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This paper focuses on the design and analysis of privacy-preserving techniques for group testing and infection status retrieval. Our work is motivated by the need to provide accurate information on the status of disease spread among a group of individuals while protecting the privacy of the infection status of any single individual involved. The paper is motivated by practical scenarios, such as controlling the spread of infectious diseases, where individuals might be reluctant to participate in testing if their outcomes are not kept confidential. The paper makes the following contributions. First, we present a differential privacy framework for the subset retrieval problem, which focuses on sharing the infection status of individuals with administrators and decision-makers. We characterize the trade-off between the accuracy of subset retrieval and the degree of privacy guaranteed to the individuals. In particular, we establish tight lower and upper bounds on the achievable level of accuracy subject to the differential privacy constraints. We then formulate the differential privacy framework for the noisy group testing problem in which noise is added either before or after the pooling process. We establish a reduction between the private subset retrieval and noisy group testing problems and show that the converse and achievability schemes for subset retrieval carry over to differentially private group testing.

In psychological practice, standardized questionnaires serve as essential tools for assessing mental constructs (e.g., attitudes, traits, and emotions) through structured questions (aka items). With the increasing prevalence of social media platforms where users share personal experiences and emotions, researchers are exploring computational methods to leverage this data for rapid mental health screening. In this study, we propose a novel adaptive Retrieval-Augmented Generation (RAG) approach that completes psychological questionnaires by analyzing social media posts. Our method retrieves the most relevant user posts for each question in a psychological survey and uses Large Language Models (LLMs) to predict questionnaire scores in a zero-shot setting. Our findings are twofold. First we demonstrate that this approach can effectively predict users' responses to psychological questionnaires, such as the Beck Depression Inventory II (BDI-II), achieving performance comparable to or surpassing state-of-the-art models on Reddit-based benchmark datasets without relying on training data. Second, we show how this methodology can be generalized as a scalable screening tool, as the final assessment is systematically derived by completing standardized questionnaires and tracking how individual item responses contribute to the diagnosis, aligning with established psychometric practices.

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.

Recent reports of colossal negative magnetoresistance (CMR) in a few magnetic semimetals and semiconductors have attracted attention, because these materials are devoid of the conventional mechanisms of CMR such as mixed valence, double exchange interaction, and Jahn-Teller distortion. New mechanisms have thus been proposed, including topological band structure, ferromagnetic clusters, orbital currents, and charge ordering. The CMR in these compounds has been reported in two forms: either a resistivity peak or a resistivity upturn suppressed by a magnetic field. Here we reveal both types of CMR in a single antiferromagnetic semiconductor Eu5In2As6. Using the transport and thermodynamic measurements, we demonstrate that the peak-type CMR is likely due to the percolation of magnetic polarons with increasing magnetic field, while the upturn-type CMR is proposed to result from the melting of a charge order under the magnetic field. We argue that similar mechanisms operate in other compounds, offering a unifying framework to understand CMR in seemingly different materials., Comment: Main manuscript has 10 pages and 4 figures. Supplemental information is included

Quantum entanglement, a fundamental feature of quantum mechanics, has become a powerful tool impacting various areas of physics. In this proceeding, we investigate the presence of quantum entanglement within the Higgs to ZZ* (HZZ*) interaction at the ATLAS experiment. Utilizing quantum tomography techniques, we extract the complete spin density matrix characterizing the HZZ* interaction from ATLAS Standard Monte Carlo (MC) HZZ* 4l Run2 samples. We then evaluate the presence of quantum entanglement in the system using the derived spin density matrix. Our investigation sheds light on the entanglement properties within the HZZ* interaction, which offers valuable insights into its quantum characteristics., Comment: Submitted to the Proceedings of the 12th Large Hadron Collider Physics Conference (LHCP 2024) hosted by PoS

Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions., Comment: 9 pages, 8 figures, AAAI workshop submission

A multi-product monopolist faces a buyer who is privately informed about his valuations for the goods. As is well-known, optimal mechanisms are in general complicated, while simple mechanisms -- such as pure bundling or separate sales -- can be far from optimal and do not admit clear-cut comparisons. We show that this changes if the monopolist observes sufficiently rich data about the buyer's valuations: Now, pure bundling always outperforms separate sales; moreover, there is a sense in which pure bundling performs essentially as well as the optimal mechanism. To formalize this, we characterize how fast the corresponding revenues converge to the first-best revenue as the monopolist's data grows rich: Pure bundling achieves the same convergence rate to the first-best as optimal mechanisms; in contrast, the convergence rate under separate sales is suboptimal.

