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In this work, we measure the spectral index of Sagittarius A* (Sgr A*) between the $H$ (1.6 $\mu$m) and $K^\prime$ (2.2 $\mu$m) broadband filters in the near-infrared (NIR), sampling over a factor $\sim 40$ in brightness, the largest range probed to date by a factor $\sim 3$. Sgr A*-NIR is highly variable, and studying the spectral index $\alpha$ (with $F_\nu \propto \nu^{\alpha}$) is essential to determine the underlying emission mechanism. For example, variations in $\alpha$ with flux may arise from shifts in the synchrotron cutoff frequency, changes in the distribution of electrons, or multiple concurrent emission mechanisms. We investigate potential variations of $\alpha_{H-K^\prime}$ with flux by analyzing 7 epochs (2005 to 2022) of Keck Observatory imaging observations from the Galactic Center Orbits Initiative (GCOI). We remove the flux contribution of known sources confused with Sgr A*-NIR, which can significantly impact color at faint flux levels. We interpolate between the interleaved $H$ and $K^\prime$ observations using Multi-Output Gaussian Processes. We introduce a flexible empirical model to quantify $\alpha$ variations and probe different scenarios. The observations are best fit by an $\alpha_{H-K^\prime} = - 0.50 \pm 0.08 _{\rm stat} \pm 0.17_{\rm sys}$ that is constant from $\sim 1$ mJy to $\sim 40$ mJy (dereddened 2 $\mu$m flux). We find no evidence for a flux-dependence of Sgr A*'s intrinsic spectral index. In particular, we rule out a model explaining NIR variability purely by shifts in the synchrotron cutoff frequency. We also constrain the presence of redder, quiescent emission from the black hole, concluding that the dereddened 2 $\mu$m flux contribution must be $\leq 0.3$ mJy at 95% confidence level., Comment: Accepted for publication in The Astrophysical Journal, 42 pages, 18 figures

The equilibrium rotation rate of a planet is determined by the sum of torques acting on its solid body. For planets with atmospheres, the dominant torques are usually the gravitational tide, which acts to slow the planet's rotation rate, and the atmospheric thermal tide, which acts to spin up the planet. Previous work demonstrated that rocky planets with thick atmospheres may produce strong enough thermal tides to avoid tidal locking, but a study of how the strength of the thermal tide depends on atmospheric properties has not been done. In this work, we use a combination of simulations from a global climate model and analytic theory to explore how the thermal tide depends on the shortwave and longwave optical depth of the atmosphere, the surface pressure, and the absorbed stellar radiation. We find that for planets in the habitable zones of M stars only high-pressure but low-opacity atmospheres permit asynchronous rotation owing to the weakening of the thermal tide at high longwave and shortwave optical depths. We conclude that asynchronous rotation may be very unlikely around low-mass stars, which may limit the potential habitability of planets around M stars.

Diabetic retinopathy is a common complication of diabetes, and monitoring the progression of retinal abnormalities using fundus imaging is crucial. Because the images must be interpreted by a medical expert, it is infeasible to screen all individuals with diabetes for diabetic retinopathy. Deep learning has shown impressive results for automatic analysis and grading of fundus images. One drawback is, however, the lack of interpretability, which hampers the implementation of such systems in the clinic. Explainable artificial intelligence methods can be applied to explain the deep neural networks. Explanations based on concepts have shown to be intuitive for humans to understand, but have not yet been explored in detail for diabetic retinopathy grading. This work investigates and compares two concept-based explanation techniques for explaining deep neural networks developed for automatic diagnosis of diabetic retinopathy: Quantitative Testing with Concept Activation Vectors and Concept Bottleneck Models. We found that both methods have strengths and weaknesses, and choice of method should take the available data and the end user's preferences into account., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:021

