We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the enhanced uv coverage in the 2021 Event Horizon Telescope (EHT) observations. The addition of the 12-m Kitt Peak Telescope and NOEMA provides two key intermediate-length baselines to SMT and the IRAM 30-m, giving sensitivity to emission structures at scales of $\sim250~\mu$as and $\sim2500~\mu$as (0.02 pc and 0.2 pc). Without these baselines, earlier EHT observations lacked the capability to constrain emission on large scales, where a "missing flux" of order $\sim1$ Jy is expected. To probe these scales, we analyzed closure phases, robust against station-based gain errors, and modeled the jet base emission using a simple Gaussian offset from the compact ring emission at separations $>100~\mu$as. Our analysis reveals a Gaussian feature centered at ($\Delta$RA $\approx320~\mu$as, $\Delta$Dec $\approx60~\mu$as), a projected separation of $\approx5500$ AU, with a flux density of only $\sim60$ mJy, implying that most of the missing flux in previous studies must arise from larger scales. Brighter emission at these scales is ruled out, and the data do not favor more complex models. This component aligns with the inferred direction of the large-scale jet and is consistent with emission from the jet base. While our findings indicate detectable jet base emission at 230 GHz, coverage from only two intermediate baselines limits reconstruction of its morphology. We therefore treat the recovered Gaussian as an upper limit on the jet base flux density. Future EHT observations with expanded intermediate-baseline coverage will be essential to constrain the structure and nature of this component.

Magic-angle twisted multilayer graphene stands out as a highly tunable class of moir\'e materials that exhibit strong electronic correlations and robust superconductivity. However, understanding the relations between the low-temperature superconducting phase and the preceding correlated phases established at higher temperatures remains a challenge. Here, we employ scanning tunneling microscopy and spectroscopy to track the formation sequence of correlated phases established by the interplay of dynamic correlations, intervalley coherence, and superconductivity in magic-angle twisted trilayer graphene (MATTG). We discover the existence of two well-resolved gaps pinned at the Fermi level within the superconducting doping range. While the outer gap, previously associated with pseudogap phase, persists at high temperatures and magnetic fields, the newly revealed inner gap is more fragile in line with superconductivity MATTG transport experiments. Andreev reflection spectroscopy taken at the same location confirms a clear trend that closely follows the doping behaviour of the inner gap, and not the outer one. Moreover, spectroscopy taken at nanoscale domain boundaries further corroborates the contrasting behavior of the two gaps, with the inner gap remaining resilient to structural variations, as expected from the finite superconducting coherence length. By comparing our findings with recent topological heavy-fermion models, we identify that the outer gap originates from the splitting of the Abrikosov-Suhl-Kondo resonance due to the breaking of the valley symmetry arising from correlation-driven effects. Our results suggest an intricate but tractable hierarchy of correlated phases in twisted multilayer graphene.

Accurate in-hand pose estimation is crucial for robotic object manipulation, but visual occlusion remains a major challenge for vision-based approaches. This paper presents an approach to robotic in-hand object pose estimation, combining visual and tactile information to accurately determine the position and orientation of objects grasped by a robotic hand. We address the challenge of visual occlusion by fusing visual information from a wrist-mounted RGB-D camera with tactile information from vision-based tactile sensors mounted on the fingertips of a robotic gripper. Our approach employs a weighting and sensor fusion module to combine point clouds from heterogeneous sensor types and control each modality's contribution to the pose estimation process. We use an augmented Iterative Closest Point (ICP) algorithm adapted for weighted point clouds to estimate the 6D object pose. Our experiments show that incorporating tactile information significantly improves pose estimation accuracy, particularly when occlusion is high. Our method achieves an average pose estimation error of 7.5 mm and 16.7 degrees, outperforming vision-only baselines by up to 20%. We also demonstrate the ability of our method to perform precise object manipulation in a real-world insertion task.

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (this https URL). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.

