Close-in warm Jupiters orbiting M dwarf stars are expected to exhibit diverse atmospheric chemistry, with clouds playing a key role in shaping their albedo, heat distribution, and spectral properties. We study WASP-80b, a warm Jupiter orbiting an M dwarf star, using the latest JWST panchromatic emission and transmission spectra to comprehensively characterise its atmosphere, including cloud coverage, chemical composition, and particle sizes, and compare the observations with predictions from the general circulation models (GCMs). We used a GCM, ADAM (ADvanced Atmospheric MITgcm, formerly known as SPARC/MITgcm), combined with the latest JWST data to study the atmosphere of WASP-80b. A cloud module with radiatively active, tracer-based clouds was integrated with the GCM to study the effects on the atmosphere and the spectrum. We find that the emission and transmission spectra of WASP-80b are only compatible with cloudless atmospheres or with clouds composed of sufficiently large particles, namely Na$_2$S ($\geq 10 μ$m), KCl ($\geq 1 μ$m), and MgSiO$_3$ ($\geq 5 μ$m). For these large-particle cloud cases, efficient gravitational settling confines the clouds to deeper atmospheric layers, resulting in weak spectral signatures. Smaller particles are ruled out due to their strong radiative feedback on the atmospheric structure. Overall, our results suggest that WASP-80b's atmosphere is either effectively cloud-free or contains clouds composed of large, settled particles whose opacity has little impact on the observable atmosphere. This underscores the importance of particle size and vertical cloud distribution in interpreting exoplanet spectra. Future observations at shorter wavelengths may help distinguish between large-particle cloud scenarios and a truly cloudless atmosphere.

We presented the optimization procedures of the baffle mounted on the GroundBIRD telescope for measuring the polarization of the Cosmic Microwave Background~(CMB). The telescope employs dual mirror reflective telescopes installed in a cryostat. The primary objectives were to minimize stray light contamination, maintain the integrity of the main beam, and ensure that thermal loading from the baffle remains significantly below that from the atmosphere. Using quasi-optical simulations, we have optimized the baffle's aperture angle to suppress stray light without degrading the main beam quality. We confirmed through Moon observations that the optimized baffle design works to eliminate the contamination of the stray light as expected. Furthermore, no measurable degradation in the noise equivalent temperature~(NET) was detected, indicating minimal thermal impact. These results show that our baffle optimization strategy effectively reduces systematic errors while maintaining observational sensitivity, providing valuable insights for future CMB experiments with similar optical architectures.

We generalize the de Sitter static patch holographic proposal and the bilayer holographic entanglement entropy prescription to de Sitter geometries containing a bubble of smaller positive or vanishing cosmological constant. When the causal patch of an observer at the center of the bubble overlaps with the ``parent'' de Sitter region, we propose that the full spacetime can be holographically encoded on two holographic screens. The leading geometrical entanglement entropy between the two screens, which can be constant or time-dependent in some cases, never exceeds the Gibbons-Hawking entropy associated with the ``parent'' de Sitter space. When the causal patch of the observer at the center of the bubble is causally disconnected from the ``parent'' de Sitter region, the holographic proposal no longer applies, and more than two holographic screens are required to encode the whole spacetime.

Complex statistical models are often built by combining multiple submodels, called modules. Here we consider modular inference where the modules contain both parametric and nonparametric components. In such cases, standard Bayesian inference can be highly sensitive to misspecification in any module, and influential prior specifications for the nonparametric components can compromise inference for the parametric components, and vice versa. We propose a novel "optimization-centric" approach to cutting feedback for semiparametric modular inference, which can address misspecification and prior-data conflicts. The proposed cut posteriors are defined via a variational optimization problem like other generalized posteriors, but regularization is based on Rényi divergence, instead of Kullback-Leibler divergence (KLD). We show empirically that defining the cut posterior using Rényi divergence delivers more robust inference than KLD, and Rényi divergence reduces the tendency to underestimate uncertainty when the variational approximations impose strong parametric or independence assumptions. Novel posterior concentration results that accommodate the Rényi divergence and allow for semiparametric components are derived, extending existing results for cut posteriors that only apply to KLD and parametric models. These new methods are demonstrated in a benchmark example and two real examples: Gaussian process adjustments for confounding in causal inference and misspecified copula models with nonparametric marginals.

Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning.

