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This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process., Comment: 15 pages, 5 main text figures, 3 supplementary figures

Ewing's sarcoma (ES), characterized by a high density of small round blue cells without structural organization, presents a significant health concern, particularly among adolescents aged 10 to 19. Artificial intelligence-based systems for automated analysis of histopathological images are promising to contribute to an accurate diagnosis of ES. In this context, this study explores the feature extraction ability of different pre-training strategies for distinguishing ES from other soft tissue or bone sarcomas with similar morphology in digitized tissue microarrays for the first time, as far as we know. Vision-language supervision (VLS) is compared to fully-supervised ImageNet pre-training within a multiple instance learning paradigm. Our findings indicate a substantial improvement in diagnostic accuracy with the adaption of VLS using an in-domain dataset. Notably, these models not only enhance the accuracy of predicted classes but also drastically reduce the number of trainable parameters and computational costs., Comment: 11 pages, 5 figures, 2 tables. Oral presentation at KES-InMed 2024 held in Madeira, Portugal

Stable $^{205}$Tl ions have the lowest known energy threshold for capturing electron neutrinos ($\nu_e$) of ${ E}_{\nu_e}\ge50.6$\,keV. The Lorandite Experiment (LOREX), proposed in the 1980s, aims at obtaining the longtime averaged solar neutrino flux by utilizing natural deposits of Tl-bearing lorandite ores. To determine the $\nu_e$ capture cross section, it is required to know the strength of the weak transition connecting the ground state of $^{205}$Tl and the 2.3 keV first excited state in $^{205}$Pb. The only way to experimentally address this transition is to measure the bound-state beta decay ($\beta_{b}$) of fully ionized $\mathrm{^{205}Tl^{81+}}$ ions. After three decades of meticulous preparation, the half-life of the $\beta_{b}$ decay of $\mathrm{^{205}Tl^{81+}}$ has been measured to be $291_{-27}^{+33}$ days using the Experimental Storage Ring (ESR) at GSI, Darmstadt. The longer measured half-life compared to theoretical estimates reduces the expected signal-to-noise ratio in the LOREX, thus challenging its feasibility.

Every day, we judge the probability of propositions. When we communicate graded confidence (e.g. "I am 90% sure"), we enable others to gauge how much weight to attach to our judgment. Ideally, people should share their judgments to reach more accurate conclusions collectively. Peer-to-peer tools for collective inference could help debunk disinformation and amplify reliable information on social networks, improving democratic discourse. However, individuals fall short of the ideal of well-calibrated probability judgments, and group dynamics can amplify errors and polarize opinions. Here, we connect insights from cognitive science, structured expert judgment, and crowdsourcing to infer the truth of propositions from human probability judgments. In an online experiment, 376 participants judged the probability of each of 1,200 general-knowledge claims for which we have ground truth (451,200 ratings). Aggregating binary judgments by majority vote already exhibits the "wisdom of the crowd"--the superior accuracy of collective inferences relative to individual inferences. However, using continuous probability ratings and accounting for individual accuracy and calibration significantly improves collective inferences. Peer judgment behavior can be modeled probabilistically, and individual parameters capturing each peer's accuracy and miscalibration can be inferred jointly with the claim probabilities. This unsupervised approach can be complemented by supervised methods relying on truth labels to learn models that achieve well-calibrated collective inference. The algorithms we introduce can empower groups of collaborators and online communities to pool their distributed intelligence and jointly judge the probability of propositions with a well-calibrated sense of uncertainty.

PSR J2030+4415 is a gamma-ray pulsar with an X-ray pulsar wind nebula elongated along the north-south direction. The system shows a prominent X-ray filament oriented at an angle of 130{\deg} to the nebula axis. To improve our understanding of the non-thermal processes occurring in the pulsar wind nebula, we attempted to determine the possible existence of a radio counterpart, study its morphology, and obtain restrictive upper limits of the pulsar and filament emission at radio wavelengths. We performed observations of the pulsar PSR J2030+4415 and its surroundings with the upgraded Giant Metrewave Radio Telescope (uGMRT) at two frequency bands, and put the results in context with findings at other wavelengths. We obtained radio images at 736 and 1274 MHz that reveal a structure trailing the pulsar, with a morphology overlapping the X-ray nebula. This radio structure is the radio counterpart of the X-ray pulsar wind nebula. The derived spectral index along this structure shows spatial variation. There are no hints of the pulsar and the filament at any of the explored radio frequencies, but we obtained restrictive upper limits. A physical scenario that combines the radio and the X-ray observations, and consistent with IR data, of the nebula and the filament is presented. We propose that particle acceleration occurs in the nebula tail due to the presence of a re-collimation shock, and the highest energy particles gradually escape from it through energy-dependent diffusion. We also find a lower limit in the energy of the particles escaping along the X-ray filament of ~GeV., Comment: 8 pages, 6 figures. Accepted for publication in A&A