The spectral functions of twisted bilayer graphene (TBG) in the absence of strain have recently been investigated in both the symmetric and symmetry-broken phases using dynamical mean-field theory (DMFT). The theoretically predicted Mott-Hubbard bands and gapless semimetallic state at half-filling have since been confirmed experimentally. Here, we develop several second-order perturbation theory approaches to the topological heavy-fermion (THF) model of TBG and twisted symmetric trilayer graphene (TSTG). In the symmetric phase, we adapt, implement, and benchmark an iterative perturbation theory (IPT) impurity solver within DMFT, enabling computationally efficient yet accurate spectral function calculations. We present momentum- and energy-resolved spectra over a broad range of temperatures and fillings for both symmetric and symmetry-broken states. In addition, we derive analytic expressions for the spectral function within the ``Hubbard-I'' approximation of the THF model and, as expected, find that while it provides a tractable description of Mott physics, it does not capture the low-energy Kondo peak or the finite lifetime broadening of the bands. Our methodology can be extended to include strain, lattice relaxation, and parameter variations, thereby allowing systematic predictions of TBG and TSTG spectral properties across a wide range of physical regimes. Because our perturbative approaches are far less computationally intensive than DMFT with numerically exact impurity solvers, they can be used to efficiently benchmark and scan extensive phase diagrams of the THF parameters, paving the way for full DMFT analyses of the TBG spectral function in the presence of strain and relaxation.

Given a sequence of $n$ numbers and $k$ parallel First-in-First-Out (FIFO) queues, how close can one bring the sequence to sorted order? It is known that $k$ queues suffice to sort the sequence if the Longest Decreasing Subsequence (LDS) of the input sequence is at most $k$. But, what if the number of queues is too small for sorting completely? - We give a simple algorithm, based on Patience Sort, that reduces the LDS by $k - 1$. We also show, that the algorithm is optimal, i.e., for any $L > 0$ there exists a sequence of LDS $L$ such that the LDS cannot be reduced below $L - k + 1$ with $k$ queues. - Merging two sorted queues is at the core of Merge Sort. In contrast, two sequences of LDS two cannot always be merged into a sequence of LDS two. We characterize when it is possible and give an algorithm to decide whether it is possible. Merging into a sequence of LDS three is always possible. - A down-step in a sequence is an item immediately followed by a smaller item. We give an optimal algorithm for reducing the number of down-steps. The algorithm is online. Our research was inspired by an application in car manufacturing.

The Resource Public Key Infrastructure (RPKI) protocol was standardized to add cryptographic security to Internet routing. With over 50% of Internet resources protected with RPKI today, the protocol already impacts significant parts of Internet traffic. In addition to its growing adoption, there is also increasing political interest in RPKI. The White House indicated in its Roadmap to Enhance Internet Routing Security, on 4 September 2024, that RPKI is a mature and readily available technology for securing inter-domain routing. The Roadmap attributes the main obstacles towards wide adoption of RPKI to a lack of understanding, lack of prioritization, and administrative barriers. This work presents the first comprehensive study of the maturity of RPKI as a viable production-grade technology. We find that current RPKI implementations still lack production-grade resilience and are plagued by software vulnerabilities, inconsistent specifications, and operational challenges, raising significant security concerns. The deployments lack experience with full-fledged strict RPKI-validation in production environments and operate in fail-open test mode. We provide recommendations to improve RPKI resilience and guide stakeholders in securing their deployments against emerging threats. The numerous issues we have discovered with the current RPKI specifications and implementations inevitably lead to the question: Is RPKI sufficiently stable to align with the expectations outlined in the White House roadmap? Certainly, it is not perfect, but is it good enough? The answer, as we will explore, varies depending on one's viewpoint.

We study the impact of lattice effects due to heterostrain and relaxation on the correlated electron physics of magic-angle twisted bilayer graphene, by applying dynamical mean-field theory to the topological heavy fermion model. Heterostrain is responsible for splitting the 8-fold degenerate flat bands into two 4-fold degenerate subsets, while relaxation breaks the particle-hole symmetry of the unperturbed THF model. The interplay of dynamical correlation effects and lattice symmetry breaking enables us to satisfactorily reproduce a wide set of experimentally observed features: splitting the flat band degeneracy has observable consequences in the form of a filling-independent maximum in the spectral density away from zero bias, which faithfully reproduces scanning tunneling microscopy and quantum twisting microscopy results alike. We also observe an overall reduction in the size and degeneracy of local moments upon lowering the temperature, in agreement with entropy measurements. The absence of particle-hole symmetry has as a consequence the stronger suppression of local moments on the hole-doped side relatively to the electron-doped side, and ultimately causes the differences in existence and stability of the correlated phases for negative and positive doping. Our results show that even fine-level structures in the experimental data can now be faithfully reproduced and understood.

