Numerical calculus algorithms which estimate derivatives and integrals from data series acquired either via measurements or by sampling functions are essential in scientific computing. To date, a few quantum algorithms have been developed to perform calculus operations based on closed form functional inputs; yet, in many practical applications, field variables are numerically described via series of samples rather than closed form expressions. This paper presents the theoretical development and the gate-level circuit implementation of novel quantum algorithms for numerical differentiation and indefinite integration with a prescribed integration constant. The methodology relies on a spectral approach that leverages the computational efficiency of the quantum Fourier transform and the parallel computing capability afforded by quantum superposition to evaluate outputs at all domain points simultaneously. The differentiation approach is also extended to enable gradient estimation, and post-processing procedures are presented to recover sign information. The primary output of the proposed algorithms are quantum state vectors directly proportional to the numerical derivative or integral of the given data; therefore, the correctly signed results are made available to proceeding quantum computations. This result lays the foundation for the proposed algorithms to serve as core subroutines in applied quantum computing operations such as image processing, data analysis, and machine learning.

Establishing the nature of a quantum phase transition in finite-size simulations -- whether continuous, first-order, or weak first-order -- is a fundamental challenge in quantum many-body computation. Especially, the weak first-order phase transition is affected by a super large correlation length and always displays as a continuous critical point in simulated finite-sizes. The core difficulty lies in the fact that there is no effective finite-size theory to distinguish these phase transitions in the realistic simulations limited by the computational resource. In this work, we have fixed this problem by introducing a unified finite-size framework based on the Renyi thermal entropy (RTE) and its derivative (DRTE) to detect and characterize quantum phase transitions. We derive complete scaling theories for the RTE and DRTE at second-order, first-order, and weak first-order transitions, showing that the DRTE naturally isolates the singular part of the free energy and strengthens the characteristics of various phase transitions in finite sizes. Using quantum Monte Carlo simulations, we demonstrate accurate data collapse and extraction of critical exponents at (2+1)-dimensional O($N$) critical points. More importantly, the DRTE provides a smoking-gun signature of weak first-order transitions through a clear double-peak structure and a crossing at zero, which we unambiguously observe in debated deconfined quantum criticality candidates such as the $J$--$Q$ models. Our approach offers a general, unbiased, and numerically efficient tool for probing the universal properties of quantum phase transitions, resolving long-standing ambiguities between continuous and weak first-order scenarios.

Macroscopic coherence is an important feature of quantum many-body systems exhibiting collective behaviors, with examples ranging from atomic Bose-Einstein condensates, and quantum liquids to superconductors. Probing many-body coherence in a dynamically unstable regime, however, presents an intriguing and outstanding challenge in out-of-equilibrium quantum many-body physics. Here, we experimentally study the first- and second-order coherence of degenerate quasi-one-dimensional (1D) Bose gases quenched from repulsive to modulationally unstable attractive interaction regimes. The resulting dynamics, monitored by in-situ density and matter-wave interference imaging, reveals phase-coherent density wave evolutions, clearly distinguished from iconic soliton trains previously observed in attractive gases. This arises from the interplay between noise-amplified density modulations and dispersive shock waves of broad interest in nonlinear physics, plasmas, granular systems, and beyond. At longer times, the gases become phase-scrambled, exhibiting a finite correlation length. Interestingly, following an interaction quench back to the repulsive regime, we observe that quasi-long-range coherence can be spontaneously re-established. This captivating rephasing dynamics can be attributed to the nucleation and annihilation of density defects in the quasi-1D geometry. These results shed light on out-of-equilibrium phase coherence in quantum many-body systems in a regime where beyond mean-field effects may arise and theoretical approaches have not been well-established.

Electronic health records (EHR) are widely used to study clinical decisions, yet unmeasured confounding remains a persistent challenge. Proxy variables offer a potential solution. In EHR data, clinicians already record many such measurements (e.g., vitals), each revealing something about a patient's underlying health. Despite this, proxy-based methods are rarely used in practice. We introduce a new way to use proxies to adjust for unmeasured confounding. Our approach uses a vector of proxies to construct covariates that capture aspects of the unmeasured confounder, which are then included in a regression model. As one implementation, we use factor analysis followed by regression. We compare this approach with existing methods, including proximal causal inference, across a range of realistic settings. In practice, assumptions rarely hold exactly, so we study what happens when models are misspecified and variables are used incorrectly: e.g., a confounder or instrument is treated as a proxy. Finally, we apply the method to EHR data to estimate the effect of hospital admission for older adults presenting to the emergency department with chest pain, a setting where unmeasured confounding is a substantial concern. This work provides a practical way to use proxies and may help bring proxy-based methods into broader use.