We provide sufficient conditions for the existence of invariant probability measures for generic stochastic differential equations with finite time delay. Applications include the Mackey--Glass equations and Nicholson's blowflies equation, each perturbed by a (small) multiplicative noise term. Solutions to these stochastic negative feedback systems persist globally and all solutions are bounded above in probability. It turns out that the occurrence of finite time blowups and boundedness in probability of solutions and solution segments are closely related. A non-trivial invariant measure is shown to exist if and only if there is at least one initial condition for which the solution remains bounded away from zero in probability. The noise driving the dynamical system is allowed to be an integrable L\'evy process., Comment: 74 pages, 1 figure

The purpose of these notes is to distribute, mostly without proofs, fundamental definitions and results concerning the theory of semimartingales and stochastic integration. The material serves as a foundational guide for those interested in applying these concepts, particularly in the study of stochastic (functional) differential equations driven by L\'evy processes. These notes are adapted from the preliminary chapter of the author's master's thesis (with only minor changes) and are intended to introduce newcomers to the essentials of c\`adl\`ag semimartingale theory while also discussing the advantages, limitations, and subtleties as compared to stochastic integration in the continuous setting., Comment: 70 pages, 7 figures, MATLAB code

We consider Dirac-type operators on manifolds with boundary, and set out to determine all local smooth boundary conditions that give rise to (strongly) regular self-adjoint operators. By combining the general theory of boundary value problems for Dirac operators as in [BB12] and pointwise considerations, for local smooth boundary conditions the question of being self-adjoint resp. regular is fully translated into linear-algebraic language at each boundary point. We analyse these conditions and classify them in low dimensions and ranks. In particular, we classify all local self-adjoint regular boundary conditions for Dirac spinors (four spinor components) in dimensions $3$ and $4$. With the same techniques we can also treat transmission boundary conditions.

We present the discovery of two mini Neptunes near a 2:1 orbital resonance configuration orbiting the K0 star TOI-1803. We describe their orbital architecture in detail and suggest some possible formation and evolution scenarios. Using CHEOPS, TESS, and HARPS-N datasets we can estimate the radius and the mass of both planets. We used a multidimensional Gaussian Process with a quasi-periodic kernel to disentangle the planetary components from the stellar activity in the HARPS-N dataset. We performed dynamical modeling to explain the orbital configuration and performed planetary formation and evolution simulations. For the least dense planet, we define possible atmospheric characterization scenarios with simulated JWST observations. TOI-1803 b and TOI-1803 c have orbital periods of $\sim$6.3 and $\sim$12.9 days, respectively, residing in close proximity to a 2:1 orbital resonance. Ground-based photometric follow-up observations revealed significant transit timing variations (TTV) with an amplitude of $\sim$10 min and $\sim$40 min, respectively, for planet -b and -c. With the masses computed from the radial velocities data set, we obtained a density of (0.39$\pm$0.10) $\rho_{earth}$ and (0.076$\pm$0.038) $\rho_{earth}$ for planet -b and -c, respectively. TOI-1803 c is among the least dense mini Neptunes currently known, and due to its inflated atmosphere, it is a suitable target for transmission spectroscopy with JWST. We report the discovery of two mini Neptunes close to a 2:1 orbital resonance. The detection of significant TTVs from ground-based photometry opens scenarios for a more precise mass determination. TOI-1803 c is one of the least dense mini Neptune known so far, and it is of great interest among the scientific community since it could constrain our formation scenarios., Comment: 26 Pages, 21 Figures Accepted for Publication in Astronomy & Astrophysics

A widespread moderation strategy by online news platforms is to feature what the platform deems high quality comments, usually called editor picks or featured comments. In this paper, we compare online discussions of news articles in which certain comments are featured, versus discussions in which no comments are featured. We measure the impact of featuring comments on the discussion, by estimating and comparing the quality of discussions from the perspective of the user base and the platform itself. Our analysis shows that the impact on discussion quality is limited. However, we do observe an increase in discussion activity after the first comments are featured by moderators, suggesting that the moderation strategy might be used to increase user engagement and to postpone the natural decline in user activity over time.

