623509 research outputs found
Sort by
Tuning THz magnons in a mixed van-der-Waals antiferromagnet
No full textAlloying stands out as a pivotal technological method employed across various compounds, be they metallic, magnetic, or semiconducting, serving to fine-tune their properties to meet specific requirements. Ternary semiconductors represent a prominent example of such alloys. They offer fine-tuning of electronic bands, the band gap in particular, thus granting the technology of semiconductor heterostructures devices, key elements in current electronics and optoelectronics. In the realm of magnetically ordered systems, akin to electronic bands in solids, spin waves exhibit characteristic dispersion relations, featuring sizeable magnon gaps in many antiferromagnets. The engineering of the magnon gap constitutes a relevant direction in current research on antiferromagnets, aiming to leverage their distinct properties for THz technologies, spintronics, or magnonics. In this study, we showcase the tunability of the magnon gap across the THz spectral range within an alloy comprising representative semiconducting van-der-Waals antiferromagnets FePS and NiPS. These constituents share identical in-plane crystal structures, magnetic unit cells and the direction of the magnetic anisotropy, but differ in the amplitude and sign of the latter. Altogether these attributes result in the wide tunability of the magnon gap in the FeNiPS alloy in which the magnetic order is imposed by stronger, perpendicular anisotropy of iron.6 pages, 1 figure, to be published in Phys. Rev.
Categorical spectra as pointed -categories
No full textLessard\u27s -categories are an analogue of -categories possessing cells in all positive and negative dimensions. Categorical spectra, developed by Stefanich, are an analogue of spectra obtained by replacing the suspension of pointed -groupoids by that of pointed -categories. We give an -categorical definition of weak -categories (alias -categories), and show categorical spectra to be equivalent to pointed -categories. In particular, we show that the stable cells of categorical spectra coincide with the natural cells of -categories, and recover Lessard\u27s description of spectra as pointed weak -groupoids.16 pages. Comments are welcome! V2: Added section (now) 3.2 on monoidal structure
How much secure randomness is in a quantum state?
No full textHow much cryptographically-secure randomness can be extracted from a quantum state? This fundamental question probes the absolute limits of quantum random number generation (QRNG) and yet, despite the technological maturity of QRNGs, it remains unsolved. In this work we consider a general adversarial model that allows for an adversary who has quantum side-information about both the source and the measurement device. Using links between randomness extraction rates and sandwiched Rényi entropies, we provide compact, easy to compute, achievable rates of secure randomness extraction from quantum states. In turn, this provides a simple to evaluate benchmarking tool for the randomness generation rates of QRNG protocols.16 (+11) pages, 3 figures, v2: fixed error in exampl
Learning Versatile Skills with Curriculum Masking
No full textMasked prediction has emerged as a promising pretraining paradigm in offline reinforcement learning (RL) due to its versatile masking schemes, enabling flexible inference across various downstream tasks with a unified model. Despite the versatility of masked prediction, it remains unclear how to balance the learning of skills at different levels of complexity. To address this, we propose CurrMask, a curriculum masking pretraining paradigm for sequential decision making. Motivated by how humans learn by organizing knowledge in a curriculum, CurrMask adjusts its masking scheme during pretraining for learning versatile skills. Through extensive experiments, we show that CurrMask exhibits superior zero-shot performance on skill prompting tasks, goal-conditioned planning tasks, and competitive finetuning performance on offline RL tasks. Additionally, our analysis of training dynamics reveals that CurrMask gradually acquires skills of varying complexity by dynamically adjusting its masking scheme.NeurIPS 2024 poster, 21 pages, 8 figure
Framer: Interactive Frame Interpolation
No full textWe propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibilities of transforming one image to another, and in turn enables finer control of local motions. Second, as the most basic form of interaction, keypoints help establish the correspondence across frames, enhancing the model to handle challenging cases (e.g., objects on the start and end frames are of different shapes and styles). It is noteworthy that our system also offers an autopilot mode, where we introduce a module to estimate the keypoints and refine the trajectory automatically, to simplify the usage in practice. Extensive experimental results demonstrate the appealing performance of Framer on various applications, such as image morphing, time-lapse video generation, cartoon interpolation, etc. The code, the model, and the interface will be released to facilitate further research.Project page: https://aim-uofa.github.io/Framer
AI in Investment Analysis: LLMs for Equity Stock Ratings
No full textInvestment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain.
