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Calculating or accurately estimating log-determinants of large positive semi-definite matrices is of fundamental importance in many machine learning tasks. While its cubic computational complexity can already be prohibitive, in modern applications, even storing the matrices themselves can pose a memory bottleneck. To address this, we derive a novel hierarchical algorithm based on block-wise computation of the LDL decomposition for large-scale log-determinant calculation in memory-constrained settings. In extreme cases where matrices are highly ill-conditioned, accurately computing the full matrix itself may be infeasible. This is particularly relevant when considering kernel matrices at scale, including the empirical Neural Tangent Kernel (NTK) of neural networks trained on large datasets. Under the assumption of neural scaling laws in the test error, we show that the ratio of pseudo-determinants satisfies a power-law relationship, allowing us to derive corresponding scaling laws. This enables accurate estimation of NTK log-determinants from a tiny fraction of the full dataset; in our experiments, this results in a $\sim$100,000$\times$ speedup with improved accuracy over competing approximations. Using these techniques, we successfully estimate log-determinants for dense matrices of extreme sizes, which were previously deemed intractable and inaccessible due to their enormous scale and computational demands.

Hyperbolic materials exhibit a very peculiar optical anisotropy with simultaneously different signs of the dielectric tensor components. This anisotropy allows the propagation of exotic surface-wave excitations like hyperbolic phonons and plasmon polaritons. While hyperbolic materials hold promise for applications in subwavelength photonics and enhanced light-matter interactions, their natural occurrence is limited to few materials, often accompanied by significant dielectric losses and limited hyperbolic spectral bandwidth. Focusing on PdCoO$_2$ and PdCrO$_2$ delafossite transition-metal oxides, in this paper we demonstrate their unique dual hyperbolic regimes: one localized around a phonon absorption in the mid-infrared spectral region, and the other extending into the visible range. Both hyperbolic regimes show exceptional properties including low dissipation and high hyperbolic quality factors. These results pave the way for innovative applications of delafossite layered metals in subwavelength photonics, imaging, and sensing.

This paper characterises the structure of every maximal weak or strong Gallai-Schur partition. The results confirm the exact values of Gallai-Schur numbers provided by Budden (2020) in the strong case, and provide corresponding values for weak Gallai-Schur numbers. The proofs are elementary and standalone., Comment: 9 pages

Hohenberg and Kohn have proven that the electronic energy and the one-particle electron density can, in principle, be obtained by minimizing an energy functional with respect to the density. Given that decades of theoretical work have so far failed to produce this elusive exact energy functional promising great computational savings, it is reasonable to try and learn it empirically. Using rotationally equivariant atomistic machine learning, we obtain for the first time a density functional that, when applied to the organic molecules in QM9, yields energies with chemical accuracy while also converging to meaningful electron densities. Augmenting the training data with densities obtained from perturbed potentials proved key to these advances. Altogether, we are now closer than ever to fulfilling Hohenberg and Kohn's promise, paving the way for more efficient calculations in large molecular systems.

Overcoming the strong chlorophyll background poses a significant challenge for measuring and optimizing plant growth. This research investigates the novel application of specialized quantum light emitters introduced into intact leaves of tobacco (Nicotiana tabacum), a well-characterized model plant system for studies of plant health and productivity. Leaves were harvested from plants cultivated under two distinct conditions: low light (LL), representing unhealthy leaves with reduced photosynthesis. and high light (HL), representing healthy leaves with highly active photosynthesis. Higher-order correlation data were collected and analyzed using machine learning (ML) techniques, specifically a Convolutional Neural Network (CNN), to classify the photon emitter states. This CNN efficiently identified unique patterns and created distinct fingerprints for Nicotiana leaves grown under LL and HL, demonstrating significantly different quantum profiles between the two conditions. These quantum fingerprints serve as a foundation for a novel unified analysis of plant growth parameters associated with different photosynthetic states. By employing CNN, the emitter profiles were able to reproducibly classify the leaves as healthy or unhealthy. This model achieved high probability values for each classification, confirming its accuracy and reliability. The findings of this study pave the way for broader applications, including the application of advanced quantum and machine learning technologies in plant health monitoring systems.

Platforms are increasingly relying on algorithms to curate the content within users' social media feeds. However, the growing prominence of proprietary, algorithmically curated feeds has concealed what factors influence the presentation of content on social media feeds and how that presentation affects user behavior. This lack of transparency can be detrimental to users, from reducing users' agency over their content consumption to the propagation of misinformation and toxic content. To uncover details about how these feeds operate and influence user behavior, we conduct an empirical audit of Reddit's algorithmically curated trending feed called r/popular. Using 10K r/popular posts collected by taking snapshots of the feed over 11 months, we find that the total number of comments and recent activity (commenting and voting) helped posts remain on r/popular longer and climb the feed. Using over 1.5M snapshots, we examine how differing ranks on r/popular correlated with engagement. More specifically, we find that posts below rank 80 showed a sharp decline in activity compared to posts above, and that posts at the top of r/popular had a higher proportion of undesired comments than those lower down. Our findings highlight that the order in which content is ranked can influence the levels and types of user engagement within algorithmically curated feeds. This relationship between algorithmic rank and engagement highlights the extent to which algorithms employed by social media platforms essentially determine which content is prioritized and which is not. We conclude by discussing how content creators, consumers, and moderators on social media platforms can benefit from empirical audits aimed at improving transparency in algorithmically curated feeds., Comment: 12 pages, 5 figures

Viewing Eratosthenes sieve as a discrete dynamic system, we show that every admissible instance of every admissible constellation of gaps arises and persists in Eratosthenes sieve. For an admissible constellation of length J, we show that its population across stages of the sieve is consistent with the Hardy and Littlewood estimates from 1923. This work strongly connects Eratosthenes sieve to the k-tuple conjecture, and it provides a compact notation, primorial coordinates, for tracking the locations of admissible instances for a constellation., Comment: Details from a short talk at JMM 2025

Besides the utilitarian aspects of online shopping, hedonic motivations play a significant role in shaping the shopping behavior of online users. With the increased popularity of voice-enabled devices, online shopping platforms have attempted to drive online shopping on voice. However, we explain why voice might be more suitable for the hedonic aspects of shopping. We introduce a prototype that enables such focus in a voice experience and share our findings from a qualitative study.

Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way to eliminate features when producing the sparse regression weights of $L_1$ models. However, despite the increasing need of privacy-preserving models for data analysis, to the best of our knowledge, no differentially private screening rule exists. In this paper, we develop the first private screening rule for linear regression. We initially find that this screening rule is too strong: it screens too many coefficients as a result of the private screening step. However, a weakened implementation of private screening reduces overscreening and improves performance., Comment: Proceedings of the 15th ACM Conference on Data and Application Security and Privacy

Conversational agents, such as chatbots, have increasingly found their way into many dimensions of our lives, including entertainment and education. In this exploratory study we built a child-friendly chatbot, "Ask Me Anything" (AMA), and investigated children's attitudes and trust toward AI-driven conversational agents. To prompt targeted questioning from students and drive engagement, AMA is a specialized chatbot that answers only topic--specific questions in three areas--astronomy, sneakers and shoes, and dinosaurs. We tested AMA with 63 students in a K-8 public school in the Northeast USA. Students worked in small groups, interacted with our tool for three to ten minutes, and completed a post survey. We identified three key themes that emerged from student conversational interactions with AMA: expressing wonder, surprise, and curiosity; building trust and developing confidence; and building relationships and anthropomorphizing. Also, we observed a broad attitude of openness and comfort. Students trusted the chatbot responses in general, indicating a high level of trust in and reliance on AI as a source of information. They described AMA as "knowledgeable," "smart," and that they could "trust it." To confirm their perception of reliability, some students tested the chatbot with questions to which they knew the answers. This behavior illustrated a fundamental aspect of children's cognitive development: the process of actively evaluating the credibility of sources. Our work extends and contributes to the existing body of literature that explores children's interactions with conversational agents., Comment: 21, 10 figures

Microcalorimeter instruments aboard future X-ray observatories will require an anti-coincidence (anti-co) detector to veto charged particle events and reduce the non-X-ray background. We have developed a large-format, TES-based prototype anti-coincidence detector that is particularly suitable for use with spatially-extended (~ 10 cm^2}) TES microcalorimeter arrays, as would be used for a future large field-of-view X-ray missions. This prototype was developed in the context of the Line Emission Mapper (LEM) probe concept, which required a ~ 14 cm^2 anti-co detector with > 95% live time and a low-energy threshold below 20 keV. Our anti-co design employs parallel networks of quasiparticle-trap-assisted electrothermal feedback TESs (QETs) to detect the athermal phonon signal produced in the detector substrate by incident charged particles. We developed multiple prototype anti-co designs featuring 12 channels and up to 6300 QETs. Here we focus on a design utilizing tungsten TESs and present characterization results. Broad energy range measurements have been performed (4.1 keV -- 5.5 MeV). Based on noise and responsivity measurements, the implied low-energy threshold is < 1 keV and a live time fraction of > 96% can be achieved up to 5.5 MeV. We also find evidence of mm-scale-or-better spatial resolution and discuss the potential utility of this for future missions. Finally, we discuss the early development of a soild-state physics model of the anti-co towards understanding phonon propagation and quasiparticle production in the detector., Comment: 26 pages, 16 figures

Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least effort. In classical OT, the optimal transportation strategy of a measure to itself is the identity. In this paper, we tackle anomaly detection by forcing samples to displace its mass, while keeping the least effort objective. We call this new transportation problem Mass Repulsing Optimal Transport (MROT). Naturally, samples lying in low density regions of space will be forced to displace mass very far, incurring a higher transportation cost. We use these concepts to design a new anomaly score. Through a series of experiments in existing benchmarks, and fault detection problems, we show that our algorithm improves over existing methods., Comment: 15 pages, 9 figures, 1 table, under review

Language is far more than a communication tool. A wealth of information - including but not limited to the identities, psychological states, and social contexts of its users - can be gleaned through linguistic markers, and such insights are routinely leveraged across diverse fields ranging from product development and marketing to healthcare. In four studies utilizing experimental and observational methods, we demonstrate that the widespread adoption of large language models (LLMs) as writing assistants is linked to notable declines in linguistic diversity and may interfere with the societal and psychological insights language provides. We show that while the core content of texts is retained when LLMs polish and rewrite texts, not only do they homogenize writing styles, but they also alter stylistic elements in a way that selectively amplifies certain dominant characteristics or biases while suppressing others - emphasizing conformity over individuality. By varying LLMs, prompts, classifiers, and contexts, we show that these trends are robust and consistent. Our findings highlight a wide array of risks associated with linguistic homogenization, including compromised diagnostic processes and personalization efforts, the exacerbation of existing divides and barriers to equity in settings like personnel selection where language plays a critical role in assessing candidates' qualifications, communication skills, and cultural fit, and the undermining of efforts for cultural preservation., Comment: arXiv admin note: text overlap with arXiv:2404.00267

