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The use of data-driven techniques for tactile data processing and classification has recently increased. However, collecting tactile data is a time-expensive and sensor-specific procedure. Indeed, due to the lack of hardware standards in tactile sensing, data is required to be collected for each different sensor. This paper considers the problem of learning the mapping between two tactile sensor outputs with respect to the same physical stimulus -- we refer to this problem as touch-to-touch translation. In this respect, we proposed two data-driven approaches to address this task and we compared their performance. The first one exploits a generative model developed for image-to-image translation and adapted for this context. The second one uses a ResNet model trained to perform a regression task. We validated both methods using two completely different tactile sensors -- a camera-based, Digit and a capacitance-based, CySkin. In particular, we used Digit images to generate the corresponding CySkin data. We trained the models on a set of tactile features that can be found in common larger objects and we performed the testing on a previously unseen set of data. Experimental results show the possibility of translating Digit images into the CySkin output by preserving the contact shape and with an error of 15.18% in the magnitude of the sensor responses., Comment: This paper was initially submitted at the International Conference on Intelligent Robots and Systems (IROS) 2023

Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions, which are crucial for reducing scan times. While deep learning techniques have advanced image reconstruction, the recent introduction of diffusion models offers new possibilities for imaging tasks, though their application in the medical field is still emerging. Notably, diffusion models have not yet been explored for the MRF problem. In this work, we propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction. Qualitative and quantitative comparisons on in-vivo brain scan data demonstrate that the proposed approach can outperform established deep learning and compressed sensing algorithms for MRF reconstruction. Extensive ablation studies also explore strategies to improve computational efficiency of our approach., Comment: 12 pages, 5 figures, 2 algorithms

Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges. Traditional analytical methods often depend on simplifying assumptions, while learning-based techniques can be computationally demanding and limit the control policies to existing data. This paper introduces a novel approach to soft robotic control, leveraging state-of-the-art policy gradient methods within parallelizable synthetic environments learned from data. We also propose a safety oriented actuation space exploration protocol via cascaded updates and weighted randomness. Specifically, our recurrent forward dynamics model is learned by generating a training dataset from a physically safe \textit{mean reverting} random walk in actuation space to explore the partially-observed state-space. We demonstrate a reinforcement learning approach towards closed-loop control through state-of-the-art actor-critic methods, which efficiently learn high-performance behaviour over long horizons. This approach removes the need for any knowledge regarding the robot's operation or capabilities and sets the stage for a comprehensive benchmarking tool in soft robotics control., Comment: soft manipulator, reinforcement learning, learned controllers

Everyday experience shows that cellular sheets are stiffened by the presence of a pressurized gas: from bicycle inner tubes to bubble wrap, the presence of an internal pressure increases the stiffness of otherwise floppy structures. The same is true of plants, with turgor pressure (due to the presence of water) taking the place of gas pressure; indeed, in the absence of water, many plants wilt. However, the mechanical basis of this stiffening is somewhat opaque: simple attempts to rationalize it suggest that the stiffness should be independent of the pressure, at odds with everyday experience. Here, we study the mechanics of sheets that are a single cell thick and show how a pressure-dependent bending stiffness may arise. Our model rationalizes observations of turgor-driven shrinkage in plant cells and also suggests that turgor is unlikely to provide significant structural support in many monolayer leaves, such as those found in mosses. However, for such systems, turgor does provide a way to control leaf shape, in accordance with observations of curling upon drying of moss leaves. Guided by our results, we also present a biomimetic actuator that un-curls upon pressurization., Comment: Paper: 14 pages, 9 figures; Supplemental Material: 8 pages, 2 figures

Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailored algorithms for removing aliasing artefacts from the compressed sampled MRF acquisitions. Within approaches found in the literature, many focus solely on two-dimensional (2D) image reconstruction, neglecting the extension to volumetric (3D) scans despite their higher relevance and clinical value. A reason for this is that transitioning to 3D imaging without appropriate mitigations presents significant challenges, including increased computational cost and storage requirements, and the need for large amount of ground-truth (artefact-free) data for training. To address these issues, we introduce StoDIP, a new algorithm that extends the ground-truth-free Deep Image Prior (DIP) reconstruction to 3D MRF imaging. StoDIP employs memory-efficient stochastic updates across the multicoil MRF data, a carefully selected neural network architecture, as well as faster nonuniform FFT (NUFFT) transformations. This enables a faster convergence compared against a conventional DIP implementation without these features. Tested on a dataset of whole-brain scans from healthy volunteers, StoDIP demonstrated superior performance over the ground-truth-free reconstruction baselines, both quantitatively and qualitatively., Comment: 10 pages, 2 figures, 1 table, 1 algorithm

