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In (super)gravity theories, T-duality relates solutions with an exact isometry which can have wildly different asymptotic behaviors: a well-known example is the duality between BTZ black holes and (non-extremal) three-dimensional black strings. Using this dual pair, we show how the knowledge of a phase space which includes one set of solutions (here, BTZ black holes embedded in the Brown-Henneaux phase space) allows to obtain a phase space for the dual set via an asymptotic notion of T-duality. The resulting asymptotic symmetry algebras can be very different. For our particular example, we find a large algebra of symmetries for the black string phase space which includes as subalgebras $\mathfrak{bms}_2$, $\mathfrak{bms}_3$, and a twisted warped conformal algebra. On the way, we show that a chiral half of the Brown-Henneaux boundary conditions are dual to the Comp\`ere-Song-Strominger ones., Comment: 19 pages + 3 appendices, ancillary Mathematica file with some computations of charges

Magnetoelectric (ME) materials displaying strong magnetically induced polarization have attracted considerable interest due to their potential applications in spintronics and various fast electrically controlled magnetic devices. CaMn$_7$O$_{12}$ (CMO) stands out for its giant spin-induced ferroelectric polarization. However, the origin of the induced electric polarization in CMO remains highly controversial and continues to be a subject of ongoing debate. In this paper, through room temperature X-ray powder diffraction (XRPD), temperature-dependent magnetic susceptibility, and thermally stimulated depolarizing current (TSDC) measurements, we provide experimental evidence for a route to tune the magnetically induced polarization by modifying the exchange-striction in CMO via Sr-doping. Our findings demonstrate that the large and broad current peaks observed near the first magnetic phase transition ($T_N1 \sim 90$ K) indicate contributions to the TSDC density from both extrinsic thermally stimulated depolarization processes and intrinsic pyroelectric current arising from magnetically induced polarization changes. We suggest that this reduction in induced electric polarization in CMO originates from the increase in the Mn$^{3+}$ -- O -- Mn$^{4+}$ bond angle due to Sr$^{2+}$ doping, weakening the exchange-striction interaction. Meanwhile, the Dzyaloshinskii-Moriya (DM) effect determines the direction of the induced electric polarization. Our result sheds light on understanding the intriguing giant-induced polarization in CMO and similar compounds with complex magnetic structures.

The efficient market hypothesis considers all available information already reflected in asset prices and limits the possibility of consistently achieving above-average returns by trading on publicly available data. We analyzed low dispersion prediction methods and their application to the M6 financial forecasting competition. Predictive averages and regression to the trend offer slight but potentially consistent advantages over the reference indexes. We put these results in the context of high variability approaches, which, if not accompanied by high information content, are bound to underperform the benchmark index as they are prone to overfit the past. In general, predicting the expected values under high uncertainty conditions, such as those assumed by the efficient market hypothesis, is more effective on average than trying to predict actual values., Comment: 12 pages, 5 figures

The vacuum entanglement entropy of a general conformal field theory (CFT) in $d=5$ spacetime dimensions contains a universal term, $F(A)$, which has a complicated and non-local dependence on the geometric details of the region $A$ and the theory. Analogously to the previously known $d=3$ case, we prove that for CFTs in $d=5$ which are holographically dual to Einstein gravity, $F(A)$ is equal to a four-dimensional version of the ``Willmore energy'' associated to a doubled and closed version of the Ryu-Takayanagi (RT) surface of $A$ embedded in $\mathbb{R}^5$. This generalized Willmore energy is shown to arise from a conformal-invariant codimension-two functional obtained by evaluating six-dimensional Conformal Gravity on the conically-singular orbifold of the replica trick. The new functional involves an integral over the doubled RT surface of a linear combination of three quartic terms in extrinsic curvatures and is free from ultraviolet divergences by construction. We verify explicitly the validity of our new formula for various entangling regions and argue that, as opposed to the $d=3$ case, $F(A)$ is not globally minimized by a round ball $A=\mathbb{B}^4$. Rather, $F(A)$ can take arbitrarily positive and negative values as a function of $A$. Hence, we conclude that the round ball is not a global minimizer of $F(A)$ for general five-dimensional CFTs., Comment: 48 pages, 5 figures; v2: typos fixed, references added. Version to appear in JHEP

