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Photonics represents one of the most promising approaches to large-scale quantum computation with millions of qubits and billions of gates, owing to the potential for room-temperature operation, high clock speeds, miniaturization of photonic circuits, and repeatable fabrication processes in commercial photonic foundries. We present an end-to-end architecture for fault-tolerant continuous variable (CV) quantum computation using only passive on-chip components that can produce photonic qubits above the fault tolerance threshold with probabilities above 90%, and encodes logical qubits using physical qubits sampled from a distribution around the fault tolerance threshold. By requiring only low photon number resolution, the architecture enables the use of high-bandwidth photodetectors in CV quantum computing. Simulations of our qubit generation and logical encoding processes show a Gaussian cluster squeezing threshold of 12 dB to 13 dB. Additionally, we present a novel magic state generation protocol which requires only 13 dB of cluster squeezing to produce magic states with an order of magnitude higher probability than existing approaches, opening up the path to universal fault-tolerant quantum computation at less than 13 dB of cluster squeezing.

Social media usage is often cited as a potential driver behind the rising suicide rates. However, distinguishing the causal effect - whether social media increases the risk of suicide - from reverse causality, where individuals already at higher risk of suicide are more likely to use social media, remains a significant challenge. In this paper, we use an instrumental variable approach to study the quasi-exogenous geographical adoption of Twitter and its causal relationship with suicide rates. Our analysis first demonstrates that Twitter's geographical adoption was driven by the presence of certain users at the 2007 SXSW festival, which led to long-term disparities in adoption rates across counties in the United States. Then, using a two-stage least squares (2SLS) regression and controlling for a wide range of geographic, socioeconomic and demographic factors, we find no significant relationship between Twitter adoption and suicide rates.

Community-based fact-checking is a promising approach to verify social media content and correct misleading posts at scale. Yet, causal evidence regarding its effectiveness in reducing the spread of misinformation on social media is missing. Here, we performed a large-scale empirical study to analyze whether community notes reduce the spread of misleading posts on X. Using a Difference-in-Differences design and repost time series data for N=237,677 (community fact-checked) cascades that had been reposted more than 431 million times, we found that exposing users to community notes reduced the spread of misleading posts by, on average, 62.0%. Furthermore, community notes increased the odds that users delete their misleading posts by 103.4%. However, our findings also suggest that community notes might be too slow to intervene in the early (and most viral) stage of the diffusion. Our work offers important implications to enhance the effectiveness of community-based fact-checking approaches on social media.

The bond distance is the simplest and most obvious indicator of the nature of a given chemical bond. However, for rare chemistry, it may happen that it is not yet firmly established. In this communication, we will show that the formally-triple protactinium(V) mono-oxo bond is predicted longer than what was previously reported in the solid state and in solution, based on robust quantum mechanical calculations, supported by an extensive methodological study. Furthermore, additional calculations are used to demonstrate that the Pa-Ooxo bond of interest is more sensitive to complexation than the supposedly analogous U-Oyl ones, not only in terms of bond distance but also of finer bond descriptors associated with the effective bond multiplicity.

\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in \texttt{scikit-learn}, making estimators and pipelines compatible with \texttt{ml\_edm}. This software is distributed over the BSD-3-Clause license, source code can be found at \url{https://github.com/ML-EDM/ml_edm}.

This paper studies the last-iterate convergence properties of the exponential weights algorithm with constant learning rates. We consider a repeated interaction in discrete time, where each player uses an exponential weights algorithm characterized by an initial mixed action and a fixed learning rate, so that the mixed action profile $p^t$ played at stage $t$ follows an homogeneous Markov chain. At first, we show that whenever a strict Nash equilibrium exists, the probability to play a strict Nash equilibrium at the next stage converges almost surely to 0 or 1. Secondly, we show that the limit of $p^t$, whenever it exists, belongs to the set of ``Nash Equilibria with Equalizing Payoffs''. Thirdly, we show that in strong coordination games, where the payoff of a player is positive on the diagonal and 0 elsewhere, $p^t$ converges almost surely to one of the strict Nash equilibria. We conclude with open questions.

In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation, and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (see \url{https://github.com/ML-EDM/ml_edm}).

