Search

We present a proposal for calculating the running of the coupling constant of the $\mathrm{SU}(3)$ pure-gauge theory, which combines the Twisted Gradient Flow (TGF) renormalization scheme with Parallel Tempering on Boundary Conditions (PTBC). The TGF is a gradient flow-based renormalization scheme formulated in an asymmetric lattice with twisted boundary conditions. Combined with step scaling, it has been successfully used to calculate the $\mathrm{SU}(3)$ $\Lambda$ parameter. As with all gradient flow-based schemes, the coupling constant is highly correlated with the topological charge and affected by topology freezing, an issue addressed by projecting the determination of the coupling onto the zero topological sector. As an alternative to the zero-charge projection, we combine TGF with PTBC, by replicating multiple copies of the same lattice, interpolating between periodic and open boundary conditions in a parallel-tempered manner. We present a first exploration of these ideas by analyzing specific ensembles of $\mathrm{SU}(3)$ lattices with and without PTBC., Comment: 11 pages, 5 figures, 40th International Symposium on Lattice Field Theory, LATTICE2023 Fermilab, Batavia, Illinois, USA, from July 31 to August 4, 2023. V2: text unchanged, some previous figures were not correct and have been updated

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ~10cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the IMAV 2022 Nanocopter AI Challenge. We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field., Comment: 8 pages, 10 Figures, 3 Tables, This paper has been accepted for publication in the IEEE Robotics and Automation Letters (RAL). Copyright 2023 IEEE

This study explores the promising potential of integrating sensing capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based networks through innovative multi-sensor fusion techniques, tracking algorithms, and resource management. A novel data fusion technique is proposed within the MIMO-OFDM system, which promotes cooperative sensing among monostatic joint sensing and communication (JSC) base stations by sharing range-angle maps with a central fusion center. To manage data sharing and control network overhead introduced by cooperation, an excision filter is introduced at each base station. After data fusion, the framework employs a three-step clustering procedure combined with a tracking algorithm to effectively handle point-like and extended targets. Delving into the sensing/communication trade-off, resources such as transmit power, frequency, and time are varied, providing valuable insights into their impact on the overall system performance. Additionally, a sophisticated channel model is proposed, accounting for complex urban propagation scenarios and addressing multipath effects and multiple reflection points for extended targets like vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA), downlink sum rate, and bit rate, offer a comprehensive assessment of the system's localization and communication capabilities, as well as network overhead.

A kinematic misalignment of the stellar and gas components is a phenomenon observed in a significant fraction of galaxies. However, the underlying physical mechanisms are not well understood. A commonly proposed scenario for the formation of a misaligned component requires any pre-existing gas disc to be removed, via fly-bys or ejective feedback from an active galactic nucleus. In this Letter, we study the evolution of a Milky Way mass galaxy in the FIREbox cosmological volume that displays a thin, counter-rotating gas disc with respect to its stellar component at low redshift. In contrast to scenarios involving gas ejection, we find that pre-existing gas is mainly removed via the conversion into stars in a central starburst, triggered by a merging satellite galaxy. The newly-accreted, counter-rotating gas eventually settles into a kinematically misaligned disc. About 4.4 (8 out of 182) of FIREbox galaxies with stellar masses larger than 5e9 Msun at z=0 exhibit gas-star kinematic misalignment. In all cases, we identify central starburst-driven depletion as the main reason for the removal of the pre-existing co-rotating gas component, with no need for feedback from, e.g., a central active black hole. However, during the starburst, the gas is funneled towards the central regions, likely enhancing black hole activity. By comparing the fraction of misaligned discs between FIREbox and other simulations and observations, we conclude that this channel might have a non-negligible role in inducing kinematic misalignment in galaxies., Comment: 9 pages; 3 figures; submitted to ApJ Letters

We study the $O(3)$ critical model and the free theory of a scalar triplet in the presence of a magnetic impurity. We use analytic bootstrap techniques to extract results in the $\varepsilon$-expansion. First, we extend by one order in perturbation theory the computation of the beta function for the defect coupling in the free theory. Then, we analyze in detail the low-lying spectrum of defect operators, focusing on their perturbative realization when the defect is constructed as a path-ordered exponential. After this, we consider two different bulk two-point functions and we compute them using the defect dispersion relation. For a free bulk theory, we are able to fix the form of the correlator at all orders in $\varepsilon$. In particular, taking $\varepsilon\to1$, we can show that in $d=3$ one does not have a consistent and non-trivial defect CFT. For an interacting bulk, we compute the correlator up to second order in $\varepsilon$. Expanding these results in the bulk and defect block expansions, we are able to extract an infinite set of defect CFT data. We discuss low-spin ambiguities that affect every result computed through the dispersion relation and we use a combination of consistency conditions and explicit diagrammatic calculations to fix this ambiguity., Comment: 58 pages, 2 figures, 2 tables