The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy

We show that equalization-enhanced phase noise manifests as a time-varying, frequency-dependent phase error, which can be modeled and reversed by a time-varying all-pass finite impulse response filter., Comment: Accepted at 50th Optical Fiber Communication Conference (OFC), San Fransisco, CA, USA, Mar. 2025

After a successful kick-off meeting in 2021. two workshops in 2022 and 2023 on the future Global Cosmic-Ray Observatory (GCOS) focused mainly on a straw man design of the detector and science possibilities for astro- and particle physics. About 100 participants gathered for in-person and hybrid panel discussions. In this report, we summarize these discussions, present a preliminary straw-man design for GCOS and collect short write-ups of the flash talks given during the focus sessions., Comment: 48 pages, 27 figures

Gradient descent is the primary workhorse for optimizing large-scale problems in machine learning. However, its performance is highly sensitive to the choice of the learning rate. A key limitation of gradient descent is its lack of natural scaling, which often necessitates expensive line searches or heuristic tuning to determine an appropriate step size. In this paper, we address this limitation by incorporating Hessian information to scale the gradient direction. By accounting for the curvature of the function along the gradient, our adaptive, Hessian-aware scaling method ensures a local unit step size guarantee, even in nonconvex settings. Near a local minimum that satisfies the second-order sufficient conditions, our approach achieves linear convergence with a unit step size. We show that our method converges globally under a significantly weaker version of the standard Lipschitz gradient smoothness assumption. Even when Hessian information is inexact, the local unit step size guarantee and global convergence properties remain valid under mild conditions. Finally, we validate our theoretical results empirically on a range of convex and nonconvex machine learning tasks, showcasing the effectiveness of the approach.

Many questions in quantitative finance, uncertainty quantification, and other disciplines are answered by computing the population mean, $\mu := \mathbb{E}(Y)$, where instances of $Y:=f(\boldsymbol{X})$ may be generated by numerical simulation and $\boldsymbol{X}$ has a simple probability distribution. The population mean can be approximated by the sample mean, $\hat{\mu}_n := n^{-1} \sum_{i=0}^{n-1} f(\boldsymbol{x}_i)$ for a well chosen sequence of nodes, $\{\boldsymbol{x}_0, \boldsymbol{x}_1, \ldots\}$ and a sufficiently large sample size, $n$. Computing $\mu$ is equivalent to computing a $d$-dimensional integral, $\int f(\boldsymbol{x}) \varrho(\boldsymbol{x}) \, \mathrm{d} \boldsymbol{x}$, where $\varrho$ is the probability density for $\boldsymbol{X}$. Quasi-Monte Carlo methods replace independent and identically distributed sequences of random vector nodes, $\{\boldsymbol{x}_i \}_{i = 0}^{\infty}$, by low discrepancy sequences. This accelerates the convergence of $\hat{\mu}_n$ to $\mu$ as $n \to \infty$. This tutorial describes low discrepancy sequences and their quality measures. We demonstrate the performance gains possible with quasi-Monte Carlo methods. Moreover, we describe how to formulate problems to realize the greatest performance gains using quasi-Monte Carlo. We also briefly describe the use of quasi-Monte Carlo methods for problems beyond computing the mean, $\mu$.

In this paper, we explore how token unmasking order influences generative quality in masked diffusion models (MDMs). We derive an expanded evidence lower bound (ELBO) that introduces a planner to select which tokens to unmask at each step. Our analysis reveals that alternative unmasking strategies can enhance generation performance. Building on this, we propose Path Planning (P2), a sampling framework that uses a pre-trained BERT model or the denoiser itself to guide unmasking decisions. P2 generalizes all known MDM sampling strategies and significantly improves performance across diverse domains, including language generation (in-context learning, code generation, story infilling, mathematical reasoning, reverse curse correction) and biological sequence generation (protein and RNA sequences).

While neural networks have demonstrated impressive performance across various tasks, accurately quantifying uncertainty in their predictions is essential to ensure their trustworthiness and enable widespread adoption in critical systems. Several Bayesian uncertainty quantification (UQ) methods exist that are either cheap or reliable, but not both. We propose a post-hoc, sampling-based UQ method for over-parameterized networks at the end of training. Our approach constructs efficient and meaningful deep ensembles by employing a (stochastic) gradient-descent sampling process on appropriately linearized networks. We demonstrate that our method effectively approximates the posterior of a Gaussian process using the empirical Neural Tangent Kernel. Through a series of numerical experiments, we show that our method not only outperforms competing approaches in computational efficiency (often reducing costs by multiple factors) but also maintains state-of-the-art performance across a variety of UQ metrics for both regression and classification tasks., Comment: 24 pages, 5 figures, 9 tables

Hydrodynamical simulations of protoplanetary disk dynamics are useful tools for understanding the formation of planetary systems, including our own. Approximations are necessary to make these simulations computationally tractable. A common assumption when simulating dust fluids is that of a constant Stokes number, a dimensionless number that characterizes the interaction between a particle and the surrounding gas. Constant Stokes number is not a good approximation in regions of the disk where the gas density changes significantly, such as near a planet-induced gap. In this paper, we relax the assumption of constant Stokes number in the popular FARGO3D code using semi-analytic equations for the drag force on dust particles, which enables an assumption of constant particle size instead. We explore the effect this change has on disk morphology and particle fluxes across the gap for both outward- and inward-drifting particles. The assumption of constant particle size, rather than constant Stokes number, is shown to make a significant difference in some cases, emphasizing the importance of the more accurate treatment., Comment: Accepted version with minor author edits; published in ApJ

