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This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

Purpose: Urban school districts often face challenges in filling principal vacancies with effective leaders, especially in high-needs schools. Prospective principals' engagement with the job application process may contribute to these challenges. The goal of this study is to better understand the job search strategies and behaviors of prospective principals and how their approaches might contribute to leadership staffing challenges in high-needs schools. Research Design and Methods: We employ a convergent mixed-methods design that draws on data from two urban school systems. We pair analysis of interviews of 36 principals who have recently navigated the districts' hiring systems with multiple years of applications and other administrative data provided by the two districts. We explored how patterns and themes that emerged from each data source were confirmed or disconfirmed with the other source. Findings: Guided by a job-search model, our analysis uncovers three main findings. First, the typical principal applicant conducted a targeted rather than a wide search, reflecting multiple strategies, preferences, and relational factors. Second, elementary educators showed a strong propensity to apply to the same grade level. Third, leaders applied to schools serving larger proportions of historically marginalized students at similar rates as other schools, reflecting their motivations to work with underserved students. Implications for Research and Practice: Considerations informing prospective principals' job searches are multifaceted. High-needs schools are desirable to many principal candidates. Identifying and strategically recruiting candidates with preferences for working in such schools can be a strategy for districts seeking to overcome challenges in filling principal vacancies.

Tempe PRE was designed as a high-quality preschool program to create more opportunity for children in Tempe. In the 2021-2022 school year, NORC at the University of Chicago conducted an evaluation of Tempe PRE to understand how the program was being implemented and its effects on children and families. They also collected students' outcome data the following year once they entered kindergarten to measure impacts on achievement compared to a control group. This evaluation documented the successful early installation of a high-quality preschool program that was designed to provide novel access to children from low-income households who would not otherwise have had such an opportunity in their community. Through close collaboration among multiple local government and philanthropic partners, and with a strong, common commitment to quality, the Tempe PRE program was structured and implemented with key components hypothesized to produce positive child outcomes.

Two-dimensional twisted bilayer moir\'e structures provide a versatile material platform for realizing a rich variety of strongly correlated electronic quantum phases intricately coupled with the periodically modulated lattice structures. In this work, we use ultrafast electron diffraction to directly reveal the photoinduced dynamic evolution of the moir\'e superlattice in $2^\circ$ and $57^\circ$ twisted WSe$_2$/MoSe$_2$ heterobilayers. Upon above-band-gap photoexcitation, the moir\'e superlattice diffraction features are enhanced within 1 ps and subsequently suppressed several picoseconds after, accompanied by a collective lattice excitation of a moir\'e phonon mode with sub-THz frequency. This unique response deviates markedly from typical photoinduced lattice heating, and suggests dynamic twisting and untwisting of the local moir\'e chiral structure. We infer large oscillations in the local twist angle, approaching $1^\circ$ peak to trough, that are driven by ultrafast charge carrier excitation and relaxation -- a phenomenon further supported by molecular dynamics simulations. Our findings suggest a novel approach for real-time dynamic reconfiguration of moire superlattices to achieve ultrafast modulation of their strongly correlated behaviors.

Advances in machine learning and the increasing availability of high-dimensional data have led to the proliferation of social science research that uses the predictions of machine learning models as proxies for measures of human activity or environmental outcomes. However, prediction errors from machine learning models can lead to bias in the estimates of regression coefficients. In this paper, we show how this bias can arise, propose a test for detecting bias, and demonstrate the use of an adversarial machine learning algorithm in order to de-bias predictions. These methods are applicable to any setting where machine-learned predictions are the dependent variable in a regression. We conduct simulations and empirical exercises using ground truth and satellite data on forest cover in Africa. Using the predictions from a naive machine learning model leads to biased parameter estimates, while the predictions from the adversarial model recover the true coefficients., Comment: 12 pages, 8 figures

We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a challenging yet practical setting in real-world applications. Our solution features a cascaded learning paradigm with camera pose serving as the critical bridge, recognizing its essential role in mapping 3D structures onto 2D image planes. Concretely, FLARE starts with camera pose estimation, whose results condition the subsequent learning of geometric structure and appearance, optimized through the objectives of geometry reconstruction and novel-view synthesis. Utilizing large-scale public datasets for training, our method delivers state-of-the-art performance in the tasks of pose estimation, geometry reconstruction, and novel view synthesis, while maintaining the inference efficiency (i.e., less than 0.5 seconds). The project page and code can be found at: https://zhanghe3z.github.io/FLARE/, Comment: 8 pages. Website: https://zhanghe3z.github.io/FLARE/

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being., Comment: 76 pages, 2 figures

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder.

We introduce ReStyle3D, a novel framework for scene-level appearance transfer from a single style image to a real-world scene represented by multiple views. The method combines explicit semantic correspondences with multi-view consistency to achieve precise and coherent stylization. Unlike conventional stylization methods that apply a reference style globally, ReStyle3D uses open-vocabulary segmentation to establish dense, instance-level correspondences between the style and real-world images. This ensures that each object is stylized with semantically matched textures. It first transfers the style to a single view using a training-free semantic-attention mechanism in a diffusion model. It then lifts the stylization to additional views via a learned warp-and-refine network guided by monocular depth and pixel-wise correspondences. Experiments show that ReStyle3D consistently outperforms prior methods in structure preservation, perceptual style similarity, and multi-view coherence. User studies further validate its ability to produce photo-realistic, semantically faithful results. Our code, pretrained models, and dataset will be publicly released, to support new applications in interior design, virtual staging, and 3D-consistent stylization., Comment: Project page: https://restyle3d.github.io/

Deep subwavelength RF imaging with atomic Rydberg sensors has overcome fundamental limitations of traditional antennas and enabled ultra-wideband detection of omni-directional time varying fields all in a compact form factor. However, in most applications, Rydberg sensors require the use of a secondary strong RF reference field to serve as a phase reference. Here, we demonstrate a new type of Rydberg sensor for angle-of-arrival (AoA) sensing which utilizes subwavelength imaging of standing wave fields. By placing a metallic plate within the Rydberg cell, we can determine the AoA independent of the strength of incoming RF field and without requiring a secondary strong RF phase reference field. We perform precision AoA measurements with a robotic antenna positioning system for 4.2, 5.0, and 5.7 GHz signals and demonstrate a 1.7 deg polar angular resolution from 0 deg to 60 deg AoA and 4.1 deg over all possible angles.