We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Our ACL model revealed some important discrepancies in survey and fishery length compositions. Our model also required large population dynamics process errors to achieve good fits to survey indices and catch estimates, which also demonstrated that additional understanding of these data and other model assumptions is required. As such, we do not propose the ACL model to provide management advice for 3LN redfish, but we do provide research recommendations that should provide a better basis to model the 3LN redfish stock dynamics. Recommendations include implementing sampling programs to determine redfish species/ecotypes in commercial and research survey catches and improving biological sampling for maturity and age., Comment: 27 pages including references, tables, and figures. In addition 12 pages figures in an Appendix

The following laboratory procedure provides students with a hands-on experience in nanomaterials chemistry and characterization. This three-day protocol is easy to follow for undergraduates with basic chemistry or materials science backgrounds and is suitable for inclusion in upper division courses in inorganic chemistry or materials science. Students use air-free chemistry procedures to synthesize and separate iron oxide magnetic nanoparticles and subsequently modify the nanoparticle surface using a chemical stripping agent. The morphology and chemical composition of the nanoparticles are characterized using electron microscopy and dynamic light scattering measurements. Additionally, magnetic characterization of the particles is performed using an open-source (3D-printed), inexpensive magnetophotometer. Possible modifications to the synthesis procedure including the incorporation of dopants to modify the magnetic response and alternative characterization techniques are discussed. The three-day synthesis, purification, and characterization laboratory will prepare students with crucial skills for advanced technology industries such as semiconductor manufacturing, nanomedicine, and green chemistry.

This paper focuses on the parameterization of the multipath propagation model (MPM) for indoor broadband power line communications (PLC), which up to now has been established in an heuristic way. The MPM model was originally proposed for outdoor channels in the band up to 20 MHz, but its number of parameters becomes extremely large when used to model indoor channel frequency responses (CFR), which are much more frequency-selective than outdoor ones, and the band is extended to 80 MHz. This work proposes a fitting procedure that addresses this problem. It allows determining the model parameters that yield the best fit to each channel of a large database of single-input single-output (SISO) experimental measurements acquired in typical home premises of different European countries. Then, the properties of the MPM parameters are analyzed. The study unveils the relation between the model parameters and the main characteristics of the actual CFR like the frequency selectivity and the average attenuation. It also estimates the probability density function (PDF) of each parameter and proposes a fitting distribution for each of them. Moreover, the relationship among the main parameters of the model is also explored. Provided results can be helpful for the development of MPM-based models for indoor broadband PLC., Comment: 9 pages, 7 figures

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged particle coincidence techniques. The exploitation, for the first time in a similar experiment, of a bidimensional map of the coincidence efficiency allows to reach an unitary value and, consequently, to strongly reduce sources of systematic uncertainties. The present results suggest a value of the radiative branching ratio of $\Gamma_{rad}/\Gamma_{tot}=4.2(6)\cdot10^{-4}$. This finding helps to resolve the tension between recent data published in the literature.

We present the first estimate of the intrinsic binary fraction of young stars across the central $\approx$ 0.4 pc surrounding the supermassive black hole (SMBH) at the Milky Way Galactic center (GC). This experiment searched for photometric variability in 102 young stars, using 119 nights of 10"-wide adaptive optics imaging observations taken at Keck Observatory over 16 years in the K'- and H-bands. We photometrically detected three binary stars, all of which are situated more than 1" (0.04 pc) from the SMBH and one of which, S2-36, is newly reported here with spectroscopic confirmation. To convert the observed binary fraction into an estimate of the underlying binary fraction, we determined experiment sensitivity through detailed light curve simulations, incorporating photometric effects of eclipses, irradiation, and tidal distortion in binaries. The simulations assumed a population of young binaries, with stellar ages (4 Myr) and masses matched to the most probable values measured for the GC young star population and underlying binary system parameters similar to those of local massive stars. The detections and simulations imply young, massive stars in the GC have a stellar binary fraction $\geq$ 71% (68% confidence), or $\geq$ 42% (95% confidence). This inferred GC young star binary fraction is consistent with that typically seen in young stellar populations in the solar neighborhood. Furthermore, our measured binary fraction is significantly higher than that recently reported by Chu et al. (2023) based on RV measurements of young stars <~1" of the SMBH. Constrained with these two studies, the probability that the same underlying young binary fraction extends across the entire region is <1.4%. This tension provides support for a radial dependence of the binary star fraction and, therefore, for the dynamical predictions of binary merger and evaporation events close to the SMBH., Comment: 51 pages, 27 figures, 7 tables. Accepted for publication in The Astrophysical Journal. Abstract revised for final manuscript

