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Understanding the cancellation of ultraviolet and infrared singularities in perturbative quantum field theory is of central importance for the development and automation of various theoretical tools that make accurate predictions for observables at high-energy colliders. The loop-tree duality aims to find an efficient solution by treating loop and tree-level contributions under the same foot to achieve a local cancellation of singularities at the integrand level, and thus avoiding dimensional regularisation. In this talk, we exploit the causal properties of vacuum amplitudes in the loop-tree duality representation to present different applications to physical processes at higher orders., Comment: 6 pages, 3 figures, conference in 42nd International Conference on High Energy Physics (ICHEP2024)

We describe an electro-optic dual-comb system that operates in the near-infrared (near-IR) region to generate optical frequency combs in the near-UV by sum frequency generation in two configurations. The near-IR frequency combs are generated using chirped pulses that down convert the optical information into the radio frequency (RF) domain by a difference in the chirp bandwidths. Near (UV) combs at twice the near-IR bandwidth are obtained by sum frequency generation in a nonlinear crystal and detected by a hybrid photon counting detection system. We compare the results of studies of Rb near 420 nm using two optical arrangements where the near-IR combs are mixed in the crystal as combined or as separated beams. While the latter method enables phase retrievals, the combined beam method is superior for phase stability, power throughput for detection, and ease of alignment. High order interleaving enables near-UV bandwidths near 4 cm-1 for faint photonic sensing and spectroscopic applications. The harmonic generation method is easily extendable across much of the titanium sapphire tuning range.

Fractional quantum statistics are a signature prediction of fractional quantum Hall states, which have long been elusive in experiments. Here, we present the observation of anyonic interference and exchange phases in a novel co-propagating 'optical-like' Mach-Zehnder Interferometer. This architecture is free of charging and backscattering effects that often plague the widely used Fabry-Perot interferometer, thus exhibiting pristine Aharonov-Bohm (AB) interference without fractional phase slips. We studied the three lowest Jain filling factors, {\nu}=1/3, 2/5, and 3/7, which host quasiparticles with fractional charges e*=e/3, e/5, and e/7, respectively. The observed AB interference patterns, plotted as a function of magnetic field B and modulation-gate voltage, VMG (known as pajamas), exhibited the expected flux periodicities: 3{\Phi}0, 5{\Phi}0, and 7{\Phi}0, with {\Phi}0 being the flux quantum. A small biased top gate (TG) deposited in the center of the interferometer induces local quasiparticles that are spatially isolated from the interfering modes. At non-zero TG voltage VTG, quantized phase slips appear in the AB pajamas approximately with every flux quantum that pierces the effective area below the TG. Moreover, when tuning VTG, at a constant magnetic field, abrupt phase jumps corresponding to adding one localized quasiparticle at a time under the top gate appear in the B-VTG pajamas.

Reliable execution of large-scale quantum algorithms requires robust underlying operations and this challenge is addressed by quantum error correction (QEC). Most modern QEC protocols rely on measurements and feed-forward operations, which are experimentally demanding, and often slow and prone to high error rates. Additionally, no single error-correcting code intrinsically supports the full set of logical operations required for universal quantum computing, resulting in an increased operational overhead. In this work, we present a complete toolbox for fault-tolerant universal quantum computing without the need for measurements during algorithm execution by combining the strategies of code switching and concatenation. To this end, we develop new fault-tolerant, measurement-free protocols to transfer encoded information between 2D and 3D color codes, which offer complementary and in combination universal sets of robust logical gates. We identify experimentally realistic regimes where these protocols surpass state-of-the-art measurement-based approaches. Moreover, we extend the scheme to higher-distance codes by concatenating the 2D color code with itself and by integrating code switching for operations that lack a natively fault-tolerant implementation. Our measurement-free approach thereby provides a practical and scalable pathway for universal quantum computing on state-of-the-art quantum processors., Comment: 16 pages, 9 figures

