Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid prediction of lab-grown tissue properties using deep learning

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set with N = 6400 cases by running detailed biophysical simulations of cell–matrix interactions using the contractile network dipole orientation model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of the pix2pix deep learning model, with an additional 100 cases that were unseen in the training of the neural network for review and testing of the trained model. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.
Grounding Object Relations in Language-Conditioned Robotic Manipulation with Semantic-Spatial Reasoning

Grounded understanding of natural language in physical scenes can greatly benefit robots that follow human instructions. In object manipulation scenarios, existing end-to-end models are proficient at understanding semantic concepts, but typically cannot handle complex instructions involving spatial relations among multiple objects. which require both reasoning object-level spatial relations and learning precise pixel-level manipulation affordances. We take an initial step to this challenge with a decoupled two-stage solution. In the first stage, we propose an object-centric semantic-spatial reasoner to select which objects are relevant for the language instructed task. The segmentation of selected objects are then fused as additional input to the affordance learning stage. Simply incorporating the inductive bias of relevant objects to a vision-language affordance learning agent can effectively boost its performance in a custom testbed designed for object manipulation with spatial-related language instructions.
3D Human Pose Estimation via Intuitive Physics

Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore the fact that bodies are typically supported by the scene. A physics engine can be used to enforce physical plausibility, but these are not differentiable, rely on unrealistic proxy bodies, and are difficult to integrate into existing optimization and learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms that can be inferred from a 3D SMPL body interacting with the scene. Inspired by biomechanics, we infer the pressure heatmap on the body, the Center of Pressure (CoP) from the heatmap, and the SMPL body's Center of Mass (CoM). With these, we develop IPMAN, to estimate a 3D body from a color image in a “stable” configuration by encouraging plausible floor contact and overlapping CoP and CoM. Our IP terms are intuitive, easy to implement, fast to compute, differentiable, and can be integrated into existing optimization and regression methods. We evaluate IPMAN on standard datasets and MoYo, a new dataset with synchronized multi-view images, ground-truth 3D bodies with complex poses, body-floor contact, CoM and pressure. IPMAN produces more plausible results than the state of the art, improving accuracy for static poses, while not hurting dynamic ones. Code and data are available for research at https://ipman.is. tue.mpg. de.
Nonstationary inversion of convection-diffusion problems -recovery from unknown nonstationary velocity fields

In this paper, we consider the reconstruction of time-varying concentration distributions under nonstationary flow conditions. Previous studies have shown that the state estimation approach that is based on stochastic process evolution models, facilitates reconstructions of rapidly time-varying targets. However, only cases with stationary velocity fields, or cases in which the velocity field can be completely specified by a velocity profile, have been studied. While simultaneous estimation of the time-varying concentration and low-dimensional representations of the flow field itself has been shown to be possible to some extent, this would be computationally too heavy for on-line process estimation and control. On the other hand, using an incorrect flow model in the evolution model may induce intolerable estimation errors. In this paper, we consider an approach in which the state evolution model is written to correspond to a stationary flow, while the actual flow is nonstationary. The associated modelling errors are handled by constructing the state noise process to accommodate to this discrepancy. We carry out a numerical feasibility study with different Reynolds numbers and show that the approach yields significant reduction of estimation errors and simultaneously facilitates using computationally efficient reduced order models.
Augmented Collective Intelligence in Collaborative Ideation: Agenda and Challenges

AI systems may be better thought of as peers than as tools. This paper explores applications of augmented collective intelligence (ACI) beneficial to collaborative ideation. Design considerations are offered for an experiment that evaluates the performance of hybrid human- AI collectives. The investigation described combines humans and large language models (LLMs) to ideate on increasingly complex topics. A promising real-time collection tool called Polis is examined to facilitate ACI, including case studies from citizen engagement projects in Taiwan and Bowling Green, Kentucky. The authors discuss three challenges to consider when designing an ACI experiment: topic selection, participant selection, and evaluation of results. The paper concludes that researchers should address these challenges to conduct empirical studies of ACI in collaborative ideation.
Constrained Optimization of Rank-One Functions with Indicator Variables