Organic based semiconductor materials offer emerging and sustainable solutions for solar energy conversion technologies and electronics. However, knowledge of their intrinsic (photo)physical properties and light-matter interactions is often limited, especially with respect to the frequency dependent dielectric properties on the relevant timescales of exciton separation and charge generation (fs-ps). By using terahertz-time-domain spectroscopy (THz-TDS), we show that the complex permittivity and THz conductivity of different polymer materials and graphitic carbon nitrides - melon and poly(heptazine imides) (K-PHI) - can be measured directly and accurately in different environmental humidities. Its effects are most strongly observed in the ionic and 2D carbon nitride, K-PHI, where both real permittivity and THz conductivity double from dry to humid conditions (~4-8 and 75-150 S/m, respectively) surpassing the intrinsic dielectric response from water or K-PHI through synergistic effects. Our findings are backed by fs-ps transient absorption spectroscopy (TAS), confirming the impact of humidity on light conversion behaviour on the ps-timescale in K-PHI. We highlight the importance of dielectric property characterization at THz frequencies as critical for understanding photophysical behaviour at exciton or charge separation time scales, especially in the presence of water and hydrated ions, which may also be beneficial for computation, exploring next generation photocatalysts, electronics or ionotronics.

Ab initio techniques have revolutionized the way how theory can help practitioners to explore critical mechanisms and devise new strategies in discovering and designing materials. Yet, their application to electrochemical systems is still limited. A well known example how novel concepts can boost our ability to perform such studies is the introduction of temperature control into ab initio simulations. The analogous technique to model electrochemical systems -- potential control -- is just emerging. In this review, we critically discuss state-of-the-art approaches to describe electrified interfaces between a solid electrode and a liquid electrolyte in realistic environments. By exchanging energy, electronic charge and ions with their environment, electrochemical interfaces are thermodynamically open systems. In addition, on the time and length scales relevant for chemical reactions, large fluctuations of the electrostatic potential and field occur. We systematically discuss the key challenges in including these features in realistic ab initio simulations, as well as the available techniques and approaches to overcome them, in order to facilitate the development and use of these novel techniques to the wider community. These methodological developments provide researchers with a new level of realism to explore fundamental electrochemical mechanisms and reactions from first principles.

In this paper we consider the anisotropic curve shortening flow in the plane in the presence of an ambient force. We consider force fields in which all their derivatives are bounded in the $L^{\infty}$ sense. We prove that closed embedded curves that have a minimum of curvature sufficiently large shrink to round points. The method of proof follows along the same lines of Gage and Hamilton, in that we study a rescaling to prove curvature bounds. We additionally show that the influence of an ambient force field may make such a result untrue, by giving sufficient conditions on the ambient field that ensures eventual non-convexity of an initially convex curve evolving under the flow., Comment: 37 pages

Preferential diffusion plays a critical role in the evolution of lean premixed hydrogen flames, influencing flame surface corrugation and overall flame behavior. Simulating such flames with tabulated chemistry (TC) methods remains challenging due to the complexity of flame dynamics. A detailed assessment of flamelet-based manifolds for capturing these dynamics is still needed. This work incorporates preferential diffusion via mixture-averaged molecular diffusion within TC to study the propagation and structure of freely propagating hydrogen flames influenced by intrinsic instabilities. Model performance is evaluated against detailed chemistry (DC) calculations, focusing on linear and non-linear regimes and sensitivity to pressure and temperature variations. The impact of mesh resolution on flame response is also examined to assess the method's capabilities without subgrid models. The linear regime is analyzed through the dispersion relation, revealing that higher temperature or pressure extends the range of wave numbers accurately predicted by the model, although some overprediction of flame wrinkling in stable regions is observed. The nonlinear regime is assessed by comparing global flame parameters and flame structure to reference solutions, showing that the model captures key flame descriptors with relative errors under 20%. Overall, the model effectively reproduces key effects governing flames with thermodiffusive instabilities, offering a viable alternative to DC at a significantly reduced computational cost., Comment: 28 pages, 8 figures, preprint, submitted to international journal of hydrogen energy

