Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Based Visual Servoing Using Trajectory Planning and Augmented Visual Servoing Controller

Robots and automation manufacturing machineries have become an inseparable part of industry, nowadays. However, robotic systems are generally limited to operate in highly structured environments. Although, sensors such as laser tracker, indoor GPS, 3D metrology and tracking systems are used for positioning and tracking in manufacturing and assembly tasks, these devices are highly limited to the working environment and the speed of operation and they are generally very expensive. Thus, integration of vision sensors with robotic systems and generally visual servoing system allows the robots to work in unstructured spaces, by producing non-contact measurements of the working area. However, projecting a 3D space into a 2D space, which happens in the camera, causes the loss of one dimension data. This initiates the challenges in vision based control. Moreover, the nonlinearities and complex structure of a manipulator robot make the problem more challenging. This project aims to develop new reliable visual servoing methods that allow its use in real robotic tasks. The main contributions of this project are in two parts; the visual servoing controller and trajectory planning algorithm. In the first part of the project, a new image based visual servoing controller called Augmented Image Based Visual Servoing (AIBVS) is presented. A proportional derivative (PD) controller is developed to generate acceleration as the controlling command of the robot. The stability analysis of the controller is conducted using Lyapanov theory. The developed controller has been tested on a 6 DOF Denso robot. The experimental results on point features and image moment features demonstrate the performance of the proposed AIBVS. Experimental results show that a damped response could be achieved using a PD controller with acceleration output. Moreover, smoother feature and robot trajectories are observed compared to those in conventional IBVS controllers. Later on, this controller is used on a moving object catching process. Visual servoing controllers have shown difficulty in stabilizing the system in global space. Hence, in the second part of the project, a trajectory planning algorithm is developed to achieve the global stability of the system. The trajectory planning is carried out by parameterizing the camera's velocity screw. The camera's velocity screw is parameterized using time-based profiles. The parameters of the velocity profile are then determined such that the velocity profile guides the robot to its desired position. This is done by minimizing the error between the initial and desired features. This method provides a reliable path for the robot considering all robotic constraints. The developed algorithm is tested on a Denso robot. The results show that the trajectory planning algorithm is able to perform visual servoing tasks which are unstable when performed using visual servoing controllers.
Molecular music: repurposing a mixed quantum-classical atomic dynamics model as an audiovisual instrument

Molecular Music is an offshoot of a long-term collaborative project, the multi- award winning danceroom Spectroscopy (dS)[1]. dS has its roots in computational molecular dynamics, and was originally conceived to serve as an interactive platform for molecular simulation [2,3,4]. It offers a multisensory immersive experience based on cutting-edge science facilitating an understanding of the principles of our microscopic world through direct experience rather than traditional academic learning. It consists of system of particles, simulated according to strict scientific principles; represented both visually and sonically, which can be interacted with through human movement. The project consists of a public installation, and also a contemporary dance piece, Hidden Fields, which is performed using the system. Hyde’s contribution to the project consists of the sonification (interactive systems and sound design) for the installation, and the composition of an interactive score for the dance piece. Molecular Music is intended to facilitate further exploration of the audiovisual relationships at play in dS and Hidden Fields and to explore more deeply how to sonify vibrations on a micro scale (where sound does not, as such, exist). We have built some highly developed algorithms based on FFT analysis of molecular vibration data outside the range of human hearing to yield subharmonics on which sonic material can be based. We also have in place a sophisticated system whereby sound can control the particle system and the particle system can in turn control the sound. We are exploring how this combination can be used to make a novel kind of feedback loop, and a network of non-trivial audiovisual relationships whereby the influence of sound on image and vice versa is mediated via the medium of an advanced mixed quantum-classical model. Using these tools we can use dS as a highly evolved ‘visual music’ instrument. The performance consists of a solo audiovisual performance of around 15 minutes duration. The paper outlines the algorithms at the heart of the dS system and their broader implications for Sci/Art visualisation/sonification and understanding, before moving on to examine how these algorithms have been adapted as an audiovisual instrument. The history of the project, including installations, dance performances and music-based collaborations, will be examined followed by a look to the future – in particular the development of dS as a large-scale permanent exhibit for ZKM in Karlsruhe to open in 2015.
Design guidelines for cast-in and post-installed anchors in Australia

The Australian anchor industry is rapidly growing, however, guidance for the design of post-installed and cast-in anchors for safety-critical applications in Australian codes of practice is minimal. The current level of guidance has resulted in a lack of consistency for product assessment and limited guidance for design. This paper summarises a design procedure for cast-in and post-installed anchors that has been endorsed by the Australian Engineered Fasteners and Anchors Council (AEFAC) for adoption in Australia. The design procedure is based on design guidelines that are intended to become a harmonised European Standard. The design guidelines are an imperative part of a framework being developed by AEFAC to enhance quality and safety standards in the Australian fastener industry.
Virtual commissioning of an existing manufacturing cell at Volvo Car Corporation using DELMIA V6