The colored Jones polynomial associated to a knot admits an expansion of knot invariants known as the large-color expansion or Melvin-Morton-Rozansky expansion. We will show how this expansion can be derived from the universal invariant arising from a Hopf algebra $\mathbb{D}$, as introduced by Bar-Natan and Van der Veen. We utilize a Mathematica implementation to compute the universal invariant $\mathbf{Z}_{\mathbb{D}}(\mathcal{K})$ up to a certain order for a given knot $\mathcal{K}$, allowing for experimental verification of our theoretical results., Comment: 19 pages

Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although recent advances in large language models (LLMs) offer the potential for more flexible interactions, their lack of controllability and transparency poses significant challenges in sensitive areas like psychotherapy. In this work, we explored how aligning LLMs with expert-crafted scripts can enhance psychotherapeutic chatbot performance. Our comparative study showed that LLMs aligned with expert-crafted scripts through prompting and fine-tuning significantly outperformed both pure LLMs and rule-based chatbots, achieving a more effective balance between dialogue flexibility and adherence to therapeutic principles. Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a flexible alignment approach that reduces reliance on fully scripted content while enhancing LLMs' therapeutic adherence and controllability. In a 10-day field study, SSAG demonstrated performance comparable to full script alignment and outperformed rule-based chatbots, empirically supporting SSAG as an efficient approach for aligning LLMs with domain expertise. Our work advances LLM applications in psychotherapy by providing a controllable, adaptable, and scalable solution for digital interventions, reducing reliance on expert effort. It also provides a collaborative framework for domain experts and developers to efficiently build expertise-aligned chatbots, broadening access to psychotherapy and behavioral interventions.

It is common practice, both in dynamical modelling and in idealised numerical simulations, to assume that galaxies and/or dark matter haloes are spherical and have isotropic velocity distributions, such that their distribution functions are ergodic. However, there is no good reason to assume that this assumption is accurate. In this paper we use idealised $N$-body simulations to study the tidal evolution of subhaloes that are anisotropic at infall. We show that the detailed velocity anisotropy has a large impact on the subhalo's mass loss rate. In particular, subhaloes that are radially anisotropic experience much more mass loss than their tangentially anisotropic counterparts. In fact, in the former case, the stripping of highly radial orbits can cause a rapid cusp-to-core transformation, without having to resort to any baryonic feedback processes. Once the tidal radius becomes comparable to the radius of the core thus formed, the subhalo is tidally disrupted. Subhaloes that at infall are tangentially anisotropic are far more resilient to tidal stripping, and are never disrupted when simulated with sufficient resolution. We show that the preferential stripping of more radial orbits, combined with re-virialisation post stripping, causes an isotropisation of the subhalo's velocity distributions. This implies that subhaloes that have experienced significant mass loss are expected to be close to isotropic, which may alleviate the mass-anisotropy degeneracies that hamper the dynamical modelling of Milky Way satellites., Comment: 16 pages, 16 figures

Efficiently learning a sequence of related tasks, such as in continual learning, poses a significant challenge for neural nets due to the delicate trade-off between catastrophic forgetting and loss of plasticity. We address this challenge with a grounded framework for sequentially learning related tasks based on Bayesian inference. Specifically, we treat the model's parameters as a nonlinear Gaussian state-space model and perform efficient inference using Gaussian filtering and smoothing. This general formalism subsumes existing continual learning approaches, while also offering a clearer conceptual understanding of its components. Leveraging Laplace approximations during filtering, we construct Gaussian posterior measures on the weight space of a neural network for each task. We use it as an efficient regularizer by exploiting the structure of the generalized Gauss-Newton matrix (GGN) to construct diagonal plus low-rank approximations. The dynamics model allows targeted control of the learning process and the incorporation of domain-specific knowledge, such as modeling the type of shift between tasks. Additionally, using Bayesian approximate smoothing can enhance the performance of task-specific models without needing to re-access any data., Comment: 20 pages, 8 figures

