Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Borders: Spatial Disparities in the Mortality Burden of the Covid-19 pandemic across 569 European Regions (2020-2021)

This article presents a detailed analysis of the global mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost (ASYLL) in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a robust forecasting approach that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). Most importantly, we estimate that eight regions suffered losses exceeding 20 years of life per 1,000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic's impact.
Free-floating or Wide-orbit? Keck Adaptive-optics Observations of Free-floating Planet Candidates Detected with Gravitational Microlensing

Recent detections of extremely short-timescale microlensing events imply the existence of a large population of Earth- to Neptune-mass planets that appear to have no host stars. However, it is currently unknown whether these objects are truly free-floating planets or whether they are in wide orbits around a distant host star. Here, we present an analysis of high-resolution imaging observations of five free-floating planet candidates collected with the Keck telescope. If these candidates were actually wide-orbit planets, then the light of the host would appear at a separation of 40–60 mas from the microlensing source star. No such stars are detected. We carry out injection and recovery simulations to estimate the sensitivity to putative host stars at different separations. Depending on the object, the presented observations rule out 11%–36% of potential hosts assuming that the probability of hosting a planet does not depend on the host mass. The results are sensitive to the latter assumption, and the probability of detecting the host star in the analyzed images may be a factor of 1.9 ± 0.1 larger, if the exoplanet hosting probability scales as the first power of the host star mass, as suggested by recent studies of planetary microlensing events. We argue that deeper observations, for example with JWST, are needed to confidently confirm or refute the free-floating planet hypothesis.
Auditable Algorithms for Approximate Model Counting

The problem of model counting, i.e., counting satisfying assignments of a Boolean formula, is a fundamental problem in computer science, with diverse applications. Given #P-hardness of the problem, many algorithms have been developed over the years to provide an approximate model count. Recently, building on the practical success of SAT-solvers used as NP oracles, the focus has shifted from theory to practical implementations of such algorithms. This has brought to focus new challenges. In this paper, we consider one such challenge – that of auditable deterministic approximate model counters wherein a counter should also generate a certificate, which allows a user (often with limited computational power) to independently audit whether the count returned by an invocation of the algorithm is indeed within the promised bounds. We start by examining a celebrated approximate model counting algorithm due to Stockmeyer that uses polynomially many calls to a \Sigma^2_P oracle, and show that it can be audited via a \Pi^2_P formula on (n^2 log^2 n) variables, where n is the number of variables in the original formula. Since n is often large (10’s to 100’s of thousands) for typical instances, we ask if the count of variables in the certificate formula can be reduced – a critical question towards potential implementation. We show that this improvement in certification can be achieved with a tradeoff in the counting algorithm’s complexity. Specifically, we develop new deterministic approximate model counting algorithms that invoke a \Sigma^3_P oracle, but can be certified using a \Pi^2_P formula on fewer variables: our final algorithm uses just (n log n) variables. Our study demonstrates that one can simplify certificate checking significantly if we allow the counting algorithm to access a slightly more powerful oracle. We believe this shows for the first time how the audit complexity can be traded for the complexity of approximate counting.
Privacy-preserving transactive energy systems: Key topics and open research challenges

This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023. We present important ideas and discussion topics in the context of transactive energy systems. Moreover, the conclusions from the discussions articulate potential aspects to be explored in future studies on transactive energy management. Particularly, these conclusions cover research topics in energy technology and energy informatics, energy law, data law, energy market and socio-psychology that are relevant to the seamless integration of renewable energy resources and the transactive energy systems-in smart microgrids-focusing on distributed frameworks such as peer-to-peer (P2P) energy trading. We clarify issues, identify barriers, and suggest possible solutions to open questions in diversified topics, such as block-chain interoperability, consumer privacy and data sharing, and participation incentivization. Furthermore, we also elaborate challenges associated with cross-disciplinary collaboration and coordination for transactive energy systems, and enumerate the lessons learned from our work so far.
Relightable Neural Actor with Intrinsic Decomposition and Pose Control