Phase singularities are universal features found across diverse wave systems. Their ensembles exhibit distance correlations governing exotic material phases. However, the full correlations in phase-space have remained unexplored and experimentally inaccessible. Here, we directly measure the ultrafast dynamics of optical singularity ensembles, capturing their phase-space correlations. Our observations reveal that phase singularities exhibit acceleration to unbounded velocities before annihilation, indicated by measurements of velocities exceeding the speed of light. These superluminal velocities are paradoxically amplified by the slow group velocity of hyperbolic phonon polaritons in our material platform, hexagonal boron nitride membranes. We demonstrate these phenomena using combined hardware and algorithmic advances in ultrafast electron microscopy, achieving spatial and temporal resolutions each an order of magnitude below the polaritonic wavelength and cycle period. Our findings enable probing topological defect dynamics at previously unattainable timescales, deepening our understanding of phase-singularity universality and suggesting phenomena of ultrafast information flow in polaritonic media.

Modified Einstein's equations implementing both an energy-dependent Newtonian and cosmological constant can be obtained via a modified action characterised by a non-minimal gravity-matter coupling. We show how different dynamics for the high energy regime - that nevertheless have in common the vanishing of the Newton constant - result in models of gravitational collapse which source the formation of non-singular geometries with different types of asymptotic core: de Sitter, Minkowski, and with "steep pressure". As we categorise their defining properties, one feature, which we prove to be actually independent of both the specific form of the non-minimal coupling and the equation of state of the collapsing fluid, stands out: the formation of a geometry with Minkowski core occurs only if the Newton constant, before finally vanishing, assumes negative values.

We present a comprehensive analysis of generic 5-dimensional Einstein-Maxwell-Dilaton-Axion (EMDA) holographic theories with exponential couplings. We find and classify exact, analytic, anisotropic solutions, both zero-temperature vacua and finite-temperature black brane backgrounds, with anisotropy sourced by scalar axions, magnetic fields, and charge densities, that can be interpreted as IR fixed points of renormalisation-group flows from UV-conformal fixed points. The resulting backgrounds feature a hyperscaling violation exponent and up to three independent Lifshitz-like exponents, generated by an equal number of independent coupling constants in the EMDA action. We derive the holographic stress-energy tensor and the corresponding equation of state, and discuss the behavior of the anisotropic speed of sound and butterfly velocity. We show that these theories can be consistently constrained by imposing several natural requirements, including energy conditions, thermodynamic stability, and causality. Additionally, we analyse hard probes in this class of theories, including Brownian motion, momentum broadening and jet quenching, and we demonstrate that a fully analytic treatment is possible, making their dependence on the underlying anisotropy explicit. We highlight the relevance of these models as benchmarks for strongly coupled anisotropic matter in nature, from the quark-gluon plasma created in heavy-ion collisions to dense QCD phases in neutron-star mergers and the cores of compact objects.

Using orbifold Hilbert schemes, we compactify all two-dimensional Hitchin systems corresponding to types A0-tilde, D4-tilde, E6-tilde, E7-tilde, and E8-tilde, thereby obtaining four rational elliptic surfaces with C*-actions. Their singular fibers and relative minimal models are listed in the main table. A particularly interesting point is that we found they can all be obtained by performing a finite number of blow-ups on the second Hirzebruch surface. To this end, we prove that Hilbert schemes of orbifold surfaces are connected smooth projective schemes under suitable conditions, and we use the Hilbert-Chow morphism to construct the minimal resolutions of the coarse moduli spaces.

Platinum-coated polystyrene Janus particles exhibit a combination of stochastic and deterministic motion in hydrogen peroxide solutions, making them promising candidates for applications in micro-scale cargo transport, drug delivery, and environmental remediation. The dynamics of Janus particles very near a boundary are dictated by conservative and non-conservative interactions that depend on particle, substrate, and solution properties. This study investigated the influence of orientational quenching by measuring the effect of changes in cap thickness and hydrogen peroxide concentration on particle velocity and maximum displacement. Janus particles with cap thicknesses of 3 nm, 7 nm, 10 nm, 20 nm, and 35 nm were analyzed in 1 wt./vol.% and 3 wt./vol.% hydrogen peroxide solutions near the bottom and top boundaries of the fluid cell. Results indicated that particles with lower cap thicknesses exhibit higher velocities, with faster particles in 3 wt./vol.% peroxide as compared to 1 wt./vol.% peroxide. Furthermore, results suggest a combined influence of activity and gravitational effects influenced whether particles moved along the top boundary i.e. ceiling or bottom boundary i.e. flooring. Heavier cap particles in lower peroxide concentration solution show less ceiling than lighter cap particles in higher peroxide concentration. We also find a global reduction in velocity for when a single surface of the two is plasma cleaned surface. These findings highlight the important interplay between cap weight, hydrodynamic interactions, and propulsion force in determining the dynamics of Janus particles.