Cosmological moduli generically come to dominate the energy density of the early universe, and thereby trigger an early matter dominated era. Such non-standard cosmological histories are expected to have profound effects on the evolution and production of axion cold dark matter and dark radiation, as well as their prospects for detection. We consider moduli-axion couplings and investigate the early history of the coupled system, considering closely the evolution of the homogeneous modulus field, the back-reaction from the axion, and the energy densities of the two fields. A particular point of interest is the enhancement of axion production from modulus decay, due to tachyonic and parametric resonant instabilities, and the implications of such production on the cosmological moduli problem, axion dark radiation, and the available parameter space for axion dark matter. Using an effective field theory approach, WKB-based semi-analytical analysis, and detailed numerical estimates of the co-evolution of the system, we evaluate the expected decay efficiency of the modulus to axions. The effects of higher-order operators are studied and implications for UV-complete frameworks such as the Large Volume Scenarios in Type IIB string theory are considered in detail., Comment: 40 pages, 15 figures; added references

The recent proliferation of short form video social media sites such as TikTok has been effectively utilized for increased visibility, communication, and community connection amongst trans/nonbinary creators online. However, these same platforms have also been exploited by right-wing actors targeting trans/nonbinary people, enabling such anti-trans actors to efficiently spread hate speech and propaganda. Given these divergent groups, what are the differences in network structure between anti-trans and pro-trans communities on TikTok, and to what extent do they amplify the effects of anti-trans content? In this paper, we collect a sample of TikTok videos containing pro and anti-trans content, and develop a taxonomy of trans related sentiment to enable the classification of content on TikTok, and ultimately analyze the reply network structures of pro-trans and anti-trans communities. In order to accomplish this, we worked with hired expert data annotators from the trans/nonbinary community in order to generate a sample of highly accurately labeled data. From this subset, we utilized a novel classification pipeline leveraging Retrieval-Augmented Generation (RAG) with annotated examples and taxonomy definitions to classify content into pro-trans, anti-trans, or neutral categories. We find that incorporating our taxonomy and its logics into our classification engine results in improved ability to differentiate trans related content, and that Results from network analysis indicate many interactions between posters of pro-trans and anti-trans content exist, further demonstrating targeting of trans individuals, and demonstrating the need for better content moderation tools, Comment: 11 pages, 4 figures. 2 tables

Palladium diselenide (PdSe$_2$) -- a layered van der Waals material -- is attracting significant attention for optoelectronics due to the wide tunability of its band gap from the infrared through the visible range as a function of the number of layers. However, there continues to be disagreement over the precise nature and value of the optical band gap of bulk PdSe$_2$, owing to the rather small value of this gap that complicates experimental measurements and their interpretation. Here, we design and employ a Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functional to investigate the electronic bandstructures and optical absorption spectra of bulk and monolayer PdSe$_2$. In particular, we account carefully for the finite exciton center-of-mass momentum within a time-dependent WOT-SRSH framework to calculate the \emph{indirect} optical gap and absorption onset accurately. Our results agree well with the best available photoconductivity measurements, as well as with state-of-the-art many-body perturbation theory calculations, confirming that bulk PdSe$_2$ has an optical gap in the mid-infrared (upper-bound of 0.44 eV). More generally, this work further bolsters the utility of the WOT-SRSH approach for predictive modeling of layered semiconductors.

We model human activity scheduling behaviour using a deep generative machine learning approach. Activity schedules, which represent the activities and associated travel behaviours of individuals, are a core component of many applied models in the transport, energy and epidemiology domains. Our data driven approach learns human preferences and scheduling logic without the need for complex interacting combinations of sub-models and custom-rules, this makes our approach significantly faster and simpler to operate that existing approaches. We find activity schedule data combines aspects of both continuous image data and also discrete text data, requiring novel approaches. We additionally contribute a novel schedule representation and comprehensive evaluation framework for generated schedules. Evaluation shows our approach is able to rapidly generate large, diverse and realistic synthetic samples of activity schedules.

Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.

This paper presents a classification framework for the architectures of planetary systems based on a complete survey of the confirmed exoplanet population. With nearly 6000 confirmed exoplanets discovered, including more than 300 multiplanet systems with three or more planets, the current observational sample has reached the point where it is both feasible and useful to build a classification system that divides the observed population into meaningful categories. This framework provides a criterion to split planetary systems into inner and outer regimes, and then further divides inner systems into dynamical classes. The resulting categories include "peas-in-a-pod systems" with uniformly small planets and "warm Jupiter systems" with a mix of large and small planets, as well as "closely-spaced systems" and "gapped systems," with further subdivisions based on the locations of gaps and other features. These categories can classify nearly all of the confirmed systems with three or more planets with minimal ambiguity. We qualitatively examine the relative prevalence of each type of system, subject to observational selection effects, as well as other notable features such as the presence of hot Jupiters. A small number of outlier systems are also discussed. Potential additional classes of systems yet to be discovered are proposed., Comment: 35 pages, 17 figures, 5 tables