The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction. Whilst state-of-the-art DL methods can successfully address the task, to fully exploit their capabilities they often require training on a paired dataset, in an area where ground truth is seldom available. In this work, we propose a method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder, can tackle the problem, resulting in a method faster and of equivalent or better accuracy than DIP-MRF., Comment: 4 pages, 3 figures 1 table, presented at ISBI 2024

Nanomaterials bring to expression unique electronic properties that promote advanced functionality and technologies. Albeit, nanoscale growth presents paramount challenges for synthesis limiting the diversity in structures and compositions. Here, we demonstrate solid-state topotactic exchange that converts Wurtzite InAs nanowires into Zintl phase Eu$_3$In$_2$As$_4$ nanowires. In situ evaporation of Eu and As over InAs nanowire cores in molecular beam epitaxy results in mutual exchange of Eu from the shell and In from the core. A continuous Eu$_3$In$_2$As$_4$ shell thereby grows that gradually consumes the InAs core and converts it into a single phase Eu$_3$In$_2$As$_4$ nanowire. Topotaxy, which facilitates the mutual exchange, is supported by the substructure of the As matrix which is similar across the Wurtzite InAs and Zintl Eu$_3$In$_2$As$_4$. We provide initial evidence of an antiferromagnetic transition at T$_N$ $\sim$ 6.5 K in the Zintl phase Eu$_3$In$_2$As$_4$ nanowires. Ab initio calculation confirms the antiferromagnetic state and classifies Eu$_3$In$_2$As$_4$ as a $C_2 T$ axion insulator hosting both chiral hinge modes and unpinned Dirac surface states. The topotactic mutual-exchange growth of Zintl Eu$_3$In$_2$As$_4$ nanowires thus enables the exploration of intricate magneto-topological states of nanomaterials. Moreover, it may open the path for topotactic mutual-exchange synthesis of nanowires made of other exotic compounds., Comment: 45 pages, 23 figures

Determining the chronology of ancient handwritten manuscripts is essential for reconstructing the evolution of ideas. For the Dead Sea Scrolls, this is particularly important. However, there is an almost complete lack of date-bearing manuscripts evenly distributed across the timeline and written in similar scripts available for palaeographic comparison. Here, we present Enoch, a state-of-the-art AI-based date-prediction model, trained on the basis of new radiocarbon-dated samples of the scrolls. Enoch uses established handwriting-style descriptors and applies Bayesian ridge regression. The challenge of this study is that the number of radiocarbon-dated manuscripts is small, while current machine learning requires an abundance of training data. We show that by using combined angular and allographic writing style feature vectors and applying Bayesian ridge regression, Enoch could predict the radiocarbon-based dates from style, supported by leave-one-out validation, with varied MAEs of 27.9 to 30.7 years relative to the radiocarbon dating. Enoch was then used to estimate the dates of 135 unseen manuscripts, revealing that 79 per cent of the samples were considered 'realistic' upon palaeographic post-hoc evaluation. We present a new chronology of the scrolls. The radiocarbon ranges and Enoch's style-based predictions are often older than the traditionally assumed palaeographic estimates. In the range of 300-50 BCE, Enoch's date prediction provides an improved granularity. The study is in line with current developments in multimodal machine-learning techniques, and the methods can be used for date prediction in other partially-dated manuscript collections. This research shows how Enoch's quantitative, probability-based approach can be a tool for palaeographers and historians, re-dating ancient Jewish key texts and contributing to current debates on Jewish and Christian origins., Comment: 16 pages of main article, 103 pages of supplementary materials; the first version of this article is originally prepared in July 2023 after the completion of all the experiments