Just like the previous generation of task-tuned models, large language models (LLMs) that are adapted to tasks via prompt-based methods like in-context-learning (ICL) perform well in some setups but not in others. This lack of consistency in prompt-based learning hints at a lack of robust generalisation. We here introduce the ICL consistency test -- a contribution to the GenBench collaborative benchmark task (CBT) -- which evaluates how consistent a model makes predictions across many different setups while using the same data. The test is based on different established natural language inference tasks. We provide preprocessed data constituting 96 different 'setups' and a metric that estimates model consistency across these setups. The metric is provided on a fine-grained level to understand what properties of a setup render predictions unstable and on an aggregated level to compare overall model consistency. We conduct an empirical analysis of eight state-of-the-art models, and our consistency metric reveals how all tested LLMs lack robust generalisation., Comment: Accepted as non-archival submission to the GenBench Workshop 2023. arXiv admin note: substantial text overlap with arXiv:2310.13486

WZW models based on super Lie algebras play an important role for the description of string theory on AdS spaces. In particular, for the case of ${\rm AdS}_3 \times {\rm S}^3$ with pure NS-NS flux the super Lie algebra of $\mathfrak{psu}(1,1|2)_k$ appears in the hybrid formalism, and higher dimensional AdS spaces can be described in terms of related supergroup cosets. In this paper we study the WZW models based on $\mathfrak{u}(2|2)_1$ and $\mathfrak{psu}(2|2)_1$ that may play a role for the worldsheet theory that is dual to free super Yang-Mills in 4D.

The process of painting fosters creativity and rational planning. However, existing generative AI mostly focuses on producing visually pleasant artworks, without emphasizing the painting process. We introduce a novel task, Collaborative Neural Painting (CNP), to facilitate collaborative art painting generation between humans and machines. Given any number of user-input brushstrokes as the context or just the desired object class, CNP should produce a sequence of strokes supporting the completion of a coherent painting. Importantly, the process can be gradual and iterative, so allowing users' modifications at any phase until the completion. Moreover, we propose to solve this task using a painting representation based on a sequence of parametrized strokes, which makes it easy both editing and composition operations. These parametrized strokes are processed by a Transformer-based architecture with a novel attention mechanism to model the relationship between the input strokes and the strokes to complete. We also propose a new masking scheme to reflect the interactive nature of CNP and adopt diffusion models as the basic learning process for its effectiveness and diversity in the generative field. Finally, to develop and validate methods on the novel task, we introduce a new dataset of painted objects and an evaluation protocol to benchmark CNP both quantitatively and qualitatively. We demonstrate the effectiveness of our approach and the potential of the CNP task as a promising avenue for future research., Comment: Submitted to Computer Vision and Image Understanding, project website at https://fodark.github.io/collaborative-neural-painting/

Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios., Comment: Accepted for IEEE RA-L, June 2024. Project's website: https://autonomousrobots.nl/paper_websites/m3p2i-aip

Observations from wide-field quasar surveys indicate that the quasar auto-correlation length increases dramatically from $z\approx2.5$ to $z\approx4$. This large clustering amplitude at $z\approx4$ has proven hard to interpret theoretically, as it implies that quasars are hosted by the most massive dark matter halos residing in the most extreme environments at that redshift. In this work, we present a model that simultaneously reproduces both the observed quasar auto-correlation and quasar luminosity functions. The spatial distribution of halos and their relative abundance are obtained via a novel method that computes the halo mass and halo cross-correlation functions by combining multiple large-volume dark-matter-only cosmological simulations with different box sizes and resolutions. Armed with these halo properties, our model exploits the conditional luminosity function framework to describe the stochastic relationship between quasar luminosity, $L$, and halo mass, $M$. Assuming a simple power-law relation $L\propto M^\gamma$ with log-normal scatter, $\sigma$, we are able to reproduce observations at $z\sim 4$ and find that: (a) the quasar luminosity-halo mass relation is highly non-linear ($\gamma\gtrsim2$), with very little scatter ($\sigma \lesssim 0.3$ dex); (b) luminous quasars ($\log_{10} L/{\rm erg s}^{-1} \gtrsim 46.5-47$) are hosted by halos with mass $\log_{10} M/{\rm M}_\odot\gtrsim 13-13.5$; and (c) the implied duty cycle for quasar activity approaches unity ($\varepsilon_{\rm DC}\approx10-60\%$). We also consider observations at $z\approx2.5$ and find that the quasar luminosity-halo mass relation evolves significantly with cosmic time, implying a rapid change in quasar host halo masses and duty cycles, which in turn suggests concurrent evolution in black hole scaling relations and/or accretion efficiency., Comment: 19 pages, 6 figures; accepted for publication in MNRAS