We present the first quantum computation of a total decay rate in high-energy physics at second order in perturbative quantum field theory. This work underscores the confluence of two recent cutting-edge advances. On the one hand, the quantum integration algorithm Quantum Fourier Iterative Amplitude Estimation (QFIAE), which efficiently decomposes the target function into its Fourier series through a quantum neural network before quantumly integrating the corresponding Fourier components. On the other hand, causal unitary in the loop-tree duality (LTD), which exploits the causal properties of vacuum amplitudes in LTD to coherently generate all contributions with different numbers of final-state particles to a scattering or decay process, leading to singularity-free integrands that are well suited for Fourier decomposition. We test the performance of the quantum algorithm with benchmark decay rates in a quantum simulator and in quantum hardware, and find accurate theoretical predictions in both settings., Comment: 6 pages (5+1), 5 figures

We report the serendipitous discovery of a bow-shock nebula around the cataclysmic variable (CV) SY Cancri. In addition, SY Cnc lies near the edge of a faint Halpha-emitting nebula with a diameter of about 15'. The orientation of the bow shock is consistent with the direction of SY Cnc's proper motion. Nebulae are extremely rare around CVs, apart from those known to have undergone classical-nova (CN) outbursts; bow shocks and off-center nebulae are even more unusual. Nevertheless, the properties of SY Cnc and its nebulosity are strikingly similar to those of V341 Ara, another CV that is also associated with a bow shock and is likewise off-center with respect to its faint Halpha nebula. Both stars are binaries with optically thick accretion disks, belonging to the classes of Z Cam CVs or nova-like variables. We discuss three scenarios to explain the properties of the nebulae. They may have resulted from chance encounters with interstellar gas clouds, with the stars leaving in their wakes material that is recombining after being photoionized by UV radiation from the CVs. Alternatively, the large nebulae could be ejecta from unobserved CN outbursts in the recent past, which have been decelerated through collisions with the interstellar medium (ISM), while the stars continue to snowplow through the gas. Or the faint Halpha nebulae may be ambient ISM that was shock-ionized by a CN outburst in the past and is now recombining., Comment: Accepted by The Astronomical Journal

Bosonic quantum error correction codes encode logical qubits in the Hilbert space of one or multiple harmonic oscillators. A prominent class of bosonic codes are Gottesman-Kitaev-Preskill (GKP) codes of which implementations have been demonstrated with trapped ions and microwave cavities. In this work, we investigate theoretically the preparation and error correction of a GKP qubit in a vibrational mode of a neutral atom stored in an optical dipole trap. This platform has recently shown remarkable progress in simultaneously controlling the motional and electronic degrees of freedom of trapped atoms. The protocols we develop make use of motional states and, additionally, internal electronic states of the trapped atom to serve as an ancilla qubit. We compare optical tweezer arrays and optical lattices and find that the latter provide more flexible control over the confinement in the out-of-plane direction, which can be utilized to optimize the conditions for the implementation of GKP codes. Concretely, the different frequency scales that the harmonic oscillators in the axial and radial lattice directions exhibit and a small oscillator anharmonicity prove to be beneficial for robust encodings of GKP states. Finally, we underpin the experimental feasibility of the proposed protocols by numerically simulating the preparation of GKP qubits in optical lattices with realistic parameters., Comment: 18 pages, 7 figures

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods. The review covered literature from several bibliographic databases, including papers published before 17/07/2024. Original research in peer-reviewed journals focused on radiology-based AI for diagnosing or prognosing primary STBT was included. Exclusion criteria were animal, cadaveric, or laboratory studies, and non-English papers. Abstracts were screened by two of three independent reviewers for eligibility. Eligible papers were assessed against guidelines by one of three independent reviewers. The search identified 15,015 abstracts, from which 325 articles were included for evaluation. Most studies performed moderately on CLAIM, averaging a score of 28.9$\pm$7.5 out of 53, but poorly on FUTURE-AI, averaging 5.1$\pm$2.1 out of 30. Imaging-AI tools for STBT remain at the proof-of-concept stage, indicating significant room for improvement. Future efforts by AI developers should focus on design (e.g. define unmet clinical need, intended clinical setting and how AI would be integrated in clinical workflow), development (e.g. build on previous work, explainability), evaluation (e.g. evaluating and addressing biases, evaluating AI against best practices), and data reproducibility and availability (making documented code and data publicly available). Following these recommendations could improve clinical translation of AI methods., Comment: 23 pages, 6 figures, 6 supplementary figures