Optimization problems involving minimization of a rank-one convex function over constraints modeling restrictions on the support of the decision variables emerge in various machine learning applications. These problems are often modeled with indicator variables for identifying the support of the continuous variables. In this paper we investigate compact extended formulations for such problems through perspective reformulation techniques. In contrast to the majority of previous work that relies on support function arguments and disjunctive programming techniques to provide convex hull results, we propose a constructive approach that exploits a hidden conic structure induced by perspective functions. To this end, we first establish a convex hull result for a general conic mixed-binary set in which each conic constraint involves a linear function of independent continuous variables and a set of binary variables. We then demonstrate that extended representations of sets associated with epigraphs of rank-one convex functions over constraints modeling indicator relations naturally admit such a conic representation. This enables us to systematically give perspective formulations for the convex hull descriptions of these sets with nonlinear separable or non-separable objective functions, sign constraints on continuous variables, and combinatorial constraints on indicator variables. We illustrate the efficacy of our results on sparse nonnegative logistic regression problems.
HD-GCN: A Hybrid Diffusion Graph Convolutional Network

The information diffusion performance of GCN and its variant models is limited by the adjacency matrix, which can lower their performance. Therefore, we introduce a new framework for graph convolutional networks called Hybrid Diffusion-based Graph Convolutional Network (HD-GCN) to address the limitations of information diffusion caused by the adjacency matrix. In the HD-GCN framework, we initially utilize diffusion maps to facilitate the diffusion of information among nodes that are adjacent to each other in the feature space. This allows for the diffusion of information between similar points that may not have an adjacent relationship. Next, we utilize graph convolution to further propagate information among adjacent nodes after the diffusion maps, thereby enabling the spread of information among similar nodes that are adjacent in the graph. Finally, we employ the diffusion distances obtained through the use of diffusion maps to regularize and constrain the predicted labels of training nodes. This regularization method is then applied to the HD-GCN training, resulting in a smoother classification surface. The model proposed in this paper effectively overcomes the limitations of information diffusion imposed only by the adjacency matrix. HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix. This method allows for more comprehensive information propagation among nodes, resulting in improved model performance. We evaluated the performance of DM-GCN on three well-known citation network datasets and the results showed that the proposed framework is more effective than several graph-based semi-supervised learning methods.
Fault detection filter design with adaptive mechanism for linear uncertain polytopic systems in finite frequency domains

This study is concerned with the fault detection (FD) filter design problem for linear uncertain polytopic systems in finite frequency domains. The H − / H ∞ FD scheme is applied to maximise the sensitivity of faults and minimise the effect of disturbances. By decomposing the unavailable state variables, a new adaptive mechanism is developed to design the FD filter with time-varying gains. Moreover, a tangent hyperplane technique is introduced to solve the non-convex problem involved in H − performance, and the FD filter design conditions with some parameters fixed are then given in terms of linear matrix inequalities. Finally, the advantages and effectiveness of the proposed FD technique are illustrated through a numerical example.
Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study

Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse. Assessing fatigue of multi-muscle coordination-involved daily exercises requires the neuromuscular features that represent the fatigue-induced characteristics of spatiotemporal adaptions of multiple muscles and the estimator that captures the time-evolving progression of fatigue. In this paper, we propose to depict fatigue by the features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model. First, we extract muscle synergy fractionation and the variance of spinal module spikings as features inspired by the prior of fatigue-induced neuromuscular adaptations. Second, we treat the features as observations and develop a Bayesian Gaussian process to capture the time-evolving progression. Third, we solve the issue of lacking supervision information by mathematically formulating the time-evolving characteristics of fatigue as the loss function. Finally, we adapt the metrics that follow the physiological principles of fatigue to quantitatively evaluate the performance. Our extensive experiments present a 0.99 similarity between days, a over 0.7 similarity with other views of fatigue and a nearly 1 weak monotonicity, which outperform other methods. This study would aim the objective assessment of muscle fatigue.
Dispersion engineering of infrared epsilon-near-zero modes by strong coupling to optical cavities

Abstract Epsilon-near-zero (ENZ) materials have recently emerged as a promising platform for infrared nanophotonics. A significant challenge in the design of ENZ-based optics is to control the dispersion of ENZ modes that otherwise have a flat profile near the ENZ frequency. Strong coupling with an optical cavity is a promising approach to ENZ dispersion engineering, which however has limitations due to the lack of tunability or nanofabrication demands of the cavity employed. Here, we theoretically and numerically show that much of the limitations of previous approaches can be overcome by strongly coupling the ENZ mode to an unpatterned Fabry–Perot cavity. We demonstrate this unprecedented ENZ dispersion control in coupled cavities by designing tunable infrared polarizers that can absorb s and reflect p-polarized components, or vice versa, for almost any oblique angle of incidence, i.e. omnidirectional polarizers. The feasibility of active control is also demonstrated using a phase change material within the cavity, which predicts dynamic switchability of polariton dispersions across multiple resonant levels at mid-infrared wavelengths. These results are expected to advance the current understanding of strongly coupled ENZ interactions and demonstrate their potential in tailoring dispersions for active and passive control of light.
Scalable Quantum Network Determination with Einstein-Podolsky-Rosen Steering