The urgent energy transition requirements towards a sustainable future stretch across various industries and are a significant challenge facing humanity. Hydrogen promises a clean, carbon-free future, with the opportunity to integrate with existing solutions in the transportation sector. However, adding hydrogen to existing technologies such as diesel engines requires additional modeling effort. Reinforcement Learning (RL) enables interactive data-driven learning that eliminates the need for mathematical modeling. The algorithms, however, may not be real-time capable and need large amounts of data to work in practice. This paper presents a novel approach which uses offline model learning with RL to demonstrate safe control of a 4.5 L Hydrogen Diesel Dual-Fuel (H2DF) engine. The controllers are demonstrated to be constraint compliant and can leverage a novel state-augmentation approach for sample-efficient learning. The offline policy is subsequently experimentally validated on the real engine where the control algorithm is executed on a Raspberry Pi controller and requires 6 times less computation time compared to online Model Predictive Control (MPC) optimization., Comment: This work has been submitted to IFAC for possible publication

In this paper, we present a self-calibrating framework that jointly optimizes camera parameters, lens distortion and 3D Gaussian representations, enabling accurate and efficient scene reconstruction. In particular, our technique enables high-quality scene reconstruction from Large field-of-view (FOV) imagery taken with wide-angle lenses, allowing the scene to be modeled from a smaller number of images. Our approach introduces a novel method for modeling complex lens distortions using a hybrid network that combines invertible residual networks with explicit grids. This design effectively regularizes the optimization process, achieving greater accuracy than conventional camera models. Additionally, we propose a cubemap-based resampling strategy to support large FOV images without sacrificing resolution or introducing distortion artifacts. Our method is compatible with the fast rasterization of Gaussian Splatting, adaptable to a wide variety of camera lens distortion, and demonstrates state-of-the-art performance on both synthetic and real-world datasets., Comment: Project Page: https://denghilbert.github.io/self-cali/

Does the intrinsic curvature of complex networks hold the key to unveiling graph anomalies that conventional approaches overlook? Reconstruction-based graph anomaly detection (GAD) methods overlook such geometric outliers, focusing only on structural and attribute-level anomalies. To this end, we propose CurvGAD - a mixed-curvature graph autoencoder that introduces the notion of curvature-based geometric anomalies. CurvGAD introduces two parallel pipelines for enhanced anomaly interpretability: (1) Curvature-equivariant geometry reconstruction, which focuses exclusively on reconstructing the edge curvatures using a mixed-curvature, Riemannian encoder and Gaussian kernel-based decoder; and (2) Curvature-invariant structure and attribute reconstruction, which decouples structural and attribute anomalies from geometric irregularities by regularizing graph curvature under discrete Ollivier-Ricci flow, thereby isolating the non-geometric anomalies. By leveraging curvature, CurvGAD refines the existing anomaly classifications and identifies new curvature-driven anomalies. Extensive experimentation over 10 real-world datasets (both homophilic and heterophilic) demonstrates an improvement of up to 6.5% over state-of-the-art GAD methods.

Actor systems are a flexible model of concurrent and distributed programming, which are efficiently implementable, and avoid many classic concurrency bugs by construction. However actor systems must still deal with the challenge of messages arriving in unexpected orderings. We describe an approach to restricting the orders in which actors send messages to each other, by equipping actor references -- the handle used to address another actor -- with a protocol restricting which message types can be sent to another actor and in which order using that particular actor reference. This endows the actor references with the properties of static (flow-sensitive) capabilities, which we call actor capabilities. By sending other actors only restricted actor references, they may control which messages are sent in which orders by other actors. Rules for duplicating (splitting) actor references ensure that these restrictions apply even in the presence of delegation. The capabilities themselves restrict message ordering, which may form the foundation for stronger forms of reasoning. We demonstrate this by layering an effect system over the base type system, where the relationships enforced between the actor capabilities and the effects of an actor's behaviour ensure that an actor's behaviour is always prepared to handle any message that may arrive., Comment: Extended version (with proof sketches) of a draft chapter for Gul Agha's festschrift

Wereportthefirstmulti-epochmilliarcsecondresolutionimagingofthe6.7-GHzclassIImethanolmaseremissionassociated with the high-mass protocluster system NGC6334I. The observations cover 4 epochs over a 10-year period between March 2010 and March 2020. We confirm the emergence of a number of new regions of 6.7-GHz methanol maser emission in the molecular gas surrounding NGC6334-MM1, which lies north of the previously known class II methanol maser sites which are associated with NGC6334-MM3 and -MM2. The new maser emission is located close to the strongest (sub)millimetre source in the NGC6334I cluster MM1B which experienced a sudden increase in intensity in 2015, produced by an episodic accretion event. We are able to compare the location and intensity of the 6.7-GHz methanol maser emission before, during, and after the flare, providing new insights into the relationship between maser flares and periodic accretion events in high-mass stars.

A major open issue concerning the active Sun is the effectiveness with which magnetic reconnection accelerates electrons in flares. A paper published by {\em{Nature}} in 2022 used microwave observations to conclude that the Sun is an almost ideal accelerator, energizing nearly all electrons within a coronal volume to nonthermal energies. Shortly thereafter, a paper published in {\em{Astrophysical Journal Letters}} used hard X-ray measurements \emph{of the same event} to reach the contradictory conclusion that less than 1\% of the available electrons were accelerated. Here we address this controversy by using spatially resolved observations of hard X-ray emission and a spectral inversion method to determine the evolution of the electron spectrum throughout the flare. So we estimated the density of the medium where electrons accelerate and, from this, the ratio of accelerated to ambient electron densities. Results show that this ratio never exceeds a percent or so in the cases analyzed.

