical ion channel. This interaction leads to the remodeling of the intracellular cytoskeleton and facilitates the production of type I interferons by dendritic cells. Consequently, the mechano-immune-driven dendritic cell backpacks, when combined with radiotherapy, induce a robust antitumor effect. This research presents an avenue for leveraging mechanotransduction to enhance combination immunotherapeutic strategies, potentially leading to groundbreaking advancements in the field.

YOLO-FD: An accurate fish disease detection method based on multi-task learning

Fish diseases often exhibit high risks of contagion, resulting in substantial economic losses. Accurate assessment of fish disease severity during diagnosis using deep learning poses a considerable challenge. Currently, deep learning models mainly focus on single tasks in fish disease detection, such as classification, object detection and segmentation. However, the accurate assessment of fish disease severity requires the integration of multiple dimensions of information, which is beyond the capabilities of traditional single-task methods. Therefore, this paper proposes YOLO-FD, a multi-task learning network specifically designed for simultaneous detection and segmentation. YOLO-FD extends the YOLOv8 backbone by integrating a novel semantic segmentation branch dedicated to precisely segmenting infected areas in diseased fish, while retaining the original object detection branch for identifying infected fish. Weight uncertainty and PCGrad are employed to balance the weights of different losses and to optimize conflicting gradients during the training process. With only a negligible increase in network parameters, YOLO-FD, tested on our constructed Nocardiosis fish dataset, achieves a detection accuracy of 94.2% mAP50 and gets mIOU of 79.4%, showcasing a 0.5% improvement over the baseline YOLOv8 and surpassing the state-of-the-art semantic segmentation network Deeplabv3plus by 4%. Notably, compared to the adapted multi-task network YOLOP, YOLO-FD demonstrates substantial improvements, displaying a 13.7% increase in mAP50-95 and a 15.1% boost in mIOU. On the VOC2012 segmentation dataset, the proposed method exhibits a 3.2% increase in mAP50 and a 2.2% rise in mAP50-95 compared to the baseline. Furthermore, results of the ablation experiment validate the effectiveness and generalization of the proposed multi-task learning approach. Source code is available at https://github.com/feifei-Lee/YOLO-FD.

Offset Multipliers and Defence Industrial Policy Efficiency

ABSTRACT Mandatory offsets are policy instruments to leverage defence procurement projects to conduct industrial policy. The prime contractor commits to generate new business in mutually acceptable sectors equivalent to a large percentage of the project value. Offset multipliers “relax” this constraint by discounting the prime contractor’s offset obligations if investments flow to sectors prioritized by the purchasing country’s industrial policy objectives. This endogenizes the relationship between the original project and the offset contracts. This paper provides a new theoretical analysis of the following three questions that have gone unaddressed in the literature. First, the efficiency of such policies depends on the absorption capacity of a targeted industry. If this capacity is low, import substitution is expensive and the prime contractor may rather choose to invest elsewhere in the economy to satisfy the overall mandatory offset constraint thereby thwarting the original objective. Second, whereas a uniform relaxation of offsets through multipliers can reduce distortions introduced by mandated offsets, multipliers may enhance distortions as an unintended consequence. Third, the prime contractor’s response to offset credit incentives may be weak due to transaction costs arising from having to find new domestic partners to satisfy the offset requirements and manage the contracts.

A parallel variable-fidelity algorithm for efficient constrained multi-objective aerodynamic design optimization

Modern aerodynamic design optimization aims to discover optimal configurations using computational fluid dynamics under complex flow conditions, which is a typical expensive multi-objective optimization problem. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) combined with efficient global optimization is a promising method but requires enhanced efficiency and faces limitations in its application to multi-objective aerodynamic design optimization (MOADO). To address the issues, an efficient parallel MOEA/D assisted with variable-fidelity optimization (VFO) is proposed for solving MOADO, called the MOEA/D-VFO algorithm. Variable-fidelity surrogates are built for objectives and constraints, achieving higher accuracy using fewer high-fidelity samples and a great number of low-fidelity samples. By retaining more good candidates, the sub-optimization problems defined by decomposing original objectives are capable of discovering more favorable samples using MOEA/D, which prompts optimization convergence. A constraint-handling strategy is developed by incorporating the probability of feasibility functions in the sub-optimizations. The selection of new samples for parallel evaluation is improved by filtering out poor candidates and selecting effective promising samples, which improves the feasibility and diversity of solved Pareto solutions. A Pareto front (PF) can be efficiently found in a single optimization run. The proposed approach is demonstrated by four analytical test functions and verified by two aerodynamic design optimizations of airfoils with and without constraints, respectively. The results indicate that the MOEA/D-VFO approach can greatly improve optimization efficiency and obtain the PF satisfying constraints within an affordable computational budget.

Transform to “What When” (TARA) Readiness from “What If” (KAMO)—Worries Beyond Multiple BCPs for Various Hazards in Japan

Since 2011, disasters have evolved into events that cannot be imagined from past experiences, and typical business continuity plans (BCPs) in Japan are unable to effectively deal with them. Through analysis of a questionnaire survey on BCPs in Japan, we found in this study that, typically private company-owned BCPs have focused on disaster response measures or plans for business itself, and are not directly relevant to the business units of individual employees and, as a result, have low feasibility. To focus on the continuity plans for business operations, which should be the original objective of a BCP, we propose a procedure to develop BCPs by moving from the perspective of “what if” (KAMO) to that of “what when” (TARA).