Research in auditory, visual, and audiovisual speech recognition (ASR, VSR, and AVSR, respectively) has traditionally been conducted independently. Even recent self-supervised studies addressing two or all three tasks simultaneously tend to yield separate models, leading to disjoint inference pipelines with increased memory requirements and redundancies. This paper proposes unified training strategies for these systems. We demonstrate that training a single model for all three tasks enhances VSR and AVSR performance, overcoming typical optimisation challenges when training from scratch. Moreover, we introduce a greedy pseudo-labelling approach to more effectively leverage unlabelled samples, addressing shortcomings in related self-supervised methods. Finally, we develop a self-supervised pre-training method within our framework, proving its effectiveness alongside our semi-supervised approach. Despite using a single model for all tasks, our unified approach achieves state-of-the-art performance compared to recent methods on LRS3 and LRS2 for ASR, VSR, and AVSR, as well as on the newly released WildVSR dataset. Code and models are available at https://github.com/ahaliassos/usr., Comment: NeurIPS 2024. Code: https://github.com/ahaliassos/usr

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface phase diagrams of metal alloys. Two alloy systems are considered: a Mg surface with Ca substitutions and a Ni surface with Nb substitutions. To construct a firm basis against which the performance of the analytical model can be leveled, we first perform Monte Carlo (MC) simulations coupled with cluster expansion of density functional theory dataset. We then demonstrate the SPEA model to reproduce the MC results accurately. Specifically, it correctly predicts the surface order-disorder transitions as well as the coexistence of the 1/3 ordered phase and the disordered phase. Finally, we compare the SPEA method to the sublattice model commonly used in the CALPHAD approach to describe ordered and random solution phases and their transitions. The proposed SPEA model provides a highly efficient approach for modeling defect phase transformations., Comment: Main: 10 pagers, 9 figures, SI: 2 pages, 5 figures

This work examines the hypothesis that intravoxel incoherent motion MRI (IVIM) can quantify local cerebral blood flow (qCBF), infarct volume, and define the ischemic penumbra for determination of the perfusion-diffusion mismatch (PWI/DWI) volume in a setting of acute ischemic stroke. Eight experiments were conducted in a pre-clinical middle cerebral artery occlusion (MCAO) model. IVIM and dynamic susceptibility contrast (DSC) imaging were acquired 2.5hr post-MCAO. IVIM was post-processed using software written in-house to produce parametric images of local qCBF, Water Transport Time (WTT), diffusion, and subsequently, PWI/DWI mismatch. These IVIM image parameters were compared with delay-and-dispersion-corrected local-AIF DSC perfusion image parameters including Tmax, qCBF, mean transit time (MTT), and mean diffusivity for DSC PWI/DWI mismatch. Final infarct volume was measured 4hrs post-occlusion. Early (2.5hr post-occlusion) DSC qCBF and IVIM qCBF in the diffusion negative MCA territory correlated strongly (slope=1.00, p=0.01,R2=0.69,Lins CCC=0.71), and both DSC and IVIM qCBF values negatively correlated with final infarct volume (R2=0.78,R2=0.61 respectively). The volume of hypoperfusion measured at 2.5 hours from DSC qCBF and from IVIM qCBF both predicted final infarct volume with good sensitivity and correlation (slope=2.08, R2=0.67, slope=2.50,R2=0.68 respectively). IVIM PWI/DWI ratio was correlated with infarct growth (R2=0.70) and WTT correlated with MTT (slope=0.82,R2=0.60). IVIM qCBF correlated strongly with local-AIF DSC qCBF and IVIM PWI/DWI correlated strongly with infarct growth. Both DSC and IVIM quantitative perfusion image acquired early after occlusion were able to predict final infarct volume, and IVIM simultaneous PWI/DWI ratio predicted infarct growth., Comment: Incomplete analysis