We present the results of combined hydrodynamic and particle tracking post-processing modeling to study the transport of small dust in a protoplanetary disk containing an embedded embryo in 3D. We use a suite of FARGO3D hydrodynamic simulations of disks containing a planetary embryo varying in mass up to 300 $M_\oplus$ on a fixed orbit in both high and low viscosity disks. We then simulate solid particles through the disk as a post-processing step using a Monte Carlo integration, allowing us to track the trajectories of individual particles as they travel throughout the disk. We find that gas advection onto the planet can carry small, well-coupled solids across the gap opened in the disk by the embedded planet for planetary masses above the pebble isolation mass. This mixing between the inner and outer disk can occur in both directions, with solids in the inner disk mixing to the outer disk as well. Additionally, in low viscosity disks, multiple pile-ups in the outer disk may preserve isotopic heterogeneities, possibly providing an outermost tertiary isotopic reservoir. Throughout Jupiter's growth, the extent of mixing between isotopic reservoirs varied depending on dust size, gas turbulence, and the Jovian embryo mass., Comment: 21 pages, 13 figures, accepted for publication in ApJ

High-throughput pheno-, geno-, and envirotyping allows routine characterization of plant varieties and the trials they are evaluated in. These datasets can be integrated into statistical models for genomic prediction in several ways. One approach is to create linear or non-linear kernels which are subsequently used in reproducing kernel hilbert spaces (RKHS) regression. Software packages implementing a Bayesian approach are typically used for these RKHS models. However, they often lack some of the flexibility offered by dedicated linear mixed model software such as ASReml-R. Furthermore, a Bayesian approach is often computationally more demanding than a frequentist model. Here we show how frequentist RKHS models can be implemented in ASReml-R and extend these models to allow for heterogeneous (i.e., trial-specific) genetic variances. We also show how an alternative to the typically Bayesian kernel averaging approach can be implemented by treating the bandwidth associated with the non-linear kernel as a parameter to be estimated using restricted maximum likelihood. We show that these REML implementations with homo- or heterogeneous variances perform similarly or better than the Bayesian models. We also show that the REML implementation comes with a significant increase in computational efficiency, being up to 12 times faster than the Bayesian models while using less memory. Finally, we discuss the significant flexibility provided by this approach and the options regarding further customization of variance models.

Electric vehicle batteries have a proven flexibility potential which could serve as an alternative to conventional electricity storage solutions. EV batteries could support the balancing of supply and demand and the integration of variable renewable energy into the electricity system. The flexibility potential from electric vehicles, in distinction to conventional battery storage, depends on the vehicle user's willingness and opportunity to make their vehicle available for flexibility. This rate of participation is often not considered in studies, despite the impact electric vehicle flexibility could have on the electricity system. This work presents a modelling study of the Norwegian electricity system, demonstrating how a future net-zero electricity system can benefit from electric vehicles in terms of integrating renewables and balancing supply and demand, while considering the rate of participation. Our findings show electric vehicles' potential to eliminate the need for stationary battery storage with just 50% participation in vehicle-to-grid. We find that the flexibility of electric vehicles contributes to relative reductions in the total cost of the electricity system by almost 4% and 15% assuming 100% participation in flexible charging and vehicle-to-grid, respectively., Comment: 32 pages, 10 figures

While nova eruptions produce some of the most common and dramatic dust formation episodes among astrophysical transients, the demographics of dust-forming novae remain poorly understood. Here, we present a statistical study of dust formation in 40 novae with high-quality optical/IR light curves, quantitatively distinguishing dust-forming from non-dust-forming novae while exploring the properties of the dust events. We find that 50-70% of novae produce dust, significantly higher than previous estimates. Dust-forming novae can be separated from those that do not show dust formation by using the largest redward ($V-K$) colour change from peak visible brightness; ($V-J$) or ($V-H$) offer useful but less sensitive constraints. This makes optical+IR photometry a powerful tool to quantify dust formation in novae. We find that novae detected in GeV $\gamma$-rays by \emph{Fermi}-LAT appear to form dust more often than novae not detected by \emph{Fermi}, implying a possible connection between $\gamma$-ray producing shocks and dust production. We also find that novae that evolve very quickly ($t_2 < 10$ days) are much less likely to form dust, in agreement with previous findings. We confirm a correlation between $t_2$ and the time of the onset of dust formation (which occurs $\sim$1 week--3 months after maximum light), but conclude that it is primarily an observational artifact driven by dust formation determining when a nova drops 2 mag below peak. The significant fraction of novae that form dust make them ideal laboratories in our Galactic backyard to tackle the puzzle of dust formation around explosive transients., Comment: 23 pages, 58 figures, 1 table, submitted to MNRAS

Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data transactions between an accelerator's on-chip buffer and DRAM. This is accomplished by grouped execution of multiple operations like convolution and activations together into single execution units - fusion groups. However, on-chip buffer capacity limits fusion group size and optimizing fusion on whole DNNs requires partitioning into multiple fusion groups. Finding the optimal groups is a complex problem where the presence of invalid solutions hampers traditional search algorithms and demands robust approaches. In this paper we incorporate Explainable AI, specifically Graph Explanation Techniques (GET), into layer fusion. Given an invalid fusion group, we identify the operations most responsible for group invalidity, then use this knowledge to recursively split the original fusion group via a greedy tree-based algorithm to minimize DRAM access. We pair our scheme with common algorithms and optimize DNNs on two types of layer fusion: Line-Buffer Depth First (LBDF) and Branch Requirement Reduction (BRR). Experiments demonstrate the efficacy of our scheme on several popular and classical convolutional neural networks like ResNets and MobileNets. Our scheme achieves over 20% DRAM Access reduction on EfficientNet-B3., Comment: DAC'23 WIP Poster; 8 pages, 5 Figures 5 Tables