Diffuse atomic orbital basis sets have proven to be essential to obtain accurate interaction energies, especially in regard to non-covalent interactions. However, they also have a detrimental impact on the sparsity of the one-particle density matrix (1-PDM), to a degree stronger than the spatial extent of the basis functions alone could explain. This is despite the fact that the matrix elements of the 1-PDM of insulators (systems with significant HOMO-LUMO gaps) are expected to decay exponentially with increasing real-space distance from the diagonal and the asymptotic decay rate is expected to have a well-defined basis set limit. The observed low sparsity of the 1-PDM appears to be independent of representation and even persists after projecting the 1-PDM onto a real-space grid, leading to the conclusion that this "curse of sparsity" is solely a basis set artifact, which, counterintuitively, becomes worse for larger basis sets, seemingly contradicting the notion of a well-defined basis set limit. We show that this is a consequence of the low locality of the contra-variant basis functions as quantified by the inverse overlap matrix $\mathbf{S}^{-1}$ being significantly less sparse than its covariant dual. Introducing the model system of an infinite non-interacting chain of helium atoms, we are able to quantify the exponential decay rate to be proportional to the diffuseness as well as local incompleteness of the basis set, meaning small and diffuse basis sets are affected the most. Finally, we propose one solution to the conundrum in the form of the complementary auxiliary basis set (CABS) singles correction in combination with compact, low l-quantum-number basis sets, showing promising results for non-covalent interactions., Comment: 27 pages, 5 figures, 3 tables

It has previously been found that the galaxy cluster environment can affect the fueling and evolution of Active Galactic Nuclei (AGN). This work examines the effect of the merging cluster environment on the properties of radio-AGN by comparing the radio morphology of cluster members in a sample of four merging and eight relaxed galaxy clusters at low redshift (z<0.2). Using 144-MHz data from the LOFAR Two-metre Sky Survey (LoTSS) and Zooniverse, we classify the radio morphology of the radio-detected cluster members using the following morphology classes: compact, compact extended, extended, jetted, and disturbed. We find that the merging cluster environment has a statistically significant, higher population proportion of disturbed (bent and head tail) sources, indicating that the merging environment can affect the morphology of cluster radio-AGN. We also investigate the number of AGN that are detected in the radio data only, and the number that are detected in both the radio and optical data in mergers and non-mergers. We find that the merging cluster environment has a higher population proportion of AGN that are identified only as radio-AGN compared to AGN that are identified as both radio and optical AGN. Overall, we find that the merging environment affects certain radio-AGN (disturbed and only radio identified AGN), but not all., Comment: Accepted for publication in the Astrophysical Journal. Table 4 is included as FITS and CSV as an ancillary file and is named `RickelMoravec+25_table4`; corresponding README is also provided. Rickel and Moravec are co-first authors. 20 pages and 3 figures

Writing good software tests can be challenging, therefore approaches that support developers are desirable. While generating complete tests automatically is such an approach commonly proposed in research, developers may already have specific test scenarios in mind and thus just require help in selecting the most suitable test assertions for these scenarios. This can be done using deep learning models to predict assertions for given test code. Prior research on assertion generation trained these models specifically for the task, raising the question how much the use of larger models pre-trained on code that have emerged since then can improve their performance. In particular, while abstracting identifiers has been shown to improve specifically trained models, it remains unclear whether this also generalises to models pre-trained on non-abstracted code. Finally, even though prior work demonstrated high accuracy it remains unclear how this translates into the effectiveness of the assertions at their intended application -- finding faults. To shed light on these open questions, in this paper we propose AsserT5, a new model based on the pre-trained CodeT5 model, and use this to empirically study assertion generation. We find that the abstraction and the inclusion of the focal method are useful also for a fine-tuned pre-trained model, resulting in test assertions that match the ground truth assertions precisely in up to 59.5\% of cases, more than twice as precise as prior models. However, evaluation on real bugs from the Defects4J dataset shows that out of 138 bugs detectable with assertions in real-world projects, AsserT5 was only able to suggest fault-finding assertions for 33, indicating the need for further improvements., Comment: Accepted for AST 2025 (https://conf.researchr.org/home/ast-2025)

Large language models (LLMs) perform remarkably well on tabular datasets in zero- and few-shot settings, since they can extract meaning from natural language column headers that describe features and labels. Similarly, TabPFN, a recent non-LLM transformer pretrained on numerous tables for in-context learning, has demonstrated excellent performance for dataset sizes up to a thousand samples. In contrast, gradient-boosted decision trees (GBDTs) are typically trained from scratch on each dataset without benefiting from pretraining data and must learn the relationships between columns from their entries alone since they lack natural language understanding. LLMs and TabPFN excel on small tabular datasets where a strong prior is essential, yet they are not competitive with GBDTs on medium or large datasets, since their context lengths are limited. In this paper, we propose a simple and lightweight approach for fusing large language models and TabPFN with gradient-boosted decision trees, which allows scalable GBDTs to benefit from the natural language capabilities and pretraining of transformers. We name our fusion methods LLM-Boost and PFN-Boost, respectively. While matching or surpassing the performance of the transformer at sufficiently small dataset sizes and GBDTs at sufficiently large sizes, LLM-Boost and PFN-Boost outperform both standalone components on a wide range of dataset sizes in between. We demonstrate state-of-the-art performance against numerous baselines and ensembling algorithms. We find that PFN-Boost achieves the best average performance among all methods we test for all but very small dataset sizes. We release our code at http://github.com/MayukaJ/LLM-Boost ., Comment: 12 pages, 6 figures