We build a novel database of around 285,000 notes from the Twitter Community Notes program to analyze the causal influence of appending contextual information to potentially misleading posts on their dissemination. Employing a difference in difference design, our findings reveal that adding context below a tweet reduces the number of retweets by almost half. A significant, albeit smaller, effect is observed when focusing on the number of replies or quotes. Community Notes also increase by 80% the probability that a tweet is deleted by its creator. The post-treatment impact is substantial, but the overall effect on tweet virality is contingent upon the timing of the contextual information's publication. Our research concludes that, although crowdsourced fact-checking is effective, its current speed may not be adequate to substantially reduce the dissemination of misleading information on social media.

This article presents a proof of the Feldman-Moore theorem on Cartan subalgebras in W*-algebras based on the non-commutative Stone equivalence between Boolean inverse semigroups and Boolean groupoids. The proof is decomposed into two parts. The first part writes the W*-algebra as the W*-algebra of the Weyl twist of the Cartan subalgebra. The second part is an extension to separable measure inverse semigroups of the realization of a separable measure algebra by a standard measure space., Comment: 35 pages

This paper describes a receiver that uses an innovative method to predict, according to history of receiver operating metrics (packet lost/well received), the optimum automatic gain control (AGC) index or most appropriate variable gain range to be used for next packet reception, anticipating an interferer appearing during the payload reception. This allows the receiver to have higher immunity to interferers even if they occur during the gain frozen payload reception period whilst still ensuring an optimum sensitivity level. As a result, the method allows setting the receiver gain to get an optimum trade-off between reception sensitivity and random interferer immunity., Comment: 17 pages, 15 figures

Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of AI in the field of music and sound technologies a doctoral day was held between Queen Mary University of London (QMUL, UK) and Sciences et Technologies de la Musique et du Son (STMS, France). Prompt questions about current trends in AI and music were generated by academics from QMUL and STMS. Students from the two institutions then debated these questions. This report presents a summary of the student debates on the topics of: Data, Impact, and the Environment; Responsible Innovation and Creative Practice; Creativity and Bias; and From Tools to the Singularity. The students represent the future generation of AI and music researchers. The academics represent the incumbent establishment. The student debates reported here capture visions, dreams, concerns, uncertainties, and contentious issues for the future of AI and music as the establishment is rightfully challenged by the next generation.

Multispectral Optoacoustic Tomography (MSOT) uniquely enables spatial mapping in high resolution of oxygen saturation (SO$_2$), with potential applications in studying pathological complications and therapy efficacy. MSOT offers seamless integration with ultrasonography, by using a common ultrasound detector array. However, MSOT relies on multiple successive acquisitions of optoacoustic (OA) images at different optical wavelengths and the low frame rate of OA imaging makes the MSOT acquisition sensitive to body/respiratory motion. Moreover, estimation of SO$_2$ is highly sensitive to noise, and artefacts related to the respiratory motion of the animal were identified as the primary source of noise in MSOT.In this work, we propose a two-step image processing method for SO$_2$ estimation in deep tissues. First, to mitigate motion artefacts, we propose a method of selection of OA images acquired only during the respiratory pause of the animal, using ultrafast ultrasound images (USIs) acquired immediately after each OA acquisition (USI acquisition duration of 1.4 ms and a total delay of 7 ms). We show that gating is more effective using USIs than OA images at different optical wavelengths. Secondly, we propose a novel method which can estimate directly the SO$_2$ value of a pixel and at the same time evaluate the amount of noise present in that pixel. Hence, the method can efficiently eliminate the pixels dominated by noise from the final SO$_2$ map. Our post-processing method is shown to outperform conventional methods for SO$_2$ estimation, and the method was validated by in vivo oxygen challenge experiments.

Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering. Indeed, along with the increasing availability of time series data, many TSC algorithms have been suggested by the research community in the literature. Besides state-of-the-art methods based on similarity measures, intervals, shapelets, dictionaries, deep learning methods or hybrid ensemble methods, several tools for extracting unsupervised informative summary statistics, aka features, from time series have been designed in the recent years. Originally designed for descriptive analysis and visualization of time series with informative and interpretable features, very few of these feature engineering tools have been benchmarked for TSC problems and compared with state-of-the-art TSC algorithms in terms of predictive performance. In this article, we aim at filling this gap and propose a simple TSC process to evaluate the potential predictive performance of the feature sets obtained with existing feature engineering tools. Thus, we present an empirical study of 11 feature engineering tools branched with 9 supervised classifiers over 112 time series data sets. The analysis of the results of more than 10000 learning experiments indicate that feature-based methods perform as accurately as current state-of-the-art TSC algorithms, and thus should rightfully be considered further in the TSC literature.

