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The study of phase transitions in frustrated magnetic systems with $O(N)\times O(2)$ symmetry has been the subject of controversy for more than twenty years, with theoretical, numerical and experimental results in disagreement. Even theoretical studies lead to different results, with some predicting a first-order phase transition while others find it to be second-order. Recently, a series of results from both numerical simulations and theoretical analyses, in particular those based on the Conformal Bootstrap, have rekindled interest in this controversy, especially as they are still not in agreement with each other. Studies based on the functional renormalization group have played a major role in this controversy in the past, and we revisit these studies, taking them a step further by adding non-trivial second order derivative terms to the derivative expansion of the effective action. We confirm the first-order nature of the phase transition for physical values of $N$, i.e. for $N=2$ and $N=3$ in agreement with the latest results obtained with the Conformal Bootstrap. We also study an other phase of the $O(N)\times O(2)$ models, called the sinusoidal phase, qualitatively confirming earlier perturbative results., Comment: 21 pages, 16 figures, 1 supplementary material

The term sample dependence describes the troublesome tendency of nominally equivalent samples to exhibit different physical properties. High entropy oxides (HEOs) are a class of materials where sample dependence has the potential to be particularly profound due to their inherent chemical complexity. In this work, we prepare a spinel HEO of identical nominal composition by five distinct methods, spanning a range of thermodynamic and kinetic conditions: solid state, high pressure, hydrothermal, molten salt, and combustion syntheses. By structurally characterizing these five samples across all length scales with a variety of x-ray methods, we find that while the average structure is unaltered, the samples vary significantly in their local structures and their microstructures. The most profound differences are observed at intermediate length scales, both in terms of crystallite morphology and cation homogeneity. As revealed by x-ray fluorescence microscopy ideal cation homogeneity is achieved only in the case of combustion synthesis. These structural differences in turn significantly alter the observed functional properties, which we demonstrate via characterization of their magnetic response. While ferrimagnetic order is retained across all five samples, the sharpness of the transition, the size of the saturated moment, and the coercivity all show marked variations with synthesis method. We conclude that the chemical flexibility inherent to HEOs is complemented by strong synthesis method dependence, providing another axis along which to optimize these materials for a wide range of applications., Comment: 14 pages, 5 figures

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we prove preserves the set of optimal policies under a more general set of functions than has been previously proven. We also present {\em Potential-Based Intrinsic Motivation} (PBIM) and {\em Generalized Reward Matching} (GRM), methods for converting IM rewards into a potential-based form that are useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey and Cliff Walking environments, we demonstrate that PBIM and GRM successfully prevent the agent from converging to a suboptimal policy and can speed up training. Additionally, we prove that GRM is sufficiently general as to encompass all potential-based reward shaping functions. This paper expands on previous work introducing the PBIM method, and provides an extension to the more general method of GRM, as well as additional proofs, experimental results, and discussion., Comment: To be submit to joint AIJ-JAIR special track for award-winning papers. arXiv admin note: substantial text overlap with arXiv:2402.07411

We prove that the automorphism group of an affine, cubic surface with equation $xyz=g(x,y)$ contains ${\mathbb Z}$ as a finite index subgroup. These equations were first studied by Mordell. v.2: small changes, references updated.

The pepenaactivity is part of the informal sector, characterized by precariousness and social invisibility; however, it is a key element in the recycling production chain, providing an economic income for many people. In the city of Chihuahua, this activity is carried out on a significant scale. Therefore, this study analyzes and dignifies the recycling of urban solid waste from the perspective of scavengers, also explaining their relationship with other social actors at the city's final disposal site. The study starts from the premise that waste picking is, in the current era of global environmental crisis, an effective way to conserve resources and reduce environmental impacts. Methodologically, participatory action research was used as a strategy to raise awareness among scavengers about the importance of their work and to present their community organization as an example to other urban waste picking groups in different locations. Finally, we found issues in these groups such as informality, lack of legal support,specific health risks, and, more broadly, the absence of public policies to recognize this activity as valuable in addressing the global environmental crisis, Comment: in Spanish language

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community., Comment: 12 pages, 2 figures

We employ molecular dynamics simulations to investigate ion and water transport driven by an electric field through quasi-two-dimensional nanoslits with a tapered geometry. Despite the absence of surface charge on the channel walls and the associated electric double layer, we do observe a robust ion selectivity. This selectivity favors the transport of cations from base to tip when the electric field is directed from base to tip, and anions from base to tip when the field direction is reversed. Additionally, we observe a corresponding electro-osmotic water flow from base to tip, regardless of the electric field direction. Intriguingly, ion selectivity and electro-osmotic flow are conventionally associated with surface charge and electric double layers. However, in uncharged tapered nanoslits, we uncover a novel mechanism for these phenomena, where ion selectivity arises from the divergence of the heterogeneous water polarization., Comment: 5 pages, 4 figures + supplementary material (8 pages, 11 figures)

Point-of-Care Ultrasound (POCUS) is the practice of clinicians conducting and interpreting ultrasound scans right at the patient's bedside. However, the expertise needed to interpret these images is considerable and may not always be present in emergency situations. This reality makes algorithms such as machine learning classifiers extremely valuable to augment human decisions. POCUS devices are becoming available at a reasonable cost in the size of a mobile phone. The challenge of turning POCUS devices into life-saving tools is that interpretation of ultrasound images requires specialist training and experience. Unfortunately, the difficulty to obtain positive training images represents an important obstacle to building efficient and accurate classifiers. Hence, the problem we try to investigate is how to explore strategies to increase accuracy of classifiers trained with scarce data. We hypothesize that training with a few data instances may not suffice for classifiers to generalize causing them to overfit. Our approach uses an Explainable AI-Augmented approach to help the algorithm learn more from less and potentially help the classifier better generalize., Comment: 7 pages, 3 figures, accepted by XAI 2024 workshop @ IJCAI

