Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling evidential cooperation in large worlds

Evidential cooperation in large worlds (ECL) refers to the idea that humans and other agents can benefit by cooperating with similar agents with differing values in causally disconnected parts of a large universe. Cooperating provides agents with evidence that other similar agents are likely to cooperate too, resulting in gains from trade for all. This could be a crucial consideration for altruists. I develop a game-theoretic model of ECL as an incomplete information bargaining problem. The model incorporates uncertainty about others' value systems and empirical situations, and addresses the problem of selecting a compromise outcome. Using the model, I investigate issues with ECL and outline open technical and philosophical questions. I show that all cooperators must maximize the same weighted sum of utility functions to reach a Pareto optimal outcome. However, I argue against selecting a compromise outcome implicitly by normalizing utility functions. I review bargaining theory and argue that the Nash bargaining solution could be a relevant Schelling point. I introduce dependency equilibria (Spohn 2007), an equilibrium concept suitable for ECL, and generalize a folk theorem showing that the Nash bargaining solution is a dependency equilibrium. I discuss gains from trade given uncertain beliefs about other agents and analyze how these gains decrease in several toy examples as the belief in another agent decreases. Finally, I discuss open issues in my model. First, the Nash bargaining solution is sometimes not coalitionally stable, meaning that a subset of cooperators can unilaterally improve payoffs by deviating from the compromise. I investigate conditions under which stable payoff vectors exist. Second, I discuss how to model agents' default actions without ECL.
The Great Dimming of the Hypergiant Star RW Cephei: CHARA Array Images and Spectral Analysis

The cool hypergiant star RW Cephei is currently in a deep photometric minimum that began several years ago. This event bears a strong similarity to the Great Dimming of the red supergiant Betelgeuse that occurred in 2019–2020. We present the first resolved images of RW Cephei that we obtained with the CHARA Array interferometer. The angular diameter and Gaia distance estimates indicate a stellar radius of 900–1760 R ⊙, which makes RW Cephei one of the largest stars known in the Milky Way. The reconstructed, near-infrared images show a striking asymmetry in the disk illumination with a bright patch offset from the center and a darker zone to the west. The imaging results depend on assumptions made about the extended flux, and we present two cases with and without allowing extended emission. We also present a recent near-infrared spectrum of RW Cep that demonstrates that the fading is much larger at visual wavelengths compared to that at near-infrared wavelengths as expected for extinction by dust. We suggest that the star’s dimming is the result of a recent surface mass ejection event that created a dust cloud that now partially blocks the stellar photosphere.
Speed of Atomic Particles and Physical Constants

This is a new updated and corrected version of the work with the same name published on January 31, 2008. With the acquired experience during the research of nuclear stability of hydrogen and helium family, I have already been able to determine with great precision the magnetic constant correlation taking as to the other well-known fundamental constants and solving an old question about the “Finestructure constant”. Another question resolved is the Planck constant that result to be exactly equal to the product of the period per the kinetic energy of the particle in rest (in the orbit of cardinal radius – quantum unitary state–). Also, I give an appropriate answer to the question on trajectory of atomic particles in orbit to the speed of the light, because in their displacement the helix forms a toroidal magnetic field, the resulting force of this magnetic field establishes the equilibrium in magnetic-dynamics form with enormous inertial resultant of particles in orbit and determined by the traverse component of spin vector speed on the spin, near to the speed of the light.
Metropolis Sampling for Constrained Diffusion Models

Denoising diffusion models have recently emerged as the predominant paradigm for generative modelling. Their extension to Riemannian manifolds has facilitated their application to an array of problems in the natural sciences. Yet, in many practical settings, such manifolds are defined by a set of constraints and are not covered by the existing (Riemannian) diffusion model methodology. Recent work has attempted to address this issue by employing novel noising processes based on logarithmic barrier methods or reflected Brownian motions. However, the associated samplers are computationally burdensome as the complexity of the constraints increases. In this paper, we introduce an alternative simple noising scheme based on Metropolis sampling that affords substantial gains in computational efficiency and empirical performance compared to the earlier samplers. Of independent interest, we prove that this new process corresponds to a valid discretisation of the reflected Brownian motion. We demonstrate the scalability and flexibility of our approach on a range of problem settings with convex and non-convex constraints, including applications from geospatial modelling, robotics and protein design.
Demonstrating Photon Ring Existence with Single-baseline Polarimetry