The thermal memory effect, TME, has been studied in Ni55Fe19Ga26 shape memory alloys, fabricated as ribbons via melt-spinning and as pellets via arc-melting, to evaluate its dependence on the martensitic structure and the macrostructure of the samples. When the reverse martensitic transformation is interrupted, a kinetic delay in the subsequent complete transformation is only evident in the ribbon samples, where the 14M modulated structure is the dominant phase. In contrast, degradation of the modulated structure or the presence of the gamma-phase significantly reduces the observed TME. In such cases, the magnitude of the TME approaches the detection limits of commercial calorimeters, and only high-resolution calorimeter at very low heating rate (40 mK h-1) can show the effect. Following the kinetic arrest and subsequent cooling, the reverse martensitic transformation was completed at several heating rates to confirm the athermal nature of the phenomenon., Comment: 30 pages (double spaced) 12 figures

Over the past decade, artificial intelligence (AI) has led to disruptive advancements in fundamental sciences and everyday technologies. Among various machine learning algorithms, deep neural networks have become instrumental in revealing complex patterns in large datasets with key applications in computer vision, natural language processing, and predictive analytics. On-chip photonic neural networks offer a promising platform that leverage high bandwidths and low propagation losses associated with optical signals to perform analog computations for deep learning. However, nanophotonic circuits are yet to achieve the required linear and nonlinear operations simultaneously in an all-optical and ultrafast fashion. Here, we report an ultrafast nanophotonic neuromorphic processor using an optical parametric oscillator (OPO) fabricated on thin-film lithium niobate (TFLN). The input data is used to modulate the optical pulses synchronously pumping the OPO. The consequent signal pulses generated by the OPO are coupled to one another via the nonlinear delayed dynamics of the OPO, thus forming the internal nodes of a deep recurrent neural network. We use such a nonlinearly coupled OPO network for chaotic time series prediction, nonlinear error correction in a noisy communication channel, as well as noisy waveform classification and achieve accuracies exceeding 93% at an operating clock rate of ~ 10 GHz. Our OPO network is capable of achieving sub-nanosecond latencies, a timescale comparable to a single clock cycle in state-of-the-art digital electronic processors. By circumventing the need for optical-electronic-optical (OEO) conversions, our ultrafast nanophotonic neural network paves the way for the next generation of compact all-optical neuromorphic processors with ultralow latencies and high energy efficiencies.

Diffusion models have demonstrated remarkable success in image generation and editing, with recent advancements enabling albedo-preserving image relighting. However, applying these models to video relighting remains challenging due to the lack of paired video relighting datasets and the high demands for output fidelity and temporal consistency, further complicated by the inherent randomness of diffusion models. To address these challenges, we introduce RelightVid, a flexible framework for video relighting that can accept background video, text prompts, or environment maps as relighting conditions. Trained on in-the-wild videos with carefully designed illumination augmentations and rendered videos under extreme dynamic lighting, RelightVid achieves arbitrary video relighting with high temporal consistency without intrinsic decomposition while preserving the illumination priors of its image backbone.

Reinforcement learning (RL) has demonstrated success in automating insulin dosing in simulated type 1 diabetes (T1D) patients but is currently unable to incorporate patient expertise and preference. This work introduces PAINT (Preference Adaptation for INsulin control in T1D), an original RL framework for learning flexible insulin dosing policies from patient records. PAINT employs a sketch-based approach for reward learning, where past data is annotated with a continuous reward signal to reflect patient's desired outcomes. Labelled data trains a reward model, informing the actions of a novel safety-constrained offline RL algorithm, designed to restrict actions to a safe strategy and enable preference tuning via a sliding scale. In-silico evaluation shows PAINT achieves common glucose goals through simple labelling of desired states, reducing glycaemic risk by 15% over a commercial benchmark. Action labelling can also be used to incorporate patient expertise, demonstrating an ability to pre-empt meals (+10% time-in-range post-meal) and address certain device errors (-1.6% variance post-error) with patient guidance. These results hold under realistic conditions, including limited samples, labelling errors, and intra-patient variability. This work illustrates PAINT's potential in real-world T1D management and more broadly any tasks requiring rapid and precise preference learning under safety constraints., Comment: 11 pages, 5 figures

When selecting a model from a set of equally performant models, how much unfairness can you really reduce? Is it important to be intentional about fairness when choosing among this set, or is arbitrarily choosing among the set of ''good'' models good enough? Recent work has highlighted that the phenomenon of model multiplicity-where multiple models with nearly identical predictive accuracy exist for the same task-has both positive and negative implications for fairness, from strengthening the enforcement of civil rights law in AI systems to showcasing arbitrariness in AI decision-making. Despite the enormous implications of model multiplicity, there is little work that explores the properties of sets of equally accurate models, or Rashomon sets, in general. In this paper, we present five main theoretical and methodological contributions which help us to understand the relatively unexplored properties of the Rashomon set, in particular with regards to fairness. Our contributions include methods for efficiently sampling models from this set and techniques for identifying the fairest models according to key fairness metrics such as statistical parity. We also derive the probability that an individual's prediction will be flipped within the Rashomon set, as well as expressions for the set's size and the distribution of error tolerance used across models. These results lead to policy-relevant takeaways, such as the importance of intentionally looking for fair models within the Rashomon set, and understanding which individuals or groups may be more susceptible to arbitrary decisions., Comment: 34 pages

We use a two sorted equational theory of algebraic effects to model concurrent shared state with preemptive interleaving, recovering Brookes's seminal 1996 trace-based model precisely. The decomposition allows us to analyse Brookes's model algebraically in terms of separate but interacting components. The multiple sorts partition terms into layers. We use two sorts: a "hold" sort for layers that disallow interleaving of environment memory accesses, analogous to holding a global lock on the memory; and a "cede" sort for the opposite. The algebraic signature comprises of independent interlocking components: two new operators that switch between these sorts, delimiting the atomic layers, thought of as acquiring and releasing the global lock; non-deterministic choice; and state-accessing operators. The axioms similarly divide cleanly: the delimiters behave as a closure pair; all operators are strict, and distribute over non-empty non-deterministic choice; and non-deterministic global state obeys Plotkin and Power's presentation of global state. Our representation theorem expresses the free algebras over a two-sorted family of variables as sets of traces with suitable closure conditions. When the held sort has no variables, we recover Brookes's trace semantics., Comment: to be published in the conference proceedings of Foundations of Software Science and Computation Structures (FoSSaCS) 2025