We present a multimethod investigation into the nature of the recently reported quantum spin liquid (QSL) phase in the spin-$1/2$ Heisenberg antiferromagnet on the Shastry-Sutherland lattice. A comprehensive projective symmetry group classification of fermionic mean-field Ansätze on this lattice yields 46 U(1) and 80 $\mathbb{Z}_{2}$ states. Motivated by density-matrix renormalization group (DMRG) calculations suggesting that the Shastry-Sutherland model and the square-lattice $J_{1}$-$J_{2}$ Heisenberg antiferromagnet putatively share the same QSL phase, we establish a mapping of our Ansätze to those of the square lattice. This enables us to identify the equivalent of the square-lattice QSL (Z2A$zz$13) in the Shastry-Sutherland system. Employing state-of-the-art variational Monte Carlo calculations with Gutzwiller-projected wavefunctions improved upon by Lanczos steps, we demonstrate the excellent agreement of energies and correlators between a gapless (Dirac) $\mathbb{Z}_{2}$ spin liquid -- characterized by only few parameters -- and approaches based on neural quantum states and DMRG. Furthermore, the real-space spin-spin correlations are shown to decay with the same power law as in the $J_{1}$-$J_{2}$ square lattice model, which also hosts a $\mathbb{Z}_{2}$ Dirac spin liquid. Finally, we apply the recently developed Keldysh formulation of the pseudo-fermion functional renormalization group to compute the dynamical spin structure factor; these correlations exhibit the features expected due to Dirac cones in the excitation spectrum, thus providing strong independent evidence for a Dirac QSL ground state. Our finding of a $d$-wave pairing $\mathbb{Z}_{2}$ Dirac QSL is consistent with the recently observed signatures of QSL behavior in Pr$_2$Ga$_2$BeO$_7$ and outlines predictions for future experiments.

A novel point cloud diffusion model for relativistic heavy-ion collisions, capable of ultra-fast generation of event-by-event collision output, is introduced. When trained on UrQMD cascade simulations, the model generates realistic collision event output containing 26 distinct hadron species, as a list of particle momentum vectors along with their particle ID. From solving inverse problems to accelerating model calculations or detector simulations, the model can be a promising general purpose tool for heavy-ion collisions beneficial to both theoretical studies and experimental applications.

QCD at nonzero isospin chemical potentials has phenomenological relevance for a series of physical systems and provides an ideal testground for the modeling of dense strongly interacting matter. The two-flavor quark-meson model is known to effectively describe the condensation of charged pions in QCD that occurs in this setting. In this paper, we derive a renormalization-group invariant mean-field formulation of the model and demonstrate that the resulting phase diagram and equation of state are in quantitative agreement with data from lattice QCD simulations at small and intermediate isospin chemical potentials. In particular, the speed of sound from the model shows an excess over the conformal bound as previously seen in lattice computations in agreement with chiral perturbation theory. We then consider the speed of sound in the limit of large isospin chemical potentials and see that it approaches the conformal limit from above, in qualitative agreement with recent lattice results and in quantitative agreement with perturbation theory in the presence of a BCS gap. Finally, we consider the phase diagram in the approach to the chiral limit. We find that within the model the chiral phase transition connects to the pion condensation phase boundary in the chiral limit and we discuss the implications for the properties of the chiral transition point.

We examine the thermodynamic behavior of a static neutral regular (non-singular) black hole enclosed in a finite isothermal cavity. The cavity enclosure helps us investigate black hole systems in a canonical or a grand canonical ensemble. Here we demonstrate the derivation of the reduced action for the general metric of a regular black hole in a cavity by considering a canonical ensemble. The new expression of the action contains quantum corrections at short distances and concludes to the action of a singular black hole in a cavity at large distances. We apply this formalism to the noncommutative Schwarzschild black hole, in order to study the phase structure of the system. We conclude to a possible small/large stable regular black hole transition inside the cavity that exists neither at the system of a classical Schwarzschild black hole in a cavity, nor at the asymptotically flat regular black hole without the cavity. This phase transition seems to be similar with the liquid/gas transition of a Van der Waals gas.