This paper studies the integration of machine-learned advice in overlay networks in order to adapt their topology to the incoming demand. Such demand-aware systems have recently received much attention, for example in the context of data structures (Fu et al. in ICLR 2025, Zeynali et al. in ICML 2024). We in this paper extend this vision to overlay networks where requests are not to individual keys in a data structure but occur between communication pairs, and where algorithms have to be distributed. In this setting, we present an algorithm that adapts the topology (and the routing paths) of the overlay network to minimize the hop distance travelled by bit, that is, distance times demand. In a distributed manner, each node receives an (untrusted) prediction of the future demand to help him choose its set of neighbors and its forwarding table. This paper focuses on optimizing the well-known skip list networks (SLNs) for their simplicity. We start by introducing continuous skip list networks (C-SLNs) which are a generalization of SLNs specifically designed to tolerate predictive errors. We then present our learning-augmented algorithm, called LASLiN, and prove that its performance is (i) similar to the best possible SLN in case of good predictions ($O(1)$-consistency) and (ii) at most a logarithmic factor away from a standard overlay network in case of arbitrarily wrong predictions ($O(\log^2 n)$-robustness, where $n$ is the number of nodes in the network). Finally, we demonstrate the resilience of LASLiN against predictive errors (ie, its smoothness) using various error types on both synthetic and real demands.

Safety alignment is critical for the ethical deployment of large language models (LLMs), guiding them to avoid generating harmful or unethical content. Current alignment techniques, such as supervised fine-tuning and reinforcement learning from human feedback, remain fragile and can be bypassed by carefully crafted adversarial prompts. Unfortunately, such attacks rely on trial and error, lack generalizability across models, and are constrained by scalability and reliability. This paper presents NeuroStrike, a novel and generalizable attack framework that exploits a fundamental vulnerability introduced by alignment techniques: the reliance on sparse, specialized safety neurons responsible for detecting and suppressing harmful inputs. We apply NeuroStrike to both white-box and black-box settings: In the white-box setting, NeuroStrike identifies safety neurons through feedforward activation analysis and prunes them during inference to disable safety mechanisms. In the black-box setting, we propose the first LLM profiling attack, which leverages safety neuron transferability by training adversarial prompt generators on open-weight surrogate models and then deploying them against black-box and proprietary targets. We evaluate NeuroStrike on over 20 open-weight LLMs from major LLM developers. By removing less than 0.6% of neurons in targeted layers, NeuroStrike achieves an average attack success rate (ASR) of 76.9% using only vanilla malicious prompts. Moreover, Neurostrike generalizes to four multimodal LLMs with 100% ASR on unsafe image inputs. Safety neurons transfer effectively across architectures, raising ASR to 78.5% on 11 fine-tuned models and 77.7% on five distilled models. The black-box LLM profiling attack achieves an average ASR of 63.7% across five black-box models, including the Google Gemini family.

In this note six Steiner systems $S(2,8,225)$ and four Steiner systems $S(2,9,289)$ are presented. This resolves two of $129$ undecided cases for block designs with block length $8$ and $9$, mentioned in Handbook of Combinatorial Designs.

Patients with rare types of melanoma such as acral, mucosal, or uveal melanoma, have lower survival rates than patients with cutaneous melanoma; these lower survival rates reflect the lower objective response rates to immunotherapy compared to cutaneous melanoma. Understanding tumor-immune dynamics in rare melanomas is critical for the development of new therapies and for improving response rates to current cancer therapies. Progress has been hindered by the lack of clinical data and the need for better preclinical models of rare melanomas. Canine melanoma provides a valuable comparative oncology model for rare types of human melanomas. We analyzed RNA sequencing data from canine melanoma patients and combined this with literature information to create a novel mechanistic mathematical model of melanoma-immune dynamics. Sensitivity analysis of the mathematical model indicated influential pathways in the dynamics, providing support for potential new therapeutic targets and future combinations of therapies. We share our learnings from this work, to help enable the application of this proof-of-concept workflow to other rare disease settings with sparse available data.