Recent experiments suggest a new paradigm towards novel colossal magnetoresistance (CMR) in a family of materials EuM$_2$X$_2$(M=Cd, In, Zn; X=P, As), distinct from the traditional avenues involving Kondo-RKKY crossovers, magnetic phase transitions with structural distortions, or topological phase transitions. Here, we use angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) calculations to explore their origin, particularly focusing on EuCd$_2$P$_2$. While the low-energy spectral weight royally tracks that of the resistivity anomaly near the temperature with maximum magnetoresistance (T$_{MR}$) as expected from transport-spectroscopy correspondence, the spectra are completely incoherent and strongly suppressed with no hint of a Landau quasiparticle. Using systematic material and temperature dependence investigation complemented by theory, we attribute this non-quasiparticle caricature to the strong presence of entangled magnetic and lattice interactions, a characteristic enabled by the $p$-$f$ mixing. Given the known presence of ferromagnetic clusters, this naturally points to the origin of CMR being the scattering of spin-polarized polarons at the boundaries of ferromagnetic clusters. These results are not only illuminating to investigate the strong correlations and topology in EuCd$_2$X$_2$ family, but, in a broader view, exemplify how multiple cooperative interactions can give rise to extraordinary behaviors in condensed matter systems.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Quantum computing is an emerging technology whose positive and negative impacts on society are not yet fully known. As government, individuals, institutions, and corporations fund and develop this technology, they must ensure that they anticipate its impacts, prepare for its consequences, and steer its development in such a way that it enables the most good and prevents the most harm. However, individual stakeholders are not equipped to fully anticipate these consequences on their own it requires a diverse community that is well-informed about quantum computing and its impacts. Collaborations and community-building across domains incorporating a variety of viewpoints, especially those from stakeholders most likely to be harmed, are fundamental pillars of developing and deploying quantum computing responsibly. This paper reviews responsible quantum computing proposals and literature, highlights the challenges in implementing these, and presents strategies developed at IBM aimed at building a diverse community of users and stakeholders to support the responsible development of this technology.

Magnetoelectric (ME) materials displaying strong magnetically induced polarization have attracted considerable interest due to their potential applications in spintronics and various fast electrically controlled magnetic devices. CaMn$_7$O$_{12}$ (CMO) stands out for its giant spin-induced ferroelectric polarization. However, the origin of the induced electric polarization in CMO remains highly controversial and continues to be a subject of ongoing debate. In this paper, through room temperature X-ray powder diffraction (XRPD), temperature-dependent magnetic susceptibility, and thermally stimulated depolarizing current (TSDC) measurements, we provide experimental evidence for a route to tune the magnetically induced polarization by modifying the exchange-striction in CMO via Sr-doping. Our findings demonstrate that the large and broad current peaks observed near the first magnetic phase transition ($T_N1 \sim 90$ K) indicate contributions to the TSDC density from both extrinsic thermally stimulated depolarization processes and intrinsic pyroelectric current arising from magnetically induced polarization changes. We suggest that this reduction in induced electric polarization in CMO originates from the increase in the Mn$^{3+}$ -- O -- Mn$^{4+}$ bond angle due to Sr$^{2+}$ doping, weakening the exchange-striction interaction. Meanwhile, the Dzyaloshinskii-Moriya (DM) effect determines the direction of the induced electric polarization. Our result sheds light on understanding the intriguing giant-induced polarization in CMO and similar compounds with complex magnetic structures.

Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly construct SBI estimators, and compute and visualize posterior distributions with only a few lines of code. In addition, the package provides functionality for conventional approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics. By virtue of being entirely written in JAX, sbijax is extremely computationally efficient, allowing rapid training of neural networks and executing code automatically in parallel on both CPU and GPU.

While COVID-19 has resulted in a significant increase in global mortality rates, the impact of the pandemic on mortality from other causes remains uncertain. To gain insight into the broader effects of COVID-19 on various causes of death, we analyze an Italian dataset that includes monthly mortality counts for different causes from January 2015 to December 2020. Our approach involves a generalized additive model enhanced with correlated random effects. The generalized additive model component effectively captures non-linear relationships between various covariates and mortality rates, while the random effects are multivariate time series observations recorded in various locations, and they embody information on the dependence structure present among geographical locations and different causes of mortality. Adopting a Bayesian framework, we impose suitable priors on the model parameters. For efficient posterior computation, we employ variational inference, specifically for fixed effect coefficients and random effects, Gaussian variational approximation is assumed, which streamlines the analysis process. The optimisation is performed using a coordinate ascent variational inference algorithm and several computational strategies are implemented along the way to address the issues arising from the high dimensional nature of the data, providing accelerated and stabilised parameter estimation and statistical inference.