Stochastic thermodynamics is the field of study relating fluctuations in stochastic systems to thermodynamic quantities. The total entropy production (EP), is central to the thermodynamic classification of systems. Non-equilibrium systems manifestly all have non-zero EP and therefore impose an "arrow of time". Thermodynamic inequalities are lower bounds on the total EP and are especially useful when only parts of systems are operationally accessible. We use a stochastic calculus approach to directly derive and generalise three classes of inequalities for Markov jump processes using correlations of path observables, e.g., currents and densities. Our theoretical predictions are compared with simulations, where a good agreement is observed. The thermodynamic bounds we investigate include the thermodynamic uncertainty relation (TUR), thermodynamic transport bound (TB), and thermodynamic correlation bound (CB). We provide insight into the saturation conditions for these bounds and to what degree saturation can be achieved. Additionally, for the TUR and TB, we show how the bounds are related, which includes identifying a diffusion coefficient for jump dynamics. %An example using a toy model shows how the CB may yield a negative lower bound on the total entropy production, contrary to the non-negative bound that the TUR and TB yield. Comparisons are drawn between the TUR and TB for relaxation and stationary processes in biologically relevant settings. Specifically, calmodulin folding dynamics and secondary active transport, where differences in long-time relaxation and convergence are observed. For a systematic way to construct models, we formulate two methods to drive systems out of equilibrium without changing the stationary probability distribution.

We complete the perturbative program for equilibrium thermodynamics of cosmological first-order phase transitions by determining the finite-temperature effective potential of gauge-Higgs theories at next-to-next-to-next-to-next-to-leading order (N$^4$LO). The computation of the three-loop effective potential required to reach this order is extended to generic models in dimensionally reduced effective theories in a companion article. Our N$^4$LO result is the last perturbative order before confinement renders electroweak gauge-Higgs theories non-perturbative at four loops. By contrasting our analysis with non-perturbative lattice results, we find a remarkable agreement. As a direct application for predictions of gravitational waves produced by a first-order transition, our computation provides the final fully perturbative results for the phase transition strength and speed of sound.

As an alternative but unified and more fundamental description for quantum physics, Feynman path integrals generalize the classical action principle to a probabilistic perspective, under which the physical observables' estimation translates into a weighted sum over all possible paths. The underlying difficulty is to tackle the whole path manifold from finite samples that can effectively represent the Feynman propagator dictated probability distribution. Modern generative models in machine learning can handle learning and representing probability distribution with high computational efficiency. In this study, we propose a Fourier-flow generative model to simulate the Feynman propagator and generate paths for quantum systems. As demonstration, we validate the path generator on the harmonic and anharmonic oscillators. The latter is a double-well system without analytic solutions. To preserve the periodic condition for the system, the Fourier transformation is introduced into the flow model to approach a Matsubara representation. With this novel development, the ground-state wave function and low-lying energy levels are estimated accurately. Our method offers a new avenue to investigate quantum systems with machine learning assisted Feynman Path integral solving.

In this work, we present a novel severe buffer-overflow vulnerability in the RPKI validator Fort, that allows an attacker to achieve Remote Code Execution (RCE) on the machine running the software. We discuss the unique impact of this RCE on networks that use RPKI, illustrating that RCE vulnerabilities are especially severe in the context of RPKI. The design of RPKI makes RCE easy to exploit on a large scale, allows compromise of RPKI validation integrity, and enables a powerful vector for additional attacks on other critical components of the network, like the border routers. We analyze the vulnerability exposing to this RCE and identify indications that the discovered vulnerability could constitute an intentional backdoor to compromise systems running the software over a benign coding mistake. We disclosed the vulnerability, which has been assigned a CVE rated 9.8 critical (CVE-2024-45237).

