Miracle-dz/UniFAR-FTUs huggingface dataset:
Local versus global stability in dynamical systems with consecutive Hopf bifurcations

Quantifying the stability of an equilibrium is central in the theory of dynamical systems as well as in engineering and control. A comprehensive picture must include the response to both small and large perturbations, leading to the concepts of local (linear) and global stability. Here, we show how systems displaying Hopf bifurcations show contrarian results on these two aspects of stability: Global stability is large close to the point where the system loses its local stability altogether. We demonstrate this effect for an elementary model system, an anharmonic oscillator and a realistic model of power system dynamics with delayed control. Detailed investigations of the bifurcation explain the seeming paradox in terms of the location of the attractors relative to the equilibrium.
Stability of hybrid systems

We introduce a model of hybrid systems as a combination of discrete state and continuous state systems. The continuous state space is divided into regions so that in every region, depending on the discrete state of hybrid system, the continuous state system dynamic functions which are called representative functions are found. The switching situation in a region is studied and the continuous state dynamics to be used in this situation is defined. It is shown that after a switching mode the system behavior may be uncertain due to the high frequency discrete state switchings. The classical stability of the origin of the continuous state space is defined. Following the Lyapunov theory, some stability theorems are provided. <<ETX>>
Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning

Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth. We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment. All experiments are performed on a dataset from clinical routine of the Radiation Oncology department of the University Hospital Erlangen (UKER). The classification is realized via the maximum-relevance minimal-redundancy (MRMR) technique and support vector machines (SVM). The pipeline leads to a classification with a mean area under the curve (AUC) score of 0.83 in internal cross-validation and allows a division of the cohort into two subcohorts that differ significantly in their median time to progression (low-risk metastasis (LRM): 17.3 months, high-risk metastasis (HRM): 9.6 months, p<0.01). The classification performance is especially enhanced by the analysis of medical images from different points in time (AUC 0.53 ->AUC 0.74). The results indicate that risk stratification of BM based on radiomics and machine learning during post-SRT follow-up is possible with good accuracy and should be further pursued to personalize and improve post-SRT follow-up.
Piecewise Deterministic Markov Processes for Bayesian Neural Networks

Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes.
Plasmon-plasmon interaction and the role of buffer in epitaxial graphene microflakes

We investigate the origin of the translational symmetry breaking in epitaxially grown single-layer graphene. Despite the surface morphology of homogeneous graphene films influenced by the presence of mutually parallel SiC surface terraces, the far-infrared magneto-plasmon absorption is almost independent of the angle between the probing light polarization and the orientation of terraces. Based on a detailed analysis of the plasmon absorption lineshape and its behavior in the magnetic field, supported by confocal Raman mapping and atomic force microscopy, we explain this discrepancy by spontaneously formed graphene micro flakes. We further support our conclusions using data collected on artificially created graphene nanoribbons: we recognize similar plasmon origin in artificial ribbons and naturally formed grains. An unexpectedly large plasmon resonance redshift was observed in nanoribbons. In a hydrogen-intercalated sample (which does not contain the buffer), this redshift is quantitatively taken into account by a plasmon-plasmon interaction. In non-intercalated samples featuring a buffer layer, this redshift is due to an interplay between the plasmon-plasmon coupling and Coulomb screening by the buffer-induced interface states. This model determines the density of interface states in good agreement with experimentally reported values.
Complex QA and language models hybrid architectures, Survey

This paper reviews the state-of-the-art of language models architectures and strategies for"complex"question-answering (QA, CQA, CPS) with a focus on hybridization. Large Language Models (LLM) are good at leveraging public data on standard problems but once you want to tackle more specific complex questions or problems (e.g. How does the concept of personal freedom vary between different cultures ? What is the best mix of power generation methods to reduce climate change ? ) you may need specific architecture, knowledge, skills, methods, sensitive data protection, explainability, human approval and versatile feedback. Recent projects like ChatGPT and GALACTICA have allowed non-specialists to grasp the great potential as well as the equally strong limitations of LLM in complex QA. In this paper, we start by reviewing required skills and evaluation techniques. We integrate findings from the robust community edited research papers BIG, BLOOM and HELM which open source, benchmark and analyze limits and challenges of LLM in terms of tasks complexity and strict evaluation on accuracy (e.g. fairness, robustness, toxicity, .) as a baseline. We discuss some challenges associated with complex QA, including domain adaptation, decomposition and efficient multi-step QA, long form and non-factoid QA, safety and multi-sensitivity data protection, multimodal search, hallucinations, explainability and truthfulness, temporal reasoning. We analyze current solutions and promising research trends, using elements such as: hybrid LLM architectural patterns, training and prompting strategies, active human reinforcement learning supervised with AI, neuro-symbolic and structured knowledge grounding, program synthesis, iterated decomposition and others.
The Le Chatelier Principle in lattices