While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including inability to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAI-Co2, a novel human-AI co-construction framework. We formalize HAI-Co2 and discuss the difficult open research problems that it faces. Finally, we present a case study of HAI-Co2 and demonstrate its efficacy compared to monolithic generative AI models., Comment: 15 pages main content; 3 pages Appendix; 5 figures

Purpose: Estimation of multi-compartment intravoxel flow in fD in ml/100g/min with multi-b-value diffusion weighted imaging and a multi-Gaussian model in the kidneys. Theory and Methods: A multi-Gaussian model of intravoxel flow using water transport time to quantify fD is presented and simulated. Multi-compartment anisotropic DWI signal is simulated analyzed with (1) a rigid bi-exponential, (2) a rigid tri-exponential, and (3) diffusion spectrum imaging model of intravoxel incoherent motion (spectral diffusion). The application is demonstrated in a two-center study of 54 kidney allografts with 9 b-value advanced DWI that were split by function (CKD-EPI 2021 eGFR<45ml/min/1.73m2) and fibrosis (Banff 2017 interstitial fibrosis and tubular atrophy score 0-6). Results: Spectral diffusion demonstrated strong correlation to truth for simulated three-compartment anisotropic diffusion (y=1.08x+0.1, R2=0.71) and two-compartment anisotropic diffusion (y=0.91x+0.6, R2=0.74), outperforming rigid models in cases of variable compartment number. Use of a fixed regularization parameter set to {\lambda}=0.1 increased computation up to 208-fold and agreed with voxel-wise cross-validated regularization (concordance correlation coefficient=0.99). Spectral diffusion of renal allografts showed significant increase in tissue parenchyma compartment fD (f-stat=3.86, p=0.02). Tubular fD was significantly decreased in allografts with impaired function (Mann-Whitney Utest t-stat=-2.14, p=0.04). Conclusions: Quantitative multi-compartment intravoxel flow can be estimated in ml/100g/min with fD from multi-Gaussian diffusion, even with moderate anisotropy such as in kidneys. The use of spectral diffusion with a multi-Gaussian model and a fixed regularization parameter shows promise in organs such as the kidney with variable numbers of physiologic compartments.

A numerical study of the effects of air dilution on soot formation and particle dynamics in a lab-scale rich-quench-lean (RQL) combustor is presented using large-eddy simulations (LES) with tabulated chemistry. The modelling approach comprises a flamelet generated manifold (FGM) turbulent combustion model and an efficient discrete sectional method with clustering of sections (CDSM) to model soot formation. Three operating conditions are studied including a reference case without secondary air dilution and two cases with varying air dilution levels. For the latter, the split between primary and dilution air is varied from 80:20 to 60:40 expressed in percentage values. The study aims to investigate the effect of air dilution on the formation and oxidation of soot for various air splits using a direct comparison with the available experimental data. The results show a good correlation between predicted flame topology and spatial distribution of the soot volume fraction with the experimental observations. The introduction of air dilution is found to limit the production of soot with a more drastic reduction for the 40% dilution case compared to the 20% condition. Predicted particle size distributions (PSD) from the case without secondary air dilution correlate well with scanning mobility particle sizer (SMPS) measurements although fewer and smaller particles are predicted with air dilution. Leaner mixtures and enhanced oxidation, resulting from the interaction with air dilution jets, favor the decrease in soot formation., Comment: 10 pages, 9 figures, Combustion Institute 40th International Symposium - Emphasizing Energy Transition

Aqueous metal corrosion is a major economic concern in modern society. A phenomenon that has puzzled generations of scientists in this field is the so-called anomalous hydrogen evolution: the violent dissolution of magnesium under electron-rich (anodic) conditions, accompanied by strong hydrogen evolution, and a key mechanism hampering Mg technology. Experimental studies have indicated the presence of univalent Mg$^+$ in solution, but these findings have been largely ignored because they defy our common chemical understanding and evaded direct experimental observation. Using recent advances in the \emph{ab initio} description of solid-liquid electrochemical interfaces under controlled potential conditions, we described the full reaction path of Mg atom dissolution from a kinked Mg surface under anodic conditions. Our study reveals the formation of a solvated [Mg$^{2+}$(OH)$^-$]$^+$ ion complex, challenging the conventional assumption of Mg$^{2+}$ ion. This insight provides an intuitive explanation for the postulated presence of (coulombically) univalent Mg$^+$ ions and the absence of protective oxide/hydroxide layers normally formed under anodic/oxidizing conditions. The discovery of this unexpected and unconventional reaction mechanism is crucial for identifying new strategies for corrosion prevention and can be transferred to other metals.