An $(m,n,k,\lambda)$-relative difference set is a lifting of a $(m,k,n\lambda)$-difference set. Lam gave a table of cyclic relative difference sets with $k \leq 50$ in 1977, all of which were liftings of $( \frac{q^d-1}{q-1},q^{d-1},q^{d-2}(q-1))$-difference sets, the parameters of complements of classical Singer difference sets. Pott found all cyclic liftings of these difference sets with $n$ odd and $k \leq 64$ in 1995. No other nontrivial difference sets are known with liftings to relative difference sets, and Pott ended his survey on relative difference sets asking whether there are any others. In this paper we extend these searches, and apply the results to the existence of circulant weighing matrices., Comment: 17 pages

To safeguard against data fabrication and enhance trust in quantitative social science, we present Data Non-Manipulation Authentication Digest (Data-NoMAD). Data-NoMAD is a tool that allows researchers to certify, and others to verify, that a dataset has not been inappropriately manipulated between the point of data collection and the point at which a replication archive is made publicly available. Data-NoMAD creates and stores a column hash digest of a raw dataset upon initial download from a survey platform (the current version works with Qualtrics and SurveyCTO), but before it is subject to appropriate manipulations such as anonymity-preserving redactions. Data-NoMAD can later be used to verify the integrity of a publicly archived dataset by identifying columns that have been deleted, added, or altered. Data-NoMAD complements existing efforts at ensuring research integrity and integrates seamlessly with extant replication practices., Comment: 18 pages, 2 figures

We analyze 23 spectroscopically confirmed Type-2 quasars (QSOs) selected from the WISE 22$\rm \mu$m band in the SDSS Stripe 82 region, focusing on their multi-band photometry and spectral energy distributions (SEDs). These objects were selected to be IR-luminous ($\rm flux_{W4} > 5mJy$, i.e., $12.62 < W4 < 14.62 \rm\ AB \, magnitude$), optically faint ($r > 23$) or with red color ($r - W4 >8.38$). Gemini/GNIRS observations were conducted for all 24 candidates, and 18/24 were also observed with Keck/LRIS. The observations confirm 23 to be real Type-2 QSOs in the redshift range $0.88 - 2.99$ (12 are at $z>2$). We collect multi-band photometry and conduct SED fitting. The composite photometry probes the wavelength from 0.1$\rm \mu$m to 10$\rm \mu$m at the rest frame. The IR emission is dominated by dust torus implying an average torus luminosity for the sample of $L_{\rm torus} 10^{46.84} \rm erg/s$. The origin of the rest-UV/optical light is not definitive, but we present three possible scenarios: scattered light, stellar emission, and the reddened accretion disk. Assuming an obscured:unobscured ratio of approximately 1:1, our targets have $L_{\rm bol} = 10^{46.28} \rm erg \,s^{-1} - 10^{47.49} \rm erg \,s^{-1}$ and around SMBH masses $\rm 10^{8.18} M_{\odot} - 10^{9.39} M_{\odot}$, assuming they accreate at the Eddington limit. Compared to previous Type-2 AGN SEDs, our targets have a brighter dust torus and redder optical-IR color. By comparing the SED to the results from JWST `little red dots' (LRDs), we find that these IR-selected Type-2 QSOs have similar SED shapes to the LRDs. This pilot Type-2 QSO survey demonstrates that mid-IR selection is an efficient way to find luminous Type-2 QSOs at $z>2$. Finally, the composite photometry and Type-2 QSOs SED model generated by this sample provide a guide for finding more Type-2 QSOs at higher redshift.

We revisit the global linear theory of the vertical shear instability (VSI) in protoplanetary discs with an imposed radial temperature gradient. We focus on the regime in which the VSI has the form of a travelling inertial wave that grows in amplitude as it propagates outwards. Building on previous work describing travelling waves in thin astrophysical discs, we develop a quantitative theory of the wave motion, its spatial structure and the physical mechanism by which the wave is amplified. We find that this viewpoint provides a useful description of the large-scale development of the VSI in global numerical simulations, which involves corrugation and breathing motions of the disc. We contrast this behaviour with that of perturbations of smaller scale, in which the VSI grows into a nonlinear regime in place without significant radial propagation., Comment: 17 pages, 5 figures. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review

Head-related transfer functions (HRTFs) with dense spatial grids are desired for immersive binaural audio generation, but their recording is time-consuming. Although HRTF spatial upsampling has shown remarkable progress with neural fields, spatial upsampling only from a few measured directions, e.g., 3 or 5 measurements, is still challenging. To tackle this problem, we propose a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject from a dataset. The HRTF of the retrieved subject at the desired direction is fed into the neural field in addition to the sound source direction itself. Furthermore, we present a neural network that can efficiently handle multiple retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate. Our experiments confirm the benefits of RANF on the SONICOM dataset, and it is a key component in the winning solution of Task 2 of the listener acoustic personalization challenge 2024., Comment: Accepted to ICASSP 2025