In this paper, we investigate D-term inflation within the framework of supergravity, employing the minimal Kahler potential. Our study reveals that this model can overcome the {\eta}-problem found in F-term models. Additionally, we explore reheating dynamics and gravitino production, emphasizing the interplay between reheating temperature, spectral index, and gravitino abundance. Our analysis indicates that gravitino production is sensitive to the equation of state during reheating, affecting the reheating temperature and subsequent dark matter relic density. Perturbation theory reveals that Primordial Gravitational Waves (PGWs) evolve according to second-order effects arising from first-order curvature perturbations. In fact, the spectral energy density of these waves is particularly relevant to Pulsar Timing Array (PTA) observations. Furthermore, we analyze gravitational waves generated by cosmic strings, providing critical constraints on early Universe dynamics and cosmic string properties. The dimensionless string tension significantly influences the stochastic gravitational wave background (SGWB) produced by cosmic strings post-inflation. Finally, the stochastic gravitational wave background (SGWB) produced by cosmic strings is influenced by the energy scales of inflation and string formation., Comment: 15 pages, 5 figures, 1 table

We prove that, given a sufficiently functorial assignment from rings to big Cohen-Macaulay algebras $R \mapsto B$, that the associated big Cohen-Macaulay closure operation on ideals $I \mapsto I B \cap R$ necessarily satisfies the Brian\c{c}on-Skoda type property. The proof combines arguments of Lipman-Teissier, Hochster, Ma, and Hochster-Huneke. Specializing to mixed characteristic, and utilizing a result of Bhatt on absolute integral closures, this recovers a slight strengthening of a result of Heitmann., Comment: Minor changes and typos fixed, 13 pages, to appear in the Michigan Mathematical Journal

The Lounesto classification is a well-established scheme for categorizing spinors based on their physical content, which are determined by their associated bilinear forms. It consists of six disjoint classes encompassing the known spinors within the context of the standard model of high-energy physics. However, advancements in theories beyond the standard model have opened the door to potential new spinorial adjoint structures, leading to new unforeseen classess. These developments indicate the potential for extending the standard Lounesto classification. In this paper, we explore all possible subclasses that could extend the Lounesto scheme. We highlight the most relevant subclasses by imposing constraints to their corresponding dual structures, thus broadening our understanding of spinor and its applications in theoretical physics., Comment: 7 pages; 4 tables

In this paper, we use big Cohen-Macaulay algebras to define a characteristic free analog of the $F$-thresholds, which we call BCM-thresholds, in the case of principal ideals. We prove that, similarly to the case of the $F$-thresholds, the set of BCM-thresholds and the set of BCM-jumping numbers agree. We also relate some BCM-thresholds to splittings of maps from the ring to a big Cohen-Macaulay algebra., Comment: 10 pages, comments welcome

The properties of an active fluid, for example, a bacterial bath or a collection of microtubules and molecular motors, can be accessed through the dynamics of passive particle probes. Here, in the perspective of analyzing experimental situations of confinement in droplets, we consider the kinematics of a negatively buoyant probe particle in an active fluid, both confined within a spherical domain. The active bath generates a fluctuating flow that pushes the particle with a velocity that is modeled as a colored stochastic noise, characterized by two parameters, the intensity and memory time of the active flow. When the particle departs a little from the bottom of the spherical domain, the configuration is well approximated by a particle in a two-dimensional harmonic trap subjected to the colored noise, in which case an analytical solution exists, which is the base for quantitative analysis. We numerically simulate the dynamics of the particle and use the planar, two-dimensional mean square displacement to recover the activity parameters of the bath. This approach yields satisfactory results as long as the particle remains relatively confined, that is, as long as the intensity of the colored noise remains low.

Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions. These fluctuations in pressure are transmitted to the optic nerve sheath (ONS), resulting in changes to its diameter, which can then be detected using ultrasound imaging devices. However, interpreting sonographic images of the ONS can be challenging. In this work, we propose two systems that actively monitor the ONS diameter throughout an ultrasound video and make a final prediction as to whether ICP is elevated. To construct our systems, we leverage subject matter expert (SME) guidance, structuring our processing pipeline according to their collection procedure, while also prioritizing interpretability and computational efficiency. We conduct a number of experiments, demonstrating that our proposed systems are able to outperform various baselines. One of our SMEs then manually validates our top system's performance, lending further credibility to our approach while demonstrating its potential utility in a clinical setting., Comment: 5 pages, 2 figures, ISBI 2024

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-based reward shaping (PBRS), has not been applicable to many IM methods, as they are often complex, trainable functions themselves, and therefore dependent on a wider set of variables than the traditional reward functions that PBRS was developed for. We present an extension to PBRS that we prove preserves the set of optimal policies under a more general set of functions than has been previously proven. We also present {\em Potential-Based Intrinsic Motivation} (PBIM), a method for converting IM rewards into a potential-based form that is useable without altering the set of optimal policies. Testing in the MiniGrid DoorKey and Cliff Walking environments, we demonstrate that PBIM successfully prevents the agent from converging to a suboptimal policy and can speed up training., Comment: Extended version of paper appearing in AAMAS 2024