3D Data Processing and Entropy Reduction for Reconstruction from Low-Resolution Spatial Coordinate Clouds in a Technical Vision System.

This paper proposes an advancement in the application of a Technical Vision System (TVS), which integrates a laser scanning mechanism with a single light sensor to measure 3D spatial coordinates. In this application, the system is used to scan and digitalize objects using a rotating table to explore the potential of the system for 3D scanning at reduced resolutions. The experiments undertaken searched for optimal scanning windows and used statistical data filtering techniques and regression models to find a method to generate a 3D scan that was still recognizable with the least amount of 3D points, balancing the number of points scanned and time, while at the same time reducing effects caused by the particularities of the TVS, such as noise and entropy in the form of natural distortion in the resulting scans. The evaluation of the experimentation results uses 3D point registration methods, joining multiple faces from the original volume scanned by the TVS and aligning it to the ground truth model point clouds, which are based on a commercial 3D camera to verify that the reconstructed 3D model retains substantial detail from the original object. This research finds it is possible to reconstruct sufficiently detailed 3D models obtained from the TVS, which contain coarsely scanned data or scans that initially lack high definition or are too noisy.

Customized Approaches for Introducing Road Maintenance Management in I-BIM Environments

Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may offer significant advantages as a convenient alternative for road maintenance management. Although existing BIM platforms are not fully equipped for this purpose, defining original modules and scripts can extend their capabilities, allowing for the handling of road condition information and maintenance management. In this context, this paper presents an operative framework designed to leverage BIM benefits for road maintenance management, particularly in terms of virtual inspection, asset condition assessment, and maintenance design. To achieve this, specific original and customized smart objects and routines were coded in I-BIM platforms, tailored to different scales, aims, and detail levels. These smart objects incorporate user-defined extended attributes related to pavement condition and maintenance planning (such as roughness, rutting, structural capacity). In particular, the authors have developed original virtual smart objects in different platforms, serving as “containers” for the survey information. These objects are adapted to display quality levels of the pavement segments in a realistic and user-friendly environment. Additionally, original routines were coded to automatically import survey data from external datasets and associate this information with the appropriate objects. This customized and extended approach, not available in commercial platforms, can effectively support maintenance operators.

Same Same, But Different: Brain Areas Underlying the Learning from Repetitive Episodic Prediction Errors.

Prediction errors (PEs) function as learning signals. It is yet unclear how varying compared to repetitive PEs affect episodic memory in brain and behavior. The current study investigated cerebral and behavioral effects of experiencing either multiple alternative versions ("varying") or one single alternative version ("repetitive") of a previously encoded episode. Participants encoded a set of episodes ("originals") by watching videos showing toy stories. During scanning, participants either experienced originals, one single, or multiple alternative versions of the previously encoded episodes. Participants' memory performance was tested through recall of original objects. Varying and repetitive PEs revealed typical brain responses to the detection of mismatching information including inferior frontal and posterior parietal regions, as well as hippocampus, which is further linked to memory reactivation, and the amygdala, known for modulating memory consolidation. Furthermore, experiencing varying and repetitive PEs triggered distinct brain areas as revealed by direct contrast. Among others, experiencing varying versions triggered activity in the caudate, a region that has been associated with PEs. In contrast, repetitive PEs activated brain areas that resembled more those for retrieval of originally encoded episodes. Thus, ACC and posterior cingulate cortex activation seemed to serve both reactivating old and integrating new but similar information in episodic memory. Consistent with neural findings, participants recalled original objects less accurately when only presented with the same, but not varying, PE during fMRI. The current findings suggest that repeated PEs interact more strongly with a recalled original episodic memory than varying PEs.

A fluid–particle decomposition approach to matching market design for crowdsourced delivery systems

This paper considers a crowdsourced delivery (CSD) system that effectively utilizes the existing trips to fulfill parcel delivery as a matching problem between CSD drivers and delivery tasks. This matching problem has two major challenges. First, it is a large-scale combinatorial optimization problem that is hard to solve in a reasonable computational time. Second, the evaluation of the objective function for socially optimal matching contains the utility of drivers for performing the tasks, which is generally unobservable private information. To address these challenges, this paper proposes a hierarchical distribution mechanism of CSD tasks that decomposes the matching problem into the task partition (master problem) and individual task-driver matching within smaller groups of drivers (sub-problems). We incorporate an auction mechanism with truth-telling and efficiency into the sub-problems so that the drivers’ perceived utilities are revealed through their bids. Furthermore, we formulate the master problem as a fluid model based on continuously approximated decision variables. By exploiting the random utility framework, we analytically represent the objective function of the problem using continuous variables, without explicitly knowing the drivers’ utilities. The numerical experiment shows that the proposed approach solved large-scale matching problems at least 100 times faster than a naive LP solver and approximated the original objective value with errors of less than 1%. The code of this work is available at https://github.com/yuki-oyama/fluid-particle-csd.