Models of reaction networks within interacting compartments (RNIC) are a generalization of stochastic reaction networks. It is most natural to think of the interacting compartments as ``cells'' that can appear, degrade, split, and even merge, with each cell containing an evolving copy of the underlying stochastic reaction network. Such models have a number of parameters, including those associated with the internal chemical model and those associated with the compartment interactions, and it is natural to want efficient computational methods for the numerical estimation of sensitivities of model statistics with respect to these parameters. Motivated by the extensive work on computational methods for parametric sensitivity analysis in the context of stochastic reaction networks over the past few decades, we provide a number of methods in the RNIC setting. Provided methods include the (unbiased) Girsanov transformation method (also called the Likelihood Ratio method) and a number of coupling methods for the implementation of finite differences. We provide several numerical examples and conclude that the method associated with the ``Split Coupling'' provides the most efficient algorithm. This finding is in line with the conclusions from the work related to sensitivity analysis of standard stochastic reaction networks. We have made all of the Matlab code used to implement the various methods freely available for download., Comment: 31 pages, a number of images

We investigate anisotropic equation of state parameterization of dark energy within the framework of an axisymmetric (planar) Bianchi-I universe. In addition to constraining the equation of state for anisotropic dark energy and other standard cosmological model parameters, we also constrain any underlying anisotropic axis using the latest Pantheon+ Type Ia Supernovae data set augmented by SH0ES Cepheid distance calibrators. The mean anisotropic dark energy equation of state and corresponding difference in the equation of states in and normal to the plane of our axisymmetric Bianchi-I space-time are found to be $\bar{w}=-0.90^{+0.16}_{-0.12}$ and $\delta_w=-0.140^{+0.11}_{-0.082}$ respectively. We also find an axis of anisotropy in this planar Bianchi-I model with anisotropic dark energy to be $\approx(279^{\circ} ,12^{\circ})$ in galactic coordinates. Our analysis of different cosmological models suggests that while a Bianchi-I universe with anisotropic dark energy (the $w_b$CDM model) shows some preference over other anisotropic models, it is still less likely than the standard $\Lambda$CDM model or a model with a constant dark energy equation of state ($w$CDM). Overall, the $\Lambda$CDM model remains the most probable model based on the Akaike Information Criterion., Comment: 12 pages, 6 figures, 2 tables; Comments are welcome

Quantum Hall plateaus at quarter fillings occur in GaAs wide quantum wells, hole-doped GaAs, and bilayer graphene. However, the interactions favoring incompressible states over compressible composite-Fermi liquids at such fillings are not well understood. We devise a method of computing the trial energies for Haldane pseudopotentials via Monte Carlo sampling. Applying it to the quarter-filled lowest Landau level, we find that tuning the third and fifth pseudopotential can stabilize anti-Pfaffian, Moore-Read, and f-wave states. The smallest deviations from pure Coulomb interactions are required by anti-Pfaffian, whose presence is indicated by daughter states in recent experiments of bilayer graphene at $\nu=\frac{3}{4}$., Comment: Page 9+2, Figures 10+2, Tables 0+3

Leveraging the advanced manufacturing capabilities of the semiconductor industry promises to help scale up silicon-based quantum processors by increasing yield, uniformity and integration. Recent studies of quantum dots fabricated on 300 mm wafer metal-oxide-semiconductor (MOS) processes have shown control and readout of individual spin qubits, yet quantum processors require two-qubit interactions to operate. Here, we use a 300 mm wafer MOS process customized for spin qubits and demonstrate coherent control of two electron spins using the spin-spin exchange interaction, forming the basis of an entangling gate such as $\sqrt{\text{SWAP}}$. We observe gate dephasing times of up to $T_2^{*}\approx500$ ns and a gate quality factor of 10. We further extend the coherence by up to an order of magnitude using an echo sequence. For readout, we introduce a dispersive readout technique, the radiofrequency electron cascade, that amplifies the signal while retaining the spin-projective nature of dispersive measurements. Our results demonstrate an industrial grade platform for two-qubit operations, alongside integration with dispersive sensing techniques.