The combined structural and electronic complexity of iron oxides poses many challenges to atomistic modeling. To leverage limitations in terms of the accessible length and time scales, one requires a physically justified interatomic potential which is accurate to correctly account for the complexity of iron-oxygen systems. Such a potential is not yet available in the literature. In this work, we propose a machine-learning potential based on the Atomic Cluster Expansion for modeling the iron-oxygen system, which explicitly accounts for magnetism. We test the potential on a wide range of properties of iron and its oxides, and demonstrate its ability to describe the thermodynamics of systems spanning the whole range of oxygen content and including magnetic degrees of freedom., Comment: Manuscript: 14 pages, 9 figures, SI: 6 pages, 7 figures

In this paper, we aim to generate clean speech frame by frame from a live video stream and a noisy audio stream without relying on future inputs. To this end, we propose RT-LA-VocE, which completely re-designs every component of LA-VocE, a state-of-the-art non-causal audio-visual speech enhancement model, to perform causal real-time inference with a 40ms input frame. We do so by devising new visual and audio encoders that rely solely on past frames, replacing the Transformer encoder with the Emformer, and designing a new causal neural vocoder C-HiFi-GAN. On the popular AVSpeech dataset, we show that our algorithm achieves state-of-the-art results in all real-time scenarios. More importantly, each component is carefully tuned to minimize the algorithm latency to the theoretical minimum (40ms) while maintaining a low end-to-end processing latency of 28.15ms per frame, enabling real-time frame-by-frame enhancement with minimal delay., Comment: Interspeech 2024

Large Language Models trained on code corpora (code-LLMs) have demonstrated impressive performance in various coding assistance tasks. However, despite their increased size and training dataset, code-LLMs still have limitations such as suggesting codes with syntactic errors, variable misuse etc. Some studies argue that code-LLMs perform well on coding tasks because they use self-attention and hidden representations to encode relations among input tokens. However, previous works have not studied what code properties are not encoded by code-LLMs. In this paper, we conduct a fine-grained analysis of attention maps and hidden representations of code-LLMs. Our study indicates that code-LLMs only encode relations among specific subsets of input tokens. Specifically, by categorizing input tokens into syntactic tokens and identifiers, we found that models encode relations among syntactic tokens and among identifiers, but they fail to encode relations between syntactic tokens and identifiers. We also found that fine-tuned models encode these relations poorly compared to their pre-trained counterparts. Additionally, larger models with billions of parameters encode significantly less information about code than models with only a few hundred million parameters.

This paper investigates the relationship between initial spatial anisotropy and final state momentum anisotropy in heavy ion collisions through the analysis of elliptic flow ($v_2$) as a function of transverse momentum ($p_T$). Building upon previous studies on thermalization in heavy ion collisions using transverse momentum distributions, we extend the analysis to the $p_T$ dependence of $v_2$ in Pb-Pb and Xe-Xe collisions. By employing a two-component model to extract the thermal and hard scattering contributions to the elliptic flow, we aim to gain further insights into the role of quantum entanglement in the rapid thermalization and collective behavior of the quark-gluon plasma (QGP)., Comment: The arXiv version of this paper incorporates additional theoretical context about quantum entanglement in heavy ion collisions based on recent literature. The core contribution is our systematic measurement and analysis of the two-component behavior in elliptic flow across different centralities in Xe-Xe and Pb-Pb collisions. A revised version of this work has been prepared for journal submission

The European carbon market plays a pivotal role in the European Union's ambitious target of achieving carbon neutrality by 2050. Understanding the intricacies of factors influencing European Union Emission Trading System (EU ETS) market prices is paramount for effective policy making and strategy implementation. We propose the use of the Information Imbalance, a recently introduced non-parametric measure quantifying the degree to which a set of variables is informative with respect to another one, to study the relationships among macroeconomic, economic, uncertainty, and energy variables concerning EU ETS prices. Our analysis shows that in Phase 3 commodity related variables such as the ERIX index are the most informative to explain the behaviour of the EU ETS market price. Transitioning to Phase 4, financial fluctuations take centre stage, with the uncertainty in the EUR/CHF exchange rate emerging as a crucial determinant. These results reflect the disruptive impacts of the COVID-19 pandemic and the energy crisis in reshaping the importance of the different variables. Beyond variable analysis, we also propose to leverage the Information Imbalance to address the problem of mixed-frequency forecasting, and we identify the weekly time scale as the most informative for predicting the EU ETS price. Finally, we show how the Information Imbalance can be effectively combined with Gaussian Process regression for efficient nowcasting and forecasting using very small sets of highly informative predictors., Comment: 17 pages, 9 figures, 3 tables