Images of supermassive black hole accretion flows contain features of both curved spacetime and plasma structure. Inferring properties of the spacetime from images requires modeling the plasma properties, and vice versa. The Event Horizon Telescope Collaboration has imaged near-horizon millimeter emission from both Messier 87* (M87*) and Sagittarius A* (Sgr A*) with very long baseline interferometry (VLBI) and has found a preference for magnetically arrested disk (MAD) accretion in each case. MAD accretion enables spacetime measurements through future observations of the photon ring, the image feature composed of near-orbiting photons. The ordered fields and relatively weak Faraday rotation of MADs yield rotationally symmetric polarization when viewed at modest inclination. In this letter, we utilize this symmetry along with parallel transport symmetries to construct a gain-robust interferometric quantity that detects the transition between the weakly lensed accretion flow image and the strongly lensed photon ring. We predict a shift in polarimetric phases on long baselines and demonstrate that the photon rings in M87* and Sgr A* can be unambiguously detected with sensitive, long-baseline measurements. For M87*, we find that photon ring detection in snapshot observations requires ∼1 mJy sensitivity on >15 Gλ baselines at 230 GHz and above, which could be achieved with space-VLBI or higher-frequency ground-based VLBI. For Sgr A*, we find that interstellar scattering inhibits photon ring detectability at 230 GHz, but ∼10 mJy sensitivity on >12 Gλ baselines at 345 GHz is sufficient and is accessible from the ground. For both sources, these sensitivity requirements may be relaxed by repeated observations and averaging.
RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision

We introduce RACH-Space, an algorithm for labelling unlabelled data in weakly supervised learning, given incomplete, noisy information about the labels. RACH-Space offers simplicity in implementation without requiring hard assumptions on data or the sources of weak supervision, and is well suited for practical applications where fully labelled data is not available. Our method is built upon a geometrical interpretation of the space spanned by the set of weak signals. We also analyze the theoretical properties underlying the relationship between the convex hulls in this space and the accuracy of our output labels, bridging geometry with machine learning. Empirical results demonstrate that RACH-Space works well in practice and compares favorably to the best existing label models for weakly supervised learning.
Differentially Private Statistical Inference through β-Divergence One Posterior Sampling

Differential privacy guarantees allow the results of a statistical analysis involving sensitive data to be released without compromising the privacy of any individual taking part. Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process. Instead of artificially introducing perturbations, sampling from Bayesian posterior distributions has been shown to be a special case of the exponential mechanism, producing consistent, and efficient private estimates without altering the data generative process. The application of current approaches has, however, been limited by their strong bounding assumptions which do not hold for basic models, such as simple linear regressors. To ameliorate this, we propose $\beta$D-Bayes, a posterior sampling scheme from a generalised posterior targeting the minimisation of the $\beta$-divergence between the model and the data generating process. This provides private estimation that is generally applicable without requiring changes to the underlying model and consistently learns the data generating parameter. We show that $\beta$D-Bayes produces more precise inference estimation for the same privacy guarantees, and further facilitates differentially private estimation via posterior sampling for complex classifiers and continuous regression models such as neural networks for the first time.
Best Arm Identification Based Beam Acquisition in Stationary and Abruptly Changing Environments

We study the initial beam acquisition problem in millimeter wave (mm-wave) networks from the perspective of best arm identification in multi-armed bandits (MABs). For the stationary environment, e.g., in mm-wave backhaul/midhaul and fixed wireless access, we propose a novel algorithm called concurrent beam exploration, <monospace>CBE</monospace>, in which multiple beams are grouped based on the beam indices and are simultaneously activated to detect the presence of the user. The best beam is then identified using a Hamming decoding strategy. For the case of orthogonal and highly directional thin beams, we characterize the performance of <monospace>CBE</monospace> in terms of the probability of missed detection and false alarm in a beam group (BG). Leveraging this, we derive the probability of beam selection error and prove that <monospace>CBE</monospace> outperforms the state-of-the-art strategies in this metric. Then, for the abruptly changing environments, e.g., in the case of moving blockages, we characterize the performance of the classical sequential halving (<monospace>SH</monospace>) algorithm. In particular, we derive the conditions on the distribution of the change for which the beam selection error is exponentially bounded. In case the change is restricted to a subset of the beams, we devise a strategy called <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-sequential halving and exhaustive search, <monospace>K-SHES</monospace>, that leads to an improved bound for the beam selection error as compared to <monospace>SH</monospace>. This policy is particularly useful when a near-optimal beam becomes optimal during the beam-selection procedure due to abruptly changing channel conditions. Finally, we demonstrate the efficacy of the proposed scheme by employing it in a tandem beam refinement and data transmission scheme.
Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information