A key technology in sperm whale (Physeter macrocephalus) monitoring is the identification of sperm whale communication signals, known as codas. In this paper we present the first automatic coda detector and annotator. The main innovation in our detector is graph-based clustering, which utilizes the expected similarity between the clicks that make up the coda. Results show detection and accurate annotation at low signal-to-noise ratios, separation between codas and echolocation clicks, and discrimination between codas from simultaneously emitting whales. Using this automatic annotator, insights into the characterization of sperm whale communication are presented. The results include new types of coda signals, analyzes of the distribution of coda types among different whales and for different years, and evidence for synchronization between communicating whales in terms of coda type and coda transmission time. These results indicate a high degree of complexity in the communication system of this cetacean species. To ensure traceability, we share the implementation code of our coda detector.

Wide-field magnetic microscopy using nitrogen-vacancy (NV) centers in diamond can yield high-quality magnetic images of DC and AC magnetic fields. The unique combination of micron-scale spatial resolution of scalar or vector fields at room temperature and parallel camera readout make this an appealing technique for applications in biology, geology, condensed-matter physics, and electronics. However, while NV magnetic microscopy has achieved great success in these areas, historically the accessible frequency range has been limited. In this paper, we overcome this limitation by implementing the recently developed technique of quantum frequency mixing. With this approach, we generate wide-field magnetic images of test structures driven by alternating currents up to 70 MHz, well outside the reach of DC and Rabi magnetometry methods. With further improvements, this approach could find utility in hyperspectral imaging for electronics power spectrum analysis, electronics diagnostics and troubleshooting, and quantum computing hardware validation., Comment: 8 pages main text, 3 pages supplement

Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images. While current benchmarks for object hallucination primarily concentrate on the presence of a single object class rather than individual entities, this work systematically investigates multi-object hallucination, examining how models misperceive (e.g., invent nonexistent objects or become distracted) when tasked with focusing on multiple objects simultaneously. We introduce Recognition-based Object Probing Evaluation (ROPE), an automated evaluation protocol that considers the distribution of object classes within a single image during testing and uses visual referring prompts to eliminate ambiguity. With comprehensive empirical studies and analysis of potential factors leading to multi-object hallucination, we found that (1). LVLMs suffer more hallucinations when focusing on multiple objects compared to a single object. (2). The tested object class distribution affects hallucination behaviors, indicating that LVLMs may follow shortcuts and spurious correlations. (3). Hallucinatory behaviors are influenced by data-specific factors, salience and frequency, and model intrinsic behaviors. We hope to enable LVLMs to recognize and reason about multiple objects that often occur in realistic visual scenes, provide insights, and quantify our progress towards mitigating the issues., Comment: Accepted to NeurIPS 2024 | Project page: https://multi-object-hallucination.github.io/

Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work., Comment: Accepted to Information, Intelligence, Systems and Applications (IISA), July 2024

Microscopic interpretation of histopathology images underlies many important diagnostic and treatment decisions. While advances in vision-language modeling raise new opportunities for analysis of such images, the gigapixel-scale size of whole slide images (WSIs) introduces unique challenges. Additionally, pathology reports simultaneously highlight key findings from small regions while also aggregating interpretation across multiple slides, often making it difficult to create robust image-text pairs. As such, pathology reports remain a largely untapped source of supervision in computational pathology, with most efforts relying on region-of-interest annotations or self-supervision at the patch-level. In this work, we develop a vision-language model based on the BLIP-2 framework using WSIs paired with curated text from pathology reports. This enables applications utilizing a shared image-text embedding space, such as text or image retrieval for finding cases of interest, as well as integration of the WSI encoder with a frozen large language model (LLM) for WSI-based generative text capabilities such as report generation or AI-in-the-loop interactions. We utilize a de-identified dataset of over 350,000 WSIs and diagnostic text pairs, spanning a wide range of diagnoses, procedure types, and tissue types. We present pathologist evaluation of text generation and text retrieval using WSI embeddings, as well as results for WSI classification and workflow prioritization (slide-level triaging). Model-generated text for WSIs was rated by pathologists as accurate, without clinically significant error or omission, for 78% of WSIs on average. This work demonstrates exciting potential capabilities for language-aligned WSI embeddings., Comment: 9 main pages and 19 pages of supplemental material; 3 main tables, 3 main figures and 11 supplemental tables, 7 supplemental figures

Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these approaches pretrain only on 2D images, while many robotics applications require 3D scene understanding. In this work, we propose 3D-MVP, a novel approach for 3D multi-view pretraining using masked autoencoders. We leverage Robotic View Transformer (RVT), which uses a multi-view transformer to understand the 3D scene and predict gripper pose actions. We split RVT's multi-view transformer into visual encoder and action decoder, and pretrain its visual encoder using masked autoencoding on large-scale 3D datasets such as Objaverse. We evaluate 3D-MVP on a suite of virtual robot manipulation tasks and demonstrate improved performance over baselines. We also show promising results on a real robot platform with minimal finetuning. Our results suggest that 3D-aware pretraining is a promising approach to improve sample efficiency and generalization of vision-based robotic manipulation policies. We will release code and pretrained models for 3D-MVP to facilitate future research. Project site: https://jasonqsy.github.io/3DMVP

Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n \times n$ dense matrix, even though the input is often $n \times n$ sparse matrices. However, the information in the solution may not correspond to a full-rank dense matrix as shown by Barvinok and Pataki. Two decades ago, Burer and Monteiro developed an SDP solver $\texttt{SDPLR}$ that optimizes over a low-rank factorization instead of the full matrix. This greatly decreases the storage cost and works well for many problems. The original solver $\texttt{SDPLR}$ tracks only the primal infeasibility of the solution, limiting the technique's flexibility to produce moderate accuracy solutions. We use a suboptimality bound for trace-bounded SDP problems that enables us to track the progress better and perform early termination. We then develop $\texttt{SDPLR+}$, which starts the optimization with an extremely low-rank factorization and dynamically updates the rank based on the primal infeasibility and suboptimality. This further speeds up the computation and saves the storage cost. Numerical experiments on Max Cut, Minimum Bisection, Cut Norm, and Lov\'{a}sz Theta problems with many recent memory-efficient scalable SDP solvers demonstrate its scalability up to problems with million-by-million decision variables and it is often the fastest solver to a moderate accuracy of $10^{-2}$., Comment: 17 pages

The integration of language and 3D perception is crucial for developing embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is the absence of large-scale datasets that provide dense grounding between language and 3D scenes. In this paper, we introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons among future models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the critical role of large-scale 3D-text datasets in advancing embodied AI research. Notably, our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with essential resources and insights, setting the stage for more reliable and better-grounded 3D-LLMs. Project website: https://3d-grand.github.io, Comment: Project website: https://3d-grand.github.io

Bilayer graphene has emerged as a key platform for non-Abelian fractional quantum Hall (FQH) states, exhibiting multiple half-filled plateaus with large energy gaps. Here, we report four unexpected quarter-filled states and complete the sequence of half-filled plateaus by observing the previously missing $\nu=-\frac{3}{2}$ and $\nu=\frac{1}{2}$ states. Identifying the half-filled plateaus according to their Levin--Halperin daughter states, we reveal an alternating pattern of non-Abelian topological orders. Whenever a pair of $N=1$ Landau levels cross, anti-Pfaffian and Pfaffian develop in the lower and higher levels, respectively. Surprisingly, quarter states occur in $N=0$ levels and are also accompanied by daughters. The mutual exclusion of half- and quarter-filled states indicates a robust competition between the interactions favoring paired states of either two-flux or four-flux composite fermions. Finally, we observe several FQH states that require strong interactions between composite fermions. The systematic pattern of non-Abelian states across two generations of even denominators strengthens the identification of their topological orders and suggests a universal origin., Comment: 17 pages, 5 figures