In ischemic stroke, leptomeningeal collaterals can provide compensatory blood flow to tissue at risk despite an occlusion, and impact treatment response and infarct growth. The purpose of this work is to test the hypothesis that local perfusion with an appropriate Local Arterial Input Function (AIF) is needed to quantify the degree of collateral blood supply in tissue distal to an occlusion. Seven experiments were conducted in a pre-clinical middle cerebral artery occlusion model. Magnetic resonance dynamic susceptibility contrast (DSC) was imaged and post-processed as cerebral blood flow maps with both a traditionally chosen single arterial input function (AIF) applied globally to the whole brain (i.e. "Global-AIF") and a novel automatic delay and dispersion corrected AIF (i.e. "Local AIF") that is sensitive to retrograde flow. Pial collateral recruitment was assessed from x-ray angiograms and infarct growth via serially acquired diffusion weighted MRI scans both blinded to DSC. The degree of collateralization at x-ray correlated strongly with quantitative perfusion determined using the Local AIF in the ischemic penumbra (R2=0.81) compared to a traditionally chosen Global-AIF (R2=0.05). Quantitative perfusion calculated using a Local-AIF was negatively correlated (less infarct progression as local perfusion increased) with infarct growth (R2 = 0.79) compared to Global-AIF (R2=0.02). Local DSC perfusion with a Local-AIF is more accurate for assessing tissue status and degree of leptomeningeal collateralization than traditionally chosen AIFs. These findings support use of a Local-AIF in determining quantitative tissue perfusion with collateral supply in occlusive disease., Comment: 13 pages, 5 figures

Linear algebra computations are foundational for neural networks and machine learning, often handled through arrays. While many functional programming languages feature lists and recursion, arrays in linear algebra demand constant-time access and bulk operations. To bridge this gap, some languages represent arrays as (eager) functions instead of lists. In this paper, we connect this idea to a formal logical foundation by interpreting functions as the usual negative types from polarized type theory, and arrays as the corresponding dual positive version of the function type. Positive types are defined to have a single elimination form whose computational interpretation is pattern matching. Just like (positive) product types bind two variables during pattern matching, (positive) array types bind variables with multiplicity during pattern matching. We follow a similar approach for Booleans by introducing conditionally-defined variables. The positive formulation for the array type enables us to combine typed partial evaluation and common subexpression elimination into an elegant algorithm whose result enjoys a property we call maximal fission, which we argue can be beneficial for further optimizations. For this purpose, we present the novel intermediate representation indexed administrative normal form (AiNF), which relies on the formal logical foundation of the positive formulation for the array type to facilitate maximal loop fission and subsequent optimizations. AiNF is normal with regard to commuting conversion for both let-bindings and for-loops, leading to flat and maximally fissioned terms. We mechanize the translation and normalization from a simple surface language to AiNF, establishing that the process terminates, preserves types, and produces maximally fissioned terms., Comment: to appear at ECOOP'24

Federated learning (FL) allows for collaborative model training across decentralized clients while preserving privacy by avoiding data sharing. However, current FL methods assume conditional independence between client models, limiting the use of priors that capture dependence, such as Gaussian processes (GPs). We introduce the Structured Independence via deep Generative Model Approximation (SIGMA) prior which enables FL for non-factorizable models across clients, expanding the applicability of FL to fields such as spatial statistics, epidemiology, environmental science, and other domains where modeling dependencies is crucial. The SIGMA prior is a pre-trained deep generative model that approximates the desired prior and induces a specified conditional independence structure in the latent variables, creating an approximate model suitable for FL settings. We demonstrate the SIGMA prior's effectiveness on synthetic data and showcase its utility in a real-world example of FL for spatial data, using a conditional autoregressive prior to model spatial dependence across Australia. Our work enables new FL applications in domains where modeling dependent data is essential for accurate predictions and decision-making., Comment: 25 pages, 7 figures, 2 tables

Mixed-consistency programming models assist programmers in designing applications that provide high availability while still ensuring application-specific safety invariants. However, existing models often make specific system assumptions, such as building on a particular database system or having baked-in coordination strategies. This makes it difficult to apply these strategies in diverse settings, ranging from client/server to ad-hoc peer-to-peer networks. This work proposes a new strategy for building programmable coordination mechanisms based on the algebraic replicated data types (ARDTs) approach. ARDTs allow for simple and composable implementations of various protocols, while making minimal assumptions about the network environment. As a case study, two different locking protocols are presented, both implemented as ARDTs. In addition, we elaborate on our ongoing efforts to integrate the approach into the LoRe mixed-consistency programming language.