Creating a digital human avatar that is relightable, drivable, and photorealistic is a challenging and important problem in Vision and Graphics. Humans are highly articulated creating pose-dependent appearance effects like self-shadows and wrinkles, and skin as well as clothing require complex and space-varying BRDF models. While recent human relighting approaches can recover plausible material-light decompositions from multi-view video, they do not generalize to novel poses and still suffer from visual artifacts. To address this, we propose Relightable Neural Actor, the first video-based method for learning a photorealistic neural human model that can be relighted, allows appearance editing, and can be controlled by arbitrary skeletal poses. Importantly, for learning our human avatar, we solely require a multi-view recording of the human under a known, but static lighting condition. To achieve this, we represent the geometry of the actor with a drivable density field that models pose-dependent clothing deformations and provides a mapping between 3D and UV space, where normal, visibility, and materials are encoded. To evaluate our approach in real-world scenarios, we collect a new dataset with four actors recorded under different light conditions, indoors and outdoors, providing the first benchmark of its kind for human relighting, and demonstrating state-of-the-art relighting results for novel human poses.
Connection between Subsurface Layers and Surface Magnetic Activity over Multiple Solar Cycles Using GONG Observations

We investigate the spatiotemporal evolution of high-degree acoustic-mode frequencies of the Sun and surface magnetic activity over the course of multiple solar cycles, to improve our understanding of the connection between the solar interior and atmosphere. We focus on high-degree p-modes due to their ability to characterize conditions in the shear layer just below the solar surface, and analyze 22 yr of oscillation frequencies obtained from the Global Oscillation Network Group. Considering 10.7 cm radio flux measurements, the sunspot number, and the local magnetic activity index as solar-activity proxies, we find strong correlation between the mode frequencies and each activity index. We further investigate the hemispheric asymmetry associated with oscillation frequencies and magnetic activity proxies, and find that both were dominant in the southern hemisphere during the descending phase of cycle 23, while in cycle 24 these quantities fluctuated between northern and southern hemispheres. Analyzing the frequencies at different latitudes with the progression of solar cycles, we observe that the variations at midlatitudes were dominant in the southern hemisphere during the maximum-activity period of cycle 24, but the values overlap as the cycle advances toward the minimum phase. The mode frequencies at the beginning of cycle 25 are found to be dominant in the southern hemisphere following the pattern of magnetic activity. The analysis provides added evidence that the variability in oscillation frequencies is caused by both strong and weak magnetic fields.
Global regularity for critical SQG in bounded domains

We prove the existence and uniqueness of global smooth solutions of the critical dissipative SQG equation in bounded domains in $\mathbb R^2$. This solves an open problem. We introduce a new methodology of transforming the single nonlocal nonlinear evolution equation in a bounded domain into an interacting system of extended nonlocal nonlinear evolution equations in the whole space. The proof then uses the method of the nonlinear maximum principle for nonlocal operators in the extended system.
Toward Responsible AI Use: Considerations for Sustainability Impact Assessment

As AI/ML models, including Large Language Models, continue to scale with massive datasets, so does their consumption of undeniably limited natural resources, and impact on society. In this collaboration between AI, Sustainability, HCI and legal researchers, we aim to enable a transition to sustainable AI development by enabling stakeholders across the AI value chain to assess and quantitfy the environmental and societal impact of AI. We present the ESG Digital and Green Index (DGI), which offers a dashboard for assessing a company's performance in achieving sustainability targets. This includes monitoring the efficiency and sustainable use of limited natural resources related to AI technologies (water, electricity, etc). It also addresses the societal and governance challenges related to AI. The DGI creates incentives for companies to align their pathway with the Sustainable Development Goals (SDGs). The value, challenges and limitations of our methodology and findings are discussed in the paper.
Poster: Reproducibility-Oriented and Privacy-Preserving Genomic Dataset Sharing

—The increasing pace in genomic research has brought a high demand for genomic datasets in recent years, yet few studies have released their datasets due to privacy concerns. This poses a problem while validating and reproducing the published results. In this work, in order to promote reproducibility of genome-related research, we propose a novel scheme for sharing genomic datasets under differential privacy. Our scheme shows great performance in terms of genome-wide association studies (GWAS) reproducibility, other data utility metrics, resistance against membership inference attacks, and running time. By constraining the privacy leakage, our mechanism is able to encourage the sharing of a genomic dataset along with the research results on it.
Travelling pulses on three spatial scales in a Klausmeier-type vegetation-autotoxicity model