Recent quantum Hall experiments have observed `daughter states' next to several plateaus at half-integer filling factors in various platforms. These states were first proposed based on model wavefunctions for the Moore-Read state by Levin and Halperin. We show that these daughters and their parents belong to an extensive family tree that encompasses all pairing channels and permits a unified description in terms of weakly interacting composite fermions. Each daughter represents a bosonic integer quantum Hall state formed by composite-fermion pairs. The pairing of the parent dictates an additional number of filled composite-fermion Landau levels. We support our field-theoretic composite-fermion treatment by using the K-matrix formalism, analysis of trial wavefunctions, and a coupled-wire construction. Our analysis yields the topological orders, quantum numbers, and experimental signatures of all daughters of paired states at half-filling and `next-generation' even-denominators. Crucially, no two daughters share the same two parents. The unique parentage implies that Hall conductance measurements alone could pinpoint the topological order of even-denominator plateaus. Additionally, we propose a numerically suitable trial wavefunction for one daughter of the SU(2)$_2$ topological order, which arises at filling factor $\nu=\frac{6}{11}$. Finally, our insights explain experimentally observed features of transitions in wide-quantum wells, such as suppression of the Jain states with the simultaneous development of half-filled and daughter states., Comment: 11 pages, 4 figures

A new degravitation mechanism within the framework of scalar tensor gravity is proposed. The mechanism eliminates all constant contributions from the potential to the Friedmann equation, leaving only the kinematic and the dynamic terms of the potential to drive cosmic acceleration. We explore a scenario involving a density-triggered phase transition in the late-time universe, and argue that the resulting effective energy density and equation of state parameter can explain late-time cosmology when extrapolated to a region of the parameter space., Comment: 5 pages, 2 figures

Parton distribution functions play a pivotal role in hadron collider phenomenology. They are non-perturbative quantities extracted from fits to available data, and their scale dependence is dictated by the DGLAP evolution equations. In this article, we discuss machine-assisted strategies to efficiently compute PDFs directly incorporating the scale evolution without the need of separately solving DGLAP equations. Analytical approximations to the PDFs as a function of $x$ and $Q^2$, including up to next-to-leading order effects in Quantum Chromodynamics, are obtained. The methodology is tested by reproducing the $\texttt{HERAPDF2.0}$ set and implementing the analytical expressions in benchmarking codes. It is found that the computational cost is reduced while the precision of the simulations stays well under control., Comment: 12 pages, 12 figures

We study the Barrow cosmological model, which proposes that quantum gravity effects create a complex, fractal structure for the universe's apparent horizon. We leverage the thermodynamics - gravity conjecture. By applying the Clausius relation to the apparent horizon of the Friedmann - Lema\^itre - Robertson - Walker universe within this framework, we derive modified field equations where the Barrow entropy is linked to the horizon. We assess the Barrow cosmology against current observations - cosmic microwave background , supernovae , and baryon acoustic oscillations data - and include projections for future Laser Interferometer Space Antenna (LISA) standard sirens (SS). Our numerical results suggest a modest improvement in the Hubble tension for Barrow cosmology with phantom dark energy behavior, compared to the standard cosmological model. Furthermore, incorporating simulated LISA SS data alongside existing observational constraints tightens the limitations on cosmological parameters, particularly the deformation exponent., Comment: 26 pages, 5 figures

The investigation of non-vacuum cosmological backgrounds containing black holes is greatly enhanced by the Kiselev solution. This solution plays a crucial role in understanding the properties of the background and its relationship with the features of the black hole. Consequently, the gravitational memory effects at large distances from the black hole offer a valuable means of obtaining information about the surrounding field parameter N and parameters related to the hair of the hairy Kiselev Black hole. This paper investigates the gravitational memory effects in the context of the Kiselev solution through two distinct approaches. At first, the gravitational memory effect at null infinity is explored by utilizing the Bondi-Sachs formalism by introducing a gravitational wave (GW) pulse to the solution. The resulting Bondi mass is then analyzed to gain further insight. Therefore, the Kiselev solution is being examined to determine the variations in Bondi mass caused by the pulse of GWs. The study of changes in Bondi mass is motivated by the fact that it is dynamic and time-dependent, and it measures mass on an asymptotically null slice or the densities of energy on celestial spheres. In the second approach, the investigation of displacement and velocity memory effects is undertaken in relation to the deviation of two neighboring geodesics and the deviation of their derivative influenced by surrounding field parameter N and the hair of hairy Kiselev black hole. This analysis is conducted within the context of a gravitational wave pulse present in the background of a hairy Kiselev black hole surrounded by a field parameter N., Comment: 18 pages, 30 figures