The standard Le Chatelier Principle states that the long-run demand for a good (in which by definition there are fewer restraints on the variables) is more elastic than short-run demand. The fundamental insight above goes well beyond demand theory, and proofs of this basic idea have been found in various settings. Nearly all of these have been continuous optimization problems requiring assumptions on the continuity of the objective function and on the convexity of the choice set. However, the statement and intuition for the original principle do not seem to rely on any such `technical' assumptions. Work by Milgrom & Shannon on monotone [ordinal] comparative statics provides an obvious framework to pursue a broader result in a discrete environment. The present paper therefore formulates and proves a very general Le Chatelier Principle in the context of lattices. A further generalization is that we allow the choice set to vary (potentially as a function of the underlying parameter).
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.Code is available at https://github.com/haoranhfut/FF-GAN.
Collocation methods for second and higher order systems

It is often unnoticed that the predominant way to use collocation methods is fundamentally flawed when applied to optimal control in robotics. Such methods assume that the system dynamics is given by a first order ODE, whereas robots are often governed by a second or higher order ODE involving configuration variables and their time derivatives. To apply a collocation method, therefore, the usual practice is to resort to the well known procedure of casting an M th order ODE into M first order ones. This manipulation, which in the continuous domain is perfectly valid, leads to inconsistencies when the problem is discretized. Since the configuration variables and their time derivatives are approximated with polynomials of the same degree, their differential dependencies cannot be fulfilled, and the actual dynamics is not satisfied, not even at the collocation points. This paper draws attention to this problem, and develops improved versions of the trapezoidal and Hermite–Simpson collocation methods that do not present these inconsistencies. In many cases, the new methods reduce the dynamics transcription error in one order of magnitude, or even more, without noticeably increasing the cost of computing the solutions.
Theory of the radiation pressure on dielectric surfaces

The radiation pressures exerted by light beams incident normally on dielectric surfaces are calculated by evaluation of the quantum-mechanical Lorentz force. The free-space surface of an almost transparent dielectric and the interface of a lossy dielectric with a transparent medium are treated in detail. Light beams in the forms of a short single-photon pulse and a continuous-mode narrow-band coherent excitation are considered. The use of a pulse excitation enables discrimination of the surface and bulk contributions to the force. It is shown that the surface force is directed inwards to the dielectric for entrance and exit of the pulse from and to free space, contrary to the conclusions of some earlier work. For the interface of lossy and transparent dielectrics, it is shown that the high-reflectivity mirror modelled by an appropriate limit of the lossy dielectric experiences a force enhanced by the refractive index of the transparent medium, in agreement with experiments on a mirror suspended in liquid dielectrics. The results for the Lorentz force are used to identify the effective momenta of photons in dielectrics.
An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.
Computing the controllability radius: a semi-definite programming approach

A semi-definite programming (SDP) approach to compute the controllability radius is proposed in this paper. The initial nonconvex optimisation problem is transformed into the minimisation of the smallest eigenvalue of a bivariate real or trigonometric polynomial with matrix coefficients. A sum-of-squares relaxation leads to the SDP formulation. A similar technique is used for the computation of the stabilisability radius. The approach is extended to the computation of the worst-case controllability radius for systems that depend polynomially on a small number of parameters. Experimental results show that the proposed methods compete well with previous ones in a complexity/accuracy trade-off.
Paint it Black: Generating paintings from text descriptions

Two distinct tasks - generating photorealistic pictures from given text prompts and transferring the style of a painting to a real image to make it appear as though it were done by an artist, have been addressed many times, and several approaches have been proposed to accomplish them. However, the intersection of these two, i.e., generating paintings from a given caption, is a relatively unexplored area with little data available. In this paper, we have explored two distinct strategies and have integrated them together. First strategy is to generate photorealistic images and then apply style transfer and the second strategy is to train an image generation model on real images with captions and then fine-tune it on captioned paintings later. These two models are evaluated using different metrics as well as a user study is conducted to get human feedback on the produced results.
Pretraining Language Models with Human Preferences

Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the trade-off between alignment and capabilities of pretrained LMs. We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.
A new phase-field model for strongly anisotropic systems

We present a new phase-field model for strongly anisotropic crystal and epitaxial growth using regularized, anisotropic Cahn–Hilliard-type equations. Such problems arise during the growth and coarsening of thin films. When the anisotropic surface energy is sufficiently strong, sharp corners form and unregularized anisotropic Cahn–Hilliard equations become ill-posed. Our models contain a high-order Willmore regularization, where the square of the mean curvature is added to the energy, to remove the ill-posedness. The regularized equations are sixth order in space. A key feature of our approach is the development of a new formulation in which the interface thickness is independent of crystallographic orientation. Using the method of matched asymptotic expansions, we show the convergence of our phase-field model to the general sharp-interface model. We present two- and three-dimensional numerical results using an adaptive, nonlinear multigrid finite-difference method. We find excellent agreement between the dynamics of the new phase-field model and the sharp-interface model. The computed equilibrium shapes using the new model also match a recently developed analytical sharp-interface theory that describes the rounding of the sharp corners by the Willmore regularization.
Beware of Pickpockets: A Practical Attack against Blocking Cards