In a recent article, Rapaport showed that there is no dimension drop for exponentially separated analytic IFSs on the real line. We show that the set of such exponentially separated IFSs in the space of analytic IFSs contains an open and dense set in the $\mathcal{C}^2$ topology. Moreover, we give a sufficient condition for the IFS to be exponentially separated which allows us to construct explicit examples which are exponentially separated. The key technical tool is the introduction of the \emph{dual IFS} which we believe has significant interest in its own right. As an application we also characterise when an analytic IFS can be conjugated to a self-similar IFS.

The integration of Internet of Things (IoT) and Non-Terrestrial Networks (NTNs) has emerged as a key paradigm to provide connectivity for sensors and actuators via satellite gateways in remote areas where terrestrial infrastructure is limited or unavailable. Among other Low-Power Wide-Area Network (LPWAN) technologies for IoT, Long Range (LoRa) holds great potential given its long range, energy efficiency, and flexibility. In this paper, we explore the feasibility and performance of LoRa to support large-scale IoT connectivity through Low Earth Orbit (LEO) satellite gateways. To do so, we developed a new ns3-LoRa-NTN simulation module, which integrates and extends the ns3-LoRa and ns3-NTN modules, to enable full-stack end-to-end simulation of satellite communication in LoRa networks. Our results, given in terms of average data rate and Packet Reception Ratio (PRR), confirm that LoRa can effectively support direct communication from the ground to LEO satellites, but network optimization is required to mitigate collision probability when end nodes use the same Spreading Factors (SFs) over long distances.

The Linear Quadratic Gaussian (LQG) regulator is a cornerstone of optimal control theory, yet its performance can degrade significantly when the noise distributions deviate from the assumed Gaussian model. To address this limitation, this work proposes a distributionally robust generalization of the finite-horizon LQG control problem. Specifically, we assume that the noise distributions are unknown and belong to ambiguity sets defined in terms of an entropy-regularized Wasserstein distance centered at a nominal Gaussian distribution. By deriving novel bounds on this Sinkhorn discrepancy and proving structural and topological properties of the resulting ambiguity sets, we establish global optimality of linear policies. Numerical experiments showcase improved distributional robustness of our control policy.

The efficient preparation of collective eigenstates of subwavelength-spaced optical dipoles is a prerequisite for observing their signature radiative properties and for their applications in quantum information processing. We theoretically investigate the deterministic preparation of superradiant and subradiant states of two dipole-coupled two-level quantum emitters at deep-subwavelength separation using the Swing-UP of Quantum Emitter Population (SUPER) excitation scheme. Utilizing suitable pulse parameters for two red-detuned, time-overlapping Gaussian pulses, the SUPER scheme enables close-to-unity population inversion in the targeted collective eigenstates. Furthermore, a tunable optical phase in the SUPER scheme enables the simultaneous inversions in both pure super- and subradiant states with finite populations, thereby resulting in the preparation of hybrid collective states. These results are possible to realize with or without an optical cavity. Our approach to populating the collective eigenstates in a cavity environment paves the way for the efficient preparation of these states in the presence of environmental decoherence. Our scheme enables single-photon generation, which is measured using the second-order correlation function. We also discuss in detail possible experimental realizations, in particular using solid-state emitters and molecules.

The Dzyaloshinskii-Moriya interaction (DMI), which originates from spin-orbit coupling and relies on broken inversion symmetry, is recognized as a key ingredient in forming magnetic skyrmions. However, most 2D magnets exhibit inversion symmetry; therefore, the DMI is suppressed. Here, we propose a strategy to induce large DMI via lithium absorption on the surface of 2D magnets -- an experimentally feasible approach. Using first-principles and atomistic spin simulations, we predict the formation of nanoscale skyrmions in lithium-decorated monolayer Fe$_3$GeTe$_2$ by imposing small out-of-plane magnetic fields ($B_z$). Notably, we find very large skyrmion energy barriers of more than 300 meV at $B_z = 0.4$ T, comparable to those observed in ferromagnet/heavy-metal interfaces. The origin of these unique skyrmions is attributed to the competition between strong DMI, exchange frustration, and small magnetocrystalline anisotropy energy. We further show that the lifetimes of metastable skyrmions exceed one hour for temperatures up to 75 K.