Ground-state auxiliary-field quantum Monte Carlo (AFQMC) methods have become key numerical tools for studying quantum phases and phase transitions in interacting many-fermion systems. Despite the broad applicability, the efficiency of these algorithms is often limited by the bottleneck associated with the {\it local update} of the field configuration. In this work, we propose two novel update schemes, the {\it delayed update} and {\it block force-bias update}, both of which can generally and efficiently accelerate ground-state AFQMC simulations. The {\it delayed update}, with a predetermined delay rank, is an elegantly improved version of the {\it local update}, accelerating the process by replacing multiple vector-vector outer products in the latter with a single matrix-matrix multiplication. The {\it block force-bias update} is a block variant of the conventional force-bias update, which is a highly efficient scheme for dilute systems but suffers from the low acceptance ratio in lattice models. Our modified scheme maintains the high efficiency while offering flexible tuning of the acceptance ratio, controlled by the block size, for any desired fermion filling. We apply these two update schemes to both the standard and spin-orbit coupled two-dimensional Hubbard models, demonstrating their speedup over the {\it local update} with respect to the delay rank and block size. We also explore their efficiencies across varying system sizes and model parameters. Our results identify a speedup of $\sim$$8$ for systems with $\sim$$1600$ lattice sites. Furthermore, we have investigated the broader applications as well as an application diagram of these update schemes to general correlated fermion systems.

Modern cosmological surveys cover extremely large volumes and map fluctuations on scales reaching gigaparsecs. As a result, it is no longer a valid assumption to ignore cosmological evolution along the line of sight from one end of the survey to the other. When extracting cosmological information, the power spectrum becomes suboptimal because it relies on the assumption of translational invariance of the observed field. For example, during the Epoch of Reionization (EoR), the 21cm brightness temperature field on the nearby (low-redshift) end of a large survey volume exhibits statistical properties that differ significantly from those at far (high-redshift) end. To overcome these limitations, we have developed a eigen decomposition-inspired non-Fourier basis that captures evolution effects. Our work demonstrates that using this new basis integrated in a new summary statistic yields tighter constraints on astrophysical parameters compared to the traditional power spectrum. Additionally, we provide an illustrative example and a practical guide for applying this basis in the context of a realistic forecast for interferometers such as the Hydrogen Epoch of Reionization Array (HERA).

In observational causal inference, domain knowledge often leaves multiple covariate adjustments plausible, yet which sets satisfy ignorability is untestable. Different adjustment sets can yield conflicting estimates of the average treatment effect, and standard remedies (adjusting for their union or intersection, or reporting the union or convex hull of confidence intervals) can fail or produce intervals whose width does not vanish with sample size. We propose a specification-robust procedure that returns a single point estimate and a confidence interval that is valid as long as at least one candidate adjustment set is valid and has width shrinking at the parametric $n^{-1/2}$ rate. Our approach mirrors how trimming and overlap weighting handle overlap violations:~We shift the target to a reweighted population, closest in KL-divergence to the original population, for which credible, specification-robust inference is feasible. We also provide diagnostic plots to assess the population shift and an extension to protect any function of the covariates used for reweighting, similar to calipers in matching. Synthetic and real-data examples demonstrate that our procedure provides substantially tighter confidence intervals than the convex hull while maintaining nominal coverage.

We discuss the Lieb-Schultz-Mattis (LSM) theorem in two-dimensional spin systems with on-site ${\mathrm U}(1)\rtimes {\mathbb Z}_2$ spin rotation symmetry and point group $C_{2v}$ symmetry about a site. We ``twist" the point group symmetry by introducing a small uniform U(1) flux to obtain a projective symmetry, similarly to the familiar magnetic translation symmetry. The LSM theorem is proved in presence of the flux and then it is demonstrated that the theorem holds also for the flux-free system. Besides, the uniform flux enables us to show the LSM theorem for the time-reversal symmetry and the site-centered $C_2$-rotation symmetry.