We present 294 pulsars found in GeV data from the Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope. Another 33 millisecond pulsars (MSPs) discovered in deep radio searches of LAT sources will likely reveal pulsations once phase-connected rotation ephemerides are achieved. A further dozen optical and/or X-ray binary systems co-located with LAT sources also likely harbor gamma-ray MSPs. This catalog thus reports roughly 340 gamma-ray pulsars and candidates, 10% of all known pulsars, compared to $\leq 11$ known before Fermi. Half of the gamma-ray pulsars are young. Of these, the half that are undetected in radio have a broader Galactic latitude distribution than the young radio-loud pulsars. The others are MSPs, with 6 undetected in radio. Overall, >235 are bright enough above 50 MeV to fit the pulse profile, the energy spectrum, or both. For the common two-peaked profiles, the gamma-ray peak closest to the magnetic pole crossing generally has a softer spectrum. The spectral energy distributions tend to narrow as the spindown power $\dot E$ decreases to its observed minimum near $10^{33}$ erg s$^{-1}$, approaching the shape for synchrotron radiation from monoenergetic electrons. We calculate gamma-ray luminosities when distances are available. Our all-sky gamma-ray sensitivity map is useful for population syntheses. The electronic catalog version provides gamma-ray pulsar ephemerides, properties and fit results to guide and be compared with modeling results., Comment: 142 pages. Accepted by the Astrophysical Journal Supplement

Polonium (Po, Z = 84) is a main-block element with poorly known physico-chemical properties. Not much information has been firmly acquired since its discovery by Marie and Pierre Curie in 1898, especially regarding its speciation in aqueous solution and spectroscopy. In this work, we revisit the absorption properties of two complexes, [PoCl5]^- and [PoCl6]^{2-}, using quantum mechanical calculations. These complexes have the potential to exhibit a maximum absorption at 418 nm in HCl medium (for 0.5 mol/L concentrations and above). Initially, we examine the electronic spectra of the Po^{4+} free ion and of its isoelectronic analogue, Bi^{3+}. In the spin-orbit configuration interaction (SOCI) framework. Our findings demonstrate that the SOCI matrix should be dressed with correlated electronic energies and that the quality of the spectra is largely improved by decontracting the reference states at the complete active space plus singles (CAS+S) level. Subsequently, we investigate the absorption properties of the [PoCl5]^- and [PoCl6]^{2-} complexes in two stages. Firstly, we perform methodological tests at the MP2/def2-TZVP gas phase geometries, indicating that the decontraction of the reference states can there be skipped without compromising the accuracy significantly. Secondly, we study the solution absorption properties by means of single-point calculations performed at the solvated geometries, obtained by an implicit solvation treatment or a combination of implicit and explicit solvation. Our results highlight the importance of saturating the first coordination sphere of the Po^{IV} ion to obtain a qualitatively correct picture. Finally, we conclude that the known-for-decades 418 nm peak could be attributed to a mixture of both the [PoCl5(H2O)]^- and [PoCl6]^{2-} complexes. This finding not only aligns with the behaviour of the analogous Bi^{III} ion under similar conditions but...

