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The stellar mass-star formation rate (M$_\star$ - SFR) plane is essential for distinguishing galaxy populations, but how galaxies move within this plane over cosmic time remains unclear. This study aims to describe galaxy migrations in the M$_\star$ - SFR plane by reconstructing star formation histories (SFHs) for a sample of galaxies out to redshift $z < 4$. This provides insights into the physical processes driving star formation. We use data from the COSMOS field, selecting 299131 galaxies at $z < 4$ with COSMOS-Web NIRCam data (m$_\mathrm{F444W} < 27$) over 0.54 deg$^2$. Using the SED modeling code CIGALE with non-parametric SFHs, we derive physical properties and migration vectors for these galaxies. These vectors describe the direction and velocity of evolutionary paths across the M$_\star$ - SFR plane. To assess the accuracy of these vectors, we compare them to results from the Horizon-AGN simulation. Galaxies within the main sequence show low migration amplitudes and dispersed movement directions, indicating oscillation within the main sequence. Most progenitors were already on the main sequence a billion years earlier. Starburst galaxies assembled half their mass in the last 350 Myr and originated from the main sequence. Passive galaxies show uniformly declining SFHs and include massive galaxies already in the passive region at $z = 3.5-4$, with increasing density over time. Using reconstructed SFHs up to $z < 4$, we propose a coherent picture of how galaxies migrate over cosmic time in the M$_\star$ - SFR plane, highlighting the connection between major phases in the star-formation history.

The current paradigm for the co-evolution of galaxies and their supermassive black holes postulates that dust-obscured active galactic nuclei (AGNs) represent a transitional phase towards a more luminous and unobscured state. However, our understanding of dusty AGNs and their host galaxies at early cosmic times is inadequate due to observational limitations. Here, we present JWST observations of CID-931, an X-ray-detected AGN at a spectroscopic redshift of $z_{\rm spec}=4.91$. Multiband NIRCam imaging from the COSMOS-Web program reveals an unresolved red core, similar to JWST-discovered dusty AGNs. Strikingly, the red core is surrounded by at least eight massive star-forming clumps spread over $1.\!\!^{\prime\prime}6 \approx 10~{\rm kpc}$, each of which has a stellar mass of $10^9-10^{10}M_\odot$ and $\sim0.1-1~{\rm kpc}$ in radius. The whole system amounts to $10^{11}M_\odot$ in stellar mass, higher than typical star-forming galaxies at the same epoch. In this system, gas inflows and/or complex merger events may trigger clump formation and AGN activity thus leading to the rapid formation of a massive galaxy hosting a supermassive black hole. Future follow-up observations will provide new insights into the evolution of the galaxy-black hole relationship during such transitional phases in the early universe., Comment: 13 pages, 10 figures, submitted to PASJ

Datasets often contain values that naturally reside in a metric space: numbers, strings, geographical locations, machine-learned embeddings in a Euclidean space, and so on. We study the computational complexity of repairing inconsistent databases that violate integrity constraints, where the database values belong to an underlying metric space. The goal is to update the database values to retain consistency while minimizing the total distance between the original values and the repaired ones. We consider what we refer to as \emph{coincidence constraints}, which include key constraints, inclusion, foreign keys, and generally any restriction on the relationship between the numbers of cells of different labels (attributes) coinciding in a single value, for a fixed attribute set. We begin by showing that the problem is APX-hard for general metric spaces. We then present an algorithm solving the problem optimally for tree metrics, which generalize both the line metric (i.e., where repaired values are numbers) and the discrete metric (i.e., where we simply count the number of changed values). Combining our algorithm for tree metrics and a classic result on probabilistic tree embeddings, we design a (high probability) logarithmic-ratio approximation for general metrics. We also study the variant of the problem where each individual value's allowed change is limited. In this variant, it is already NP-complete to decide the existence of any legal repair for a general metric, and we present a polynomial-time repairing algorithm for the case of a line metric.

Venus has become a target of astrobiological interest because it is physically accessible to direct exploration, unlike exoplanets. So far this interest has been motivated not by the explicit expectation of finding life, but rather a desire to understand the limits of biology. The Venusian surface is sterilizing, but the cloud deck includes regions with temperatures and pressures conventionally considered compatible with life. However, the Venusian clouds are thought to consist of concentrated sulfuric acid. To determine if any fundamental features of life as we understand them here on Earth could in principle exist in these extreme solvent conditions, we have tested several simple lipids for resistance to solvolysis and their ability to form structures in concentrated sulfuric acid. We find that single-chain saturated lipids with sulfate, alcohol, trimethylamine, and phosphonate head groups are resistant to sulfuric acid degradation at room temperature. Furthermore, we find that they form stable higher-order structures typically associated with lipid membranes, micelles, and vesicles. Finally, results from molecular dynamics simulations suggest a molecular explanation for the observed robustness of the lipid structures formed in concentrated sulfuric acid. We conclude with implications for the study of Venus as a target of experimental astrobiology., Comment: Submitted to Astrobiology