Extrusion is a critical process in the manufacturing of various organic materials, including animal feed, human food, pharmaceuticals, and biomass for bioenergy. During extrusion, a dense particle-fluid mixture, or paste, is pushed through a narrow channel under high pressure. Despite its widespread use, many questions remain about how particle-fluid interactions and frictional forces at the wall influence high-pressure paste flow, particularly whether laboratory-scale findings translate to more complex industrial settings. Here, we quantify for the first time how a micrometer thin fluid lubrication layer emerges from particle-fluid interactions during the extrusion of organic biomass, controlling friction at the paste-channel interface. This lubrication layer forms through a pressure-induced phase segregation process and is significantly thinner than the particle size, affecting their contact dynamics, making friction at the interface tunable. Our results reveal how the thickness of the lubrication layer is influenced by paste composition, enhancing our fundamental understanding of phase segregation during industrial extrusion processes and offering potential pathways for reducing energy consumption and product failure in the industrial material production.

The abundance of satellite galaxies is set by the hierarchical assembly of their host halo. We leverage this to investigate the low mass end ($M_{\mathrm{H}} < 10^{11} M_{\odot}$) of the Stellar-to-Halo Mass Relation (SHMR), which is key to constraining theories of galaxy formation and cosmology. We argue that recent analyses of satellite galaxies in the Local Group environment have not adequately modelled the dominant source of scatter in satellite stellar mass functions: the variance in accretion histories for a fixed host halo mass. We present a novel inference framework that not only properly accounts for this halo-to-halo variance but also naturally identifies the amount of host halo mass mixing, which is generally unknown. Specifically, we use the semi-analytical SatGen model to construct mock satellite galaxy populations consistent with the third data release of the Satellites Around Galactic Analogs (SAGA) survey. We demonstrate that even under the most idealized circumstances, the halo-to-halo variance makes it virtually impossible to put any meaningful constraints on the scatter in the SHMR. Even a satellite galaxy survey made up 100 hosts can at best only place an upper limit of $\sim 0.5$dex on the scatter (at the 95\% confidence level). This is because the large variance in halo assembly histories dominates over the scatter in the SHMR. This problem can be overcome by increasing the sample size of the survey by an order of magnitude ($\sim 1000$ host galaxies), something that should be fairly straightforward with forthcoming spectroscopic surveys.

The scenario of feedback-free starbursts (FFB), which predicts excessively bright galaxies at cosmic dawn as observed using JWST, may provide a natural setting for black hole (BH) growth. This involves the formation of intermediate-mass seed BHs and their runaway mergers into super-massive BHs with high BH-to-stellar mass ratios and low AGN luminosities. We present a scenario of merger-driven BH growth in FFB galaxies and study its feasibility. BH seeds form within the building blocks of the FFB galaxies, namely, thousands of compact star clusters, each starbursting in a free-fall time of a few Myr before the onset of stellar and supernova feedback. The BH seeds form by rapid core collapse in the FFB clusters, in a few free-fall times, sped up by the migration of massive stars due to the young, broad stellar mass function and stimulated by a `gravo-gyro' instability due to internal cluster rotation and flattening. BHs of $10^4 M_\odot$ are expected in $10^6 M_\odot$ FFB clusters within sub-kpc galactic disks at $z \sim 10$. The BHs then migrate to the galaxy center by dynamical friction, hastened by the compact FFB stellar galactic disk configuration. Efficient mergers of the BH seeds will produce $10^{6-8} M_\odot$ BHs with a BH-to-stellar mass ratio $\sim 0.01$ by $z \sim 4-7$, as observed. The growth of the central BH by mergers can overcome the bottleneck introduced by gravitational wave recoils if the BHs inspiral within a relatively cold disk or if the escape velocity from the galaxy is boosted by a wet compaction event. Such events, common in massive galaxies at high redshifts, can also help by speeding up the inward BH migration and by providing central gas to assist with the final parsec problem. The cold disk version of the FFB scenario provides a feasible route for the formation of supermassive BHs., Comment: 24 pages, 14 figures