The electronic orders appearing in condensed matter systems are originating from the precise arrangement of atoms constituting the crystal as well as their nature. This teneous relationship can lead to highly different phases in condensed matter, and drive electronic phase transitions. Here, we show that a very slight deformation of the crystal structure of TbTe$_3$ can have a dramatic influence on the electronic order that is stabilized. In particular, we show that the Charge Density Wave (CDW) developping along the $\vec{c}$ axis in the pristine state, switches to an orientation along $\vec{a}$ when the naturally orthorhombic system is turned into a tetragonal system. This is achieved by performing true biaxial mechanical deformation of a TbTe$_3$ sample from 250K to 375K, and by measuring both structural and electronic parameters with x-ray diffraction and transport measurements. We show that this switching transition is driven by the tetragonality parameter $a/c$, and that the transition occurs for $a=c$, with a coexistence region for $0.9985< a/c < 1.002$. The CDW transition temperature $T_c$ is found to have a linear dependence with $a/c$, with no saturation in the deformed states investigated here, while the gap saturates out of the coexistence region. The linear dependence of $T_c$ is accounted for within a tight-binding model. Our results question the relationship between the gap and $T_c$ in RTe$_3$ systems. More generally, our method of applying true biaxial deformation at cryogenic temperatures can be applied to many systems displaying electronic phase transitions, and opens a new route towards the study of coexisting or competing electronic orders in condensed matter.

The accuracy and explainability of data-driven nowcasting models are of great importance in many socio-economic sectors reliant on weather-dependent decision making. This paper proposes a novel architecture called Small Attention Residual UNet (SAR-UNet) for precipitation and cloud cover nowcasting. Here, SmaAt-UNet is used as a core model and is further equipped with residual connections, parallel to the depthwise separable convolutions. The proposed SAR-UNet model is evaluated on two datasets, i.e., Dutch precipitation maps ranging from 2016 to 2019 and French cloud cover binary images from 2017 to 2018. The obtained results show that SAR-UNet outperforms other examined models in precipitation nowcasting from 30 to 180 minutes in the future as well as cloud cover nowcasting in the next 90 minutes. Furthermore, we provide additional insights on the nowcasts made by our proposed model using Grad-CAM, a visual explanation technique, which is employed on different levels of the encoder and decoder paths of the SAR-UNet model and produces heatmaps highlighting the critical regions in the input image as well as intermediate representations to the precipitation. The heatmaps generated by Grad-CAM reveal the interactions between the residual connections and the depthwise separable convolutions inside of the multiple depthwise separable blocks placed throughout the network architecture., Comment: 9 pages, 8 figures

No quantum system can be considered totally isolated from its environment. In most cases the interaction between the system of interest and the external degrees of freedom deeply changes its dynamics, as described by open quantum system theory. Nevertheless, engineered environment can be turned into beneficial effects for some quantum information tasks. Here we demonstrate an optical simulator of a quantum system coupled to an arbitrary and reconfigurable environment built as a complex network of quantum interacting systems. We experimentally retrieve typical features of open quantum system dynamics like the spectral density and quantum non-Markovianity, by exploiting squeezing and entanglement correlation of a continuous variables optical platform. This opens the way to the experimental tests of open quantum systems in reconfigurable environments that are relevant in, among others, quantum information, quantum thermodynamics, quantum transport and quantum synchronization., Comment: 15 pages, 7 figures

Protactinium ($Z$ = 91) is a very rare actinide with peculiar physico-chemical properties. Indeed, although one may naively think that it behaves similarly to either thorium or uranium by its position in the periodic table, it may in fact follow its own rules. Because of the quite small energy gap between its valence shells (in particular the 5$f$ and 6$d$ ones) and also the strong influence of relativistic effects on its properties, it is actually a challenging element for theoretical chemists. In this article, we combine experimental information, chemical arguments and standard first-principle calculations, complemented by implicit and explicit solvation, to revisit the stepwise complexation of aqueous protactinium(V) with sulfate and oxalate dianionic ligands (SO$_4$$^{2-}$ and C$_2$O$_4$$^{2-}$, respectively). From a methodological viewpoint, we notably conclude that it is necessary to at least saturate the coordination sphere of protactinium(V) to reach converged equilibrium constant values. Furthermore, in the case of single complexations (i.e. with one sulfate or oxalate ligand bound in the bidentate fashion), we show that it is necessary to maintain the coordination of one hydroxyl group, present in the supposed [PaO(OH)]$^{2+}$ precursor, to obtain coherent complexation constants. Therefore, we predict that this hydroxyl group is maintained in the formation of 1:1 complexes while we confirm that it is withdrawn when coordinating three sulfate or oxalate ligands. Finally, we stress that this work is a first step toward the future use of theoretical predictions to elucidate the enigmatic chemistry of protactinium in solution.