Search

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation., Comment: Comments on the manuscript welcome!

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. The aim of this study is to compare the efficacy of several transformer models in identifying cyberbullying targeting LGBTQ+ individuals. We seek to determine the relative merits and demerits of these existing methods in addressing complex and subtle kinds of cyberbullying by assessing their effectiveness with real social media data., Comment: 10 pages, 4 tables, 1 figure, 17th International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation

The TESS mission searches for transiting exoplanets by monitoring the brightness of hundreds of thousands of stars across the entire sky. M-type planet hosts are ideal targets for this mission due to their smaller size and cooler temperatures, which makes it easier to detect smaller planets near or within their habitable zones. Additionally, M~dwarfs have a smaller contrast ratio between the planet and the star, making it easier to measure the planet's properties accurately. Here, we report the validation analysis of 13 TESS exoplanet candidates orbiting around M dwarfs. We studied the nature of these candidates through a multi-colour transit photometry transit analysis using several ground-based instruments (MuSCAT2, MuSCAT3, and LCO-SINISTRO), high-spatial resolution observations, and TESS light curves. We present the validation of five new planetary systems: TOI-1883b, TOI-2274b, TOI2768b, TOI-4438b, and TOI-5319b, along with compelling evidence of a planetary nature for TOIs 2781b and 5486b. We also present an empirical definition for the Neptune desert boundaries. The remaining six systems could not be validated due to large true radius values overlapping with the brown dwarf regime or, alternatively, the presence of chromaticity in the MuSCAT2 light curves.

Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification. Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide. Yet the resulting representation remains constrained by the limited clinical and biological diversity of the views. Instead, we postulate that slides stained with multiple markers, such as immunohistochemistry, can be used as different views to form a rich task-agnostic training signal. To this end, we introduce Madeleine, a multimodal pretraining strategy for slide representation learning. Madeleine is trained with a dual global-local cross-stain alignment objective on large cohorts of breast cancer samples (N=4,211 WSIs across five stains) and kidney transplant samples (N=12,070 WSIs across four stains). We demonstrate the quality of slide representations learned by Madeleine on various downstream evaluations, ranging from morphological and molecular classification to prognostic prediction, comprising 21 tasks using 7,299 WSIs from multiple medical centers. Code is available at https://github.com/mahmoodlab/MADELEINE., Comment: ECCV'24

Let $\varphi$ be a univalent non-elliptic self-map of the unit disc $\mathbb D$ and let $(\psi_{t})$ be a continuous one-parameter semigroup of holomorphic functions in $\mathbb D$ such that $\psi_{1}\neq\mathrm{id}_{\mathbb D}$ commutes with $\varphi$. This assumption does not imply that all elements of the semigroup $(\psi_t)$ commute with $\varphi$. In this paper, we provide a number of sufficient conditions that guarantee that ${\psi_t\circ\varphi=\varphi\circ\psi_t}$ for all ${t>0}$: this holds, for example, if $\varphi$ and $\psi_1$ have a common boundary (regular or irregular) fixed point different from their common Denjoy-Wolff point $\tau$, or when $\psi_1$ has a boundary regular fixed point ${\sigma\neq\tau}$ at which $\varphi$ is isogonal, or when $(\varphi-\mathrm{id}_{\mathbb D})/(\psi_1-\mathrm{id}_{\mathbb D})$ has an unrestricted limit at $\tau$. In addition, we analyze how $\varphi$ behaves in the petals of the semigroup $(\psi_t)$.

Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric composition and climates of temperate terrestrial planets. We report the discovery of an Earth-sized planet transiting the nearby (12 pc) inactive M3.0 dwarf Gliese 12 (TOI-6251) with an orbital period ($P_{\rm{orb}}$) of 12.76 days. The planet, Gliese 12b, was initially identified as a candidate with an ambiguous $P_{\rm{orb}}$ from TESS data. We confirmed the transit signal and $P_{\rm{orb}}$ using ground-based photometry with MuSCAT2 and MuSCAT3, and validated the planetary nature of the signal using high-resolution images from Gemini/NIRI and Keck/NIRC2 as well as radial velocity (RV) measurements from the InfraRed Doppler instrument on the Subaru 8.2 m telescope and from CARMENES on the CAHA 3.5 m telescope. X-ray observations with XMM-Newton showed the host star is inactive, with an X-ray-to-bolometric luminosity ratio of $\log L_{\rm X}/L_{\rm bol} \approx -5.7$. Joint analysis of the light curves and RV measurements revealed that Gliese 12b has a radius of 0.96 $\pm$ 0.05 $R_\oplus$, a 3$\sigma$ mass upper limit of 3.9 $M_\oplus$, and an equilibrium temperature of 315 $\pm$ 6 K assuming zero albedo. The transmission spectroscopy metric (TSM) value of Gliese 12b is close to the TSM values of the TRAPPIST-1 planets, adding Gliese 12b to the small list of potentially terrestrial, temperate planets amenable to atmospheric characterization with JWST., Comment: 29 pages (20 pages in main body), 13 figures (10 figures in main body). Equal contributions from M. K. and A. F.. Accepted for Publication in ApJL at 2024 March 21

Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing. The former results from topological bottlenecks that hamper the information flow from distant nodes and are mitigated by spectral gap maximization, primarily, by means of edge additions. However, such additions often promote over-smoothing that renders nodes of different classes less distinguishable. Inspired by the Braess phenomenon, we argue that deleting edges can address over-squashing and over-smoothing simultaneously. This insight explains how edge deletions can improve generalization, thus connecting spectral gap optimization to a seemingly disconnected objective of reducing computational resources by pruning graphs for lottery tickets. To this end, we propose a more effective spectral gap optimization framework to add or delete edges and demonstrate its effectiveness on large heterophilic datasets.

String diagrams are a graphical language used to represent processes that can be composed sequentially or in parallel, which correspond graphically to horizontal or vertical juxtaposition. In this paper we demonstrate how to compute the layout of a string diagram by folding over its algebraic representation in terms of sequential and parallel composition operators. The algebraic representation can be seen as a term of a free monoidal category or a proof tree for a small fragment of linear logic. This contrasts to existing non-compositional approaches that use graph layout techniques. The key innovation is storing the diagrams in binary space-partition trees, maintaining a right-trapezoidal shape for the diagram's outline as an invariant. We provide an implementation in Haskell, using an existing denotational graphics library called Diagrams. Our renderer also supports adding semantics to diagrams to serve as a compiler, with matrix algebra used as an example.

The tectonic interaction between the Rivera and North American plates north of the Bahia de Banderas is poorly understood. The nature of the crust and where the subduction ends in the western part of the Islas Marias Archipelago are still controversial. Based on new geophysical data provided by the TsuJal project, we present the shallow and deep crustal structure of the Rivera North American plate contact zone along two seismic transects, TS09b and RTSIM01b, and the bathymetry obtained across the northern region of Maria Madre Island. Detailed bathymetric analysis allowed mapping of a series of lineaments along the study region, with two main preferred tendencies (020 050 and 290 320) associated with the evolution of the Pacific-Rivera rise and the transform faults of the Gulf of California, respectively. The shallow structure is characterized by five sedimentary basins without deformation, whose horizons are subparallel, suggesting that the sediment deposition occurred after the extension process ended. The deep structure corresponds to a transition between oceanic crust (Rivera Plate), with an average thickness of 10 km to the Islas Marias Escarpment, and a thinned continental crust, whose thickness increases toward the continent until it reaches 28 km, with a dip angle of 7 10. The absence of an accretionary prism suggests that the subduction process of the Rivera Plate beneath the North American Plate to the north of Islas Marias has ceased. In this study, we determined that the morphological expression of the northern limit of the Rivera Plate corresponds to the Islas Marias Escarpment., Comment: Frontiers in Earth Science, 2021