This paper studies the over-the-air computation (AirComp) in an orthogonal frequency division multiplexing (OFDM) system with imperfect channel state information (CSI), in which multiple single-antenna wireless devices (WDs) simultaneously send uncoded signals to a multi-antenna access point (AP) for distributed functional computation over multiple subcarriers. In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively. Towards this end, we jointly optimize the transmit coefficients at the WDs and the receive beamforming vectors at the AP over subcarriers, subject to the maximum transmit power constraints at individual WDs. First, for the special case with a single receive antenna at the AP, we propose the semi-closed-form globally optimal solutions to the two problems using the Lagrange-duality method. It is shown that at each subcarrier, the WDs' optimized power control policy for average MSE minimization follows a regularized channel inversion structure, while that for computation outage probability minimization follows an on-off regularized channel inversion, with the regularization dependent on the transmit power budget and channel estimation error. Next, for the general case with multiple receive antennas at the AP, we present efficient algorithms based on alternating optimization and convex optimization to find converged solutions to both problems.
Heat Transfer of Bingham Fluids in an Annular Duct with Viscous Dissipation

ABSTRACT Analytical expressions for the velocity and temperature profiles in a fully-developed laminar Poiseuille flow through a concentric annular duct of a Bingham fluid with constant wall heat flux at the inner and outer wall, in the presence of viscous dissipation are deduced and presented. It is found that the proportion of the heat generated by viscous dissipation near the outer wall increases with an increase of the dimensionless flow parameter, and a decrease of the duct radius ratio. The Nusselt numbers are first calculated based on a single bulk temperature for the entire duct cross section. The possibility of performing calculations of the relevant parameters discussed in this work is available via the Supplementary Material as an Excel file. Also in this work a new approach is employed, where two different bulk temperatures are used, one for each side of the radial location in the temperature profile whose derivative is zero. With this new approach the Nusselt number behavior is free of either unphysical discontinuities or negative values. As a consequence, the Nusselt number values better reflect the actual heat transfer coefficient at the walls and are more comparable with the heat transfer inside ducts when the temperature profile is symmetric.
My3DGen: A Scalable Personalized 3D Generative Model

In recent years, generative 3D face models (e.g., EG3D) have been developed to tackle the problem of synthesizing photo-realistic faces. However, these models are often unable to capture facial features unique to each individual, highlighting the importance of personalization. Some prior works have shown promise in personalizing generative face models, but these studies primarily focus on 2D settings. Also, these methods require both fine-tuning and storing a large number of parameters for each user, posing a hindrance to achieving scalable personalization. Another challenge of personalization is the limited number of training images available for each individual, which often leads to overfitting when using full fine-tuning methods. Our proposed approach, My3DGen, generates a personalized 3D prior of an individual using as few as 50 training images. My3DGen allows for novel view synthesis, semantic editing of a given face (e.g. adding a smile), and synthesizing novel appearances, all while preserving the original person's identity. We decouple the 3D facial features into global features and personalized features by freezing the pre-trained EG3D and training additional personalized weights through low-rank decomposition. As a result, My3DGen introduces only $\textbf{240K}$ personalized parameters per individual, leading to a $\textbf{127}\times$ reduction in trainable parameters compared to the $\textbf{30.6M}$ required for fine-tuning the entire parameter space. Despite this significant reduction in storage, our model preserves identity features without compromising the quality of downstream applications.
Deep Probabilistic Movement Primitives with a Bayesian Aggregator

Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of move-ments (reproducing movements faster or slower), blending (merging two movements into one), via-point conditioning (constraining a movement to meet some particular via-points) and context conditioning (generation of movements based on an observed variable, e.g., position of an object). Previous works have proposed neural network-based motor primitive models, having demonstrated their capacity to perform tasks with some forms of input conditioning or time-modulation representations. However, there has not been a single unified deep movement primitive's model proposed that is capable of all previous operations, limiting neural movement primitive's potential applications. This paper proposes a deep movement primitive architecture that encodes all the operations above and uses a Bayesian context aggregator that allows a more sound context conditioning and blending. Our results demonstrate our approach can scale to reproduce complex motions on a larger variety of input choices compared to baselines while maintaining operations of linear movement primitives provide.
Benchmarking Algorithms for Federated Domain Generalization

While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges. Additionally, we explore domain-based heterogeneity in clients' local datasets - a realistic Federated DG scenario. Prior Federated DG evaluations are limited in terms of the number or heterogeneity of clients and dataset diversity. To address this gap, we propose an Federated DG benchmark methodology that enables control of the number and heterogeneity of clients and provides metrics for dataset difficulty. We then apply our methodology to evaluate 13 Federated DG methods, which include centralized DG methods adapted to the FL context, FL methods that handle client heterogeneity, and methods designed specifically for Federated DG. Our results suggest that despite some progress, there remain significant performance gaps in Federated DG particularly when evaluating with a large number of clients, high client heterogeneity, or more realistic datasets. Please check our extendable benchmark code here: https://github.com/inouye-lab/FedDG_Benchmark.
Cone-beam computed tomography versus orthopantomography in sinus lift procedures: Two-dimensional versus three-dimensional imaging