Context. The UVJ color-color diagram is a widely used diagnostic to separate star-forming and quiescent galaxies. Observational data from photometric surveys reveal a strong stellar mass trend, with higher-mass star-forming galaxies being systematically more dust-reddened. Aims. We analyze the UVJ diagram in the TNG100 cosmological simulation at cosmic noon ($z\approx2$). Specifically, we focus on the trend between UVJ colors and mass which has not been reproduced in any cosmological simulation thus far. Methods. We applied the SKIRT dust radiative transfer code to the TNG100 simulation to generate rest-frame UVJ fluxes. These UVJ colors were then compared to observational data from several well-studied extragalactic fields from the CANDELS/3D-HST programs, augmented by recent JWST/NIRCam photometry. Results. Quiescent and low-mass ($M_\star\lesssim10^{10.5}\,\mathrm{M}_\odot$) galaxies at cosmic noon do not require significant levels of dust reddening, as opposed to massive ($M_\star\gtrsim10^{11}\,\mathrm{M}_\odot$) star-forming galaxies. An extensive range of possible dust models fall short of the required dust reddening in V-J color for massive star-forming galaxies, with the simulated galaxies being too blue by $\approx0.9\,\mathrm{mag}$. Conclusions. We find that only variations in the star-to-dust geometries of the simulated galaxies can yield V-J colors that are red enough to match the observations. A toy model with isolated dust screens around younger stellar populations (with ages below $\sim1\,\mathrm{Gyr}$) can reproduce the observational data, while all conventional dust radiative transfer models (where the dust distribution follows the metals in the interstellar medium) fail to achieve the required V-J colors., Comment: Main text 17 pages, 12 figures. Accepted to A&A. Our analysis is publicly available at https://github.com/andreagebek/TNG100_UVJ

Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review the major contributions and advancements in the past three decades in the speech, audio, and music SS research field. We will cover both single- and multi-channel SS approaches. We will also look back on key efforts to foster a culture of scientific evaluation in the research field, including challenges, performance metrics, and datasets. We will conclude by discussing current trends and future research directions., Comment: Accepted by IEEE ICASSP 2025

Astrophysical discs which are sufficiently massive and cool are linearly unstable to the formation of axisymmetric structures. In practice, linearly stable discs of surface density slightly below the threshold needed for this instability often form spiral structures, and can subsequently fragment or exhibit a state of self-sustained turbulence, depending on how rapidly the disc cools. This has raised the question of how such turbulence is possible in the linearly stable regime. We suggest a nonlinear mechanism for this phenomenon. We find analytically weakly nonlinear axisymmetric subcritical solitary equilibria which exist in linearly stable 3D discs that are close to the instability threshold. The energy of these 'soliton' solutions is only slightly higher than that of a uniform disc, and the structures themselves are expected to be unstable to non-axisymmetric perturbations. In this way, these subcritical solitary equilibria highlight a nonlinear instability and provide a possible pathway to a turbulent state in linearly stable discs., Comment: 20 pages, 6 figures, published GAFD

Massive, multi-language, monolithic repositories form the backbone of many modern, complex software systems. To ensure consistent code quality while still allowing fast development cycles, Continuous Integration (CI) is commonly applied. However, operating CI at such scale not only leads to a single point of failure for many developers, but also requires computational resources that may reach feasibility limits and cause long feedback latencies. To address these issues, developers commonly split test executions across multiple pipelines, running small and fast tests in pre-submit stages while executing long-running and flaky tests in post-submit pipelines. Given the long runtimes of many pipelines and the substantial proportion of passing test executions (98% in our pre-submit pipelines), there not only a need but also potential for further improvements by prioritizing and selecting tests. However, many previously proposed regression optimization techniques are unfit for an industrial context, because they (1) rely on complex and difficult-to-obtain features like per-test code coverage that are not feasible in large, multi-language environments, (2) do not automatically adapt to rapidly changing systems where new tests are continuously added or modified, and (3) are not designed to distinguish the different objectives of pre- and post-submit pipelines: While pre-submit testing should prioritize failing tests, post-submit pipelines should prioritize tests that indicate non-flaky changes by transitioning from pass to fail outcomes or vice versa. To overcome these issues, we developed a lightweight and pipeline-aware regression test optimization approach that employs Reinforcement Learning models trained on language-agnostic features. We evaluated our approach on a large industry dataset collected over a span of 20 weeks of CI test executions. When predicting..., Comment: This paper is accepted on the Industry Track of the 18th IEEE International Conference on Software Testing, Verification and Validation (ICST 2025)

Granular materials in nature are nearly always non-spherical, but particle shape effects in granular flow remain largely elusive. This study uses discrete element method simulations to investigate how elongated particle shapes affect the mobility of dense granular flows down a rough incline. For a range of systematically varied particle length-to-diameter aspect ratios (AR), we run simulations with various flow thicknesses $h$ and slope angles $\theta$ to extract the well-known $h_\textrm{stop}(\theta)$ curves (below which the flow ceases) and the $Fr$-$h/h_\textrm{stop}$ relations following Pouliquen's approach, where $Fr=u/\sqrt{gh}$ is the Froude number, $u$ is the mean flow velocity, and $g$ is the gravitational acceleration. The slope $\beta$ of the $Fr$-$h/h_\textrm{stop}$ relations shows an intriguing S-shaped dependence on AR, with two plateaus at small and large AR, respectively, transitioning with a sharp increase. We understand this S-shaped dependence by examining statistics of particle orientation, alignment, and hindered rotation. We find that the rotation ability of weakly elongated particles ($\textrm{AR}\lesssim1.3$) remains similar to spheres, leading to the first plateau in the $\beta$-AR relation, whereas the effects of particle orientation saturates beyond $\textrm{AR}\approx2.0$, explaining the second plateau. An empirical sigmoidal function is proposed to capture this non-linear dependence. The findings are expected to enhance our understanding of how particle shape affects the flow of granular materials from both the flow- and particle-scale perspectives.