Quantum networks of entangled end nodes serve stronger than the classical correlation for unparalleled quantum internet applications. However, practical quantum networking exists noise, at worst, causing end nodes to be described in pre-existing classical data. In such untrusted networks, determining the quantum network fidelity and genuine multi-node entanglement becomes crucial problems. Here, we theoretically and experimentally show that when the network nodes aim to be entangled in a star topology, detecting truly $N$-node Einstein-Podolsky-Rosen steering in networks with high noise tolerance requires only $N+1$ measurement settings. With this small experimental effort, steering detection determines the quantum network fidelity and genuine multi-node entanglement in the presence of untrusted measurement devices. Experimentally, using spontaneous parametric down-conversion entanglement sources, we demonstrate the determinations of genuine 3-photon and 4-photon quantum networks via genuine multi-photon steering and the false positives of the widely used entanglement witnesses. Our results help accurately evaluate setting up realistic entanglement-backbone quantum networks.
How to measure research performance of single scientists? A proposal for an index based on scientific prizes: The Prize Winner Index (PWI)

In this study, we propose a new index for measuring excellence in science which is based on collaborations (co-authorship distances) in science. The index is based on the Erd\H{o}s number - a number that was introduced several years ago. We propose to focus with the new index on laureates of prestigious prizes in a certain field and to measure co-authorship distances between the laureates and other scientists. To exemplify and explain our proposal, we computed the proposed index in the field of quantitative science studies (PWIPM). The Derek de Solla Price Memorial Award (Price Medal, PM) is awarded to outstanding scientists in the field. We tested the convergent validity of the PWIPM. We were interested whether the indicator is related to an established bibliometric indicator: P(top 10%). The results show that the coefficients for the correlation between PWIPM and P(top 10%) are high (in cases when a sufficient number of papers have been considered for a reliable assessment of performance). Therefore, measured by an established indicator for research excellence, the new PWI indicator seems to be convergently valid and, therefore, might be a possible alternative for established (bibliometric) indicators - with a focus on prizes.
Quantum Thermal State Preparation

Preparing ground states and thermal states is essential for simulating quantum systems on quantum computers. Despite the hope for practical quantum advantage in quantum simulation, popular state preparation approaches have been challenged. Monte Carlo-style quantum Gibbs samplers have emerged as an alternative, but prior proposals have been unsatisfactory due to technical obstacles rooted in energy-time uncertainty. We introduce simple continuous-time quantum Gibbs samplers that overcome these obstacles by efficiently simulating Nature-inspired quantum master equations (Lindbladians). In addition, we construct the first provably accurate and efficient algorithm for preparing certain purified Gibbs states (called thermal field double states in high-energy physics) of rapidly thermalizing systems; this algorithm also benefits from a quantum walk speedup. Our algorithms' costs have a provable dependence on temperature, accuracy, and the mixing time (or spectral gap) of the relevant Lindbladian. We complete the first rigorous proof of finite-time thermalization for physically derived Lindbladians by developing a general analytic framework for nonasymptotic secular approximation and approximate detailed balance. Given the success of classical Markov chain Monte Carlo (MCMC) algorithms and the ubiquity of thermodynamics, we anticipate that quantum Gibbs sampling will become indispensable in quantum computing.
Delay-Agnostic Asynchronous Distributed Optimization

Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees under both partial and total asynchrony. The practical performance of our algorithms is demonstrated by numerical experiments.
Sodium Brightening of (3200) Phaethon near Perihelion