Reaction-diffusion models describing interactions between vegetation and water reveal the emergence of several types of patterns and travelling wave solutions corresponding to structures observed in real-life. Increasing their accuracy by also considering the ecological factor known as autotoxicity has lead to more involved models supporting the existence of complex dynamic patterns. In this work, we include an additional carrying capacity for the biomass in a Klausmeier-type vegetation-water-autotoxicity model, which induces the presence of two asymptotically small parameters: $\varepsilon$, representing the usual scale separation in vegetation-water models, and $\delta$, directly linked to autotoxicity. We construct three separate types of homoclinic travelling pulse solutions based on two different scaling regimes involving $\varepsilon$ and $\delta$, with and without a so-called superslow plateau. The relative ordering of the small parameters significantly influences the phase space geometry underlying the construction of the pulse solutions. We complement the analysis by numerical continuation of the constructed pulse solutions, and demonstrate their existence (and stability) by direct numerical simulation of the full PDE model.
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments

Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments. However, most models used within this domain are able to identify positive or negative sentiment associated with a textual appraisal as a whole, without inferring information about specific urban aspects contained within it, or the sentiment associated with them. While Aspect Based Sentiment Analysis (ABSA) is becoming increasingly popular, most existing ABSA models are trained on non-urban themes such as restaurants, electronics, consumer goods and the like. This body of research develops an ABSA model capable of extracting urban aspects contained within geo-located textual urban appraisals, along with corresponding aspect sentiment classification. We annotate a dataset of 2500 crowdsourced reviews of public parks, and train a Bidirectional Encoder Representations from Transformers (BERT) model with Local Context Focus (LCF) on this data. Our model achieves significant improvement in prediction accuracy on urban reviews, for both Aspect Term Extraction (ATE) and Aspect Sentiment Classification (ASC) tasks. For demonstrative analysis, positive and negative urban aspects across Boston are spatially visualized. We hope that this model is useful for designers and planners for fine-grained urban sentiment evaluation.
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing

Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to find and then resolve urban infrastructural problems such as fallen street trees, flooded basements, or rat infestations. Without additional assumptions, there is no way to distinguish events that occur but are not reported from events that truly did not occur--a fundamental problem in settings with positive-unlabeled data. Because disparities in reporting rates correlate with resident demographics, addressing incidents only on the basis of reports leads to systematic neglect in neighborhoods that are less likely to report events. We show how to overcome this challenge by leveraging the fact that events are spatially correlated. Our framework uses a Bayesian spatial latent variable model to infer event occurrence probabilities and applies it to storm-induced flooding reports in New York City, further pooling results across multiple storms. We show that a model accounting for under-reporting and spatial correlation predicts future reports more accurately than other models, and further induces a more equitable set of inspections: its allocations better reflect the population and provide equitable service to non-white, less traditionally educated, and lower-income residents. This finding reflects heterogeneous reporting behavior learned by the model: reporting rates are higher in Census tracts with higher populations, proportions of white residents, and proportions of owner-occupied households. Our work lays the groundwork for more equitable proactive government services, even with disparate reporting behavior.
Searching for Axial Neutral Current Non-Standard Interactions of neutrinos by DUNE-like experiments

The increasingly precise neutrino experiments raise the hope for searching for new physics through studying the impact of Neutral Current (NC) Non-Standard Interactions (NSI) of neutrinos with matter fields. Neutrino oscillation experiments along with the Elastic Coherent $\nu$ Nucleus Scattering (CE$\nu$NS) experiments already set strong bounds on all the flavor elements of the"vector"NC NSI. However,"axial"NC NSI can hide from these experiments. We show how a DUNE-like experiment can probe these couplings by studying NC Deep Inelastic Scattering (DIS) events. We find that strong bounds can be set on the axial NC NSI of neutrinos with the $u$, $d$, and $s$ quarks. We show that using both the near and far detectors, a DUNE-like experiment can significantly improve the present bounds on all the flavor elements.
3D-LFM: Lifting Foundation Model

The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP) problems, but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3PDO and PAUL) with resilience to noise, occlusions, and perspective distortions. All these techniques, however, have been limited by the fundamental need to establish correspondences across the 3D training data -- significantly limiting their utility to applications where one has an abundance of"in-correspondence"3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying number of points per 3D data instance, withstands occlusions, and generalizes to unseen categories. We demonstrate state of the art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind.
Long-living prethermalization in nearly integrable spin ladders