Speech recordings are being more frequently used to detect and monitor disease, leading to privacy concerns. Beyond cryptography, protection of speech can be addressed by approaches, such as perturbation, disentanglement, and re-synthesis, that eliminate sensitive information of the speaker, leaving the information necessary for medical analysis purposes. In order for such privacy protective approaches to be developed, clear and systematic specifications of assumptions concerning medical settings and the needs of medical professionals are necessary. In this paper, we propose a Scenario of Use Scheme that incorporates an Attacker Model, which characterizes the adversary against whom the speaker's privacy must be defended, and a Protector Model, which specifies the defense. We discuss the connection of the scheme with previous work on speech privacy. Finally, we present a concrete example of a specified Scenario of Use and a set of experiments about protecting speaker data against gender inference attacks while maintaining utility for Parkinson's detection., Comment: Accepted and published at SPSC Symposium 2024 4th Symposium on Security and Privacy in Speech Communication. Interspeech 2024

Hybrid quantum-classical optimization techniques, which incorporate the pre-optimization of Variational Quantum Algorithms (VQAs) using Tensor Networks (TNs), have been shown to allow for the reduction of quantum computational resources. In the particular case of large optimization problems, commonly found in real-world use cases, this strategy is almost mandatory to reduce the otherwise unfathomable execution costs and improve the quality of the results. We identify the coherence entropy as a crucial metric in determining the suitability of quantum states as effective initialization candidates. Our findings are validated through extensive numerical tests for the Quantum Approximate Optimization Algorithm (QAOA), in which we find that the optimal initialization states are pure Gibbs states. Further, these results are explained with the inclusion of a simple and yet novel notion of expressivity adapted to classical optimization problems. Based on this finding, we propose a quantum-classical optimization protocol that significantly improves on previous approaches for such tasks, with specific focus on its effectiveness., Comment: 14 pages, 10 figures

The transportation sector has the potential to enable a greener future if aligned with increasing mobility needs. Making public transport an attractive alternative to individual transportation requires real-world data to investigate reasons and indicators of positive and negative travel experiences. These experiences manifest not only in subjective evaluations but also in physiological reactions like cardiac activity. We present a geo-referenced dataset where participants wore electrocardiograms and reported real-time stress, satisfaction, events, and emotions while traveling by tram, train, and bus. An interactive experience map helps to visually explore the data, with benchmark analyses identifying significant stress hot spots and satisfaction cold spots during journeys. Events and emotions in these spots highlight positive and negative travel experiences in an ecologically valid setting. Data on age and self-identified gender provide insights into differences between user groups. Despite including only 44 participants, the dataset offers a valuable foundation for transportation researchers and mobility providers to combine qualitative and quantitative methods for identifying public transportation users' needs., Comment: 27 pages, 10 figures

Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss changes in overweight and obese individuals. Using wearable data from a 1-month trial involving around 100 subjects from the AI4FoodDB database, including biomarkers, vital signs, and behavioral data, we identify key differences between those achieving weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms reveal promising results, with the Gradient Boosting classifier achieving 84.44% Area Under the Curve (AUC). The integration of multiple data sources (e.g., vital signs, physical and sleep activity, etc.) enhances performance, suggesting the potential of wearable devices and AI in personalized healthcare., Comment: 15 pages, 5 figures, 6 tables, 1 appendix

We use Ramsey interferometry to study spin dynamics in the strongly interacting regime of spin-orbit-coupled quantum gases in one-dimensional optical lattices. We observe an intrinsic many-body dephasing mechanism immune to spin-echo in two-component Mott insulators. We ascribe the dephasing to the motion of hole-like defects in an otherwise inert Mott insulator, the spinless nature of the holes explaining the ineffectiveness of spin echo to restore it. We show that a model of spin-orbit-coupled hardcore bosons can explain quantitatively our experimental observations., Comment: Supplementary Material available as ancillary file

Basilisk is a novel Bayesian hierarchical method for inferring the galaxy-halo connection, including its scatter, using the kinematics of satellite galaxies extracted from a redshift survey. In this paper, we introduce crucial improvements, such as updated central and satellite selection, advanced modelling of impurities and interlopers, extending the kinematic modelling to fourth order by including the kurtosis of the line-of-sight velocity distribution, and utilizing satellite abundance as additional constraint. This drastically enhances Basilisk's performance, resulting in an unbiased recovery of the full conditional luminosity function (central and satellite) and with unprecedented precision. After validating Basilisk's performance using realistic mock data, we apply it to the SDSS-DR7 data. The resulting inferences on the galaxy-halo connection are consistent with, but significantly tighter than, previous constraints from galaxy group catalogues, galaxy clustering and galaxy-galaxy lensing. Using full projected phase-space information, Basilisk breaks the mass-anisotropy degeneracy, thus providing precise global constraint on the average orbital velocity anisotropy of satellite galaxies across a wide range of halo masses. Satellite orbits are found to be mildly radially anisotropic, in good agreement with the mean anisotropy for subhaloes in dark matter-only simulations. Thus, we establish Basilisk as a powerful tool that is not only more constraining than other methods on similar volumes of data, but crucially, is also insensitive to halo assembly bias which plagues the commonly used techniques like galaxy clustering and galaxy-galaxy lensing., Comment: 25+4 pages, 17+3 figures