Climate change is increasingly disrupting worldwide agriculture, making global food production less reliable. To tackle the growing challenges in feeding the planet, cutting-edge management strategies, such as precision agriculture, empower farmers and decision-makers with rich and actionable information to increase the efficiency and sustainability of their farming practices. Crop-type maps are key information for decision-support tools but are challenging and costly to generate. We investigate the capabilities of Meta AI's Segment Anything Model (SAM) for crop-map prediction task, acknowledging its recent successes at zero-shot image segmentation. However, SAM being limited to up-to 3 channel inputs and its zero-shot usage being class-agnostic in nature pose unique challenges in using it directly for crop-type mapping. We propose using clustering consensus metrics to assess SAM's zero-shot performance in segmenting satellite imagery and producing crop-type maps. Although direct crop-type mapping is challenging using SAM in zero-shot setting, experiments reveal SAM's potential for swiftly and accurately outlining fields in satellite images, serving as a foundation for subsequent crop classification. This paper attempts to highlight a use-case of state-of-the-art image segmentation models like SAM for crop-type mapping and related specific needs of the agriculture industry, offering a potential avenue for automatic, efficient, and cost-effective data products for precision agriculture practices., Comment: Accepted at NeurIPS 2023 AI for Science Workshop

Back-action refers to a response that retro-acts on a system to tailor its properties with respect to an external stimulus. This self-induced effect generally belongs to both the natural and technological realm, ranging from neural networks to optics and electronic circuitry. In electronics, back-action mechanisms are at the heart of many classes of devices such as amplifiers, oscillators, and sensors. Here, we demonstrate that back-action can be successfully exploited to achieve $\textit{non-reciprocal}$ transport in superconducting circuits. Our device realizes a supercurrent diode, since the dissipationless current flows in one direction whereas dissipative transport occurs in the opposite direction. Supercurrent diodes presented so far rely on magnetic elements or vortices to mediate charge transport or external magnetic fields to break time-reversal symmetry. In our implementation, back-action solely turns a conventional reciprocal superconducting weak link with no asymmetry between the current bias directions into a diode, where the critical current amplitude depends on the bias sign. The self-interaction of the supercurrent with the device stems from the gate tunability of the critical current, which uniquely promotes up to $\sim$88% of magnetic field-free signal rectification and diode functionality with selectable polarity. The concept we introduce is very general and can be applied directly to a large variety of devices, thereby opening novel functionalities in superconducting electronics.

We present a study of the superconducting penetration depth $\lambda$ in aluminum thin films of varying thickness. The range of thicknesses chosen spans from the thin-film regime to the regime approaching bulk behavior. The penetration depths observed range from $\lambda = 163.3\pm0.4~\rm{nm}$ for the thinnest $20~\rm{nm}$ samples down to $\lambda = 53.6\pm0.4~\rm{nm}$ for the $200~\rm{nm}$-thick ones. In order to accurately determine $\lambda$, we performed complementary measurements using the frequency of superconducting $LC$ resonators as well as the resistance of normal-state meanders. Both methods yield comparable results, providing a well-characterized set of values of $\lambda$ in aluminum in the relevant range for applications in fields such as quantum computing and microwave radiation detector technologies.