We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on human pose control, X-Dyna addresses key shortcomings causing the loss of dynamic details, enhancing the lifelike qualities of human video animations. At the core of our approach is the Dynamics-Adapter, a lightweight module that effectively integrates reference appearance context into the spatial attentions of the diffusion backbone while preserving the capacity of motion modules in synthesizing fluid and intricate dynamic details. Beyond body pose control, we connect a local control module with our model to capture identity-disentangled facial expressions, facilitating accurate expression transfer for enhanced realism in animated scenes. Together, these components form a unified framework capable of learning physical human motion and natural scene dynamics from a diverse blend of human and scene videos. Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna outperforms state-of-the-art methods, creating highly lifelike and expressive animations. The code is available at https://github.com/bytedance/X-Dyna., Comment: Project page:https://x-dyna.github.io/xdyna.github.io/ Code:https://github.com/bytedance/X-Dyna Model:https://huggingface.co/Boese0601/X-Dyna

Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining search-based software testing principles with neuroevolution to optimise neural networks that serve as test cases. However, since NEATEST is designed as a single-objective algorithm, it may require a long time to cover fairly simple program states or may even get stuck trying to reach unreachable program states. In order to resolve these shortcomings of NEATEST, this work aims to transform the algorithm into a many-objective search algorithm that targets several program states simultaneously. To this end, we combine the neuroevolution algorithm NEATEST with the two established search-based software testing algorithms, MIO and MOSA. Moreover, we adapt the existing many-objective neuroevolution algorithm NEWS/D to serve as a test generator. Our experiments on a dataset of 20 SCRATCH programs show that extending NEATEST to target several objectives simultaneously increases the average branch coverage from 75.88% to 81.33% while reducing the required search time by 93.28%., Comment: 11 pages, 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025

Photochemistry in the earth's atmosphere is driven by the sun, continuously altering the concentration and spatial distribution of pollutants. Precisely monitoring their atmospheric abundance relies predominantly on optical sensing, which requires the knowledge of exact absorption cross sections. One key pollutant which impacts many photochemical reaction-pathways is formaldehyde. Agreement on formaldehyde absolute absorption cross section remains elusive in the photochemically-relevant ultraviolet spectral region, hampering sensitive concentration tracking. Here, we introduce free-running ultraviolet dual comb spectroscopy, combining high spectral resolution (1 GHz), broad spectral coverage (12 THz), and fast acquisition speed (500 ms), as a novel method for absolute absorption cross section determination with unprecedented fidelity. Within this bandwidth, our method uncovers almost one order of magnitude more rovibrational transitions than detected before which leads to refined rotational constants for high-level quantum simulations of molecular eigenstates. This ultra-resolution method can be generalized to provide a universal tool for fast electronic fingerprinting of atmospherically-relevant species, both for sensing applications and to benchmark improvements of ab-initio quantum theory., Comment: 19 pages, 8 figures, 1 table

Networks of coupled nonlinear optical resonators have emerged as an important class of systems in ultrafast optical science, enabling richer and more complex nonlinear dynamics compared to their single-resonator or travelling-wave counterparts. In recent years, these coupled nonlinear optical resonators have been applied as application-specific hardware accelerators for computing applications including combinatorial optimization and artificial intelligence. In this work, we rigorously prove a fundamental result showing that coupled nonlinear optical resonators are Turing-complete computers, which endows them with much greater computational power than previously thought. Furthermore, we show that the minimum threshold of hardware complexity needed for Turing-completeness is surprisingly low, which has profound physical consequences. In particular, we show that several problems of interest in the study of coupled nonlinear optical resonators are formally undecidable. These theoretical findings can serve as the foundation for better understanding the promise of next-generation, ultrafast all-optical computers.

We use an infinite $k$-ary tree with a self-loop at the root as our underlying graph. We consider a chip-firing process starting with $N$ chips at the root. We describe the stable configurations. We calculate the number of fires for each vertex and the total number of fires. We study a sequence of the number of root fires for a given $k$ as a function of $N$ and study its properties. We do the same for the total number of fires., Comment: 22 pages, 1 figure

The pairing hierarchy of perfect pairing (PP), perfect quadruples (PQ) and perfect hextuples (PH) are sparsified coupled cluster models that are exact in a pairing active space for 2, 4, and 6 electron clusters, respectively. We describe and implement three extensions for radicals. First is the trivial generalization that does not correlate radical orbitals. The second model (PQr, PHr) includes terms that entangle pair indices and radical indices such that their maximum total number is 2 for PQ and 3 for PH (like their closed-shell versions). The third family of extended radical models (PPxr, PQxr, and PHxr) include cluster amplitudes that entangle up to 1, 2, and 3 pair indices with up to 1, 2, and 3 radical indices. Notably, PPxr and PQxr are exact for (3e,3o) and (5e,5o), respectively, while still having only $O(N)$ and $O(N^{2}$) amplitudes like their parent models (for $N$ paired electrons). Orbital optimization is considered for PPxr. A series of large-scale numerical tests of these models are presented for spin gaps, and ionization energies of polyenes and polyenyl radicals, ranging in size from ethene and allyl radical up to C$_{22}$H$_{24}$ in full-valence active spaces up to (122e,122o). The xR models perform best., Comment: 19 pages, 4 figures

We present a catalog of individual stellar and dust extinction properties along close to 500,000 sight lines in the southwest bar of the Small Magellanic Cloud (SMC). The catalog is based on multiband Hubble Space Telescope photometric data spanning near-ultraviolet to near-infrared wavelengths from the Small Magellanic Cloud Investigation of Dust and Gas Evolution survey (SMIDGE) covering a 100 x 200 pc area. We use the probabilistic technique of the Bayesian Extinction And Stellar Tool (BEAST) to model the spectral energy distributions of individual stars in SMIDGE and include the effects of observational uncertainties in the data. We compare BEAST-derived dust extinction properties with tracers of the interstellar medium, such as the emission from the 12CO (2-1) transition (I(CO)), the dust mass surface density ({\Sigma}dust) from far-IR emission, the H I column density (N(HI)) from the 21cm transition, and the mass fraction of polycyclic aromatic hydrocarbons (PAHs; qPAH, derived from IR emission). We find that the dust extinction (A(V )) in the SMIDGE field is strongly correlated with {\Sigma}dust and I(CO), and less so with N(HI) and qPAH, and suggest potential explanations. Our extinction measurements are also sensitive to the presence of the 2175 {\AA} bump in the extinction curve toward UV bright stars. While most do not show evidence for the bump, we identify ~200 lines of sight that are 2175 {\AA} bump candidates. Furthermore, we find distinct structures in the dust extinction-distance distributions that provide insights into the 3D geometry of the SMC., Comment: 20 pages, 13 figures, 4 tables