Nerve impulses, the currency of information flow in the brain, are generated by an instability of the neuronal membrane potential dynamics. Neuronal circuits exhibit collective chaos that appears essential for learning, memory, sensory processing, and motor control. However, the factors controlling the nature and intensity of collective chaos in neuronal circuits are not well understood. Here we use computational ergodic theory to demonstrate that basic features of nerve impulse generation profoundly affect collective chaos in neuronal circuits. Numerically exact calculations of Lyapunov spectra, Kolmogorov-Sinai-entropy, and upper and lower bounds on attractor dimension show that changes in nerve impulse generation in individual neurons moderately impact information encoding rates but qualitatively transform phase space structure. Specifically, we find a drastic reduction in the number of unstable manifolds, Kolmogorov-Sinai entropy, and attractor dimension. Beyond a critical point, marked by the simultaneous breakdown of the diffusion approximation, a peak in the largest Lyapunov exponent, and a localization transition of the leading covariant Lyapunov vector, networks exhibit sparse chaos: prolonged periods of near stable dynamics interrupted by short bursts of intense chaos. Analysis of large, more realistically structured networks supports the generality of these findings. In cortical circuits, biophysical properties appear tuned to this regime of sparse chaos. Our results reveal a close link between fundamental aspects of single-neuron biophysics and the collective dynamics of cortical circuits, suggesting that nerve impulse generation mechanisms are adapted to enhance circuit controllability and information flow.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

We present a novel perspective on the universal approximation theorem for rough path functionals, introducing a polynomial-based approximation class. We extend universal approximation to non-geometric rough paths within the tensor algebra. This development addresses critical needs in finance, where no-arbitrage conditions necessitate It\^o integration. Furthermore, our findings motivate a hypothesis for payoff functionals in financial markets, allowing straightforward analysis of signature payoffs proposed in \cite{arribas2018derivativespricingusingsignature}.

Radial drift of icy pebbles can have a large impact on the chemistry of the inner regions of protoplanetary disks. Compact dust disks ($\lesssim$50 au) are suggested to have a higher (cold) H$_2$O flux than more extended disks, likely due to efficient radial drift bringing H$_2$O-rich material to the inner disk, where it can be observed with JWST. We present JWST MIRI/MRS observations of the disk CX Tau taken as a part of the Mid-INfrared Disk Survey (MINDS) GTO program, a prime example of a drift-dominated disk. This compact disk seems peculiar: the source possesses a bright CO$_2$ feature instead of the bright H$_2$O expected based on its efficient radial drift. We aim to provide an explanation for this finding. We detect molecular emission from H$_2$O, $^{12}$CO$_2$, $^{13}$CO$_2$, C$_2$H$_2$, HCN, and OH in this disk, and even demonstrate a potential detection of CO$^{18}$O. Analysis of the $^{12}$CO$_2$ and $^{13}$CO$_2$ emission shows the former to be tracing a temperature of $\sim$450 K, whereas the $^{13}$CO$_2$ traces a significantly colder temperature ($\sim$200 K). H$_2$O is also securely detected both at shorter and longer wavelengths, tracing a similar temperature of $\sim$500-600 K as the CO$_2$ emission. We also find evidence for a colder, $\sim$200 K H$_2$O component at longer wavelengths, which is in line with this disk having strong radial drift. The cold $^{13}$CO$_2$ and H$_2$O emission indicate that radial drift of ices likely plays an important role in setting the chemistry of the inner disk of CX Tau. Potentially, the H$_2$O-rich gas has already advected onto the central star, which is now followed by an enhancement of comparatively CO$_2$-rich gas reaching the inner disk, explaining the enhancement of CO$_2$ emission in CX Tau. The comparatively weaker H$_2$O emission can be explained by the source's low accretion luminosity. (abridged), Comment: 23 pages, 17 figures, accepted for publication in Astronomy & Astrophysics

The first JWST/MIRI photometric observations of TRAPPIST-1 b allowed for the detection of the thermal emission of the planet at 15 $\mu m$, suggesting that the planet could be a bare rock with a zero albedo and no redistribution of heat. These observations at 15 $\mu m$ were acquired as part of GTO time that included a twin program at 12.8 $\mu m$ in order to have a measurement in and outside the CO$_2$ absorption band. Here we present five new occultations of TRAPPIST-1 b observed with MIRI in an additional photometric band at 12.8 $\mu m$. We perform a global fit of the 10 eclipses and derive a planet-to-star flux ratio and 1-$\sigma$ error of 452 $\pm$ 86 ppm and 775 $\pm$ 90 ppm at 12.8 $\mu m$ and 15 $\mu m$, respectively. We find that two main scenarios emerge. An airless planet model with an unweathered (fresh) ultramafic surface, that could be indicative of relatively recent geological processes fits well the data. Alternatively, a thick, pure-CO2 atmosphere with photochemical hazes that create a temperature inversion and result in the CO2 feature being seen in emission also works, although with some caveats. Our results highlight the challenges in accurately determining a planet's atmospheric or surface nature solely from broadband filter measurements of its emission, but also point towards two very interesting scenarios that will be further investigated with the forthcoming phase curve of TRAPPIST-1 b., Comment: 49 pages, 3 main text figure, 2 extended figures, 10 supplementary figures, accepted for publication in Nature Astronomy on October 29, 2024

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions., Comment: 20 pages, 6 figures, 4 tables

Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has led many to see potential in deploying LLMs for clinical use. However, medicine is a setting where accurate reasoning is paramount. Many researchers are questioning the effectiveness of multiple choice question answering (MCQA) benchmarks, frequently used to test LLMs. Researchers and clinicians alike must have complete confidence in LLMs' abilities for them to be deployed in a medical setting. To address this need for understanding, we introduce a knowledge graph (KG)-based method to evaluate the biomedical reasoning abilities of LLMs. Essentially, we map how LLMs link medical concepts in order to better understand how they reason. We test GPT-4, Llama3-70b, and PalmyraMed-70b, a specialized medical model. We enlist a panel of medical students to review a total of 60 LLM-generated graphs and compare these graphs to BIOS, a large biomedical KG. We observe GPT-4 to perform best in our human review but worst in our ground truth comparison; vice-versa with PalmyraMed, the medical model. Our work provides a means of visualizing the medical reasoning pathways of LLMs so they can be implemented in clinical settings safely and effectively., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 19 pages

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team of researchers spanning computer science and political science--present the first principled framework termed Political-LLM to advance the comprehensive understanding of integrating LLMs into computational political science. Specifically, we first introduce a fundamental taxonomy classifying the existing explorations into two perspectives: political science and computational methodologies. In particular, from the political science perspective, we highlight the role of LLMs in automating predictive and generative tasks, simulating behavior dynamics, and improving causal inference through tools like counterfactual generation; from a computational perspective, we introduce advancements in data preparation, fine-tuning, and evaluation methods for LLMs that are tailored to political contexts. We identify key challenges and future directions, emphasizing the development of domain-specific datasets, addressing issues of bias and fairness, incorporating human expertise, and redefining evaluation criteria to align with the unique requirements of computational political science. Political-LLM seeks to serve as a guidebook for researchers to foster an informed, ethical, and impactful use of Artificial Intelligence in political science. Our online resource is available at: http://political-llm.org/., Comment: 54 Pages, 9 Figures

Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in suboptimal performance of monolingual and cross-lingual sentence embedding models for these languages. To address this issue, we compile a relatively small but high-quality human-generated cross-lingual parallel dataset to train LuxEmbedder, an enhanced sentence embedding model for Luxembourgish with strong cross-lingual capabilities. Additionally, we present evidence suggesting that including low-resource languages in parallel training datasets can be more advantageous for other low-resource languages than relying solely on high-resource language pairs. Furthermore, recognizing the lack of sentence embedding benchmarks for low-resource languages, we create a paraphrase detection benchmark specifically for Luxembourgish, aiming to partially fill this gap and promote further research., Comment: Accepted at COLING 2025

We investigate the impact of Mutual Coupling (MC) between antennas on the time-delay power spectrum response of the core of the SKA-Low radio telescope. Using two in-house tools - Fast Array Simulation Tool (FAST) (a fast full-wave electromagnetic solver) and OSKAR (a GPU-accelerated radio telescope simulator) - we simulate station beams and compute visibilities for various array layouts (regular, sunflower, and random). Simulations are conducted in an Epoch of Reionisation subband between 120-150~MHz, with a fine frequency resolution of 100~kHz, enabling the investigation of longer delays. Our results show that MC effects significantly increase foreground leakage into longer delays, especially for regular station layouts. For 21-cm science, foreground spill-over into the 21-cm window extends beyond $k_{\parallel} \sim 2$~h$^{-1}$Mpc for all station layouts and across all $k_{\perp}$ modes, completely obscuring the detection window. We find that attempting to remove the foreground contribution from the visibilities using an approximated beam model, based on the average embedded element pattern or interpolating the embedded element patterns from a coarse channel rate of 781~kHz, results in residuals around 1% ($\sim 10^{11}~\mathrm{mK}^2$h$^{-3}\mathrm{Mpc}^3$) which is still around 7 orders of magnitude brighter than the expected level of the EoR signal ($\sim 10^{4}~\mathrm{mK}^2$h$^{-3}\mathrm{Mpc}^3$). We also find that station beam models with at least 4-5 significant digits in the far-field pattern and high spectral resolution are needed for effective foreground removal. Our research provides critical insights into the role of MC in SKA-Low experiments and highlights the computational challenges of fully integrating array patterns that account for MC effects into processing pipelines., Comment: 18 pages, 17 figures

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment located in the Atacama Desert in Chile at an elevation of 5200 meters, nominally consisting of an array of three 0.42-meter small aperture telescopes (SATs) and one 6-meter large aperture telescope (LAT). SO will make accurate measurements of the CMB temperature and polarization spanning six frequency bands ranging from 27 to 280 GHz, fielding a total of $\sim$68,000 detectors covering angular scales between one arcminute to tens of degrees. In this paper, we focus on the SATs, which are tailored to search for primordial gravitational waves, with the primary science goal of measuring the primordial tensor-to-scalar ratio \textit{r} at a target level of $\sigma(r) \approx 0.003$. We discuss the design drivers, scientific impact, and current deployment status of the three SATs, which are scheduled to start taking data in the coming year. The SATs aim to map 10\% of the sky at a 2 $\mu$K-arcmin noise level observing at mid-frequencies (93/145 GHz), with additional ultra-high-frequency (225/280 GHz) and low-frequency (27/39 GHz) targets to yield galactic foreground-subtracted measurements.