Cardiovascular diseases (CVD) and depression exhibit significant comorbidity, which is highly predictive of poor clinical outcomes. Yet, the underlying biological pathways remain challenging to decipher, presumably due to the non-linear associations across multiple mechanisms. In this study, we introduced a multipartite projection method based on mutual information correlations to construct multilayer disease networks as a novel approach to explore such intricate relationships. We applied this method to a cross-sectional dataset from a wave of the Young Finns Study, which includes data on CVD and depression, along with related risk factors and two omics of biomarkers: metabolites and lipids. Rather than directly correlating CVD-related phenotypes and depressive symptoms, we extended the notion of bipartite networks to create a multipartite network, linking these phenotypes and symptoms to intermediate biological variables. Projecting from these intermediate variables results in a weighted multilayer network, where each link between CVD and depression variables is marked by its layer (i.e., metabolome or lipidome). Applying this projection method, we identified potential mediating biomarkers that connect CVD with depression. These biomarkers may therefore play significant roles in the biological pathways underlying CVD-depression comorbidity. Additionally, the network projection highlighted sex and BMI as key risk factors, or confounders, in this comorbidity. Our method is scalable to incorporate any number of omics layers and various disease phenotypes, offering a comprehensive, system-level perspective on the biological pathways contributing to comorbidity.

The relationship between galaxy size and environment has remained enigmatic, with over a decade of conflicting results. We present one of the first comprehensive studies of the variation of galaxy radius with environment beyond the local Universe and demonstrate that large-scale environmental density is correlated with galaxy radius independent of stellar mass and galaxy morphology. We confirm with $>5\sigma$ confidence that galaxies in denser environments are up to $\sim25\%$ larger than their equally massive counterparts with similar morphology in less dense regions of the Universe. We achieve this result by correlating projected two-dimensional densities over $\sim360$ deg$^2$ with the structural parameters of $\sim3$ million Hyper Suprime-Cam galaxies at $0.3 \leq z < 0.7$ with $\log M/M_{\odot} \geq 8.9$. Compared to most previous studies, this sample is $\sim100-10,000$ times larger and goes $\sim1$ dex deeper in mass-completeness. We demonstrate that past conflicting results have been driven by small sample sizes and a lack of robust measurement uncertainties. We verify the presence of the above correlation separately for disk-dominated, bulge-dominated, star-forming, and quiescent subpopulations. We find the strength of the correlation to be dependent on redshift, stellar mass, and morphology. The correlation is strongest at lower redshifts and systematically weakens or disappears beyond $z \geq 0.5$. At $z\geq0.5$, more massive galaxies still display a statistically significant correlation. Although some existing theoretical frameworks can be selectively invoked to explain some of the observed correlations, our work demonstrates the need for more comprehensive theoretical investigations of the correlation between galaxy size and environment., Comment: 29 pages, 13 figures. Published in The Astrophysical Journal. We welcome comments and constructive criticism

The $2$-Edge Connected Spanning Subgraph problem (2ECSS) is among the most basic survivable network design problems: given an undirected unweighted graph, find a subgraph with the minimum number of edges which is 2-edge-connected (i.e., it remains connected after the removal of any single edge). This NP-hard problem is well-studied in terms of approximation algorithms. The current-best approximation factor for 2ECSS is $1.3+\varepsilon$ for any constant $\varepsilon >0$ [Garg, Grandoni, Jabal-Ameli'23; Kobayashi,Noguchi'23]. In this paper we present a much simpler $9/7$ approximation algorithm, and a more complex $5/4$ one. Our algorithms are also faster: their running time is $n^{O(1)}$ instead of $n^{O(1/\varepsilon)}$., Comment: 61 pages, 5 figures