Robust Bayesian analysis has been mainly devoted to detecting and measuring robustness to the prior distribution. Indeed, many contributions in the literature aim to define suitable classes of priors which allow the computation of variations of quantities of interest while the prior changes within those classes. The literature has devoted much less attention to the robustness of Bayesian methods to the likelihood function due to mathematical and computational complexity, and because it is often arguably considered a more objective choice compared to the prior. In this contribution, a new approach to Bayesian local robustness to the likelihood function is proposed and extended to robustness to the prior and to both. This approach is based on the notion of distortion function introduced in the literature on risk theory, and then successfully adopted to build suitable classes of priors for Bayesian global robustness to the prior. The novel robustness measure is a local sensitivity measure that turns out to be very tractable and easy to compute for certain classes of distortion functions. Asymptotic properties are derived and numerical experiments illustrate the theory and its applicability for modelling purposes.

The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typically at a small scale, the ID is very large, as the data are affected by measurement errors. At large scale, the ID can also be erroneously large, due to the curvature and the topology of the manifold containing the data. In this work, we introduce an automatic protocol to select the sweet spot, namely the correct range of scales in which the ID is meaningful and useful. This protocol is based on imposing that for distances smaller than the correct scale the density of the data is constant. In the presented framework, to estimate the density it is necessary to know the ID, therefore, this condition is imposed self-consistently. We derive theoretical guarantees and illustrate the usefulness and robustness of this procedure by benchmarks on artificial and real-world datasets.

This study evaluates the effectiveness of non-thermal plasma at atmospheric pressure (NTP APPJ) for treating PFAS - contaminated water in different matrices. Successful removal of several perfluoroalkyl carboxylic acids (PFCAs) (C6 to C4), perfluroalkane sulfonic acids (PFSAs) (C8 to C4) and perfluropolyethers (PFPEs) (GenX and ADONA) PFAS compounds was achieved in laboratory scale experiments. Complex matrix effects influence degradation rates. Byproducts from the plasma treatment were investigated, revealing distinct degradation mechanisms for various PFAS compounds. For PFSAs and PFCAs, degradation involved electron transfer, bond breaking and subsequent reactions. Conversely, ADONA and GenX degradation initiated with ether-group cleavage, followed by additional transformation processes. Plasmabased technology shows potential for degradation of PFAS, especially for newer substitute compounds like ADONA and GenX. However, further research is needed to optimize plasma performance for complete mineralization of PFAS. This study also proposes a degradation mechanism for ADONA, marking a novel investigation into ether-group PFAS degradation with potential implications for further research and understanding toxicological implications.

Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introduces the first comprehensive framework for fitting Bayesian models in the VFL setting. We propose a novel approach that leverages data augmentation techniques to transform VFL problems into a form compatible with existing Bayesian federated learning algorithms. We present an innovative model formulation for specific VFL scenarios where the joint likelihood factorizes into a product of client-specific likelihoods. To mitigate the dimensionality challenge posed by data augmentation, which scales with the number of observations and clients, we develop a factorized amortized variational approximation that achieves scalability independent of the number of observations. We showcase the efficacy of our framework through extensive numerical experiments on logistic regression, multilevel regression, and a novel hierarchical Bayesian split neural net model. Our work paves the way for privacy-preserving, decentralized Bayesian inference in vertically partitioned data scenarios, opening up new avenues for research and applications in various domains., Comment: 30 pages, 5 figures, 3 tables

We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing -- the removal of furniture items from indoor panorama images. Specifically, we illustrate how increased context, domain-specific model fine-tuning, and improved image blending can produce high-fidelity inpaints that are geometrically plausible without needing to rely on room layout estimation. We demonstrate qualitative and quantitative improvements over other furniture removal techniques., Comment: Accepted at CVPR 2024 workshops. Project page: https://matterport.github.io/automatic-defurnishing-of-indoor-panoramas/