Virtual commissioning is a process which allows a comprehensive evaluation of production systems before performing physical commissioning. The programmable logic controller (PLC) code can be debugged before using it in a real production system. A growing number of companies have recently started taking interest in this technology as it reduces the time and cost of introducing new products and different scenarios can be performed to validate the manufacturing controllers in the virtual environments prior to the physical commissioning. This work discusses an approach to create and simulate an existing Bodyin-White manufacturing workcell in Volvo Car Corporation (VCC) using Dassault Systèmes digital manufacturing and production solution DELMIA V6. Also the migration process of other 3D model formats such as Process Simulate, CATIA V4, and V5 into DELMIA V6 has been investigated. It is shown that it is possible to create a simulation using the given resources; all models could be used. Also, a large amount of the robot functionality could be imported after editing certain functions which are not supported. One of the substantial properties for a successful simulation model is to replicate the actual controller, robot programs and HMI applications. These aspects of the Dassault Systèmes PLM solution are validated in this project. The latest release of DELMIA V6 is also tested in terms of connectivity to an actual PLC through the OPC communication protocol.
Study of transport process and discharge structure of inductively coupled electronegative plasmas via fluid model and analytic theory collaboration

Study of transport process and discharge structure of inductively coupled electronegative plasmas via fluid model and analytic theory collaboration Shu-Xia Zhao and An-Qi Tang 1. Key Laboratory of Material Modification by Laser, Ion, and Electron Beams (Ministry of Education), School of Physics, Dalian University of Technology, 116024, Dalian, China 2. China Key System & Integrated Circuit Co., Ltd, 214026, Wuxi, China a Correspondence: zhaonie@dlut.edu. cn Abstract The discharge structure of inductively coupled plasma is studied via fluid simulation and analytic theory collaboration. At low pressure, the discharge is stratified by the double layer, which is modelled as dipole moment. The parabolic profile is formed in the discharge core when recombination loss is negligible and both the electron and anions are the Boltzmann balanced. At increasing the pressure, the main characteristics, i.e., parabolic, elliptic and flat-topped profile, are experienced, predicted by the simulation and analytics. Self-coagulation is accompanied at all considered pressures. It is more a chemistry process and provides new means of constricting plasma. At its influence, electron density deviates from the Boltzmann equilibrium. For satisfying the neutrality of bulk plasma, the ambi-polar self-coagulation mechanism is suggested. At high pressure, the self-coagulation-to-coil scheme causes the mass point behavior in the plasma. Minor cations re-self-coagulate at certain conditions and the correlation with the celestial bodies’ formation is hypothesized.
Lattice QED in external electromagnetic fields

We study QED in external electromagnetic fields using methods developed for simulating lattice QCD. Our first project is to simulate QED in a constant (in space and time) external magnetic field on a euclidean space-time lattice using the Rational Hybrid Monte Carlo (RHMC) method. Observables we measure include the condensate $\langle\bar{\psi}\psi\rangle$ and the effective electron action after integrating out the fermion fields. We look for evidence that the combined effect of the magnetic field and the electron-positron attraction from QED produces a non-zero condensate in the limit of zero electron mass, a non-perturbative effect analogous to spontaneous chiral symmetry breaking. Very preliminary evidence is that such a condensate exists, at least for strong external magnetic fields and unphysically large electric charge. In addition, we are storing field configurations to measure the expected distortions and screenings of the coulomb field of a charged particle due to the vacuum polarization asymmetries produced by the magnetic field. We hope also to measure the dynamical contribution to the electron mass produced by the same mechanism that produces a finite condensate in the zero input mass limit.
Cooperative Transportation with Multiple Aerial Robots and Decentralized Control for Unknown Payloads

Cooperative transportation by multiple aerial robots has the potential to support various payloads and to reduce the chance of them being dropped. Furthermore, autonomously controlled robots make the system scalable with respect to the payload. In this study, a cooperative transportation system was developed using rigidly attached aerial robots, and a decentralized controller was proposed to guarantee asymptotic stability of the tracking error for unknown strictly positive real systems. A feedback controller was used to transform unstable systems into strictly positive real ones using the shared attachment positions. First, the cooperative transportation of unknown payloads with different shapes larger than the carrier robots was investigated through numerical simulations. Second, cooperative transportation of an unknown payload (with a weight of about 2.7 kg and maximum length of 1.6 m) was demonstrated using eight robots, even under robot failure. Finally, it was shown that the proposed system carried an unknown payload, even if the attachment positions were not shared, that is, even if the asymptotic stability was not strictly guaranteed.
A reduced order Schwarz method for nonlinear multiscale elliptic equations based on two-layer neural networks

Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts, including solution of partial differential equations (PDEs). We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition, an accelerated Schwarz framework, and two-layer neural networks to approximate the boundary-to-boundary map for the subdomains, which is the key step in the Schwarz procedure. Conventionally, the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain. By leveraging the compressibility of multiscale problems, our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map. Our method is applied to a multiscale semilinear elliptic equation and a multiscale $p$-Laplace equation. In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.
Getting a Grip: in Materio Evolution of Membrane Morphology for Soft Robotic Jamming Grippers

The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing bespoke gripping performance, however the other main contributing factor, membrane morphology, has not been studied due to its particular complexities in both accurate modelling and fabrication. This research presents the first study that optimises membrane morphology for granular jamming grippers, combining multi-material 3D printing and an evolutionary algorithm to search through a varied morphology design space in materio. Entire generations are printed in a single run and gripper retention force is tested and used as a fitness measure. Our approach is relatively scalable, circumvents the need for modelling, and guarantees the real-world performance of the grippers considered. Results show that membrane morphology is a key determinant of gripper performance. Common high performance designs are seen to optimise all three of the main identified mechanisms by which granular grippers generate grip force, are significantly different from a standard gripper morphology, and generalise well across a range of test objects.
Instability of Majorana states in Shiba chains due to leakage into a topological substrate

We revisit the problem of Majorana states in chains of scalar impurities deposited on a superconductor with a mixed s-wave and p-wave pairing. We also study the formation of Majorana states for magnetic impurity chains. We find that the magnetic impurity chains exhibit well-localized Majorana states when the substrate is trivial, but these states hybridize and get dissolved in the bulk when the substrate is topological. Most surprisingly, and contrary to previous predictions, the scalar impurity chain does not support fully localized Majorana states except for very small and finely tuned parameter regimes, mostly for a non-topological substrate close to the topological transition. Our results indicate that a purely p-wave or a dominant p-wave substrate are not good candidates to support either magnetic or scalar impurity topological Shiba chains.
Utility Maximization in Multivariate Volterra Models

This paper is concerned with portfolio selection for an investor with power utility in multi-asset financial markets in a rough stochastic environment. We investigate Merton's portfolio problem for different multivariate Volterra models, covering the rough Heston model. First we consider a class of multivariate affine Volterra models introduced in [E. Abi Jaber et al., SIAM J. Financial Math., 12, 369-409, (2021)]. Based on the classical Wishart model described in [N. B\"auerle and Li, Z. , J. Appl. Probab. , 50, 1025-1043 (2013)], we then introduce a new matrix-valued stochastic volatility model, where the volatility is driven by a Volterra-Wishart process. Due to the non-Markovianity of the underlying processes, the classical stochastic control approach cannot be applied in these settings. To overcome this issue, we provide a verification argument using calculus of convolutions and resolvents. The resulting optimal strategy can then be expressed explicitly in terms of the solution of a multivariate Riccati-Volterra equation. We thus extend the results obtained by Han and Wong to the multivariate case, avoiding restrictions on the correlation structure linked to the martingale distortion transformation used in [B. Han and Wong, H. Y. , Finance Res. Lett. , 39 (2021)]. We also provide existence and uniqueness theorems for the occurring Volterra processes and illustrate our results with a numerical study.
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1Dipartimento di Fisica e Astronomia (DIFA), University of Bologna, via Gobetti 93/2, I-40129 Bologna, Italy 2Istituto Nazionale di Astrofisica (INAF) Osservatorio di Astrofisica e Scienza dello Spazio (OAS), via Gobetti 101, I-40129 Bologna, Italy 3Istituto Nazionale di Astrofisica (INAF) Istituto di Radioastronomia (IRA), via Gobetti 101, I-40129 Bologna, Italy 4University of California Observatories/Lick Observatory, Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA
Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.
Multimodal urban mobility and multilayer transport networks

Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modeling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open-source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.
Equivariant Deep Dynamical Model for Motion Prediction

Learning representations through deep generative modeling is a powerful approach for dynamical modeling to discover the most simplified and compressed underlying description of the data, to then use it for other tasks such as prediction. Most learning tasks have intrinsic symmetries, i.e., the input transformations leave the output unchanged, or the output undergoes a similar transformation. The learning process is, however, usually uninformed of these symmetries. Therefore, the learned representations for individually transformed inputs may not be meaningfully related. In this paper, we propose an SO(3) equivariant deep dynamical model (EqDDM) for motion prediction that learns a structured representation of the input space in the sense that the embedding varies with symmetry transformations. EqDDM is equipped with equivariant networks to parameterize the state-space emission and transition models. We demonstrate the superior predictive performance of the proposed model on various motion data.
Rethinking the Image Feature Biases Exhibited by Deep CNN Models

In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, such deep neural models are still regarded as black box in most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how they are processed by CNNs. It is widely accepted that CNN models combine low-level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, i.e., specific tasks exhibit a feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify anticipated biases. We devised experiments comprising many tasks to test these biases for the ResNet and DenseNet models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.