Context: The present study was undertaken to do a comparative evaluation of the role of cone-beam computed tomography (CBCT) imaging and orthopantomography (OPG) for preoperative implant planning in combination with sinus grafting procedures to assess sinus anatomy and morphology and the existing bone height in three-dimensions (3D). Aim: The aim of the present study was to assess sinus anatomy and morphology and the existing bone height in all the 3D. Materials and Methods: Pre- and postoperative assessment of maxillary sinuses was done in 17 patients who underwent implant therapy in combination with sinus augmentation procedures using CBCT and OPG. These patients were subjected to orthopantomographs and CBCT imaging both preoperatively and postoperatively. CBCT imaging helped decide the type of sinus augmentation procedure best suited for the patients as per the individual needs and depending on the residual alveolar bone height, timing of implant placement, sinus morphology, anticipation of complication, and comparative analysis between pre- and postprocedural gain in vertical alveolar bone height and increase in bone density. Statistical Analysis Used: The results were tabulated and statistically analyzed using paired and unpaired t-tests. Results: In the majority of cases, there was a concordance between the treatment types based on pre- and postoperative CBCT scans. The assessment of sinus morphology revealed a significantly higher detection rate of aberrations in the form of sinus mucosal hypertrophy and septae on CBCT which were imperceptible on routine radiographs. The most appealing result was that vertical alveolar bone height could be measured precisely, and there was a significant increase in surgical confidence and a significantly better prediction of complications when using CBCT imaging. Conclusions: A preoperative planning based on CBCT imaging seems to improve sinus diagnostics and helps execute a better treatment plan. Furthermore, it is a good tool for the comparison of vertical alveolar bone height pre- and postoperatively following sinus augmentation procedures using various graft materials.
Nodal Solutions for a Singularly Perturbed Nonlinear Elliptic Problem on Riemannian Manifolds

Abstract Let (M, g) be a smooth compact Riemannian N−manifold, N ≥ 2. We show that if the scalar curvature of g is not constant, the problem −ε2∆gu + u = up−1 in M has a positive solution with two positive peaks ξ1ε and ξ2ε, and a sign changing solution with one positive peak η1ε and one negative peak η2ε, such that as ε goes to zero Scalg(ξ1ε), Scalg(η1ε) → Scalg(ξ) and Scalg(ξ2ε), Scalg(η2ε) → Scalg(ξ). Here p > 2 if N = 2 and
Students at risk: how do students’ perception of socio-familiar characteristics condition their attitudes and behaviour in the class?

Dropping out of school early is a major social problem that needs to be addressed, given the negative consequences for the individual, as well as the high social costs involved. Combatting early school leaving is one of Europe’s Sustainable Development Indicators and a challenge for many schools. We present the results of the project “Pupils at risk of dropping out” carried out in Andalusia (Spain). This community has a very high dropout rate (21.6% on average) compared to the European average (around 10%). We analysed the risk factors for school dropout among pupils in this community by relating their attitudes and
Observations of Birds Feeding in Gumbo Limbo and Huevos de Caballo in Costa Rica

A study of bird frugivory in Desamparaditos de Puriscal in Costa Rica in 2021 showed the importance of two tropical trees, Gumbo Limbo (Bursera simaruba) and Huevos de Caballo (the common Spanish name of Tabernaemontana donnell-smithii) within habited areas. The study also focused on which species fed on lipid-rich fruits and which fed on lipid-poor fruits — Gumbo Limbo fruits are lipid-poor, whereas Huevos de Caballo “fruits” are lipid-rich — and whether the feeding choices of various birds in this community would reﬂect the distinction in lipid content. The study was done during the beginning of the rainy season, and compared with a recent analysis of bird frugivory studies. Results showed numerous similarities regarding the birds feeding on Gumbo Limbo, but more diﬀerences were found for the birds feeding on Huevos de Caballo. Most signiﬁcant was that a number of species were observed feeding on both of these fruits that had not been observed in the other studies. Additionally, the study indicated that both trees might be important nutritional sources for young birds. A follow-up study was conducted in 2022. That study showed more feeding by some species on Gumbo Limbo fruits, and demonstrated the importance of Gumbo Limbo for these species during the late dry season. The 2022 study began on 26 January and ended on 7 March, the date when the 2021 study began. Data from 2022 (summarized in Appendices 3 and 3A) will not be fully analyzed, but may be referred to in the discussion.