In 2021, the City of Atlanta and Atlanta Police Foundation launched plans to build a large police training facility in the South River Forest in unincorporated DeKalb County, GA. Residents of Atlanta and DeKalb County, environmental activists, police and prison abolitionists, and other activists and concerned individuals formed the movement in opposition to the facility, known as the Stop Cop City / Defend the Atlanta Forest movement. Social media and digital maps became common tools for communicating information about the facility and the movement. Here, we examine online maps about the facility and the opposition movement, originating from grassroots organizations, the City of Atlanta, news media outlets, the Atlanta Police Foundation, and individuals. We gather and examine 32 publicly available maps collected through the Google Search API, Twitter (now X), Instagram and reddit. Using a framework of critical cartography, we conduct a content analysis of these maps to identify the mapping technologies and techniques (data, cartographic elements, styles) used by different stakeholders and roles that maps and mapping technologies can play in social movements. We examine the extent to which these maps provide data to confirm or contradict concerns raised by grassroots organizations and local residents about the facility. We find that stakeholders and mapmakers use geospatial tools in different ways and likely have varied access to mapping technologies. We argue that documenting the use of maps to communicate information about a contentious project can help enumerate community positions and perspectives, and we advocate for accessible mapmaking tools. We conclude by discussing the implications of accessibility of mapping technology and posting maps to social media, and share example map images that extend the geographic information systems (GIS) techniques seen in the retrieved maps.

This study introduces a pioneering machine learning (ML)-based approach for predicting the impact of nanoparticle (NP) carriers on the functionality of attached small biomolecules. It was hypothesised that NP interactions induce measurable perturbations in the atomic environment of the small biomolecules, which are reliably captured by chemical shifts in 13C and 1H NMR spectroscopy. Ten datasets were generated by combining 13C, 1H NMR spectroscopy data, derived from SMILES notations and molecular features provided by PubChem. The resulting datasets were used to train predictive models via traditional ML algorithms (Scikit-learn) and Deep Neural Network DNN (PyTorch). The methodology was demonstrated through a quantitative high-throughput screening (qHTS) focused on DNA Damage-Inducible Transcript 3 (CHOP) inhibitors. The optimal ML performance was achieved by the Random Forest Classifier, which was trained on 19,184 samples and tested on 4,000, resulting in 81.1% accuracy, 83.4% precision, 77.7% recall, 80.4% F1-score, 81.1% ROC, and a five-fold cross-validation score of 0.821. Complementing the main study, two computational approaches were developed to enhance CHOP inhibitor prediction. The first identifies the most desirable/undesirable functional groups for CHOP inhibition. The second, a CID_SID ML model, achieved 90.1% accuracy in predicting whether compounds designed for other purposes possess CHOP inhibition potential.

Our understanding of neural computation is founded on the assumption that neurons fire in response to a linear summation of inputs. Yet experiments demonstrate that some neurons are capable of complex functions that require interactions between inputs. Here we show, across multiple brain regions and species, that direct dependencies (without interactions between inputs) explain most of the variability in neuronal activity. Neurons are quantitatively described by models that capture the measured dependence on each input individually, but assume nothing about combinations of inputs. These minimal models, which are equivalent to logistic artificial neurons, predict complex higher-order dependencies and recover known features of synaptic connectivity. The inferred neural network is sparse, indicating a highly redundant neural code that is robust to perturbations. These results suggest that, despite intricate biophysical details, most neurons are described by simple artificial models.

We introduce an algebraic structure which encodes a collection of countable graphs through a set of states, generators and relations. These structures, which we call blueprints, can capture standard algebraic objects such as groups, monoids or small categories, as well as geometric tiling spaces with finite local complexity. We provide a general framework for symbolic dynamics on blueprints under a partial monoid action, and for transferring invariants of their symbolic dynamics through quasi-isometries. In particular, we show that the undecidability of the domino problem, the existence of strongly aperiodic subshifts of finite type, and the existence of subshifts of finite type without computable points are all quasi-isometry invariants for finitely presented blueprints. As an application of this model, we show that two variants of the domino problem for geometric tilings of $\mathbb{R}^d$ are undecidable for $d \geq 2$ on any underlying tiling space with finite local complexity.

