Search

We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance on every individual data point. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance., Comment: Published at AISTATS 2025. Code available at https://github.com/juan43ramirez/feasible-learning

Gaseous Optical Time Projection Chambers (OTPCs) aimed at Neutrino Physics and Rare Event Searches will likely exceed the tonne scale during the next decade. This will make their performance sensitive to gas contamination levels as low as 100 ppb, that is challenging at room temperature due to outgassing from structural materials. In this work we discuss gas distribution and impurity mitigation on a 5 m-length/5 m-diameter 10 bar TPC filled with Ar/CF$_4$ admixed at 99/1 per volume (1.75 tonne), loaded with technical plastics in order to enhance light collection and readout. Different distributor topologies, outgassing and flow rates are discussed. Specifically, our work is aimed at illustrating the conceptual viability of the optical readout of ND-GAr's TPC (within the DUNE Near-Detector complex), in terms of material compatibility. For our proposal, with perforated distributors aligned with the electric field, and under realistic assumptions, the concentration of contaminants can be controlled within a week from chamber filling. In the case of N$_2$, injection of fresh gas at %-level seems to represent the safest strategy to keep the concentration within operability limits., Comment: 33 pages, 17 figures

These notes are an introduction to knot theory from the perspective of surfaces. The notes cover fundamental concepts such as isotopies, Reidemeister moves, torus knots, and (orientable, connected) surfaces with one boundary component. They also present knot invariants defined through Seifert surfaces and their associated matrices, including the 3-genus, the Alexander polynomial, and the signature. Finally, starting from the notion of concordance and the connected sum operation, a group structure on the set of knots is introduced., Comment: These are lecture note for the mini-course "Nudos y Superficies'' part of the first ECOGyT that took place in Bogot\'a on Julio de 2024, in Spanish language

The possibility of exciting stationary solitons due to the Brewster effect is investigated. The analysis is performed by computing the transmission coefficient of an electromagnetic monochromatic wave obliquely incident on the optical system and linearly polarized with the transverse-electric polarization. The optical system is supposed to be surrounded by a vacuum. Two different optical systems are studied. First, the system under consideration is assumed to be composed of the junction of a single dispersive layer with a linear electromagnetic response and a single nonlinear layer exhibiting Kerr nonlinearity. The obtained results suggest the excitation of stationary solitons in the nonlinear layer induced by the occurrence of the Brewster phenomenon in the linear medium. The second system is considered as a single Kerr nonlinear layer. The Brewster effect is observed for both self-focusing and self-defocusing nonlinearities, which leads to the excitation of soliton states within the nonlinear layer. The Brewster-angle dependence of the intensity of the incident electromagnetic wave is also shown. Finally, the excitation of omnidirectional solitons in the case of single nonlinear layers is discussed. It is shown that matching the magnetic permeability of the surrounding medium and the nonlinear layer leads to maximum transmission states, independent of the incidence angle, for the input-intensity value that causes the homogenization of the electric permittivity., Comment: 7 pages, 7 figures

We demonstrate that including the second viscosity of an electron gas in the hydrodynamic model allows for highly accurate modeling of the optical response of heavily doped semiconductors. In our setup, which improves resonance visibility compared to previous approaches, plasmon resonances become more distinct, allowing for detailed analysis of the underlying physics. With advanced fitting techniques based on a physics-informed cost function and a tailored optimization algorithm, we obtain close agreement between simulations and experimental data across different sample thicknesses. This enhanced resonance visibility, combined with our integrated approach, shows that key parameters such as doping level and effective electron mass can be retrieved from a single optical measurement. The spatial dispersion taken into account in the hydrodynamic framework is essential for accurately describing the optical response of plasmonic materials in this frequency range and is likely to become a standard modeling approach.