This paper examines the alignment of inductive biases in machine learning (ML) with structural models of economic dynamics. Unlike dynamical systems found in physical and life sciences, economics models are often specified by differential equations with a mixture of easy-to-enforce initial conditions and hard-to-enforce infinite horizon boundary conditions (e.g. transversality and no-ponzi-scheme conditions). Traditional methods for enforcing these constraints are computationally expensive and unstable. We investigate algorithms where those infinite horizon constraints are ignored, simply training unregularized kernel machines and neural networks to obey the differential equations. Despite the inherent underspecification of this approach, our findings reveal that the inductive biases of these ML models innately enforce the infinite-horizon conditions necessary for the well-posedness. We theoretically demonstrate that (approximate or exact) min-norm ML solutions to interpolation problems are sufficient conditions for these infinite-horizon boundary conditions in a wide class of problems. We then provide empirical evidence that deep learning and ridgeless kernel methods are not only theoretically sound with respect to economic assumptions, but may even dominate classic algorithms in low to medium dimensions. More importantly, these results give confidence that, despite solving seemingly ill-posed problems, there are reasons to trust the plethora of black-box ML algorithms used by economists to solve previously intractable, high-dimensional dynamical systems -- paving the way for future work on estimation of inverse problems with embedded optimal control problems.

We present a novel approach for single-image mesh texturing, which employs a diffusion model with judicious conditioning to seamlessly transfer an object's texture from a single RGB image to a given 3D mesh object. We do not assume that the two objects belong to the same category, and even if they do, there can be significant discrepancies in their geometry and part proportions. Our method aims to rectify the discrepancies by conditioning a pre-trained Stable Diffusion generator with edges describing the mesh through ControlNet, and features extracted from the input image using IP-Adapter to generate textures that respect the underlying geometry of the mesh and the input texture without any optimization or training. We also introduce Image Inversion, a novel technique to quickly personalize the diffusion model for a single concept using a single image, for cases where the pre-trained IP-Adapter falls short in capturing all the details from the input image faithfully. Experimental results demonstrate the efficiency and effectiveness of our edge-aware single-image mesh texturing approach, coined EASI-Tex, in preserving the details of the input texture on diverse 3D objects, while respecting their geometry., Comment: ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2024. Project Page: https://sairajk.github.io/easi-tex/

Periodic waves are standing wave solutions of nonlinear Schr\''odinger equations whose profile is periodic in space dimension one. We consider general nonlinearities and provide variational characterizations for the periodic wave profiles. This involves minimizing energy while keeping mass and momentum constant, as well as minimizing the action over the Nehari manifold. These variational approaches are considered both in the periodic and anti-periodic settings, and for focusing and defocusing nonlinearities. In appendix, we study the existence properties of periodic solutions of the triple power nonlinearity.

As evidenced by recent survey results, majority of asteroids are slow rotators (P>12 h), but lack spin and shape models due to selection bias. This bias is skewing our overall understanding of the spins, shapes, and sizes of asteroids, as well as of their other properties. Also, diameter determinations for large (>60km) and medium-sized asteroids (between 30 and 60 km) often vary by over 30% for multiple reasons. Our long-term project is focused on a few tens of slow rotators with periods of up to 60 hours. We aim to obtain their full light curves and reconstruct their spins and shapes. We also precisely scale the models, typically with an accuracy of a few percent. We used wide sets of dense light curves for spin and shape reconstructions via light-curve inversion. Precisely scaling them with thermal data was not possible here because of poor infrared data: large bodies are too bright for WISE mission. Therefore, we recently launched a campaign among stellar occultation observers, to scale these models and to verify the shape solutions, often allowing us to break the mirror pole ambiguity. The presented scheme resulted in shape models for 16 slow rotators, most of them for the first time. Fitting them to stellar occultations resolved previous inconsistencies in size determinations. For around half of the targets, this fitting also allowed us to identify a clearly preferred pole solution, thus removing the ambiguity inherent to light-curve inversion. We also address the influence of the uncertainty of the shape models on the derived diameters. Overall, our project has already provided reliable models for around 50 slow rotators. Such well-determined and scaled asteroid shapes will, e.g. constitute a solid basis for density determinations when coupled with mass information. Spin and shape models continue to fill the gaps caused by various biases., Comment: Accepted to Astronomy & Astrophysics. 12 pages + appendices

