arXiv subjects

Rolf Pfister

Publications and source records attributed to Rolf Pfister.

5 recordsLinked to original sources

arXiv2026

Unusual upper critical field in UTe2 revealed by magnetotransport measurements up to 42 T

The heavy-fermion superconductor UTe2 is unique in that, at ambient pressure, it exhibits three distinct superconducting phases, two of which are induced by magnetic field. When the field is applied along the crystallographic b axis in the orthorhombic structure, the field-induced phase SC2 develops above approximately 20 T and persists up to the metamagnetic transition at Hm about 34 T. When the magnetic field is tilted towards the c axis, another superconducting phase, SC3, emerges at very high fields above about 40 T over a certain angular range. The origin of this exotic phase remains under debate. One of the key open questions regarding the origin of SC3 is whether it is confined to the spin-polarized state above Hm, or whether it already develops at lower fields. Here, we report magnetoresistance measurements performed on a high-quality single crystal of UTe2 in static magnetic fields up to 42 T applied in the (bc) plane at temperatures down to 0.35 K. At this temperature, we find that the SC3 phase first appears at an angle of 20 deg from the b axis. At larger angles, the onset of the SC3 phase, defined by a maximum in resistivity, occurs below Hm. However, zero resistivity is reached only above Hm throughout the entire angular range investigated. These results are summarized in the resulting field-angle phase diagram. Furthermore, we find that at 21 deg the SC3 phase is rapidly suppressed with increasing temperature, whereas at 24 deg it becomes considerably more robust and persists up to about 1 K. Finally, we observe Shubnikov de Haas (SdH) oscillations in the vicinity of the c axis. The observed oscillation frequencies are in good agreement with our previous results. The field dependence of the strongest SdH frequency and of the effective mass is discussed.

cond-mat.str-el↗

arXiv2025

A Representationalist, Functionalist and Naturalistic Conception of Intelligence as a Foundation for AGI

The article analyses foundational principles relevant to the creation of artificial general intelligence (AGI). Intelligence is understood as the ability to create novel skills that allow to achieve goals under previously unknown conditions. To this end, intelligence utilises reasoning methods such as deduction, induction and abduction as well as other methods such as abstraction and classification to develop a world model. The methods are applied to indirect and incomplete representations of the world, which are obtained through perception, for example, and which do not depict the world but only correspond to it. Due to these limitations and the uncertain and contingent nature of reasoning, the world model is constructivist. Its value is functionally determined by its viability, i.e., its potential to achieve the desired goals. In consequence, meaning is assigned to representations by attributing them a function that makes it possible to achieve a goal. This representational and functional conception of intelligence enables a naturalistic interpretation that does not presuppose mental features, such as intentionality and consciousness, which are regarded as independent of intelligence. Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.

cs.AI↗

arXiv2025

Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI

OpenAI's o3 achieves a high score of 87.5 % on ARC-AGI, a benchmark proposed to measure intelligence. This raises the question whether systems based on Large Language Models (LLMs), particularly o3, demonstrate intelligence and progress towards artificial general intelligence (AGI). Building on the distinction between skills and intelligence made by Fran\c{c}ois Chollet, the creator of ARC-AGI, a new understanding of intelligence is introduced: an agent is the more intelligent, the more efficiently it can achieve the more diverse goals in the more diverse worlds with the less knowledge. An analysis of the ARC-AGI benchmark shows that its tasks represent a very specific type of problem that can be solved by massive trialling of combinations of predefined operations. This method is also applied by o3, achieving its high score through the extensive use of computing power. However, for most problems in the physical world and in the human domain, solutions cannot be tested in advance and predefined operations are not available. Consequently, massive trialling of predefined operations, as o3 does, cannot be a basis for AGI - instead, new approaches are required that can reliably solve a wide variety of problems without existing skills. To support this development, a new benchmark for intelligence is outlined that covers a much higher diversity of unknown tasks to be solved, thus enabling a comprehensive assessment of intelligence and of progress towards AGI.

cs.AI↗

arXiv2024

Counting and Algorithmic Generalization with Transformers

Algorithmic generalization in machine learning refers to the ability to learn the underlying algorithm that generates data in a way that generalizes out-of-distribution. This is generally considered a difficult task for most machine learning algorithms. Here, we analyze algorithmic generalization when counting is required, either implicitly or explicitly. We show that standard Transformers are based on architectural decisions that hinder out-of-distribution performance for such tasks. In particular, we discuss the consequences of using layer normalization and of normalizing the attention weights via softmax. With ablation of the problematic operations, we demonstrate that a modified transformer can exhibit a good algorithmic generalization performance on counting while using a very lightweight architecture.

cs.LG↗

arXiv2022

Hydration Dynamics and IR Spectroscopy of 4-Fluorophenol

Halogenated groups are relevant in pharmaceutical applications and potentially useful spectroscopic probes for infrared spectroscopy. In this work, the structural dynamics and infrared spectroscopy of $para$-fluorophenol (F-PhOH) and phenol (PhOH) is investigated in the gas phase and in water using a combination of experiment and molecular dynamics (MD) simulations. The gas phase and solvent dynamics around F-PhOH and PhOH is characterized from atomistic simulations using empirical energy functions with point charges or multipoles for the electrostatics, Machine-Learning (ML) based parametrization and with full $\textit{ab initio}$ (QM) and mixed Quantum Mechanical/Molecular Mechanics (QM/MM) simulations with a particular focus on the CF- and OH-stretch region. The CF-stretch band is heavily mixed with other modes whereas the OH-stretch in solution displays a characteristic high-frequency peak around 3600 cm$^{-1}$ most likely associated with the -OH group of PhOH and F-PhOH together with a characteristic progression below 3000 cm$^{-1}$ due to coupling with water modes which is also reproduced by several of the simulations. Solvent and radial distribution functions indicate that the CF-site is largely hydrophobic except for simulations using point charges which renders them unsuited for correctly describing hydration and dynamics around fluorinated sites.

physics.chem-ph↗