In the context of the ATHENA X-IFU Cryogenic AntiCoincidence Detector (CryoAC) development, we have studied the thermalization properties of a 2mm x 2mm SQUID chip. The chip is glued on a front-end PCB and operated on the cold stage of a dilution refrigerator (TBASE < 20 mK). We performed thermal conductance measurements by using different materials to glue the SQUID chip on the PCB. These have been repeated in subsequent cryostat runs, to highlight degradation effects due to thermal cycles. Here, we present the results obtained by glues and greases widely used in cryogenic environments, i.e. GE 7031 Varnish Glue, Apiezon N Grease and Rubber Cement., Comment: Accepted for publication in the Journal of Low Temperature Physics for LTD-20 special issue

We are developing the Cryogenic AntiCoincidence detector (CryoAC) of the ATHENA X-IFU spectrometer. It is a TES-based particle detector aimed to reduce the background of the instrument. Here, we present the result obtained with the last CryoAC single-pixel prototype. It is based on a 1 cm2 silicon absorber sensed by a single 2mm x 1mm Ir/Au TES, featuring an on-chip heater for calibration and diagnostic purposes. We have illuminated the sample with 55Fe (6 keV line) and 241Am (60 keV line) radioactive sources, thus studying the detector response and the heater calibration accuracy at low energy. Furthermore, we have operated the sample in combination with a past-generation CryoAC prototype. Here, by analyzing the coincident detections between the two detectors, we have been able to characterize the background spectrum of the laboratory environment and disentangle the primary (i.e. cosmic muons) and secondaries (mostly secondary photons and electrons) signatures in the spectral shape., Comment: Accepted for publication in the Journal of Low Temperature Physics for LTD-20 special issue

In deep learning, classification tasks are formalized as optimization problems often solved via the minimization of the cross-entropy. However, recent advancements in the design of objective functions allow the usage of the $f$-divergence to generalize the formulation of the optimization problem for classification. We adopt a Bayesian perspective and formulate the classification task as a maximum a posteriori probability problem. We propose a class of objective functions based on the variational representation of the $f$-divergence. Furthermore, driven by the challenge of improving the state-of-the-art approach, we propose a bottom-up method that leads us to the formulation of an objective function corresponding to a novel $f$-divergence referred to as shifted log (SL). We theoretically analyze the objective functions proposed and numerically test them in three application scenarios: toy examples, image datasets, and signal detection/decoding problems. The analyzed scenarios demonstrate the effectiveness of the proposed approach and that the SL divergence achieves the highest classification accuracy in almost all the considered cases., Comment: Accepted at ICML 2024

Magnetic nanoparticles form the foundation of a range of biomedical and aerospace technologies. To ensure reproducible system-level performance, quality control (QC) analysis of the nanoparticles' magnetic properties is critical. Although benchtop methods exist to characterize some physical properties of these materials, measuring magnetic susceptibility requires high-performance instrumentation. Here, we detail and validate the design of an open-source, low-cost benchtop magnetophotometer (MAP) that can characterize the magnetic susceptibility of nanoparticles suspended in solution. A mathematical model to calculate the magnetic susceptibility from the MAP data was developed and validated using control measurements. All designs and software are available on open-source platforms., Comment: 39 total pages, Main text and SI included, 21 figures, 6 tables, Design files provided at GitHub link in PDF