Accurate physical parameters of exoplanet systems are essential for further exploration of planetary internal structure, atmospheres, and formation history. We aim to use simultaneous multicolour transit photometry to improve the estimation of transit parameters, to search for transit timing variations (TTVs), and to establish which of our targets should be prioritised for follow-up transmission spectroscopy. We performed time series photometric observations of 12 transits for the hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b using the simultaneous four-colour camera MuSCAT2 on the Telescopio Carlos S\'anchez. We collected 56 additional transit light curves from TESS photometry. To derive transit parameters, we modelled the MuSCAT2 light curves with Gaussian processes to account for correlated noise. To derive physical parameters, we performed EXOFASTv2 global fits to the available transit and radial velocity data sets, together with the Gaia DR3 parallax, isochrones, and spectral energy distributions. To assess the potential for atmospheric characterisation, we compared the multicolour transit depths with a flat line and a clear atmosphere model. We consistently refined the transit and physical parameters. We improved the orbital period and ephemeris estimates, and found no evidence for TTVs or orbital decay. The MuSCAT2 broadband transmission spectra of HAT-P-19b and HAT-P-65b are consistent with previously published low-resolution transmission spectra. We also found that, except for HAT-P-65b, the assumption of a planetary atmosphere can improve the fit to the MuSCAT2 data. In particular, we identified HAT-P-55b as a priority target among these four planets for further atmospheric studies using transmission spectroscopy., Comment: Corrected a typo in T0 of HAT-P-65b (the correct value is 2458139.35067)

The embeddability problem is a very old and hard problem in discrete holomorphic iteration which deals with determining general conditions on a given univalent self-map $\varphi$ of the unit disc $\mathbb D$ in order to be contained in a continuous one-parameter semigroup. In this paper, we tackle this embedding problem by establishing different dichotomy results about the centralizer of $\varphi$ (i.e. the set of all univalent self-maps commuting with $\varphi$) which depend strongly on the dynamical character of $\varphi$. Our approach is, in part, based on a new technique to obtain simultaneous linearizations of two non-elliptic univalent self-maps of the unit disc, which might be interesting on their own. We also introduce and study several closed additive subsemigroups of the complex plane that collect the main features of the centralizer of $\varphi$ and which play a prominent position in those dichotomy results., Comment: Ver3: The most important changes concern our viewpoint on the result about the simultaneous linearization of commuting self-maps, Theorem 4.1. Accordingly, a part of Introduction and Section 4 have been re-organized. Ver2: an error in the list of references and a misprint in the Introduction are corrected; references [7,23] are added

The rewards a blockchain miner earns vary with time. Most of the time is spent mining without receiving any rewards, and only occasionally the miner wins a block and earns a reward. Mining pools smoothen the stochastic flow of rewards, and in the ideal case, provide a steady flow of rewards over time. Smooth block rewards allow miners to choose an optimal mining power growth strategy that will result in a higher reward yield for a given investment. We quantify the economic advantage for a given miner of having smooth rewards, and use this to define a maximum percentage of rewards that a miner should be willing to pay for the mining pool services., Comment: 15 pages, 1 figure

In this paper, we outline a framework for modeling utility-based blockchain-enabled economic systems using Agent Based Modeling (ABM). Our approach is to model the supply dynamics based on metrics of the cryptoeconomy. We then build autonomous agents that make decisions based on those metrics. Those decisions, in turn, impact the metrics in the next time-step, creating a closed loop that models the evolution of cryptoeconomies over time. We apply this framework as a case-study to Filecoin, a decentralized blockchain-based storage network. We perform several experiments that explore the effect of different strategies, capitalization, and external factors to agent rewards, that highlight the efficacy of our approach to modeling blockchain based cryptoeconomies., Comment: 14 pages, 5 figures

We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions across a heterogeneous device ecosystem. Federated learning is an approach to machine learning based on a strong data sovereignty principle, i.e., that privacy and security of data is best enabled by storing it at its origin, whether on end-user devices or in segregated cloud storage silos. Federated learning enables model training across devices and silos while the training data remains within its security boundary, by distributing a model snapshot to a client running inside the boundary, running client code to update the model, and then aggregating updated snapshots across many clients in a central orchestrator. Deploying a FL solution requires implementation of complex privacy and security mechanisms as well as scalable orchestration infrastructure. Scale and performance is a paramount concern, as the model training process benefits from full participation of many client devices, which may have a wide variety of performance characteristics. Project Florida aims to simplify the task of deploying cross-device FL solutions by providing cloud-hosted infrastructure and accompanying task management interfaces, as well as a multi-platform SDK supporting most major programming languages including C++, Java, and Python, enabling FL training across a wide range of operating system (OS) and hardware specifications. The architecture decouples service management from the FL workflow, enabling a cloud service provider to deliver FL-as-a-service (FLaaS) to ML engineers and application developers. We present an overview of Florida, including a description of the architecture, sample code, and illustrative experiments demonstrating system capabilities.