Today, we rely on contactless smart cards to perform several critical operations (e.g., payments and accessing buildings). Attacking smart cards can have severe consequences, such as losing money or leaking sensitive information. Although the security protections embedded in smart cards have evolved over the years, those with weak security properties are still commonly used. Among the different solutions, blocking cards are affordable devices to protect smart cards. These devices are placed close to the smart cards, generating a noisy jamming signal or shielding them. Whereas vendors claim the reliability of their blocking cards, no previous study has ever focused on evaluating their effectiveness. In this paper, we shed light on the security threats on smart cards in the presence of blocking cards, showing the possibility of being bypassed by an attacker. We analyze blocking cards by inspecting their emitted signal and assessing a vulnerability in their internal design. We propose a novel attack that bypasses the jamming signal emitted by a blocking card and reads the content of the smart card. We evaluate the effectiveness of 11 blocking cards when protecting a MIFARE Ultralight smart card and a MIFARE Classic card. Of these 11 cards, we managed to bypass 8 of them and successfully dump the content of a smart card despite the presence of the blocking card. Our findings highlight that the noise type implemented by the blocking cards highly affects the protection level achieved by them. Based on this observation, we propose a countermeasure that may lead to the design of effective blocking cards. To further improve security, we released the tool we developed to inspect the spectrum emitted by blocking cards and set up our attack.
Distances for Markov Chains, and Their Differentiation

(Directed) graphs with node attributes are a common type of data in various applications and there is a vast literature on developing metrics and efficient algorithms for comparing them. Recently, in the graph learning and optimization communities, a range of new approaches have been developed for comparing graphs with node attributes, leveraging ideas such as the Optimal Transport (OT) and the Weisfeiler-Lehman (WL) graph isomorphism test. Two state-of-the-art representatives are the OTC distance proposed in (O'Connor et al., 2022) and the WL distance in (Chen et al., 2022). Interestingly, while these two distances are developed based on different ideas, we observe that they both view graphs as Markov chains, and are deeply connected. Indeed, in this paper, we propose a unified framework to generate distances for Markov chains (thus including (directed) graphs with node attributes), which we call the Optimal Transport Markov (OTM) distances, that encompass both the OTC and the WL distances. We further introduce a special one-parameter family of distances within our OTM framework, called the discounted WL distance. We show that the discounted WL distance has nice theoretical properties and can address several limitations of the existing OTC and WL distances. Furthermore, contrary to the OTC and the WL distances, our new discounted WL distance can be differentiated after a entropy-regularization similar to the Sinkhorn distance, making it suitable to use in learning frameworks, e.g., as the reconstruction loss in a graph generative model.
Enabling a Multi-Purpose High-Energy Neutron Source Based on High-Current Compact Cyclotrons

The current and future need for high-energy neutrons has been a subject of increasing discussion and concern. Immediate applications for such an intense neutron source include medical isotope production, high-energy physics (HEP) research, and for materials development and to support qualification for fission reactors. Also, and of the utmost importance, is the need for such a source to inform critical gaps in our understanding of the transmutation materials science issues facing fusion power reactors. A 14 MeV fusion prototypical neutron source (FPNS) has been a critical, yet unresolved need of the fusion program for more than 40 years. Given the narrowing timeline for construction of pilot and fusion power plants the urgency and necessity of such a neutron source has become increasingly time sensitive. One possibility to address this need is a scaled-down version of IFMIF technology ("IFMIF-Lite"), operating at 125 mA with the beam and target technology leveraging technology developed under the IFMIF/EVEDA program. Within this white paper, a blueprint of necessary R&D to enable a transformational change in both the capital and operating cost of this IFMIF-Lite driver concept is presented. Enabling this transformation is the replacement of the historic RFQ/LINAC components with multiple compact 35+ MeV D+ drivers, based on compact cyclotrons.
Optimum dissipation by cruising in dense suspensions

Dense suspensions tend to shear jam at large packing fractions. However, it has recently been shown that various oscillation protocols can unjam as well as reduce viscosity and dissipation. In this numerical work, we ask ourselves what is the optimum shear protocol in terms of dissipation. We show that many cruising protocols' dissipation are similar to shear protocols with a steady primary shear and superimposed cross oscillations, even though the latter's viscosity reduction is more considerable. Furthermore, we find that alternating between primary and perpendicular oscillations yields a much higher dissipation than the two protocols mentioned above, yet has similar viscosity as the cross-oscillatory one. While self-organization has been shown to minimize viscosity, our findings challenge the idea that random organization is the underlying mechanism for reducing dissipation. Instead, shear ``fragility'' combined with geometry seems to be the key ingredients, which explains the counter-intuitive decoupling of the minima of viscosity and dissipation for the cruising protocol. This work paves the way for a new class of highly-energy efficient flow protocols.