Reconfigurable photonics have rapidly become an invaluable tool for information processing. Light-based computing accelerators are promising for boosting neural network learning and inference and optical interconnects are foreseen as a solution to the information transfer bottleneck in high-performance computing. In this study, we demonstrate the successful programming of a transformation implemented using a reconfigurable photonic circuit with a non-conventional architecture. The core of most photonic processors is an MZI-based architecture that establishes an analytical connection between the controllable parameters and circuit transformation. However, several architectures that are substantially more difficult to program have improved robustness to fabrication defects. We use two algorithms that rely on different initial datasets to reconstruct the circuit model of a complex interferometer, and then program the required unitary transformation. Both methods performed accurate circuit programming with an average fidelity greater than 99% and 97%, respectively. Our results provide a strong foundation for the introduction of non-conventional interferometric architectures for photonic information processing.

I investigate the possibility that explicit solutions of stochastic reaction-diffusion equations can be found by multiplying the deterministic travelling waves with a stochastic exponent. This approach has become widespread in the literature in recent years. I will conclude that this approach is, in general, not a valid Ansatz and only works in the case of NLS-type equations in the Stratonovich interpretation.

Physical reservoir computing provides a powerful machine learning paradigm that exploits nonlinear physical dynamics for efficient information processing. By incorporating quantum effects, quantum reservoir computing offers superior potential for machine learning applications, as quantum dynamics are exponentially costly to simulate classically. Here, we present a novel quantum reservoir computing approach based on correlated quantum spin systems, exploiting natural quantum many-body interactions to generate reservoir dynamics, thereby circumventing the practical challenges of deep quantum circuits. Our experimental implementation supports nontrivial quantum entanglement and exhibits sufficient dynamical complexity for high-performance machine learning. We achieve state-of-the-art performance in experiments on standard time-series benchmarks, reducing prediction error by 1 to 2 orders of magnitude compared to previous quantum reservoir experiments. In long-term weather forecasting, our 9-spin quantum reservoir delivers greater prediction accuracy than classical reservoirs with thousands of nodes. This represents the first experimental demonstration of quantum machine learning outperforming large-scale classical models on real-world tasks.

We report experimental and theoretical investigations on the quaternary Heusler alloy CoRuTiGe, synthesized using the arc melting technique. Crystal structure analysis reveals a tetragonal structure at room temperature. Magnetization measurements as a function of temperature and magnetic field indicate ferromagnetic nature with a saturation magnetization of 0.681 mB/f.u. at 5 K. The temperature dependence of electrical resistivity shows a nearly linear decrease in the high-temperature range, indicating the spin gapless semiconductor like behavior of the material. This SGS nature is further supported by the temperature-independent carrier concentration and mobility. Hall effect analysis reveals that the anomalous Hall effect in CoRuTiGe arises from both intrinsic and extrinsic mechanisms. Additionally, a well-defined symmetric negative magnetoresistance is observed at low temperatures. These findings suggest that CoRuTiGe holds significant promise for spintronic applications.

The Audio-to-3D-Gesture (A2G) task has enormous potential for various applications in virtual reality and computer graphics, etc. However, current evaluation metrics, such as Fréchet Gesture Distance or Beat Constancy, fail at reflecting the human preference of the generated 3D gestures. To cope with this problem, exploring human preference and an objective quality assessment metric for AI-generated 3D human gestures is becoming increasingly significant. In this paper, we introduce the Ges-QA dataset, which includes 1,400 samples with multidimensional scores for gesture quality and audio-gesture consistency. Moreover, we collect binary classification labels to determine whether the generated gestures match the emotions of the audio. Equipped with our Ges-QA dataset, we propose a multi-modal transformer-based neural network with 3 branches for video, audio and 3D skeleton modalities, which can score A2G contents in multiple dimensions. Comparative experimental results and ablation studies demonstrate that Ges-QAer yields state-of-the-art performance on our dataset.

Dynamical mean-field theory (DMFT) is a useful tool to analyze models of strongly correlated fermions like the Hubbard model. In DMFT, the lattice of the model is replaced by a single impurity site embedded in an effective bath. The resulting single impurity Anderson model (SIAM) can then be solved self-consistently with a quantum-classical hybrid algorithm. This procedure involves repeatedly preparing the ground state on a quantum computer and evolving it in time to measure the Greens function. We here develop an approximation of the time evolution operator for this setting by training a Hamiltonian variational ansatz. The parameters of the ansatz are obtained via a variational quantum algorithm that utilizes a small number of time steps, given by the Suzuki-Trotter expansion of the time evolution operator, to guide the evolution of the parameters. The resulting circuit has a fixed depth for the time evolution depending on the size of the bath and is significantly shallower than a comparable Suzuki-Trotter expansion.