Eclipse timing variations (ETV) have been a successful tool for detecting circumbinary companions to eclipsing binaries (EB). While TESS and Kepler have been prolific for ETV searches, they sometimes can be limited by time and sky coverage which can be addressed by specialised ground-based ETV surveys. We present the initial results from the Solaris photometric survey which uses four 0.5m robotic telescopes in the southern hemisphere to look for circumbinary companions. We present the method of light curve extraction, detrending, and EB modelling using the observations from the Solaris network. Using these light curves we extract precise eclipse timing for 7 EB and look for companions using a Lomb-Scargle periodogram search. We find two possible periodic signals for the target GSC 08814-01026. With the system having strong activity, we check for the feasibility of orbital solutions at these two periods. We find that the 245 +/- 1 d period is due to an M-dwarf mass companion. This makes GSC 08814-01026 a candidate compact hierarchical triple system. The other periodic signal at 146 +/- 1 d is an artefact of stellar activity., Comment: 14 pages, 9 figures, Accepted for publication in MNRAS. This is a pre-copyedited, author-produced PDF of an article accepted for publication in MNRAS following peer review

Droplet microfluidics are a powerful approach for hydrogel cell encapsulations. Much of the field has focused on single-cell encapsulations with pico-nanoliter droplet volumes necessary for single-cell sequencing or high-throughput screening. These small volumes, however, limit the use of hydrogel droplets for tissue engineering or cell therapies. We describe simple droplet microfluidics to generate millimeter-sized alginate droplets and demonstrate their use for cell encapsulations. This effort builds on our recent efforts, specifically by replacing the glass slide forming the bottom layer of the chamber with a more hydrophobic acrylic (PMMA) layer to improve the alginate-in-oil droplet formation. Using glass layer and PMMA layer devices, we characterized the tunable production of water-in-oil droplets (average droplet lengths ranged from 0.8 to 3.7 mm). Next, PMMA layer devices were used to demonstrate the tunable generation of alginate-in-oil droplets (average droplet lengths ranged from 3-6 mm). Increasing the flow ratio (Q.ratio = Q.oil/Q.alginate) led to more uniform droplets as measured by the coefficient of variance, which was approximately 5%. Finally, a proof-of-use experiment used HUVEC-encapsulated alginate droplets as part of a scratch-healing assay. Specifically, HUVEC-encapsulated droplets (AH droplets) led to the recovery of 3T3 fibroblast monolayers compared to no droplets or cell-free droplets (A droplets). Our results extended the use of simple microfluidics to generate and retrieve millimeter-sized alginate droplets for effective cell encapsulations., Comment: 13 pages, 6 figures

Let $X$ be a random walk on the torus of side length $N$ in dimension $d\geq 3$ with uniform starting point, and $t_{\text{cov}}$ be the expected value of its cover time, which is the first time that $X$ has visited every vertex of the torus at least once. For $\alpha > 0$, the set $\mathcal{L}^{\alpha}$ of $\alpha$-late points consists of those points not visited by $X$ at time $\alpha t_{\text{cov}}$. We prove the existence of a value $\alpha_* \in (\frac12,1)$ across which $\mathcal{L}^{\alpha}$ trivialises as follows: for all $\alpha > \alpha_*$ and $\epsilon\geq N^{-c}$ there exists a coupling of $\mathcal{L}^\alpha$ and two occupation sets $\mathcal{B}^{\alpha_\pm}$ of i.i.d. Bernoulli fields having the same density as $\mathcal{L}^{\alpha\pm \epsilon}$, which is asymptotic to $N^{-(\alpha\pm\epsilon)d}$, with the property that the inclusion $ \mathcal{B}^{\alpha_+} \subseteq \mathcal{L}^{\alpha} \subseteq \mathcal{B}^{\alpha_-}$ holds with high probability as $N \to \infty$. On the contrary, when $\alpha \leq \alpha_*$ there is no such coupling. Corresponding results also hold for the vacant set of random interlacements at high intensities. The transition at $\alpha_*$ corresponds to the (dis-)appearance of `double-points' (i.e. neighboring pairs of points) in $\mathcal{L}^\alpha$. We further describe the law of $\mathcal{L}^{\alpha}$ for $\alpha>\frac12$ by adding independent patterns to $\mathcal{B}^{\alpha_{\pm}}$. In dimensions $d \geq 4$ these are exactly all two-point sets. When $d=3$ one must also include all connected three-point sets, but no other., Comment: 68 pages, 2 figures