We give a geometric characterisation of those groups that arise as fixed subgroups of finite-order untwisted automorphisms of right-angled Artin groups (RAAGs). They are precisely the fundamental groups of a class of compact special cube complexes that we term "divisible". The main corollary is that surface groups arise as fixed subgroups of finite-order automorphisms of RAAGs, as do all commutator subgroups of right-angled Coxeter groups. These appear to be the first examples of such fixed subgroups that are not themselves isomorphic to RAAGs. Using a variation of canonical completions, we also observe that every special group arises as the fixed subgroup of an automorphism of a finite-index subgroup of a RAAG., Comment: 27 pages, 5 figures; v2: minor edits, to appear in Transactions of the AMS

We extend our previous work by building a smooth complete manifold $(M^6,g,p)$ with $\mathrm{Ric}\geq 0$ and whose fundamental group $\pi_1(M^6)=\mathbb{Q}/\mathbb{Z}$ is infinitely generated. The example is built with a variety of interesting geometric properties. To begin the universal cover $\tilde M^6$ is diffeomorphic to $S^3\times \mathbb{R}^3$, which turns out to be rather subtle as this diffeomorphism is increasingly twisting at infinity. The curvature of $M^6$ is uniformly bounded, and in fact decaying polynomially. The example is {\it locally} noncollapsed, in that $\mathrm{Vol}(B_1(x))>v>0$ for all $x\in M$. Finally, the space is built so that it is {\it almost } globally noncollapsed. Precisely, for every $\eta>0$ there exists radii $r_j\to \infty$ such that $\mathrm{Vol}(B_{r_j}(p))\geq r_j^{6-\eta}$. The broad outline for the construction of the example will closely follow the scheme introduced in our previous work. The six-dimensional case requires a couple of new points, in particular the corresponding Ricci curvature control on the equivariant mapping class group is harder and cannot be done in the same manner., Comment: arXiv admin note: text overlap with arXiv:2303.15347

This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs). This evaluation is accomplished via a two-tier approach leveraging Unity simulations. The first level tests for language grounding by assessing a model's ability to relate simple textual descriptions with visual information. The second level evaluates the model's understanding of "Intuitive Physics" principles, such as object permanence and continuity. In addition to releasing the benchmark, we use it to evaluate several state-of-the-art multimodal LLMs. Our evaluation reveals significant shortcomings in the language grounding and intuitive physics capabilities of these models. Although they exhibit at least some grounding capabilities, particularly for colors and shapes, these capabilities depend heavily on the prompting strategy. At the same time, all models perform below or at the chance level of 50% in the Intuitive Physics tests, while human subjects are on average 80% correct. These identified limitations underline the importance of using benchmarks like GRASP to monitor the progress of future models in developing these competencies.

In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates as a monostatic radar system, enabling comprehensive scanning of the monitored area and generating range-angle maps that provide information regarding the position of a group of heterogeneous objects. The acquired maps are subsequently fused in the FC. Then, a convolutional neural network (CNN) is employed to infer the category of the targets, e.g., pedestrians or vehicles, and such information is exploited by an adaptive clustering algorithm to group the detections originating from the same target more effectively. Finally, two multi-target tracking algorithms, the probability hypothesis density (PHD) filter and multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the targets. Numerical results demonstrated that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm, while keeping communication services to UEs with a reduction of the communication capacity in the order of 10% to 20%. The impact of the number of BSs engaged in sensing is also examined, and we show that in the specific case study, 3 BSs ensure a localization error below 1 m.

This paper studies a multiaccess coded caching (MACC) where the connectivity topology between the users and the caches can be described by a class of combinatorial designs. Our model includes as special cases several MACC topologies considered in previous works. The considered MACC network includes a server containing $N$ files, $\Gamma$ cache nodes and $K$ cacheless users, where each user can access $L$ cache nodes. The server is connected to the users via an error-free shared link, while the users can retrieve the cache content of the connected cache-nodes while the users can directly access the content in their connected cache-nodes. Our goal is to minimise the worst-case transmission load on the shared link in the delivery phase. The main limitation of the existing MACC works is that only some specific access topologies are considered, and thus the number of users $K$ should be either linear or exponential to $\Gamma$. We overcome this limitation by formulating a new access topology derived from two classical combinatorial structures, referred to as the $t$-design and the $t$-group divisible design. In these topologies, $K$ scales linearly, polynomially, or even exponentially with $\Gamma$. By leveraging the properties of the considered combinatorial structures, we propose two classes of coded caching schemes for a flexible number of users, where the number of users can scale linearly, polynomially or exponentially with the number of cache nodes. In addition, our schemes can unify most schemes for the shared link network and unify many schemes for the multi-access network except for the cyclic wrap-around topology., Comment: 48 pages