The traditional and most common view of economists on the issue of (bad) uncertainty and its effects has been one of partial equilibrium. When the topic is approached from a macroeconomic perspective, the most frequent has been the examination of the effects of uncertainty shocks on short-term dynamics with various methods, but mainly with neo-Keynesian and statistical (vector autoregressive) models. This document responds to another concern and has two objectives: 1. to reflect on this issue with some instruments of the macroeconomist's toolbox related to a medium or long-term horizon, and 2. report a ciphering of the social cost of uncertainty in the Colombian case., Comment: 13 pages, 4 figures, 2 tables

Foundation models have demonstrated remarkable generalization, data efficiency, and robustness properties across various domains. In this paper, we explore the feasibility of foundation models for applications in the control domain. The success of these models is enabled by large-scale pretaining on Internet-scale datasets. These are available in fields like natural language processing and computer vision, but do not exist for dynamical systems. We address this challenge by pretraining a transformer-based foundation model exclusively on synthetic data and propose to sample dynamics functions from a reproducing kernel Hilbert space. Our pretrained model generalizes for prediction tasks across different dynamical systems, which we validate in simulation and hardware experiments, including cart-pole and Furuta pendulum setups. Additionally, the model can be fine-tuned effectively to new systems to increase performance even further. Our results demonstrate the feasibility of foundation models for dynamical systems that outperform specialist models in terms of generalization, data efficiency, and robustness., Comment: 10 pages

Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with patient portal messages to generate research ideas that prioritize important patient issues. We further quantified the quality of AI-generated research topics. To define patient clinical concerns, we analyzed 614,464 patient messages from 25,549 individuals with breast or skin cancer obtained from a large academic hospital (2013 to 2024), constructing a 2-staged unsupervised NLP topic model. Then, we generated research topics to resolve the defined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024 version) with prompt-engineering strategies. We guided AI to perform multi-level tasks: 1) knowledge interpretation and summarization (e.g., interpreting and summarizing the NLP-defined topics), 2) knowledge generation (e.g., generating research ideas corresponding to patients issues), 3) self-reflection and correction (e.g., ensuring and revising the research ideas after searching for scientific articles), and 4) self-reassurance (e.g., confirming and finalizing the research ideas). Six highly experienced breast oncologists and dermatologists assessed the significance and novelty of AI-generated research topics using a 5-point Likert scale (1-exceptional, 5-poor). One-third of the AI-suggested research topics were highly significant and novel when both scores were lower than the average. Two-thirds of the AI-suggested topics were novel in both cancers. Our findings demonstrate that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.

The emission rate of atom-like photon sources can be significantly improved by coupling them to plasmonic resonant nanostructures. These arrangements function as nanoantennas, serving the dual purpose of enhancing light--matter interactions and decoupling the emitted photons. However, there is a contradiction between the requirements for these two tasks. A small resonator volume is necessary for maximizing interaction efficiency, while a large antenna mode volume is essential to achieve high emission directivity. In this work, we analyze a hybrid structure composed of a noble metal plasmonic resonant nanoparticle coupled to the atom-like emitter, which is designed to enhance the emission rate, alongside a corner reflector aimed at optimizing the angular distribution of the emitted photons. A comprehensive numerical study of silver and gold corner reflector nanoantennas, employing the finite difference time domain method, is presented. The results demonstrate that a well-designed corner reflector can significantly enhance photon emission directivity while also substantially boosting the emission rate.

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications. AI red teaming raises many questions about the human factor, such as how red teamers are selected, biases and blindspots in how tests are conducted, and harmful content's psychological effects on red teamers. A growing body of HCI and CSCW literature examines related practices-including data labeling, content moderation, and algorithmic auditing. However, few, if any have investigated red teaming itself. Future studies may explore topics ranging from fairness to mental health and other areas of potential harm. We aim to facilitate a community of researchers and practitioners who can begin to meet these challenges with creativity, innovation, and thoughtful reflection., Comment: Updated with camera-ready version

Since the Transformer architecture emerged, language model development has grown, driven by their promising potential. Releasing these models into production requires properly understanding their behavior, particularly in sensitive domains like medicine. Despite this need, the medical literature still lacks practical assessment of pre-trained language models, which are especially valuable in settings where only consumer-grade computational resources are available. To address this gap, we have conducted a comprehensive survey of language models in the medical field and evaluated a subset of these for medical text classification and conditional text generation. The subset includes 53 models with 110 million to 13 billion parameters, spanning the Transformer-based model families and knowledge domains. Different approaches are employed for text classification, including zero-shot learning, enabling tuning without the need to train the model. These approaches are helpful in our target settings, where many users of language models find themselves. The results reveal remarkable performance across the tasks and datasets evaluated, underscoring the potential of certain models to contain medical knowledge, even without domain specialization. This study thus advocates for further exploration of model applications in medical contexts, particularly in computational resource-constrained settings, to benefit a wide range of users. The code is available on https://github.com/anpoc/Language-models-in-medicine.