A computer's clock rate ultimately determines the minimum time between sequential operations or instructions. Despite exponential advances in electronic computer performance owing to Moore's Law and increasingly parallel system architectures, computer clock rates have remained stagnant at $\sim5~\mathrm{GHz}$ for almost two decades. This poses an intractable problem for applications requiring real-time processing or control of ultrafast information systems. Here we break this barrier by proposing and experimentally demonstrating computing based on an end-to-end and all-optical recurrent neural network harnessing the ultrafast nature of linear and nonlinear optical operations while avoiding electronic operations. The all-optical computer realizes linear operations, nonlinear functions, and memory entirely in the optical domain with $>100~\mathrm{GHz}$ clock rates. We experimentally demonstrate a prototypical task of noisy waveform classification as well as perform ultrafast in-situ analysis of the soliton states from integrated optical microresonators. We further illustrate the application of the architecture for generative artificial intelligence based on quantum fluctuations to generate images even in the absence of input optical signals. Our results highlight the potential of all-optical computing beyond what can be achieved with digital electronics by utilizing ultrafast linear, nonlinear, and memory functions and quantum fluctuations.

We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems., Comment: 30 pages

Tidal synchronization plays a fundamental role in the evolution of binary star systems. However, key details such as the timescale of synchronization, efficiency of tidal dissipation, final rotational period, and dependence on stellar mass are not well constrained. We present a catalog of rotation periods, orbital periods, and eccentricities from eclipsing binaries (EBs) that can be used to study the role of tides in the rotational evolution of low-mass dwarf (FGKM spectral type) binaries. This study presents the largest catalog of EB orbital and rotational periods (Porb and Prot) measured from the Transiting Exoplanet Satellite Survey (TESS). We first classify 4584 light curves from the TESS Eclipsing Binary Catalog according to out-of-eclipse stellar variability type: starspot modulation, ellipsoidal variability, non-periodic variability, and "other" variability (e.g. pulsations). We then manually validate each light curve classification, resulting in a sample of 1039 candidates with 584 high confidence EBs that exhibit detectable star-spot modulation. From there, we measure and compare the rotation period of each starspot-modulated EB using three methods: Lomb-Scargle periodograms, autocorrelation function, and phase dispersion minimization. We find that our period distributions are consistent with previous work that used a sample of 816 starspot EBs from Kepler to identify two populations: a synchronous population (with Porb \approx Prot) and a subsynchronous population (with 8Porb \approx 7Prot). Using Bayesian model comparison, we find that a bimodal distribution is a significantly better fit than a unimodal distribution for Kepler and TESS samples, both individually or combined, confirming that the subsynchronous population is statistically significant., Comment: 27 pages with 21 figures

Context. The Spectrometer/Telescope for Imaging X-Rays (STIX) onboard Solar Orbiter was designed to observe solar flares in the X-ray range of 4-150 keV, providing spectral, temporal and spatial information. Besides 30 imaging detectors, STIX has two additional detectors, the coarse flare locator (CFL) and the background (BKG) detector. Flares observed from Earth are classified using their peak X-ray flux observed by the GOES satellites. Roughly half of all flares observed by STIX are located on the backside of the Sun. These flares lack a GOES-class classification. Aims. In this paper, we describe the calibration of the BKG detector aperture sizes. Using the calibrated measurements of the BKG detector, we explore the relationship between the peak flux for flares jointly observed by STIX and GOES. This allows us to estimate the GOES flare classes of backside flares using STIX measurements. Methods. We looked at the 500 largest flares observed by both STIX and GOES in the time range Feb. 21 to Apr. 23. Aperture size calibration is done by comparing 4-10 keV counts of the BKG detector with the CFL measurements. In a second step, we correlate the calibrated STIX BKG peak flux with the GOES peak flux for individual flares. Results. We calibrated the BKG detector aperture sizes of STIX. Further, we showed that for the larger flares a close power law fit exists between the STIX BKG and GOES peak flux with a Pearson correlation coefficient of 0.97. This correlation provides a GOES proxy with a one sigma uncertainty of 11%. We were able to show that the BKG detector can reliably measure a broad range of GOES flare classes from roughly B5 up to at least X85 (assuming a radial distance of 1AU), making it an interesting detector-concept for future space weather missions. The largest flare observed by STIX to date is an estimated X16.5 $\pm$ 1.8 backside flare on the 20 Mai 2024., Comment: Accepted for publication in A&A. 11 pages, 7 figures, 2 tables

We consider the challenge of estimating the model parameters and latent states of general state-space models within a Bayesian framework. We extend the commonly applied particle Gibbs framework by proposing an efficient particle generation scheme for the latent states. The approach efficiently samples particles using an approximate hidden Markov model (HMM) representation of the general state-space model via a deterministic grid on the state space. We refer to the approach as the grid particle Gibbs with ancestor sampling algorithm. We discuss several computational and practical aspects of the algorithm in detail and highlight further computational adjustments that improve the efficiency of the algorithm. The efficiency of the approach is investigated via challenging regime-switching models, including a post-COVID tourism demand model, and we demonstrate substantial computational gains compared to previous particle Gibbs with ancestor sampling methods.

Humanoid robots have great potential to perform various human-level skills. These skills involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. Therefore, a timely overview of current progress and future trends in this fast-evolving field is essential. This survey first summarizes the model-based planning and control that have been the backbone of humanoid robotics for the past three decades. We then explore emerging learning-based methods, with a focus on reinforcement learning and imitation learning that enhance the versatility of loco-manipulation skills. We examine the potential of integrating foundation models with humanoid embodiments, assessing the prospects for developing generalist humanoid agents. In addition, this survey covers emerging research for whole-body tactile sensing that unlocks new humanoid skills that involve physical interactions. The survey concludes with a discussion of the challenges and future trends.