We analyze the role of the general relativity (GR) on the nodal librations of test particles located at the Habitable Zone (HZ) around a solar-mass star, which evolve under the influence of an eccentric planetary-mass perturber with a semimajor axis of 0.1 au. Based on a secular Hamiltonian up to quadrupole level, we derive analytical criteria that define the nodal libration region of a HZ particle as a function of its eccentricity $e_2$ and inclination $i_2$, and the mass $m_1$ and the eccentricity $e_1$ of the perturber. We show that a HZ particle can experience nodal librations with orbital flips or purely retrograde orbits for any $m_1$ and $e_1$ by adopting a suitable combination of $e_2$ and $i_2$. For $m_1 <$ 0.84 M$_\textrm{Jup}$, the greater the $m_1$ value, the smaller the $e_2$ value above which nodal librations are possible for a given $e_1$. For $m_1 >$ 0.84 M$_{\textrm{Jup}}$, a HZ test particle can undergo nodal librations for any $e_2$ and appropriate values of $e_1$ and $i_2$. The same correlation between $m_1$ and $e_2$ is obtained for nodal librations with orbital flips, but a mass limit for $m_1$ of 1.68 M$_{\textrm{Jup}}$ is required in this case. Moreover, the more massive the inner perturber, the greater the nodal libration region associated with orbital flips in the ($e_1$, $i_2$) plane for a given value of $e_2$. Finally, we find good agreements between the analytical criteria and results from N-body simulations for values of $m_1$ ranging from Saturn-like planets to super-Jupiters.

Scalable and effective calibration is a fundamental requirement for Low Cost Air Quality Monitoring Systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensors specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in Air Quality (AQ) monitoring networks. Concept and sensor drift often force calibration process to be frequently repeated. These issues lead to unbearable calibration costs which denies their massive deployment when accuracy is a concern. In this work, We propose a zero transfer samples, global calibration methodology as a technological enabler for IoT AQ multisensory devices which relies on low cost Particulate Matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning concepts and can be universally applied to all units of the same type. A multi season test campaign shown that, when applied to different sensors, this methodology performances match those of state of the art methodology which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded on board of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long range data transfer needs.

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives., Comment: 14 pages, 3 figures, Camera-ready version in ACIS 2023

We present a multi-frequency, multi-epoch catalog of extragalactic sources. The catalog is based on 150, 220, and 280 GHz observations carried out in 2008, 2009, and 2010 using the Millimeter Bolometric Array Camera on the Atacama Cosmology Telescope. We also present and release 280 GHz maps from 2008 and 2010. The catalog contains 483 sources found in a sky area of ${\sim}600$ square degrees. It was obtained by cross-matching sources found in 11 sub-catalogs, one for each season and frequency band. We also include co-added data from ${\sim}150$ and ${\sim}160$ square degrees using two and three years of overlapping observations. We divide the sources into two populations, synchrotron and dusty emitters, based on their spectral behavior in the 150-280 GHz frequency range. We find 284 synchrotron sources and 183 dusty source candidates. Our cross-matching with catalogs from radio to X-ray results in 251 synchrotron sources (88%) and 92 dusty sources (51%) with counterparts and suggests that 91 dusty candidates are not in existing catalogs. We study the variability and number counts of each population. In the case of synchrotron sources, we find year-to-year variability, with a mean value around 35%. As expected, we find no evidence of dusty source variability. Our number counts generally agree with previous measurements and models, except for dusty sources at 280 GHz, where some models overestimate our results., Comment: 24 pages, 16 figures, for associated data products see https://lambda.gsfc.nasa.gov/product/act/act_prod_table.html -- Accepted on Astronomy and Astrophysics

