Model: gpham/all-mpnet-base-v2-setfit-arxiv
Author: gpham
Pipeline: text-classification
Library: setfit
Tags: setfit, safetensors, mpnet, sentence-transformers, text-classification, generated_from_setfit_trainer, arxiv:2209.11055, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, model-index, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: tags: setfit, sentence-transformers, text-classification, generated_from_setfit_trainer
widget: Review on Quantum Computing for Lattice Field Theory
In these proceedings, we review recent advances in applying quantum computing
to lattice field theory. Quantum computing offers the prospect to simulate
lattice field theories in parameter regimes that are largely inaccessible with
the conventional Monte Carlo approach, such as the sign-problem afflicted
regimes of finite baryon density, topological terms, and out-of-equilibrium
dynamics. First proof-of-concept quantum computations of lattice gauge theories
in (1+1) dimensions have been accomplished, and first resource-efficient
quantum algorithms for lattice gauge theories in (1+1) and (2+1) dimensions
have been developed. The path towards quantum computations of (3+1)-dimensional
lattice gauge theories, including Lattice QCD, requires many incremental steps
of improving both quantum hardware and quantum algorithms. After reviewing
these requirements and recent advances, we discuss the main challenges and
future directions., Beating full state tomography for unentangled spectrum estimation
How many copies of a mixed state $\rho \in \mathbb{C}^{d \times d}$ are
needed to learn its spectrum? To date, the best known algorithms for spectrum
estimation require as many copies as full state tomography, suggesting the
possibility that learning a state's spectrum might be as difficult as learning
the entire state. We show that this is not the case in the setting of
unentangled measurements, by giving a spectrum estimation algorithm that uses
$n = O(d^3\cdot (\log\log(d) / \log(d))^4 )$ copies of $\rho$, which is
asymptotically fewer than the $n = \Omega(d^3)$ copies necessary for full state
tomography. Our algorithm is inspired by the technique of local moment matching
from classical statistics, and shows how it can be applied in the quantum
setting.
  As an important subroutine in our spectrum estimation algorithm, we give an
estimator of the $k$-th moment $\operatorname{tr}(\rho^k)$ which performs
unentangled measurements and uses $O(d^{3-2/k})$ copies of $\rho$ in order to
achieve a constant multiplicative error. This directly translates to an
additive-error estimator of quantum Renyi entropy of order $k$ with the same
number of copies.
  Finally, we present numerical evidence that the sample complexity of spectrum
estimation can only improve over full state tomography by a sub-polynomial
factor. Specifically, for spectrum learning with fully entangled measurements,
we run simulations which suggest a lower bound of $\Omega(d^{2 - \gamma})$
copies for any constant $\gamma > 0$. From this, we conclude the current best
lower bound of $\Omega(d)$ is likely not tight., Automated Bug Report Prioritization in Large Open-Source Projects
Large open-source projects receive a large number of issues (known as bugs),
including software defect (i.e., bug) reports and new feature requests from
their user and developer communities at a fast rate. The often limited project
resources do not allow them to deal with all issues. Instead, they have to
prioritize them according to the project's priorities and the issues'
severities. In this paper, we propose a novel approach to automated bug
prioritization based on the natural language text of the bug reports that are
stored in the open bug repositories of the issue-tracking systems. We conduct
topic modeling using a variant of LDA called TopicMiner-MTM and text
classification with the BERT large language model to achieve a higher
performance level compared to the state-of-the-art. Experimental results using
an existing reference dataset containing 85,156 bug reports of the Eclipse
Platform project indicate that we outperform existing approaches in terms of
Accuracy, Precision, Recall, and F1-measure of the bug report priority
prediction., Nearby open clusters with tidal features: golden sample selection and 3D
  structure
Open clusters offer unique opportunities to study stellar dynamics and
evolution under the influence of their internal gravity, the Milky Way's
gravitational field, and the interactions with encounters. Using the Gaia DR3
data for a catalog of open clusters within 500 parsecs that exhibit tidal
features reported by the literature, we apply a novel method based on 3D
principal component analysis to select a ``golden sample'' of nearby open
clusters with minimal line-of-sight distortions. This approach ensures a
systematic comparison of 3D and 2D structural parameters for tidally perturbed
clusters. The selected golden sample includes Blanco 1, Melotte 20, Melotte 22,
NGC 2632, NGC 7092, NGC 1662, Roslund 6 and Melotte 111. We analyze these
clusters by fitting both 2D and 3D King Profiles to their stellar density
distributions. Our results reveal systematic discrepancies: most of the golden
sample clusters exhibit larger 3D tidal radii compared to their 2D
counterparts, demonstrating that the 2D projection effects bias the measured
cluster size. Furthermore, the 3D density profiles show stronger deviations
from King profiles at the tidal radii ($\Delta \rho_{\rm 3D} > \Delta \rho_{\rm
2D}$), highlighting enhanced sensitivity to tidal disturbances. Additionally,
we investigate the spatial distribution of cluster members relative to their
bulk motion in the Galactic plane. We find that some clusters exhibit tidal
features oriented perpendicular to their direction of motion, which can be
attributed to the fact that the current surveys only detect the curved inner
regions of the tidal features. In conclusion, this work offers a golden sample
of nearby open clusters that are most reliable for 3D structure analysis and
underscores the necessity of 3D analysis in characterizing OC morphological
asymmetries, determining cluster size, and identifying tidal features., Revisiting the physical properties of (LaS)1+d(NbS2) misfit-layered
  compounds
Electrical transport in polycrystalline and single-crystalline (LaS)1+d(NbS2)
misfit-layered compounds was measured. Polycrystalline samples were synthesized
using S raw materials of different purities (2N or 6N), and single-crystalline
samples were grown using two types of transport agents (2NH4Cl+PbCl2 or NH4Cl)
via the chemical vapor transport method. The temperature dependence on
resistivity dropped at 1.3-2.0 K for some of the samples, which might be
affected by the unknown impurity. (LaS)1+d(NbS2) misfit-layered compounds for
the main phase of those obtained samples exhibited no superconductivity above
0.2 K by the resistivity measurement.
metrics: f1
pipeline_tag: text-classification
library_name: setfit
inference: yes
base_model: sentence-transformers/all-mpnet-base-v2
model-index: name: SetFit with sentence-transformers/all-mpnet-base-v2
results: task: type: text-classification
name: Text Classification
dataset: name: Unknown
type: unknown
split: test
metrics: type: f1
value: 0.5294216467829347
name: F1
verified: no
Likes: 0 | All-time downloads: 8
Created: 2025-05-04 07:14:57