Relaxation rates in nearly integrable systems usually increase quadratically with the strength of the perturbation that breaks integrability. We show that the relaxation rates can be significantly smaller in systems that are integrable along two intersecting lines in the parameter space. In the vicinity of the intersection point, the relaxation rates of certain observables increase with the fourth power of the distance from this point, whereas for other observables one observes standard quadratic dependence on the perturbation. As a result, one obtains exceedingly long-living prethermalization but with a reduced number of the nearly conserved operators. We show also that such a scenario can be realized in spin ladders.
ACCL+: an FPGA-Based Collective Engine for Distributed Applications

FPGAs are increasingly prevalent in cloud deployments, serving as Smart NICs or network-attached accelerators. Despite their potential, developing distributed FPGA-accelerated applications remains cumbersome due to the lack of appropriate infrastructure and communication abstractions. To facilitate the development of distributed applications with FPGAs, in this paper we propose ACCL+, an open-source versatile FPGA-based collective communication library. Portable across different platforms and supporting UDP, TCP, as well as RDMA, ACCL+ empowers FPGA applications to initiate direct FPGA-to-FPGA collective communication. Additionally, it can serve as a collective offload engine for CPU applications, freeing the CPU from networking tasks. It is user-extensible, allowing new collectives to be implemented and deployed without having to re-synthesize the FPGA circuit. We evaluated ACCL+ on an FPGA cluster with 100 Gb/s networking, comparing its performance against software MPI over RDMA. The results demonstrate ACCL+'s significant advantages for FPGA-based distributed applications and highly competitive performance for CPU applications. We showcase ACCL+'s dual role with two use cases: seamlessly integrating as a collective offload engine to distribute CPU-based vector-matrix multiplication, and serving as a crucial and efficient component in designing fully FPGA-based distributed deep-learning recommendation inference.
Path Planning for Continuum Rods Using Bernstein Surfaces

This paper presents a method for optimal motion planning of continuum robots by employing Bernstein surfaces to approximate the system's dynamics and impose complex constraints, including collision avoidance. The main contribution is the approximation of infinite-dimensional continuous problems into their discrete counterparts, facilitating their solution using standard optimization solvers. This discretization leverages the unique properties of Bernstein surface, providing a framework that extends previous works which focused on ODEs approximated by Bernstein polynomials. Numerical validations are conducted through several numerical scenarios. The presented methodology offers a promising direction for solving complex optimal control problems in the realm of soft robotics.
Exact Tunneling Solutions in Multi-Field Potentials

The tunneling potential formalism makes it easy to construct exact solutions to the vacuum decay problem in potentials with multiple fields. While some exact solutions for single-field decays were known, we present the first nontrivial analytic examples with two and three scalar fields, and show how the method can be generalized to include gravitational corrections. Our results illuminate some analytic properties of the tunneling potential functions and can have a number of uses, among others: to serve as simple approximations to realistic potentials; to learn about parametric dependencies of decay rates; to check conjectures on vacuum decay; as benchmarks for multi-field numerical codes; or to study holographic interpretations of vacuum decay.
The Near-infrared Extinction Law at High and Low Galactic Latitudes

The Milky Way dust extinction curve in the near-infrared (NIR) follows a power-law form, but the value of the slope, β NIR, is debated. Systematic variations in the slope of the Milky Way UV extinction curve are known to be correlated with variations in the optical slope (through R V ), but whether such a dependence extends to the NIR is unclear. Finally, because of low dust column densities, the NIR extinction law is poorly understood at high Galactic latitudes where most extragalactic work takes place. In this paper, we construct extinction curves from 56,649 stars with Sloan Digital Sky Survey (SDSS) and Two Micron All Sky Survey photometry, based on stellar parameters from SDSS spectra. We use dust maps to identify dust-free stars, from which we calibrate the relation between stellar parameters and intrinsic colors. Furthermore, to probe the low-dust regime at high latitudes, we use aggregate curves based on many stars. We find no systematic variation of β NIR across low-to-moderate dust columns (0.02 < E(B − V) ≲ 1), and report average β NIR = 1.85 ± 0.01, in agreement with the law in the 2019 Fitzpatrick et al. study, but steeper than the Cardelli et al. and 1999 Fitzpatrick laws. Star-to-star scatter in β NIR is relatively small (σ(β NIR) = 0.13). We also find no intrinsic correlation between β NIR and R V (there is an apparent correlation that is the result of the correlated uncertainties in the two values). These results hold for typical sightlines; we do not probe very dusty regions near the Galactic Center, nor rare sightlines with R V > 4. Finally, we find R H = 0.345 ± 0.007 and comment on its bearing on Cepheid calibrations and the determination of H 0.