We investigate single crystals of the trigonal antiferromagnet EuZn$_2$P$_2$ ($P\overline{3}m1$) by means of electrical transport, magnetization measurements, X-ray magnetic scattering, optical reflectivity, angle-resolved photoemission spectroscopy (ARPES) and ab-initio band structure calculations (DFT+U). We find that the electrical resistivity of EuZn$_2$P$_2$ increases strongly upon cooling and can be suppressed in magnetic fields by several orders of magnitude (CMR effect). Resonant magnetic scattering reveals a magnetic ordering vector of $q = (0\, 0\, \frac{1}{2})$, corresponding to an $A$-type antiferromagnetic (AFM) order, below $T_{\rm N} = 23.7\,\rm K$. We find that the moments are canted out of the $a-a$ plane by an angle of about $40^{\circ}\pm 10^{\circ}$ degrees and aligned along the [100] in the $a-a$ plane. We observe nearly isotropic magnetization behavior for low fields and low temperatures which is consistent with the magnetic scattering results. The magnetization measurements show a deviation from the Curie-Weiss behavior below $\approx 150\,\rm K$, the temperature below which also the field dependence of the material's resistivity starts to increase. An analysis of the infrared reflectivity spectrum at $T=295\,\rm K$ allows us to resolve the main phonon bands and intra-/interband transitions, and estimate indirect and direct band gaps of $E_i^{\mathrm{opt}}=0.09\,\rm{eV}$ and $E_d^{\mathrm{opt}}=0.33\,\rm{eV}$, respectively, which are in good agreement with the theoretically predicted ones. The experimental band structure obtained by ARPES is nearly $T$-independent above and below $T_{\rm N}$. The comparison of the theoretical and experimental data shows a weak intermixing of the Eu 4$f$ states close to the $\Gamma$ point with the bands formed by the phosphorous 3$p$ orbitals leading to an induction of a small magnetic moment at the P sites.

We extend the concept of superadiabatic dynamics, or transitionless quantum driving, to quantum open systems whose evolution is governed by a master equation in the Lindblad form. We provide the general framework needed to determine the control strategy required to achieve superadiabaticity. We apply our formalism to two examples consisting of a two-level system coupled to environments with time-dependent bath operators.

The mechanism of the peculiar transport properties around the magnetic ordering temperature of semiconducting antiferromagnetic EuCd$_2$P$_2$ is not yet understood. With a huge peak in the resistivity observed above the Néel temperature, $T_{\rm N}=10.6\,\rm K$, it exhibits a colossal magnetoresistance effect. Recent reports on observations of ferromagnetic contributions above $T_{\rm N}$ as well as metallic behavior below this temperature have motivated us to perform a comprehensive characterization of this material, including its resistivity, heat capacity, magnetic properties and electronic structure. Our transport measurements revealed quite different temperature dependence of resistivity with the maximum at $14\,\rm K$ instead of previously reported $18\,\rm K$. Low-field susceptibility data support the presence of static ferromagnetism above $T_{\rm N}$ and show a complex behavior of the material at small applied magnetic fields. Namely, signatures of reorientation of magnetic domains are observed up to $T=16\,\rm K$. Our magnetization measurements indicate a magnetocrystalline anisotropy which also leads to a preferred alignment of the magnetic clusters above $T_{\rm N}$. The momentum-resolved photoemission experiments at temperatures from $24\,\rm K$ down to $2.5\,\rm K$ indicate the permanent presence of a fundamental band gap without change of the electronic structure when going through $T_N$ that is in contradiction with previous results. We performed \textit{ab initio} band structure calculations which are in good agreement with the measured photoemission data when assuming an antiferromagnetic ground state. Calculations for the ferromagnetic phase show a much smaller bandgap, indicating the importance of possible ferromagnetic contributions for the explanation of the colossal magnetoresistance effect in the related EuZn$_2$P$_2$.

We consider a largely untapped potential for the improvement of traffic networks that is rooted in the inherent uncertainty of travel times. Travel times are subject to stochastic uncertainty resulting from various parameters such as weather condition, occurrences of road works, or traffic accidents. Large mobility services have an informational advantage over single network users as they are able to learn traffic conditions from data. A benevolent mobility service may use this informational advantage in order to steer the traffic equilibrium into a favorable direction. The resulting optimization problem is a task commonly referred to as signaling or Bayesian persuasion. Previous work has shown that the underlying signaling problem can be NP-hard to approximate within any non-trivial bounds, even for affine cost functions with stochastic offsets. In contrast, we show that in this case, the signaling problem is easy for many networks. We tightly characterize the class of single-commodity networks, in which full information revelation is always an optimal signaling strategy. Moreover, we construct a reduction from optimal signaling to computing an optimal collection of support vectors for the Wardrop equilibrium. For two states, this insight can be used to compute an optimal signaling scheme. The algorithm runs in polynomial time whenever the number of different supports resulting from any signal distribution is bounded to a polynomial in the input size. Using a cell decomposition technique, we extend the approach to a polynomial-time algorithm for multi-commodity parallel link networks with a constant number of commodities, even when we have a constant number of different states of nature.