Modern applications often leverage multiple views of a subject of study. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental principles about the characteristics of each representation and about the system. However, existing methods to characterize such relationships either lack the expressivity required to capture complex nonlinearities, describe only sources of variance that are shared between views, or discard geometric information that is crucial to interpreting the data. Here, we develop a nonlinear neural network-based method that, given paired samples of high-dimensional views, disentangles low-dimensional shared and private latent variables underlying these views while preserving intrinsic data geometry. Across multiple simulated and real datasets, we demonstrate that our method outperforms competing methods. Using simulated populations of lateral geniculate nucleus (LGN) and V1 neurons we demonstrate our model's ability to discover interpretable shared and private structure across different noise conditions. On a dataset of unrotated and corresponding but randomly rotated MNIST digits, we recover private latents for the rotated view that encode rotation angle regardless of digit class, and places the angle representation on a 1-d manifold, while shared latents encode digit class but not rotation angle. Applying our method to simultaneous Neuropixels recordings of hippocampus and prefrontal cortex while mice run on a linear track, we discover a low-dimensional shared latent space that encodes the animal's position. We propose our approach as a general-purpose method for finding succinct and interpretable descriptions of paired data sets in terms of disentangled shared and private latent variables.

Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transforms and have built a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is able to capture the influence of business cycles and the heterogeneity of the marketplace, improving prediction accuracy by more than fivefold, with an estimated annualized saving of \$50MM.

Some magnetic systems display a shift in the center of their magnetic hysteresis loop away from zero field, a phenomenon termed exchange bias. Despite the extensive use of the exchange bias effect, particularly in magnetic multilayers, for the design of spin-based memory/electronics devices, a comprehensive mechanistic understanding of this effect remains a longstanding problem. Recent work has shown that disorder-induced spin frustration might play a key role in exchange bias, suggesting new materials design approaches for spin-based electronic devices that harness this effect. Here, we design a spin glass with strong spin frustration induced by magnetic disorder by exploiting the distinctive structure of Fe intercalated ZrSe2, where Fe(II) centers are shown to occupy both octahedral and tetrahedral interstitial sites and to distribute between ZrSe2 layers without long-range structural order. Notably, we observe behavior consistent with a magnetically frustrated, and multi-degenerate ground state in these Fe0.17ZrSe2 single crystals, which persists above room temperature. Moreover, this magnetic frustration leads to a robust and tunable exchange bias up to 250 K. These results not only offer important insights into the effects of magnetic disorder and frustration in magnetic materials generally, but also highlight as design strategy the idea that a large exchange bias can arise from an inhomogeneous microscopic environment without discernible long-range magnetic order. In addition, these results show that intercalated TMDs like Fe0.17ZrSe2 hold potential for spintronics technologies that can achieve room temperature applications., Comment: Manuscript, 22 pages, 6 figures; Supporting information, 41 pages, 19 figures

The four approaches of engaging in formative assessment, deeper learning, culturally responsive and sustaining education, and personalized learning share a common aspirational commitment--to provide student learning experiences rooted in: (1) developing identity; (2) prioritizing learner agency; (3) educating for equity; and (4) honoring and activating community assets. All four approaches support student and teacher roles that enable young people to be self-directed, empowered learners. Each is rooted in learning science and the understanding that engaging students in thinking about how they understand their learning themselves (metacognition) can help students be more efficient and effective in their learning, leading to improved outcomes. Cognitive science and learning research provide a foundation for reimagining the student role as one of a knowledgeable member of the learning community so that all content does not have to come from, or go through, the teacher. These approaches all strive for student learning to be rigorous, relevant, and aligned to appropriate content standards. Each approach requires teachers to move away from a traditional teacher-directed instructional model in order to develop multiple ways for students to learn. This paper attempts to serve as a resource for school communities that are introducing or considering integrating different types of student-centered learning practices in a way that honors the capacities and strengths of each approach and recognizes that multiple approaches may benefit students.

Filaments are ubiquitous in the interstellar medium (ISM), yet their formation and evolution remains the topic of intense debate. In order to obtain a more comprehensive view of the 3D morphology and evolution of the Musca filament, we model the C$^{18}$O(2-1) emission along the filament crest with several large-scale velocity field structures. This indicates that Musca is well described by a 3D curved cylindrical filament with longitudinal mass inflow to the center of the filament unless the filament is a transient structure with a lifetime $\lesssim$~0.1 Myr. Gravitational longitudinal collapse models of filaments appear unable to explain the observed velocity field. To better understand these kinematics, we further analyze a map of the C$^{18}$O(2-1) velocity field at the location of SOFIA HAWC+ dust polarization observations that trace the magnetic field in the filament. This unveils an organized magnetic field that is oriented roughly perpendicular to the filament crest. Although the velocity field is also organized, it progressively changes its orientation by more than 90$^{o}$ when laterally crossing the filament crest and thus appears disconnected from the magnetic field in the filament. This strong lateral change of the velocity field over the filament remains unexplained and might be associated with important longitudinal motion in the filament that can be associated to the large-scale kinematics along the filament., Comment: 16 pages, 11 figures, 1 table, accepted in ApJ