Studies of electronic transport in width-restricted channels of PdCoO$_2$ have recently revealed a novel `directional ballistic' regime, in which ballistic propagation of electrons on an anisotropic Fermi surface breaks the symmetries of bulk transport. Here we introduce a magnetic field to this regime, in channels of PdCoO$_2$ and PtCoO$_2$ along two crystallographically distinct directions and over a wide range of widths. We observe magnetoresistance distinct from that in the bulk, with features strongly dependent on channel orientation and becoming more pronounced the narrower the channel. Comparison to semi-classical theory establishes that magnetoresistance arises from field-induced modification of boundary scattering, and helps connect features in the data with specific electronic trajectories. However, the role of bulk scattering in our measurements is yet to be fully understood. Our results demonstrate that finite-size magnetotransport is sensitive to the anisotropy of Fermi surface properties, motivating future work to fully understand and exploit this sensitivity.

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents' objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach.

We respond to Holst et al.'s critique that the decline in scientific disruptiveness documented in Park et al. (Nature, 2023) is an artifact of including works with zero backward citations. Using their advocated dataset, metric, and exclusion criteria, we find declines equivalent to major benchmark transformations in science. Their own regression model--designed to address their concerns about zero-citation works--yields large and significant declines for both papers and patents (p<0.001), a result found in their supplementary tables yet left unaddressed, despite directly contradicting their central claim. Their critique is further undermined by severe quality issues in their data, which contain three times more zero-citation works than ours. We trace this excess to their inclusion of at least 2.8 million editorials, obituaries, and comments, 1.5 million books and proceedings, and 254,000 product and artistic reviews--in all, 20% of their sample is non-research content that almost by definition lacks backward citations. Simple keyword searches confirm the problem's severity, identifying among others 456 For Dummies guides, 50 Dr. Seuss and Curious George books, and the Captain Underpants series--all zero-citation entries in their sample. Applying granular document type classification to their data reveals that such non-research content fell from 40% to 8% of their sample between 1945 and 2010--a shift sufficient to generate the decline in zero-citation prevalence they attribute to metadata errors in our study. Standard practice excludes such content to guard against the metadata quality concerns at the center of their critique--concerns their dataset exemplifies rather than addresses. Declining disruptiveness has been documented in nearly 100 studies across multiple databases, metrics, and non-citation-based measures. The weight of evidence does not support an artifact-based explanation.

Given a compact surface of revolution with Laplace-beltrami operator $Δ$, we consider the spectral projector $P_{λ,δ}$ on a polynomially narrow frequency interval $[λ-δ,λ+ δ]$, which is associated to the self-adjoint operator $\sqrt{-Δ}$. For a large class of surfaces of revolution, and after excluding small disks around the poles, we prove that the $L^2 \to L^{\infty}$ norm of $P_{λ,δ}$ is of order $λ^{\frac{1}{2}} δ^{\frac{1}{2}}$ up to $δ\geq λ^{-\frac{1}{32}}$. We adapt the microlocal approach introduced by Sogge for the case $δ= 1$, by using the Quantum Completely Integrable structure of surfaces of revolution introduced by Colin de Verdière. This reduces the analysis to a number of estimates of explicit oscillatory integrals, for which we introduce new quantitative tools.This is the first sharp result in the case $δ\ll 1$ beyond the case of locally symmetric surfaces (torus, sphere, arithmetic hyperbolic surfaces).

When dealing with a multi-objective optimization problem, obtaining a comprehensive representation of the set of Pareto optimal solutions can be computationally expensive. However, identifying the most representative solutions can be difficult and sometimes ambiguous, since what constitutes a representative solution depends on the decision maker's preferences. A popular selection are the so-called Pareto knee solutions, which correspond to nondominated points on the Pareto front where a small improvement in any objective leads to a large deterioration in at least one other objective. In this paper, using Pareto sensitivity, we show how to compute Pareto knee solutions according to their verbal (informal) definition of least maximal change. We refer to the resulting approach as the sensitivity knee (snee) approach, and we apply it to unconstrained and constrained problems. Pareto sensitivity can also be used to compute local most-changing Pareto sub-fronts around a nondominated point, where points on the sub-fronts are distributed along directions of maximum change. Our approach is still restricted to scalarized methods, in particular to the weighted-sum or epsilon-constrained methods, and requires the computation or approximations of first- and second-order derivatives. We include numerical results from synthetic problems that illustrate the benefits of our approach.

