Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-plasma effect on energy transfer from laser beam to shock wave generated in solid target

Efficiency of the laser radiation energy transport into the shock wave generated in layered planar targets (consisting of massive Cu over coated by thin CH layer) was investigated. The targets were irradiated using two laser pulses. The 1ω pulse with the energy of ∼50 J produced a pre-plasma, imitating the corona of the pre-compressed inertial confinement fusion target. The second main pulse used the 1ω or 3ω laser harmonics with the energy of ∼200 J. The influence of the pre-plasma on parameters of the shock wave was determined from the crater volume measurements and from the electron density distribution measured by 3-frame interferometry. The experimental results show that the energy transport by fast electrons provides a definite contribution to the dynamics of the ablative process, to the shock wave generation, and to the ablation pressure in dependence on the target irradiation conditions. The strong influence of the pre-plasma on the investigated process was observed in the 1ω case. Theoretical analysis supports the explanation of experimental results.
Isomorphisms of 4d N=2 SCFTs from 6d

There exist 4d $\mathcal{N}=2$ SCFTs in class $\mathcal{S}$ which have different constructions as punctured Riemann surfaces, but which nevertheless appear to describe the same physics. Some of these class $\mathcal{S}$ theories have an alternative construction as torus-compactifications of 6d $(1,0)$ SCFTs. We demonstrate that the 6d SCFTs are isomorphic. Each 6d SCFT in question can be obtained from a parent 6d SCFT by Higgs branch renormalization group flow, and the parent theory possesses a discrete symmetry under which the relevant Higgs branch flows are exchanged. The existence of this discrete symmetry, which may be embedded in an enhanced continuous symmetry, proves that the original pair of class $\mathcal{S}$ theories are, in fact, isomorphic.
Impossibility Theorems for Feature Attribution

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear-for example, Integrated Gradients and Shapley Additive Explanations (SHAP)-can provably fail to improve on random guessing for inferring model behavior. Our results apply to common end-tasks such as characterizing local model behavior, identifying spurious features, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks: Once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.
Haptic Shared Control for Dissipating Phantom Traffic Jams

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called phantom traffic jams are traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.
Numerical approximations of thin structure deformations

We review different (reduced) models for thin structures using bending as principal mechanism to undergo large deformations. Each model consists in the minimization of a fourth order energy, potentially subject to a nonconvex constraint. Equilibrium deformations are approximated using local discontinuous Galerkin (LDG) finite elements. The design of the discrete energies relies on a discrete Hessian operator defined on discontinuous functions with better approximation properties than the piecewise Hessian. Discrete gradient flows are put in place to drive the minimization process. They are chosen for their robustness and ability to preserve the nonconvex constraint. Several numerical experiments are presented to showcase the large variety of shapes that can be achieved with these models.
Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction

Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing either the single-step model or the multi-step algorithm in the process. In this work, we establish a bridge between both tasks by benchmarking the performance and transfer of different single-step retrosynthesis models to the multi-step domain by leveraging two common search algorithms, Monte Carlo Tree Search and Retro*. We show that models designed for single-step retrosynthesis, when extended to multi-step, can have a tremendous impact on the route finding capabilities of current multi-step methods, improving performance by up to +30% compared to the most widely used model. Furthermore, we observe no clear link between contemporary single-step and multi-step evaluation metrics, showing that single-step models need to be developed and tested for the multi-step domain and not as an isolated task to find synthesis routes for molecules of interest.
The ngEHT Analysis Challenges

The next-generation Event Horizon Telescope (ngEHT) will be a significant enhancement of the Event Horizon Telescope (EHT) array, with ∼10 new antennas and instrumental upgrades of existing antennas. The increased uv-coverage, sensitivity, and frequency coverage allow a wide range of new science opportunities to be explored. The ngEHT Analysis Challenges have been launched to inform the development of the ngEHT array design, science objectives, and analysis pathways. For each challenge, synthetic EHT and ngEHT datasets are generated from theoretical source models and released to the challenge participants, who analyze the datasets using image reconstruction and other methods. The submitted analysis results are evaluated with quantitative metrics. In this work, we report on the first two ngEHT Analysis Challenges. These have focused on static and dynamical models of M87* and Sgr A* and shown that high-quality movies of the extended jet structure of M87* and near-horizon hourly timescale variability of Sgr A* can be reconstructed by the reference ngEHT array in realistic observing conditions using current analysis algorithms. We identify areas where there is still room for improvement of these algorithms and analysis strategies. Other science cases and arrays will be explored in future challenges.
SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named"Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on"Disparity Height"and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://github.com/Yang-Kaixing/SHLE.
Harmonic measure in a multidimensional gambler's problem