Integration of variable energy resources -- e.g., solar, wind, and hydro -- and end-use electrification increase modern energy systems' weather-dependence. Identifying critical infrastructure constraining the power grid's ability to meet electricity demand under weather-induced shocks and stressors is essential for understanding risks and guiding adaptation. We use transfer entropy to identify predictive pressure points: grid components whose utilization patterns provide early signals of downstream power shortages. We apply this method to simulations of New York State's proposed future grid under various meteorological and technological scenarios, showing that pressure points often arise from complex, system-wide interactions between generation, transmission, and demand. While transfer entropy does not support conclusions about causality, the identified pressure points align with known bottlenecks and offer insight into failure pathways. Furthermore, these pressure points are not easily predicted by high-level scenario features alone, underscoring the need for holistic and adaptive approaches to reliability planning in power systems with intermittent resources.

Information and communication technologies are by now employed in most human activities, including economics and finance. Modern computers have reached an extraordinary power in terms of information processing, storage, retrieval, and transmission. However, several results of theoretical computer science imply the impossibility of certifying software quality in general. With the exception of safety-critical systems, this has primarily concerned information processed by confined systems, with limited socio-economic consequences. In the emerging era of technologies for exchanging tokenized assets and digital money over the Internet, such as in particular central bank digital currency (CBDC), even a minor bug could trigger a financial collapse. Although the aforementioned impossibility results cannot be overcome in an absolute sense, there exist formal methods that can provide correctness assertions for software system models under suitable conditions. We advocate their use to validate the operational resilience of software infrastructures enabling CBDC, with special emphasis on offline payments as they constitute a very critical issue.

The relationship between Salem numbers and short geodesics has been fruitful in quantitative studies of arithmetic hyperbolic orbifolds, particularly in dimensions 2 and 3. In this article, we push these connections even further. The primary goals are: (1) to bound the proportion of Salem numbers of degree up to $n+1$ in the commensurability class of classical arithmetic lattices in any odd dimension $n$; (2) to improve lower bounds for the strong exponential growth of averages of multiplicities in the geodesic length spectrum of non-compact arithmetic orbifolds. In order to accomplish these goals, we bound, for a fixed square-free integer $D$, the count of Salem numbers with minimal polynomial $f$ satisfying $f(1)f(-1)=-D$ in $\mathbb{Q}^{\times}/\mathbb{Q}^{\times 2}$. To do this, we make use of results on the distribution of Salem numbers, as well as classical methods for counting Pythagorean triples and Gauss' lattice-counting argument. To this end, we give a generalization of the count of Pythagorean triples and provide an elementary proof which may be of independent interest.

We propose a universal gate set for quantum computing that operates in the presence of decoherence without the overhead of active error correction. We show that a broad class of anisotropic system--bath couplings can be effectively decoupled by preparing an appropriate system--bath entangled initial state. The initially established entanglement serves as a resource to cancel out the dominant decoherence during evolution, enabling quantum computation to proceed as if the system were effective decoupled from its environment.

Galactic microlensing has the capability to determine the position angle of the detected planets in a sky reference frame. By a broad enough statistics, it is possible to investigate possible anisotropies in the distribution of the orbital planes of the planetary systems. We select 66 published microlensing planets suitable for such study and test the hypothesis that such orientations are randomly distributed against the possibility that the orbital planes follow some preferential alignment. The whole sample seems to be overall isotropically distributed, but by re-binning according to the distance along the line of sight, we find some local anisotropy peaks. Excluding those coming from very poor statistics or possible systematics, the anisotropy at 3 kpc may suggest a preferential alignment of planetary orbits in the Scutum-Centaurus spiral arm of the Milky Way with the Galactic plane. Special orientations of the orbital planes may be reminiscent of the specific conditions that triggered and drove the star formation processes and how these are related to local and global Galactic kinematics. Using the method proposed here, the future Roman microlensing survey will be able to identify and quantify preferential orientations in all structures from the Sun to the bulge with high confidence and accuracy.