The article proposes a conceptual approach for evaluating the impact of engineered nanoparticles (NPs) on the functionality of small biomolecules. The developed machine learning (ML) model is based on in-silico 13C NMR spectroscopy chemical shifts derived by the SMILES notations on small biomolecules. The rationale behind this approach is that 13C NMR provide information about the atom environment of the carbon atoms. Thus, decomposing the small biomolecules into their fundamental 13C NMR spectral data, and performing classification based on the count and position of chemical peaks, establishes a baseline for evaluating the impact of NPs on the functionality of small biomolecules, even if the ML model is not based on nano data. The approach mitigates not only the scarcity of nano-bio data but also hold potential for building of NP`s portfolio by utilising data collected from various in vitro, in situ, in vivo, and organ-on-a-chip environments across multiple timeframes. Such a framework enables predictive modeling based on these multi-environmental datasets, facilitating a deeper understanding of NP behaviour. The methodology was demonstrated using data from bioassay focused on human dopamine D1 receptor antagonists provided by PubChem. The model was train with 26,766 samples and test on 5,466 samples, achieving Accuracy of 70.8%, Precision of 74.3%, recall of 63.6%, F1-score of 68.5% and ROC of 70.8% were achieved by the Support Vector classifier, with an Area Under the Curve (AUC) of 76% and Matthews Correlation Coefficient, MCC=0.4204. A secondary, non-NP-related ML model was developed to complement the study case. It uses PubChem compound and substance identifiers (CIDs and SIDs) to predict whether pre-designed small biomolecules have the potential to be human dopamine D1 receptor antagonists.

Modeling the spectral energy distribution (SED) of active galactic nuclei (AGN) plays a very important role in constraining modern cosmological simulations of galaxy formation. Here, we utilize an advanced supermassive black hole (SMBH) accretion disk model to compute the accretion flow structure and AGN SED across a wide range of black hole mass ($M_{\rm SMBH}$) and dimensionless accretion rates $\dot{m}(\equiv \dot{M}_{\rm acc}/\dot{M}_\mathrm{Edd})$, where $\dot{M}_{\rm acc}$ is the mass flow rate through the disk and $\dot{M}_\mathrm{Edd}$ is the Eddington mass accretion rate. We find that the radiative efficiency is mainly influenced by $\dot m$, while contributions of $M_{\rm SMBH}$ and $\dot{m}$ to the bolometric luminosity are comparably important. We have developed new scaling relationships that relate the bolometric luminosity of an AGN to its luminosities in the hard X-ray, soft X-ray, and optical bands. Our results align with existing literature at high luminosities but suggest lower luminosities in the hard and soft X-ray bands for AGNs with low bolometric luminosities than commonly reported values. Combining with the semi-analytical model of galaxy formation \textsc{L-Galaxies} and Millennium dark matter simulation for the distribution of ($M_{\rm SMBH}, \dot{m}$) at different redshift, we find the model predictions align well with observational data at redshifts below 1 but deviates for higher redshifts regarding AGN detection fraction and luminosity functions. This deviation may arise from improper treatment of SMBH growth at high redshifts in the model or bias from limited observational data. This AGN SED calculation can be readily applied in other cosmological simulations.

In this paper, we study the recurrence of Open Quantum Walks induced by finite-dimensional coins on the line ($\mathbb{Z}$) and on the grid ($\mathbb{Z}^2$). Two versions are considered: discrete-time open quantum walks (OQW) and continuous-time open quantum walks (CTOQW). We present three distinct recurrence criteria for OQWs on $\mathbb{Z}$, each adapted to different types of coins. The first criterion applies to coins whose auxiliary map has a unique invariant state, resulting in the first recurrence criterion for Lazy OQWs. The second one applies to Lazy OQWs of dimension 2, where we provide a complete characterization of the recurrence for this low-dimensional case. The third one presents a general criterion for finite-dimensional coins in the non-lazy case, which generalizes many of the previously known results for OQWs on $\mathbb{Z}$. Also, we present a general recurrence criterion for OQWs on $\mathbb{Z}^2$ via the open quantum jump chain, obtained from a recurrence criterion for CTOQWs on $\mathbb{Z}^2$.