This paper presents chemical abundances of twelve elements (C, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, and Fe) for 80 FGK dwarfs in the Pleiades open cluster, which span a temperature range of $\sim$2000 K in T$_{\rm eff}$, using the high-resolution (R$\sim$22,500) near-infrared SDSS-IV/APOGEE spectra ($\lambda$1.51--1.69 \micron). Using a 1D LTE abundance analysis, we determine an overall metallicity of [Fe/H]=+0.03$\pm$0.04 dex, with the elemental ratios [$\alpha$/Fe]=+0.01$\pm$0.05, [odd-z/Fe]=-0.04$\pm$0.08, and [iron peak/Fe]=-0.02$\pm$0.08. These abundances for the Pleiades are in line with the abundances of other open clusters at similar galactocentric distances as presented in the literature. Examination of the abundances derived from each individual spectral line revealed that several of the stronger lines displayed trends of decreasing abundance with decreasing $T_{\rm eff}$. The list of spectral lines that yield abundances that are independent of $T_{\rm eff}$ are presented and used for deriving the final abundances. An investigation into possible causes of the temperature-dependent abundances derived from the stronger lines suggests that the radiative codes and the APOGEE line list we employ may inadequately model van der Waals broadening, in particular in the cooler K dwarfs., Comment: 16 pages, 7 figures, and 4 tables. Accepted for publication in MNRAS

Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accompanies the first-ever release of a LLM-generated, MBR-decoded and QE-reranked dataset with both sentence-level and multi-sentence examples. We perform extensive experiments to demonstrate the quality of our dataset in terms of its downstream impact on NMT model performance. We find that training from scratch on our (machine-generated) dataset outperforms training on the (web-crawled) WMT'23 training dataset (which is 300 times larger), and also outperforms training on the top-quality subset of the WMT'23 training dataset. We also find that performing self-distillation by finetuning the LLM which generated this dataset outperforms the LLM's strong few-shot baseline. These findings corroborate the quality of our dataset, and demonstrate the value of high-quality machine-generated data in improving performance of NMT models.

The DArk Matter In CCDs at Modane (DAMIC-M) experiment is designed to search for light dark matter (m$_{\chi}$<10\,GeV/c$^2$) at the Laboratoire Souterrain de Modane (LSM) in France. DAMIC-M will use skipper charge-coupled devices (CCDs) as a kg-scale active detector target. Its single-electron resolution will enable eV-scale energy thresholds and thus world-leading sensitivity to a range of hidden sector dark matter candidates. A DAMIC-M prototype, the Low Background Chamber (LBC), has been taking data at LSM since 2022. The LBC provides a low-background environment, which has been used to characterize skipper CCDs, study dark current, and measure radiopurity of materials planned for DAMIC-M. It also allows testing of various subsystems like readout electronics, data acquisition software, and slow control. This paper describes the technical design and performance of the LBC.

This article aims at presenting a new local subcell monolithic Discontinuous-Galerkin/Finite-Volume (DG/FV) convex property preserving scheme solving system of conservation laws on 2D unstructured grids. This is known that DG method needs some sort of nonlinear limiting to avoid spurious oscillations or nonlinear instabilities which may lead to the crash of the code. The main idea motivating the present work is to improve the robustness of DG schemes, while preserving as much as possible its high accuracy and very precise subcell resolution. To do so, a convex blending of high-order DG and first-order FV scheme will be locally performed, at the subcell scale, where it is needed. To this end, by means of the theory developed in our previous work, we first recall that it is possible to rewrite DG scheme as a subcell FV scheme on a subgrid provided with some specific numerical fluxes referred to as DG reconstructed fluxes. Then, the monolithic DG/FV method will be defined as following: each face of each subcell will be assigned with two fluxes, a 1st-order FV one and a high-order reconstructed one, that in the end will be blended in a convex way. The goal is then to determine, through analysis, optimal blending coefficients to achieve the desire properties. Numerical results on various type problems will be presented to assess the very good performance of the design method. A particular emphasis will be put on entropy consideration. By means of this subcell monolithic framework, we will attempt to address the following questions: is this possible through this monolithic framework to ensure any entropy stability? what do we mean by entropy stability? What is the cost of such constraints? Is this absolutely needed while aiming for high-order accuracy?

Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application. This paper presents a novel approach for approximating MBR decoding using matrix completion techniques, focusing on the task of machine translation. We formulate MBR decoding as a matrix completion problem, where the utility metric scores between candidate hypotheses and pseudo-reference translations form a low-rank matrix. First, we empirically show that the scores matrices indeed have a low-rank structure. Then, we exploit this by only computing a random subset of the scores and efficiently recover the missing entries in the matrix by applying the Alternating Least Squares (ALS) algorithm, thereby enabling a fast approximation of the MBR decoding process. Our experimental results on machine translation tasks demonstrate that the proposed method requires 1/16 utility metric computations compared to vanilla MBR decoding while achieving equal translation quality measured by COMET22 on the WMT22 dataset (en<>de and en<>ru). We also benchmark our method against other approximation methods and we show gains in quality when comparing to them.

Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real-time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public dataset which is currently either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB which was created using sensory data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria, Spain. It consists of a multi-sensor dataset built on data collected using an Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the collection environment, sampling technique, information regarding the sensors, the applications used for data conversion and storage make the dataset a transparent one. This transparency of data can thus be used for further development of techniques for lameness detection for dairy cows which can be objectively compared. Aside from the public sharing of the dataset, we have also shared a machine-learning technique which classifies the caws in healthy and lame by using the raw sensory data. Hence validating the major objective which is to establish the relationship between sensor data and lameness.

Continuous adaptation to variable environments is crucial for the survival of living organisms. Here, we analyze how adaptation, forecasting, and resource mobilization towards a target state, termed actionability, interact to determine biological function. We develop a general theory and show that it is possible for organisms to continuously track their optimal state in a dynamic environment by adapting towards an actionable target that incorporates just current information on the optimal state and its rate of change. If the environmental information is precise and readily actionable, it is possible to implement perfect tracking without anticipatory mechanisms, irrespective of the adaptation rate. In contrast, predictive functions, like those of circadian rhythms, are beneficial if sensing the environment is slow or unreliable, as they allow better adaptation with fewer resources. To explore potential actionable forecasting mechanisms, we develop a general approach that implements the adaptation dynamics with forecasting through a dynamics-informed neural network., Comment: 20 pages, 7 figures

We show that gauge fields after a spontaneous symmetry breaking (SSB) mechanism do not have a Gribov problem along the broken directions. In order to make this proof, we describe a gauge fixing procedure inspired on Morse theory leading to the concept of a gauge fixing generating functional. This approach is specially suited in order to understand the unitary gauges of t Hooft. The conclusion is that after a SSB process, the generalized Faddeev-Popov operator acquires a positive definite value along the broken directions. We show how this works in the eletroweak $SU(2)XU(1)$ theory, where such a result is in fact expected as in this case the gauge fields acquire masses after the SSB. Then we apply this development to the $SL(3,c)$ confining model of [1]. The final result explains why such confining mechanism is actually a solution to Gribov problem for the confined gauge fields. These cases show that although confinement is not a general effect of the solution of the Gribov problem, as we can infer from the eletroweak example, its solution indeed seems to be necessary in order to achieve confinement. In the end, these conclusions allow us to speculate that the strong interaction confinement may be the result of some hidden SSB mechanism yet to be described., Comment: No figure , 17 pages, accepted final version Physical Review D