We present the first proof-of-concept application to decay processes at higher perturbative orders of LTD causal unitary, a novel methodology that exploits the causal properties of vacuum amplitudes in the loop-tree duality (LTD) and is directly well-defined in the four physical dimensions of the space-time. The generation of loop- and tree-level contributions to the differential decay rates from a kernel multiloop vacuum amplitude is shown in detail, and explicit expressions are presented for selected processes that are suitable for a lightweight understanding of the method. Specifically, we provide a clear physical interpretation of the local cancellation of soft, collinear and unitary threshold singularities, and of the local renormalisation of ultraviolet singularities. The presentation is illustrated with numerical results that showcase the advantages of the method., Comment: 11 pages, 6 figures

Recent technological advances allow us to view chemical mass-action systems as analog computers. In this context, the inputs to a computation are encoded as initial values of certain chemical species while the outputs are the limiting values of other chemical species. In this paper, we design chemical systems that carry out the elementary arithmetic computations of: identification, inversion, $m$th roots (for $m \ge 2$), addition, multiplication, absolute difference, rectified subtraction over non-negative real numbers, and partial real inversion over real numbers. We prove that these ``elementary modules'' have a speed of computation that is independent of the inputs to the computation. Moreover, we prove that finite sequences of such elementary modules, running in parallel, can carry out composite arithmetic over real numbers, also at a rate that is independent of inputs. Furthermore, we show that the speed of a composite computation is precisely the speed of the slowest elementary step. Specifically, the scale of the composite computation, i.e. the number of elementary steps involved in the composite, does not affect the overall asymptotic speed -- a feature of the parallel computing nature of our algorithm. Our proofs require the careful mathematical analysis of certain non-autonomous systems, and we believe this analysis will be useful in different areas of applied mathematics, dynamical systems, and the theory of computation. We close with a discussion on future research directions, including numerous important open theoretical questions pertaining to the field of computation with reaction networks.

The analysis of bubbly two-phase flows is challenging due to their turbulent nature and the need for intrusive phase-detection probes. However, accurately characterizing these flows is crucial for safely designing critical infrastructure such as dams and their appurtenant structures. The combination of dual-tip intrusive phase-detection probes with advanced signal processing algorithms enables the assessment of pseudo-instantaneous 1-D velocity time series; for which the limitations are not fully fathomed. In this investigation, we theoretically define four major sources of error, which we quantify using synthetically generated turbulent time series, coupled with the simulated response of a phase detection probe. Our findings show that typical high-velocity flows in hydraulic structures hold up to 15% error in the mean velocity estimations and up to 35% error in the turbulence intensity estimations for the most critical conditions, typically occurring in the proximity of the wall. Based on thousands of simulations, our study provides a novel data-driven tool for the estimation of these baseline errors (bias and uncertainties) in real-word phase-detection probe measurements., Comment: 35 pages, 10 figures

Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely, methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale, but at the expense of reduced precision. We show how to combine the best of both methods; our approach yields results that are both precise and robust, while also accurately inferring translation scales. At the heart of our model lies a Transformer that (1) learns to balance between solved and learned pose estimations, and (2) provides a prior to guide a solver. A comprehensive analysis supports our design choices and demonstrates that our method adapts flexibly to various feature extractors and correspondence estimators, showing state-of-the-art performance in 6DoF pose estimation on Matterport3D, InteriorNet, StreetLearn, and Map-free Relocalization., Comment: Accepted to CVPR 2024. Project Page: https://crockwell.github.io/far/