This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware., Comment: 15 pages, 8 figures, presented at QTML 2023

Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problems are solved via their min-max Lagrangian formulations, which exhibit unstable oscillatory dynamics when optimized using gradient descent-ascent. The adoption of constrained optimization techniques in the machine learning community is currently limited by the lack of reliable, general-purpose update schemes for the Lagrange multipliers. This paper proposes the $\nu$PI algorithm and contributes an optimization perspective on Lagrange multiplier updates based on PI controllers, extending the work of Stooke, Achiam and Abbeel (2020). We provide theoretical and empirical insights explaining the inability of momentum methods to address the shortcomings of gradient descent-ascent, and contrast this with the empirical success of our proposed $\nu$PI controller. Moreover, we prove that $\nu$PI generalizes popular momentum methods for single-objective minimization. Our experiments demonstrate that $\nu$PI reliably stabilizes the multiplier dynamics and its hyperparameters enjoy robust and predictable behavior., Comment: Published at ICML 2024. Code available at https://github.com/motahareh-sohrabi/nuPI

The present paper is devoted to a study of the equilibrium configurations of slowly rotating anisotropic stars in the framework of general relativity. For that purpose, we provide the equations of structure where the rotation is treated to second order in the angular velocity. These equations extend those first derived by Hartle for slowly rotating isotropic stars. As an application of the new formalism, we study the rotational properties of Bowers-Liang fluid spheres. A result of particular interest is that the ellipticity and mass quadrupole moment are negative for certain highly anisotropic configurations; thus, such systems are prolate rather than oblate. Furthermore, for configurations with high anisotropy and compactness close to their critical value, quantities like the moment of inertia, change of mass, and mass quadrupole moment approach to the corresponding Kerr black hole values, similar to other ultracompact systems like sub-Buchdahl Schwarzschild stars and analytic rotating gravastars., Comment: 27 pages, 30 figures. Discussion in Appendix B extended. Matches the published version in PRD

Many of the world's workers rely on digital platforms for their income. In Venezuela, a nation grappling with extreme inflation and where most of the workforce is self-employed, data production platforms for machine learning have emerged as a viable opportunity for many to earn a flexible income in US dollars. Platform workers are deeply interconnected within a vast network of firms and entities that act as intermediaries for wage payments in digital currencies and its subsequent conversion to the national currency, the bolivar. Past research on embeddedness has noted that being intertwined in multi-tiered socioeconomic networks of companies and individuals can offer significant rewards to social participants, while also connoting a particular set of limitations. This paper furnishes qualitative evidence regarding how this deep embeddedness impacts platform workers in Venezuela. Given the backdrop of a national crisis and rampant hyperinflation, the perks of receiving wages through various financial platforms include access to a more stable currency and the ability to save and invest outside the national financial system. However, relying on numerous digital and local intermediaries often diminishes income due to transaction fees. Moreover, this introduces heightened financial risks, particularly due to the unpredictable nature of cryptocurrencies as an investment. The over-reliance on external financial platforms erodes worker autonomy through power dynamics that lean in favor of the platforms that set the transaction rules and prices. These findings present a multifaceted perspective on deep embeddedness in platform labor, highlighting how the rewards of financial intermediation often come at a substantial cost for the workers in unstable situations, who are saddled with escalating financial risks.

Waveguide geometries combining bent asymmetric coupled structures and adiabatic transitions shaped as partial Euler bends are put forward and theoretically analyzed in this work. Designs aiming to reduce the radiation loss, with application in curved waveguide sections and Q-enhanced microresonators, and polarization selection geometries, both for the silicon nitride platform, are studied using highly accurate numerical techniques., Comment: Final published version

Room temperature photoluminescence and optical absorption spectra have been measured in wurtzite In$_{x}$Al$_{y}$Ga$_{1-x-y}$N (x $\approx$ 0.06, 0.02 < y < 0.27) layers grown by molecular beam epitaxy. Photoluminescence spectra show both an enhancement of the integrated intensity and an increasing Stokes shift with the Al content. Both effects arise from an Al-enhanced exciton localization revealed by the S- and W-shaped temperature dependences of the photoluminescence emission energy and bandwidth respectively. Present results point to these materials as a promising choice for the active region in efficient light emitters. An In-related bowing parameter of 1.6 eV was derived from optical absorption data.