Mechanical and elastic properties of materials are among the most fundamental quantities for many engineering and industrial applications. Here, we present a formulation that is efficient and accurate for calculating the elastic and bending rigidity tensors of crystalline solids, leveraging interatomic force constants and long-wavelength perturbation theory. Crucially, in the long-wavelength limit, lattice vibrations induce macroscopic electric fields which further couple with the propagation of elastic waves, and a separate treatment on the long-range electrostatic interactions is thereby required to obtain elastic properties under the appropriate electrical boundary conditions. A cluster expansion of the charge density response and dielectric screening function in the long-wavelength limit has been developed to efficiently extract multipole and dielectric tensors of arbitrarily high order. We implement the proposed method in a first-principles framework and perform extensive validations on silicon, NaCl, GaAs and rhombohedral BaTiO$_3$ as well as monolayer graphene, hexagonal BN, MoS$_2$ and InSe, obtaining good to excellent agreement with other theoretical approaches and experimental measurements. Notably, we establish that multipolar interactions up to at least octupoles are necessary to obtain the accurate short-circuit elastic tensor of bulk materials, while higher orders beyond octupole interactions are required to converge the bending rigidity tensor of 2D crystals. The present approach greatly simplifies the calculations of bending rigidities and will enable the automated characterization of the mechanical properties of novel functional materials.

Virtual-to-physical address translation is a critical performance bottleneck in paging-based virtual memory systems. The Translation Lookaside Buffer (TLB) accelerates address translation by caching frequently accessed mappings, but TLB misses lead to costly page walks. Hardware and software techniques address this challenge. Hardware approaches enhance TLB reach through system-level support, while software optimizations include TLB prefetching, replacement policies, superpages, and page size adjustments. Prefetching Page Table Entries (PTEs) for future accesses reduces bottlenecks but may incur overhead from incorrect predictions. Integrating an Agile TLB Prefetcher (ATP) with SBFP optimizes performance by leveraging page table locality and dynamically identifying essential free PTEs during page walks. Predictive replacement policies further improve TLB performance. Traditional LRU replacement is limited to near-instant references, while advanced policies like SRRIP, GHRP, SHiP, SDBP, and CHiRP enhance performance by targeting specific inefficiencies. CHiRP, tailored for L2 TLBs, surpasses other policies by leveraging control flow history to detect dead blocks, utilizing L2 TLB entries for learning instead of sampling. These integrated techniques collectively address key challenges in virtual memory management.

In this paper we address the question: How many pairwise non-isomorphic extremely amenable groups are there which are separable metrizable or even Polish? We show that there are continuum many such groups. In fact we construct continuum many pairwise non-isomorphic extremely amenable groups as automorphism groups of countable structures. We also consider this classification problem from the point of view of descriptive set theory by showing that the class of all extremely amenable closed subgroups of $S_\infty$ is Borel and their isomorphism relation is more complex than any isomorphism relation of countable structures in the Borel reducibility hierarchy.

Systems driven far from equilibrium may exhibit anomalous density fluctuations: active matter with orientational order display giant density fluctuations at large scale, while systems of interacting particles close to an absorbing phase transition may exhibit hyperuniformity, suppressing large-scale density fluctuations. We show that these seemingly incompatible phenomena can coexist in nematically ordered active systems, provided activity is conditioned to particle contacts. We characterize this unusual state of matter and unravel the underlying mechanisms simultaneously leading to spatially enhanced (on large length scales) and suppressed (on intermediate length scales) density fluctuations. Our work highlights the potential for a rich phenomenology in active matter systems in which particles' activity is triggered by their local environment, and calls for a more systematic exploration of absorbing phase transitions in orientationally-ordered particle systems.

We review the basic features of a logarithmic conformal field theory that arise in the context of the scaling limit of lattice models. The theory of interest is the symplectic fermions, whose central charge is $-2$. We provide an explicit construction of its space of fields as a logarithmic Fock space, and discuss its logarithmic structure as a representation of the Virasoro algebra. The construction of the correlation functions is presented following the ideas of the bootstrap approach. The text aims to be accessible to readers with little or no expertise in conformal field theory.

