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Recently, in Malik23, a dataset of several EoS for purely nucleonic stellar matter based on a non-linear RMF model prescription, and constrained to properties of nuclear matter, to state-of-the-art chiral effective field theory calculations for low-density neutron matter, and to astrophysical data, were proposed. In this work, twenty one unified neutron star EoS were chosen from that dataset, in such a way that a large range of values of the slope of the symmetry energy at saturation is covered. Several quantities are calculated and discussed, such as the the proton fraction and the direct Urca behavior, the density dependence of the speed of sound and the trace anomaly, the crust-core transition properties, the compatibility with astrophysical observations, and the neutron matter properties from $\chi$EFT calculations and pQCD constraints. We construct unified EoS, where the outer crust is given by the BSk22 functional, and the inner crust is calculated from a CLD approximation. The core is purely nucleonic, made of protons, neutrons, electrons and muons, under charge neutrality and in $\beta-$equilibrium conditions. The correlation of the slope of the symmetry energy at saturation with the crust-core transition density and proton fraction is analysed, and equations that translate these relations are proposed. Moreover, the spectral representation for all the EOS according to the format proposed in Lindblom10 is given, which is a convenient representation to study quasi-periodic oscillations with realistic EOS. It is shown that several of these EoS have in the center of the most massive NS a speed of sound squared of the order of $\lesssim 0.5$. Most of the EoS predict a maximum central density of the order of about 6 times the nuclear saturation density. Three of the EoS satisfy all of the constraints imposed. All these EoS will be made available in the CompOSE platform., Comment: 14 pages, 12 figures, 7 tables

The equations of state (EoSs) governing neutron star (NS) matter obtained for both non-relativistic and relativistic mean-field models are systematically confronted with a diverse set of terrestrial data and astrophysical observations within the Bayesian framework. The terrestrial data, spans from bulk properties of finite nuclei to the heavy-ion collisions, constrain the symmetric nuclear matter EoS and the symmetry energy up to twice the saturation density ($\rho_0$= 0.16 fm$^{-3}$). The astrophysical observations encompass the NS radius, the tidal deformability, and the lower bound on maximum mass. Three distinct posterior distributions of EoSs are generated by gradually updating the priors with different constraints: (i) only the maximum NS mass, (ii) incorporating additional terrestrial data, (iii) combining both the terrestrial data and astrophysical observations. These EoS distributions are then compared using the Kullback-Liebler divergence which highlights the significant constraints imposed on the EoSs by the currently available lower bound of NS maximum mass and terrestrial data. The remaining astrophysical observations marginally refine the EoS within the density range $\sim$ 2-3$\rho_0$. It is observed that the relativistic mean field model yields stiffer EoS around the saturation density, but predict smaller values of the speed of sound and proton fraction in the interior of massive stars.

Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memory-intensive for tasks with high-resolution data, e.g., video understanding, small object detection, and point cloud analysis. In this paper, we propose Dynamic Reversible Dual-Residual Networks, or Dr$^2$Net, a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr$^2$Net contains two types of residual connections, one maintaining the residual structure in the pretrained models, and the other making the network reversible. Due to its reversibility, intermediate activations, which can be reconstructed from output, are cleared from memory during training. We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr$^2$Net on various pretrained models and various tasks, and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage.

Real-time traffic flow prediction holds significant importance within the domain of Intelligent Transportation Systems (ITS). The task of achieving a balance between prediction precision and computational efficiency presents a significant challenge. In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. The FLAGCN model employs a spatial-temporal graph convolution technique to asynchronously address spatio-temporal dependencies within traffic data effectively. To efficiently handle the computational requirements associated with this deep learning model, this study used a graph federated learning technique known as GraphFL. This approach is designed to facilitate the training process. The experimental results obtained from conducting tests on two distinct traffic datasets demonstrate that the utilization of FLAGCN leads to the optimization of both training and inference durations while maintaining a high level of prediction accuracy. FLAGCN outperforms existing models with significant improvements by achieving up to approximately 6.85% reduction in RMSE, 20.45% reduction in MAPE, compared to the best-performing existing models., Comment: I request to withdraw my paper from arXiv due to significant updates and improvements identified post-submission. These enhancements will substantially elevate the work's quality and impact. I plan to resubmit the revised paper upon completion of these updates. Thank you for accommodating this request

We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z <$1.13, which is the largest sample of supernovae from any single survey and increases the number of known $z>0.5$ supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are $\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=$0.017 in a flat $\Lambda$CDM model, and $(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time., Comment: 39 pages, 19 figures; Submitted to ApJ; companion paper Dark Energy Collaboration et al. on consecutive arxiv number 2401.02929

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.100.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025

Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. Despite LLMs' successes in other domains, it is challenging to develop LLM-based products for social networks for numerous reasons, and it has been relatively under-reported in the research community. We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second is entertainment tasks where users want to consume interesting content, such as getting entertaining notification content. Third is foundational tasks that need to be done to moderate and operate the social networks, such as content annotation and LLM monitoring. For each task, we share the challenges we found, solutions we developed, and lessons we learned. To the best of our knowledge, this is the first comprehensive paper about developing LLM applications for social networks.

The U.S. CMS collaboration has designed a novel internship program for undergraduates to enhance the participation of students from under-represented populations, including those at minority serving institutions, in High Energy Physics (HEP). These students traditionally face several barriers including lack of research infrastructure and opportunities, insufficient mentoring, lack of support networks, and financial hardship, among many others, resulting in a lack of participation in STEM fields. We had recently reported about a fully virtual 10-week internship pilot program called "U.S. CMS - PURSUE (Program for Undergraduate Research SUmmer Experience)" to address dismantling such barriers. The 2023 iteration of this program builds on it by imparting not only an in-person summer internship experience but extends it into the academic semester as well. Students are selected predominantly from Minority Serving Institutions with no research program in HEP and from under-represented groups. They experience a structured hands-on research experience with an initial two-week "bootcamp" on software training modules followed by an 8-week HEP project targeting physics analysis, software, computing or instrumentation work on the CMS Experiment. A subset of interns continue the experience into the academic semester, enabling a further in-depth knowledge of the field and a motivation to persist in STEM areas. In this paper, we describe our recent experience with this upgraded internship program. The paper is dedicated to the memory of Prof. Meenakshi Narain (Brown University) who was the driving force behind this internship program and U.S CMS diversity, equity and inclusion efforts.

It is essential for items in assessments of mathematics' teacher knowledge to evoke the desired response processes -- to be interpreted and responded to by teachers as intended by item developers. In this study, we sought to unpack evidence that middle school mathematics teachers were not consistently interacting as intended with constructed response (i.e. open-ended) items designed to assess their pedagogical content knowledge (PCK). We analyzed recent data derived from think-aloud interviews with 13 teachers involving 38 assessment items designed to tap PCK regarding proportional reasoning. Five key issues associated with undesired response processes were identified: (1) scenarios provided insufficient information, (2) content knowledge (CK) and PCK elements were confounded, (3) questions asked about the scenarios lacked specificity, (4) items contained distracting text and/or visual elements, and (5) differences between math education research and classroom teacher work cultures led to unanticipated interpretations of items. These issues were associated with teacher responses that were problematic (e.g. vague, off topic, etc.). In addition, we suggest that obtaining response process evidence is critical, and the way it is obtained may impact the average difficulty of the final pool of assessment items developed. [This is the online version of an article published in "Investigations in Mathematics Learning."]