Sunskirting asteroid (3200) Phaethon has been repeatedly observed in Solar Terrestrial Relations Observatory (STEREO) Heliospheric Imager 1 (HI1) imagery to anomalously brighten and produce an antisunward tail for a few days near each perihelion passage, phenomena previously attributed to the ejection of micron-sized dust grains. Color imaging by the Solar and Heliospheric Observatory (SOHO) Large Angle Spectrometric Coronagraph (LASCO) during the 2022 May apparition indicates that the observed brightening and tail development instead capture the release of sodium atoms, which resonantly fluoresce at the 589.0/589.6 nm D lines. While HI1's design bandpass nominally excludes the D lines, filter degradation has substantially increased its D line sensitivity, as quantified by the brightness of Mercury’s sodium tail in HI1 imagery. Furthermore, the expected fluorescence efficiency and acceleration of sodium atoms under solar radiation readily reproduce both the photometric and morphological behaviors observed by LASCO and HI1 during the 2022 apparition and the 17 earlier apparitions since 1997. This finding connects Phaethon to the broader population of sunskirting and sungrazing comets observed by SOHO, which often also exhibit bright sodium emission with minimal visible dust, but distinguishes it from other sunskirting asteroids without detectable sodium production under comparable solar heating. These differences may reflect variations in the degree of sodium depletion of near-surface material and thus the extent and/or timing of any past or present resurfacing activity.
Analytic modelling of Quantum Capacitance and Carrier Concentration for $\beta_{12}$-Borophene FET based Gas Sensor

In this work, we investigate the physical and electronic properties of $\beta_{12}$-borophene FET-based gas sensor using a theoretical quantum capacitance model based on tight-binding approach. We study the impact of adsorbed NH$_3$, NO, NO$_2$ and CO gas molecule on its density of states, carrier concentration, quantum capacitance and I-V characteristics. We found a remarkable variation in the energy band structure and the density of states (DOS) of the $\beta_{12}$-borophene in the presence of the adsorbed gas molecule. The appearance of non-identical Van-Hove singularities in the DOS in the presence of adsorbed gas molecules strongly indicates the high sensitivity of $\beta_{12}$-borophene. We found a significant increase in the carrier concentration for NH$_3$ gas while it decreases for all other gases. Moreover, a drastic change in quantum capacitance and current-voltage relation is also observed in adsorbed gases. The different properties of the given gas molecules are compared with the pristine borophene and found to exhibit distinct wrinkles in each case, thereby indicating the strong selectivity of our proposed gas sensor. Though $\beta_{12}$ - borophene is found to be highly sensitive for all studied gases, the NO gas is found to be most sensitive compared to the others.
On Hausdorff dimension of invariant sets for expanding maps of a circle

Abstract Given an orientation preserving C2 expanding mapping g: S1 → Sl of a circle we consider the family of closed invariant sets Kg(ε) defined as those points whose forward trajectory avoids the interval (0, ε). We prove that topological entropy of g|Kg(ε) is a Cantor function of ε. If we consider the map g(z) = zq then the Hausdorff dimension of the corresponding Cantor set around a parameter ε in the space of parameters is equal to the Hausdorff dimension of Kg(ε). In § 3 we establish some relationships between the mappings g|Kg(ε) and the theory of β-transformations, and in the last section we consider DE-bifurcations related to the sets Kg(ε).
Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow

In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal knowledge, visual anomaly detection has wide applications in industrial scenarios and has attracted significant attention. However, most existing methods fail to meet the requirements. In contrast, the proposed DTDF presents a new paradigm: it firstly leverages a pre-trained network to acquire multi-scale prior embeddings, followed by the development of a vision Transformer with dual attention mechanisms, namely self-attention and memorial-attention, to achieve two-level reconstruction for prior embeddings with the sequential and normality association. Additionally, we propose using normalizing flow to establish discriminative likelihood for the joint distribution of prior and reconstructions at each scale. The DADF achieves 98.3/98.4 of image/pixel AUROC on Mvtec AD; 83.7 of image AUROC and 67.4 of pixel sPRO on Mvtec LOCO AD benchmarks, demonstrating the effectiveness of our proposed approach.
Explicit solution of min–max model predictive control for uncertain systems

For a discrete-time linear system with uncertain model perturbations and additive disturbances, the authors develop an approach to evaluate the explicit state feedback solution of the constrained min–max model predictive control problem. By considering a quadratic cost function and a robust reformulation of constraints, the problem is transferred to an equivalent multi-parametric programming problem. The control policy is determined to be a continuous and piecewise affine function of the state vector. Meanwhile, the feasible state space is partitioned into polyhedral cones corresponding to the control law. The results are shown via computer simulations by applying the method to a numerical example.