Next-generation megawatt-scale neutrino beams open the way to studying neutrino-nucleus scattering using gaseous targets for the first time. This represents an opportunity to improve the knowledge of neutrino cross sections in the energy region between hundreds of MeV and a few GeV, of interest for the upcoming generation of long-baseline neutrino oscillation experiments. The challenge is to accurately track and (especially) time the particles produced in neutrino interactions in large and seamless volumes down to few-MeV energies. We propose to accomplish this through an optically-read time projection chamber (TPC) filled with high-pressure argon and equipped with both tracking and timing functions. In this work, we present a detailed study of the time-tagging capabilities of such a device, based on end-to-end optical simulations that include the effect of photon propagation, photosensor response, dark count rate and pulse reconstruction. We show that the neutrino interaction time can be reconstructed from the primary scintillation signal with a precision in the range of 1-2.5 ns ($\sigma$) for point-like deposits with energies down to 5 MeV. A similar response is observed for minimum-ionizing particle tracks extending over lengths of a few meters. A discussion on previous limitations towards such a detection technology, and how they can be realistically overcome in the near future thanks to recent developments in the field, is presented. The performance demonstrated in our analysis seems to be well within reach of next-generation neutrino-oscillation experiments, through the instrumentation of the proposed TPC with conventional reflective materials and a silicon photomultiplier array behind a transparent cathode.

The idea of implementing electroluminescence-based amplification through transparent multi-hole structures (FAT-GEMs) has been entertained for some time. Arguably, for such a technology to be attractive it should perform at least at a level comparable to conventional alternatives based on wires or meshes. We present now a detailed calorimetric study carried out for 5.9~keV X-rays in xenon, for pressures ranging from 2 to 10~bar, resorting to different geometries, production and post-processing techniques. At a reference voltage 5~times above the electroluminescence threshold ($E_{EL,th}\sim0.7$~kV/cm/bar), the number of photoelectrons measured for the best structure was found to be just 18\%~below that obtained for a double-mesh with the same thickness and at the same distance. The energy resolution stayed within 10\% (relative) of the double-mesh value. An innovative characteristic of the structure is that vacuum ultraviolet (VUV) transparency of the polymethyl methacrylate (PMMA) substrate was achieved, effectively, through tetraphenylbutadiene (TPB) coating of the electroluminescence channels combined with indium tin oxide (ITO) coating of the electrodes. This resulted in a $\times 2.25$-increased optical yield (compared to the bare structure), that was found to be in good agreement with simulations if assuming a TPB wavelength-shifting-efficiency at the level of WLSE=0.74-1.28, compatible with expected values. This result, combined with the stability demonstrated for the TPB coating under electric field (over 20~h of continuous operation), shows great potential to revolutionize electroluminescence-based instrumentation.

Spontaneous emission is one of the most fundamental out-of-equilibrium processes in which an excited quantum emitter relaxes to the ground state due to quantum fluctuations. In this process, a photon is emitted that can interact with other nearby emitters and establish quantum correlations between them, e.g., via super and subradiance effects. One way to modify these photon-mediated interactions is to alter the dipole radiation patterns of the emitter, e.g., by placing photonic crystals near them. One recent example is the generation of strong directional emission patterns-key to enhancing super and subradiance effects-in two dimensions by employing photonic crystals with band structures characterized by linear isofrequency contours and saddle-points. However, these studies have predominantly used oversimplified toy models, overlooking the electromagnetic field's intricacies in actual materials, including aspects like geometrical dependencies, emitter positions, and polarization. Our study delves into the interaction between these directional emission patterns and the aforementioned variables, revealing the untapped potential to fine-tune collective quantum optical phenomena., Comment: 10 pages and 6 Figures. Submmitted to Nanophotonics journal