In this paper, we evaluate the capability of large language models to conduct personalized phishing attacks and compare their performance with human experts and AI models from last year. We include four email groups with a combined total of 101 participants: A control group of arbitrary phishing emails, which received a click-through rate (recipient pressed a link in the email) of 12%, emails generated by human experts (54% click-through), fully AI-automated emails 54% (click-through), and AI emails utilizing a human-in-the-loop (56% click-through). Thus, the AI-automated attacks performed on par with human experts and 350% better than the control group. The results are a significant improvement from similar studies conducted last year, highlighting the increased deceptive capabilities of AI models. Our AI-automated emails were sent using a custom-built tool that automates the entire spear phishing process, including information gathering and creating personalized vulnerability profiles for each target. The AI-gathered information was accurate and useful in 88% of cases and only produced inaccurate profiles for 4% of the participants. We also use language models to detect the intention of emails. Claude 3.5 Sonnet scored well above 90% with low false-positive rates and detected several seemingly benign emails that passed human detection. Lastly, we analyze the economics of phishing, highlighting how AI enables attackers to target more individuals at lower cost and increase profitability by up to 50 times for larger audiences.

A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent. In this work, we ask if there is a way for anti-virus developers to similarly re-purpose existing work to improve their malware detection capability. We show that this is plausible via YARA rules, which use human-written signatures to detect specific malware families, functionalities, or other markers of interest. By extracting sub-signatures from publicly available YARA rules, we assembled a set of features that can more effectively discriminate malicious samples from benign ones. Our experiments demonstrate that these features add value beyond traditional features on the EMBER 2018 dataset. Manual analysis of the added sub-signatures shows a power-law behavior in a combination of features that are specific and unique, as well as features that occur often. A prior expectation may be that the features would be limited in being overly specific to unique malware families. This behavior is observed, and is apparently useful in practice. In addition, we also find sub-signatures that are dual-purpose (e.g., detecting virtual machine environments) or broadly generic (e.g., DLL imports)., Comment: To appear in BigData'24 CyberHunt 2024

Maize farming is a major livelihood activity for many farmers in Ghana. Unfortunately, farmers usually do not obtain the expected returns on their investment due to reliance on rudimentary, labor-intensive, and inefficient methods of production. Using cross-sectional data from 359 maize farmers, this study investigates the profitability and determinants of the use of tractor services for maize production in Ejura-Sekyedumase, Ashanti Region of Ghana. Results from descriptive and profitability analyses reveal that tractor services such as ploughing and shelling are widely used, but their profitability varies significantly among farmers. Key factors influencing profitability include farm size, fertilizer quantity applied, and farmer experience. Results from a multivariate probit analysis also showed that farming experience, fertilizer quantity, and profit per acre have a positive influence on tractor service use for shelling, while household size, farm size, and FBO have a negative influence. Farming experience, fertilizer quantity, and profit per acre positively influence tractor service use for ploughing, while farm size has a negative influence. A t-test result reveals a statistically significant difference in profit between farmers who use tractor services and those who do not. Specifically, farmers who utilize tractor services on their maize farm had a return to cost of 9 percent more than those who do not (p-value < 0.05). The Kendall's result showed a moderate agreement among the maize farmers (first ranked being financial issues) in their ability to access/utilize tractor services on their farm.

Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies. Traditional mathematical models, while accurate, are often computationally expensive and time-consuming, limiting their practical application. Recent advancements in machine learning, particularly in deep learning, offer promising alternatives for accelerating these predictions. This study explores the use of a deep operator network (DeepONet), a type of neural operator, as a surrogate model for finite element simulations, aimed at predicting post-burn contraction across multiple wound shapes. A DeepONet was trained on three distinct initial wound shapes, with enhancement made to the architecture by incorporating initial wound shape information and applying sine augmentation to enforce boundary conditions. The performance of the trained DeepONet was evaluated on a test set including finite element simulations based on convex combinations of the three basic wound shapes. The model achieved an $R^2$ score of $0.99$, indicating strong predictive accuracy and generalization. Moreover, the model provided reliable predictions over an extended period of up to one year, with speedups of up to 128-fold on CPU and 235-fold on GPU, compared to the numerical model. These findings suggest that DeepONets can effectively serve as a surrogate for traditional finite element methods in simulating post-burn wound evolution, with potential applications in medical treatment planning.

In this paper, we present a framework for learning the solution map of a backward parabolic Cauchy problem. The solution depends continuously but nonlinearly on the final data, source, and force terms, all residing in Banach spaces of functions. We utilize Fr\'echet space neural networks (Benth et al. (2023)) to address this operator learning problem. Our approach provides an alternative to Deep Operator Networks (DeepONets), using basis functions to span the relevant function spaces rather than relying on finite-dimensional approximations through censoring. With this method, structural information encoded in the basis coefficients is leveraged in the learning process. This results in a neural network designed to learn the mapping between infinite-dimensional function spaces. Our numerical proof-of-concept demonstrates the effectiveness of our method, highlighting some advantages over DeepONets., Comment: 19 pages

We classify all trilinear singular Brascamp-Lieb forms, completing the classification in the two dimensional case by Demeter and Thiele in arXiv:0803.1268. We use known results in the representation theory of finite dimensional algebras, namely the classification of indecomposable representations of the four subspace quiver. Our classification lays out a roadmap for achieving bounds for all degenerate higher dimensional bilinear Hilbert transforms. As another step towards this goal, we prove new bounds for a particular class of forms that arises as a natural next candidate from our classification. We further prove conditional bounds for forms associated with mutually related representations. For this purpose we develop a method of rotations that allows us to decompose any homogeneous $d$-dimensional singular integral kernel into $(d-1)$-dimensional kernels on hyperplanes., Comment: 44 pages