Systems with P2$_{1}$3 symmetry are characterized by the realization of chiral edge modes, propagating in one direction along closed loops around some high symmetry points of the Brillouin zone. We study the phononic and electronic properties of EuPtSi, which crystallizes with P2$_{1}$3 symmetry. EuPtSi is also characterized by intriguing magnetic properties, such as the realization of the skyrmion lattice. Here, using ab initio techniques, we study bulk and slab properties of EuPtSi. The bulk phononic and electronic band structures exhibit a spin-1 Weyl point and a charge-2 Dirac point at the $Γ$ and R points, respectively. Consequently, the surface states exhibit chiral edge modes. Such features are present in both the phononic and electronic surface spectra. The chiral phononic edge mode is associated with the vibration of the atoms in close vicinity, while the chiral electronic surface states correspond to carrier accumulation at the edge of chiral atomic chains.

X-ray binaries (XRBs) often exhibit spectral residuals in the 0.5 to 2 keV range, known as the ``1 keV residual/1 keV feature", with variable centroid and intensity across different systems. Yet a comprehensive scientific explanation of the variability of the 1 keV feature has remained largely elusive. In this paper, we explain for the first time the origin and variability of the 1 keV feature in XRBs using the spectral synthesis code \textsc{Cloudy}. We constructed line blends for the emission and absorption lines and study the variability of these blends with ionization parameters, temperature, and column density. We conducted a sample study involving five XRBs including two ultraluminous X-ray sources (ULXs): NGC 247 ULX-1, NGC 1313 X-1, a binary X-ray pulsar : Hercules X-1, and two typical low-mass X-ray binaries (LMXBs): Cygnus X-2, and Serpens X-1. Our analysis establishes a self-consistent framework explaining the variability of the 1 keV spectral feature, attributing its diversity to differences in spectral energy distribution, ionization parameter, temperature, column density, and disk reflection properties. This framework provides a comprehensive explanation for the observed 1 keV feature across these diverse XRB systems, offering insights into the underlying physical mechanisms at play.

This paper studies a loss-averse version of the multiplicative habit formation preference and the corresponding optimal investment and consumption strategies over an infinite horizon. The agent's consumption preference is depicted by a general S-shaped utility function of her consumption-to-habit ratio. By considering the concave envelope of the S-shaped utility and the associated dual value function, we provide a thorough analysis of the HJB equation for the concavified problem via studying a related nonlinear free boundary problem. Based on established properties of the solution to this free boundary problem, we obtain the optimal consumption and investment policies in feedback form. Some new and technical verification arguments are developed to cope with generality of the utility function. The equivalence between the original problem and the concavified problem readily follows from the structure of the feedback controls. We also discuss some quantitative properties of the optimal policies, complemented by illustrative numerical examples and their financial implications.

A fast-paced policy context is characteristic of energy and climate research, which strives to develop solutions to wicked problems such as climate change. Funding agencies in the European Union recognize the importance of linking research and policy in climate and energy research. This calls for an increased understanding of how stakeholder engagement can effectively be used to co-design research questions that include stakeholders' concerns. This paper reviews the current literature on stakeholder engagement, from which we create a set of criteria. These are used to critically assess recent and relevant papers on stakeholder engagement in climate and energy projects. We obtained the papers from a scoping review of stakeholder engagement through workshops in EU climate and energy research. With insights from the literature and current EU climate and energy projects, we developed a workshop programme for stakeholder engagement. This programme was applied to the European Climate and Energy Modelling Forum project, aiming to co-design the most pressing and urgent research questions according to European stakeholders. The outcomes include 82 co-designed and ranked research questions for nine specific climate and energy research themes. Findings from the scoping review indicate that papers rarely define the term 'stakeholder'. Additionally, the concepts of co-creation, co-design, and co-production are used interchangeably and often without definition. We propose that workshop planners use stakeholder identification and selection methods from the broader stakeholder engagement literature.

In this paper, we consider a six parameter family of affine Segre surfaces embedded in $\mathbb C^6$. For generic values of the parameters, this family is associated to the $q$-difference sixth Painlevé equation. We show that different limiting forms of this family give Segre surfaces that are isomorphic as affine varieties to the the monodromy manifolds of each Painlevé differential equation.