We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias.
Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights.9 pages, 5 figures, ICAIF24: 5th ACM International Conference on AI in Financ
Federated Learning with Relative Fairness
No full textThis paper proposes a federated learning framework designed to achieve \textit{relative fairness} for clients. Traditional federated learning frameworks typically ensure absolute fairness by guaranteeing minimum performance across all client subgroups. However, this approach overlooks disparities in model performance between subgroups. The proposed framework uses a minimax problem approach to minimize relative unfairness, extending previous methods in distributionally robust optimization (DRO). A novel fairness index, based on the ratio between large and small losses among clients, is introduced, allowing the framework to assess and improve the relative fairness of trained models. Theoretical guarantees demonstrate that the framework consistently reduces unfairness. We also develop an algorithm, named \textsc{Scaff-PD-IA}, which balances communication and computational efficiency while maintaining minimax-optimal convergence rates. Empirical evaluations on real-world datasets confirm its effectiveness in maintaining model performance while reducing disparity.43 page
Again About Singularity Crossing In Gravitation And Cosmology
No full textWe discuss the problem of singularity crossing in isotropic and anisotropic universes. We study at which conditions singularities can disappear in quantum cosmology and how quantum particles behave in the vicinity of singularities. Some attempts to develop general approach to the connection between the field reparametrization and the elimination of singularities is presented as well.23 pages, based on the talk given by the author at the Second International conference to celebrate the Legacy of G. Lemaître. Black Holes, Gravitational Waves and Space-Time Singularities (June 17-21, 2024, Castel Gandolfo, Specola Vaticana
Euclid: The - relation as a function of redshift. I. The black hole in NGC 1272
No full textCore ellipticals, massive
early-type galaxies have an almost constant inner surface brightness
profile. The size of the core region correlates with
the mass of the finally merged black hole.
Here we report the first
Euclid-based dynamical mass determination of a supermassive black
hole. We study the centre of NGC 1272, the
second most luminous elliptical galaxy in the Perseus cluster,
combining the Euclid VIS photometry coming from the Early Release
Observations of the Perseus cluster with VIRUS spectroscopic
observations at the Hobby-Eberly Telescope.
The core of NGC 1272 is detected
on the Euclid VIS image. Its size is
or 0.45 kpc, determined by
fitting PSF-convolved core-Sérsic and Nuker-law functions. The
two-dimensional stellar kinematics of the galaxy is measured from
the VIRUS spectra by deriving optimally regularized non-parametric
line-of-sight velocity distributions. Dynamical models of the
galaxy are constructed using our axisymmetric and triaxial
Schwarzschild codes.
We measure a black hole mass of ,
in line with the expectation from the
- correlation, but eight times larger than
predicted by the - correlation (at significance).
The core size, rather than the velocity dispersion, allows one to
select galaxies harboring the most massive black holes. The
spatial resolution, wide area coverage, and depth of the \Euclid
(Wide and Deep) surveys allow us to find cores of passive galaxies
larger than 2 kpc up to redshift 1.Accepted for publication in A&
Enhancing Bayesian parameter estimation by adapting to multiple energy scales in RHIC and LHC heavy-ion collisions
No full textImproved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at ~GeV and Pb--Pb collisions at ~TeV and ~TeV. Additionally, the calibration of centrality is now carried out separately for all parametrizations. The inclusion of an Au--Au collision system with an order of magnitude lower beam energy, along with separate centrality calibration, suggests a preference for smaller values of nucleon width, minimum volume per nucleon, and free-streaming time. The results with the acquired \textit{maximum a posteriori} parameters show improved agreement with the data for the second-order flow coefficient, identified particle yields, and mean transverse momenta. This work contributes to a more comprehensive understanding of heavy-ion collision dynamics and sets the stage for future improvements in theoretical modeling and experimental measurements.17 pages, 19 figure