We study parallel fault-tolerant quantum computing for families of homological quantum low-density parity-check (LDPC) codes defined on 3-manifolds with constant or almost-constant encoding rate. We derive generic formula for a transversal $T$ gate of color codes on general 3-manifolds, which acts as collective non-Clifford logical CCZ gates on any triplet of logical qubits with their logical-$X$ membranes having a $\mathbb{Z}_2$ triple intersection at a single point. The triple intersection number is a topological invariant, which also arises in the path integral of the emergent higher symmetry operator in a topological quantum field theory: the $\mathbb{Z}_2^3$ gauge theory. Moreover, the transversal $S$ gate of the color code corresponds to a higher-form symmetry supported on a codimension-1 submanifold, giving rise to exponentially many addressable and parallelizable logical CZ gates. We have developed a generic formalism to compute the triple intersection invariants for 3-manifolds and also study the scaling of the Betti number and systoles with volume for various 3-manifolds, which translates to the encoding rate and distance. We further develop three types of LDPC codes supporting such logical gates: (1) A quasi-hyperbolic code from the product of 2D hyperbolic surface and a circle, with almost-constant rate $k/n=O(1/\log(n))$ and $O(\log(n))$ distance; (2) A homological fibre bundle code with $O(1/\log^{\frac{1}{2}}(n))$ rate and $O(\log^{\frac{1}{2}}(n))$ distance; (3) A specific family of 3D hyperbolic codes: the Torelli mapping torus code, constructed from mapping tori of a pseudo-Anosov element in the Torelli subgroup, which has constant rate while the distance scaling is currently unknown. We then show a generic constant-overhead scheme for applying a parallelizable universal gate set with the aid of logical-$X$ measurements., Comment: 40 pages, 32 figures. In the updated version v2, we have simplified the TQFT derivation of the logical gates via an operator-valued cochain formalism in Sec. III, which also gives rise to the explicit construction of constant-depth circuits corresponding to logical CCZ and CZ gates in three copies of identical toric codes defined on arbitrary 3-manifolds

Much of what is known of the chemical composition of the universe is based on emission line spectra from star forming galaxies. Emission-based inferences are, nevertheless, model-dependent and they are dominated by light from luminous star forming regions. An alternative and sensitive probe of the metallicity of galaxies is through absorption lines imprinted on the luminous afterglow spectra of long gamma ray bursts (GRBs) from neutral material within their host galaxy. We present results from a JWST/NIRSpec programme to investigate for the first time the relation between the metallicity of neutral gas probed in absorption by GRB afterglows and the metallicity of the star forming regions for the same host galaxy sample. Using an initial sample of eight GRB host galaxies at z=2.1-4.7, we find a tight relation between absorption and emission line metallicities when using the recently proposed $\hat{R}$ metallicity diagnostic (+/-0.2dex). This agreement implies a relatively chemically-homogeneous multi-phase interstellar medium, and indicates that absorption and emission line probes can be directly compared. However, the relation is less clear when using other diagnostics, such as R23 and R3. We also find possible evidence of an elevated N/O ratio in the host galaxy of GRB090323 at z=3.58, consistent with what has been seen in other $z>4$ galaxies. Ultimate confirmation of an enhanced N/O ratio and of the relation between absorption and emission line metallicities will require a more direct determination of the emission line metallicity via the detection of temperature-sensitive auroral lines in our GRB host galaxy sample., Comment: Accepted for publication in MNRAS; 24 pages, 15 figures

Finding the best way of adapting pre-trained language models to a task is a big challenge in current NLP. Just like the previous generation of task-tuned models (TT), models that are adapted to tasks via in-context-learning (ICL) are robust in some setups but not in others. Here, we present a detailed analysis of which design choices cause instabilities and inconsistencies in LLM predictions. First, we show how spurious correlations between input distributions and labels -- a known issue in TT models -- form only a minor problem for prompted models. Then, we engage in a systematic, holistic evaluation of different factors that have been found to influence predictions in a prompting setup. We test all possible combinations of a range of factors on both vanilla and instruction-tuned (IT) LLMs of different scale and statistically analyse the results to show which factors are the most influential, interactive or stable. Our results show which factors can be used without precautions and which should be avoided or handled with care in most settings.

The Kondo effect describes the scattering of conduction electrons by magnetic impurities, manifesting as an electronic resonance at the Fermi energy with a distinctive temperature evolution. In this letter, we present a critical evaluation of the current methodology employed to demonstrate Kondo behavior in transport measurements, underscoring the limitations of established theoretical frameworks and the influence of extrinsic broadening. We introduce a novel approach for analyzing spectroscopic indicators of the Kondo effect, employing the Hurwitz-Fano lineshape as a model for the Kondo resonance in the presence of extrinsic broadening. Through precise scanning tunneling spectroscopy measurements on an exemplary spin-1/2 Kondo system, phenalenyl on Au(111), we demonstrate the efficacy of our proposed protocol in extracting accurate intrinsic Kondo linewidths from finite-temperature measurements. The extracted linewidths exhibit a robust fit with a recently derived expression for the temperature-dependent intrinsic Kondo linewidth, providing compelling evidence for the validity of the underlying theory., Comment: 6 pages, 4 figures; plus supplemental material (13 pages, 9 Figures)