We present an analysis of archival Spitzer InfraRed Spectrograph (IRS) observations of the recurrent nova RS Ophiuchi obtained on several occasions, beginning about 7 months after the outburst in 2006. These data show atomic emission lines, absorption bands due to photospheric SiO, and the well known silicate dust features at $9.7\,\mu$m and $18\,\mu$m. The dust emission, arising in the wind of the secondary star, is fitted by Dusty models for mass-loss rates in the range $1.0-1.7\times10^{-7}$M$_{\odot}$yr$^{-1}$. The silicate features are similar in profile to those seen in circumstellar environments of isolated late-type stars and some dusty symbiotic binaries, although the longer wavelength feature peaks at $17\,\mu$m,, instead of the usual $18\,\mu$m, indicating peculiar grain properties. The dust features are variable, appearing stronger in 2006-2007 during outburst than in 2008-2009 when the system was in the quiescent state. This variability is attributed to changes in the ultraviolet output and the reformation of the accretion disk, although a decline in the mass-loss rate of the red giant secondary star could also play a role. Further observations, in the aftermath of the 2021 eruption, could provide a definitive conclusion., Comment: 10 pages, 8 figures. Accepted for publication in MNRAS

XeBRA is a flexible cryogenic platform designed to perform research and development for liquid xenon detectors searching for rare events. Its extra-large outer cryostat makes it possible to install a wide variety of detector designs. We present the system, including its cryogenic, gas handling, data acquisition and slow control subsystems. Two dual phase time projection chambers with sensitive masses at the 1 kg scale have so far been operated in XeBRA. Using data from these, we determine the field-dependence of the electron drift velocity in liquid xenon. We also measure the relative charge and light yields for 41.5 keV energy deposits from Kr-83m with electric drift fields between 50 V/cm and 677 V/cm., Comment: 19 pages, 12 figures, accepted for publication by JINST

Learning a Bayesian network is an NP-hard problem and with an increase in the number of nodes, classical algorithms for learning the structure of Bayesian networks become inefficient. In recent years, some methods and algorithms for learning Bayesian networks with a high number of nodes (more than 50) were developed. But these solutions have their disadvantages, for instance, they only operate one type of data (discrete or continuous) or their algorithm has been created to meet a specific nature of data (medical, social, etc.). The article presents a BigBraveBN algorithm for learning large Bayesian Networks with a high number of nodes (over 100). The algorithm utilizes the Brave coefficient that measures the mutual occurrence of instances in several groups. To form these groups, we use the method of nearest neighbours based on the Mutual information (MI) measure. In the experimental part of the article, we compare the performance of BigBraveBN to other existing solutions on multiple data sets both discrete and continuous. The experimental part also represents tests on real data. The aforementioned experimental results demonstrate the efficiency of the BigBraveBN algorithm in structure learning of Bayesian Networks., Comment: The article contains 10 pages and 10 figures

We present an analysis of the red giant component of the recurrent nova V3890 Sgr, using data obtained before and after its 2019 eruption. Its effective temperature is $T_{\rm eff}=3050\pm$200 K for $\log{g}=0.7$, although there are modest changes in $T_{\rm eff}$. There is an overabundance of both carbon ($0.20\pm0.05$~dex) and sodium ($1.0\pm0.3$~dex) relative to their solar values, possibly the result of ejecta from the 1990 nova eruption being entrained into the red giant photosphere. We find $^{12}$C/$^{13}$C $=25\pm2$, a value similar to that found in red giants in other recurrent novae. The interpretation of the quiescent spectrum in the 5--38$\,mu$m region requires the presence of photospheric SiO absorption and cool ($\sim400$~K) dust in the red giant environment. The spectrum in the region of the Na{\sc i} D lines is complex, and includes at least six interstellar components, together with likely evidence for interaction between ejecta from the 2019 eruption and material accumulated in the plane of the binary. Three recurrent novae with giant secondaries have been shown to have environments with different dust content, but photospheres with similar $^{12}$C/$^{13}$C ratios. The SiO fundamental bands most likely have a photospheric origin in the all three stars., Comment: Accepted for publication in MNRAS on 22 July 2022. 15 pages, 11 figs

A dedicated single-cell SiPM structure is designed and measured to investigate the radiation damage effects on the gain and breakdown voltage of SiPMs exposed to a reactor neutron fluence up to $\Phi$ = 5e13 cm$^{-2}$. The cell has a pitch of 15 $\mu$m. Results of the measurements and analysis of the IV-curves are presented. Impact of the self-heating effect was investigated. The radiation damage uniformity of 1 cell and 120 cells was checked up to $U_\mathit{ov}$ = 1.7 V. Fluence dependence of the breakdown voltage from the current measurements $U^{IV}_\mathit{bd}$ was extracted and compared to that of the breakdown voltage from the gain measurements $U^{G}_\mathit{bd}$., Comment: arXiv admin note: text overlap with arXiv:2111.00483