As a result of the progressive implantation of the Industry 4.0 paradigm, many industries are experimenting a revolution that shipyards cannot ignore. Therefore, the application of the principles of Industry 4.0 to shipyards are leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the 10 largest shipbuilders in the world, is updating its whole inner workings to keep up with the near-future challenges that a Shipyard 4.0 will have to face. Such challenges can be divided into three groups: the vertical integration of production systems, the horizontal integration of a new generation of value creation networks, and the re-engineering of the entire production chain, making changes that affect the entire life cycle of each piece of a ship. Pipes, which exist in a huge number and varied typology on a ship, are one of the key pieces, and its monitoring constitutes a prospective cyber-physical system. Their improved identification, traceability, and indoor location, from production and through their life, can enhance shipyard productivity and safety. In order to perform such tasks, this article first conducts a thorough analysis of the shipyard environment. From this analysis, the essential hardware and software technical requirements are determined. Next, the concept of smart pipe is presented and defined as an object able to transmit signals periodically that allows for providing enhanced services in a shipyard. In order to build a smart pipe system, different technologies are selected and evaluated, concluding that passive and active RFID are currently the most appropriate technologies to create it. Furthermore, some promising indoor positioning results obtained in a pipe workshop are presented, showing that multi-antenna algorithms and Kalman filtering can help to stabilize Received Signal Strength (RSS) and improve the overall accuracy of the system., Comment: 43 pages, 25 figures, accepted version of Sensors journal article

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud., Comment: Accepted version of an IEEE Access journal article

The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario., Comment: Accepted version of an IEEE Access journal paper

The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The first proposed method considers any $L_p$ norm for functional data to measure the distance between curves, the second one is designed to take different variabilities along the curve into account, and the third one takes advantage of the notion of depth of a functional data. The regression model with functional response on which our proposed prediction regions are based is rather general: it allows to include both endogenous and exogenous functional variables, as well as exogenous scalar variables; in addition, the effect of such variables on the response one is modeled in a parametric, nonparametric or semi-parametric way. A comparative study is carried out to analyse the performance of these prediction regions for the electricity market of mainland Spain, in year 2012. This work extends and complements the methods and results in Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018) (focused on prediction intervals), which use the same database as here.

We refine and extend a recent construction of sets of black hole microstates with semiclassical interiors that span a Hilbert space of dimension $e^S$, where $S$ is the black hole entropy. We elaborate on the definition and properties of microstates in statistical and black hole mechanics. The gravitational description of microstates employs matter shells in the interior of the black hole, and we argue that in the limit where the shells are very heavy, the construction acquires universal validity. To this end, we show it for very wide classes of black holes: we first extend the construction to rotating and charged black holes, including extremal and near-extremal solutions, with or without supersymmetry, and we sketch how the construction of microstates can be embedded in String Theory. We then describe how the approach can include general quantum corrections, near or far from extremality. For supersymmetric black holes, the microstates we construct differ from other recent constructions in that the interior excitations are not confined within the near-extremal throat., Comment: 17 pages + appendices; v2: typos corrected, references added, added paragraph at the end of Sec. VB

The Fermilab Proton-Improvement-Plan-II (PIP-II) is being implemented in order to support the precision neutrino oscillation measurements at the Deep Underground Neutrino Experiment, the U.S. flagship neutrino experiment. The PIP-II LINAC is presently under construction and is expected to provide 800~MeV protons with 2~mA current. This white paper summarizes the outcome of the first workshop on May 10 through 13, 2023, to exploit this capability for new physics opportunities in the kinematic regime that are unavailable to other facilities, in particular a potential beam dump facility implemented at the end of the LINAC. Various new physics opportunities have been discussed in a wide range of kinematic regime, from eV scale to keV and MeV. We also emphasize that the timely establishment of the beam dump facility at Fermilab is essential to exploit these new physics opportunities.