We consider a random walk in a truncated cone K N , which is obtained by slicing cone K by a hyperplane at a growing level of order N . We study the behaviour of the Green function in this truncated cone as N increases. Using these results we also obtain the asymptotic behaviour of the harmonic measure. The obtained results are applied to a multidimensional gambler’s problem studied by Diaconis and Ethier (2022). In particular we conﬁrm their conjecture that the probability of eliminating players in a particular order has the same exact asymptotic behaviour as for the Brownian motion approximation. We also provide a rate of convergence of this probability towards this approximation.
Bridge Girth: A Unifying Notion in Network Design

A classic 1993 paper by Althöfer et al. proved a tight reduction from spanners, emulators, and distance oracles to the extremal function $\gamma$ of high-girth graphs. This paper initiated a large body of work in network design, in which problems are attacked by reduction to $\gamma$ or the analogous extremal function for other girth concepts. In this paper, we introduce and study a new girth concept that we call the bridge girth of path systems, and we show that it can be used to significantly expand and improve this web of connections between girth problems and network design. We prove two kinds of results:•We write the maximum possible size of an n-node, p-path system with bridge girth $\gt k$ as $\beta(n, p, k)$, and we write a certain variant for “ordered” path systems as $\beta^{*}(n, p, k)$. We identify several arguments in the literature that implicitly show upper or lower bounds on $\beta, \beta^{*}$, and we provide some polynomial improvements to these bounds. In particular, we construct a tight lower bound for $\beta(n, p, 2)$, and we polynomially improve the upper bounds for $\beta(n, p, 4)$ and $\beta^{*}(n, p, \infty)$. •We show that many state-of-the-art results in network design can be recovered or improved via black-box reductions to $\beta$ or $\beta^{*}$. Examples include bounds for distance/reachability preservers, exact hopsets, shortcut sets, the flow-cut gaps for directed multicut and sparsest cut, an integrality gap for directed Steiner forest.We believe that the concept of bridge girth can lead to a stronger and more organized map of the research area. Towards this, we leave many open problems related to both bridge girth reductions and extremal bounds on the size of path systems with high bridge girth.
The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent nonlinearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in literature, principally via the introduction of measurements between layers of unitary transformations. In this article, we introduce the quantum path kernel (QPK), a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of quantum neural tangent kernel, which has been used to study the dynamics of classical and quantum machine learning models. The QPK exploits the parameter trajectory, i.e., the curve delineated by model parameters as they evolve during training, enabling the representation of differential layerwise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian xor mixtures: an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.
Learning to swim efficiently in a nonuniform flow field

Microswimmers can acquire information on the surrounding fluid by sensing mechanical queues. They can then navigate in response to these signals. We analyze this navigation by combining deep reinforcement learning with direct numerical simulations to resolve the hydrodynamics. We study how local and nonlocal information can be used to train a swimmer to achieve particular swimming tasks in a nonuniform flow field, in particular, a zigzag shear flow. The swimming tasks are (1) learning how to swim in the vorticity direction, (2) learning how to swim in the shear-gradient direction, and (3) learning how to swim in the shear-flow direction. We find that access to laboratory frame information on the swimmer's instantaneous orientation is all that is required in order to reach the optimal policy for tasks (1) and (2). However, information on both the translational and rotational velocities seems to be required to accomplish task (3). Inspired by biological microorganisms, we also consider the case where the swimmers sense local information, i.e., surface hydrodynamic forces, together with a signal direction. This might correspond to gravity or, for microorganisms with light sensors, a light source. In this case, we show that the swimmer can reach a comparable level of performance to that of a swimmer with access to laboratory frame variables. We also analyze the role of different swimming modes, i.e., pusher, puller, and neutral.
High-resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b

Terrestrial exoplanets orbiting M-dwarf stars are promising targets for transmission spectroscopy with existing or near-future instrumentation. The atmospheric composition of such rocky planets remains an open question, especially given the high X-ray and ultraviolet flux from their host M dwarfs that can drive atmospheric escape. The 1.3 R ⊕ exoplanet GJ 486b (T eq ∼ 700 K), orbiting an M3.5 star, is expected to have one of the strongest transmission spectroscopy signals among known terrestrial exoplanets. We observed three transits of GJ 486b using three different high-resolution spectrographs: IRD on Subaru, IGRINS on Gemini-South, and SPIRou on the Canada–France–Hawai’i Telescope. We searched for atmospheric absorption from a wide variety of molecular species via the cross-correlation method, but did not detect any robust atmospheric signals. Nevertheless, our observations are sufficiently sensitive to rule out several clear atmospheric scenarios via injection and recovery tests, and extend comparative exoplanetology into the terrestrial regime. Our results suggest that GJ 486b does not possess a clear H2/He-dominated atmosphere, nor a clear 100% water-vapor atmosphere. Other secondary atmospheres with high mean molecular weights or H2/He-dominated atmospheres with clouds remain possible. Our findings provide further evidence suggesting that terrestrial planets orbiting M-dwarf stars may experience significant atmospheric loss.
Novel Deep Learning Framework For Bovine Iris Segmentation

Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic vision. Most recent works focus on designing curricula based on difficulty levels in input samples or smoothing the feature maps. However, smoothing labels to control the learning utility in a curriculum manner is still unexplored. In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a heavy smoothing penalty in the true label and limits learning less information. Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger smoothing value at the beginning of training and gradually decreased it to zero to control the model learning utility from lower to higher. We also designed a confidence-aware pacing function and combined it with our CBLS to investigate the benefits of various curricula. The proposed techniques are validated on four robotic surgery datasets of multi-class, multi-label classification, captioning, and segmentation tasks. We also investigate the robustness of our method by corrupting validation data into different severity levels. Our extensive analysis shows that the proposed method improves prediction accuracy and robustness.
An improved bound on the electron’s electric dipole moment

The imbalance of matter and antimatter in our Universe provides compelling motivation to search for undiscovered particles that violate charge-parity symmetry. Interactions with vacuum fluctuations of the fields associated with these new particles will induce an electric dipole moment of the electron (eEDM). We present the most precise measurement yet of the eEDM using electrons confined inside molecular ions, subjected to a huge intramolecular electric field, and evolving coherently for up to 3 seconds. Our result is consistent with zero and improves on the previous best upper bound by a factor of ~2.4. Our results provide constraints on broad classes of new physics above 1013 electron volts, beyond the direct reach of the current particle colliders or those likely to be available in the coming decades. Description Editor’s summary The puzzling imbalance between matter and antimatter in the universe can be explained by the breaking of charge parity symmetry. The standard model of particle physics does predict a slight breaking of this symmetry but is insufficient to explain the observations. Many extensions to the standard model have been proposed to resolve this discrepancy. To test such model extensions, tabletop experiments that measure the electron’s electric dipole moment (eEDM), a measure of symmetry breaking, have looked very promising. Roussy et al. exploited the large electric fields inside the polar molecular ions of hafnium fluoride to measure the eEDM to extremely high precision (see the Perspective by Fan and Jayich). The uncertainty of the measurement compares favorably to those achievable through accelerator-based experiments. —JS Spectroscopy of molecular ions of hafnium fluoride was used to deduce the asymmetry of the electron’s charge distribution.
On asymptotic behavior for 1‐dimensional functional of Ginzburg‐Landau type with internally‐externally created oscillations of minimizers

In this note we provide a kind of generalization of the well‐known notion of internally (externally, resp.) created oscillations of minimizers of non‐convex integrands in the calculus of variations. As an example, we consider a class of 1‐dimensional Ginzburg‐Landau functionals (the simplest case being considered in the paper G. Alberti, S. Muller: A new approach to variational problems with multiple scales, Comm. Pure Appl. Math. 54, 761–825 (2001)). We describe asymptotic behavior leading to multiple small scale separation as parameter epsilon tends to zero. (© 2012 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
CHAOTIC DIFFERENCE EQUATIONS: GENERIC ASPECTS

It is shown that in the set of all continuous selfmaps of a compact acyclic polyhedron (i.e. the homology groups of the space vanish in all dimensions > 0) the chaotic maps form a dense set. The notion of chaos used here is that of Li and Yorke. If this notion is slightly weakened ("almost chaotic") the density result can be improved by the theorem that the set of almost chaotic (continuous) selfmaps of a compact acyclic polyhedron P contains a residual subset of the space of all continuous selfmaps of P. Moreover, the topological entropy of such a generic almost chaotic map is shown to be infinite. The basic ingredients of the proofs are from fixed point index theory.
Three-dimensional hydrodynamical simulations of colliding stars. III: Collisions and tidal captures of unequal-mass main-sequence stars

A 3D smooth-particle hydrodynamic code with 7000 particles is used to simulate collisions between lower main sequence (MS) stars whose masses differ by a factor of five. Collisions between point-mass intruders and MS stars are simulated to study how the physics of the encounters depends on the finite radius of the smaller MS stars. It is found that the two MS stars become gravitationally bound in a physical collision if their relative velocity at infinity is less than a critical velocity Vd. Vd decreases from 1000 km/s in a head-on collision to 150 km/s in a grazing one. If the less massive star is replaced by a point mass, Vd remains the same for grazing collision and tidal encounters but drops to about 600 km/s in head-on collisions. The collision and coalescence of two MS stars resets the nuclear clock of the coalesced star, so it contracts to the MS. Tidal capture of binary stars in globular clusters can only occur in encounters in which the closest approach of the two stars to their center of mass is less than 2.0 times the sum of their radii. 17 refs.