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arxiv_dataset-183002304.11954 | Spikingformer: Spike-driven Residual Learning for Transformer-based
Spiking Neural Network
cs.NE cs.AI
Spiking neural networks (SNNs) offer a promising energy-efficient alternative
to artificial neural networks, due to their event-driven spiking computation.
However, state-of-the-art deep SNNs (including Spikformer and SEW ResNet)
suffer from non-spike computations (integer-float multiplications) caused by
the structure of their residual connection. These non-spike computations
increase SNNs' power consumption and make them unsuitable for deployment on
mainstream neuromorphic hardware, which only supports spike operations. In this
paper, we propose a hardware-friendly spike-driven residual learning
architecture for SNNs to avoid non-spike computations. Based on this residual
design, we develop Spikingformer, a pure transformer-based spiking neural
network. We evaluate Spikingformer on ImageNet, CIFAR10, CIFAR100, CIFAR10-DVS
and DVS128 Gesture datasets, and demonstrate that Spikingformer outperforms the
state-of-the-art in directly trained pure SNNs as a novel advanced backbone
(75.85$\%$ top-1 accuracy on ImageNet, + 1.04$\%$ compared with Spikformer).
Furthermore, our experiments verify that Spikingformer effectively avoids
non-spike computations and significantly reduces energy consumption by
57.34$\%$ compared with Spikformer on ImageNet. To our best knowledge, this is
the first time that a pure event-driven transformer-based SNN has been
developed.
| arxiv topic:cs.NE cs.AI |
arxiv_dataset-183012304.12054 | Differential Equations for Gaussian Statistical Models with Rational
Maximum Likelihood Estimator
math.AG math.ST stat.TH
We study multivariate Gaussian statistical models whose maximum likelihood
estimator (MLE) is a rational function of the observed data. We establish a
one-to-one correspondence between such models and the solutions to a nonlinear
first-order partial differential equation (PDE). Using our correspondence, we
reinterpret familiar classes of models with rational MLE, such as directed (and
decomposable undirected) Gaussian graphical models. We also find new models
with rational MLE. For linear concentration models with rational MLE, we show
that homaloidal polynomials from birational geometry lead to solutions to the
PDE. We thus shed light on the problem of classifying Gaussian models with
rational MLE by relating it to the open problem in birational geometry of
classifying homaloidal polynomials.
| arxiv topic:math.AG math.ST stat.TH |
arxiv_dataset-183022304.12154 | Explainable AI Insights for Symbolic Computation: A case study on
selecting the variable ordering for cylindrical algebraic decomposition
cs.SC cs.LG
In recent years there has been increased use of machine learning (ML)
techniques within mathematics, including symbolic computation where it may be
applied safely to optimise or select algorithms. This paper explores whether
using explainable AI (XAI) techniques on such ML models can offer new insight
for symbolic computation, inspiring new implementations within computer algebra
systems that do not directly call upon AI tools. We present a case study on the
use of ML to select the variable ordering for cylindrical algebraic
decomposition. It has already been demonstrated that ML can make the choice
well, but here we show how the SHAP tool for explainability can be used to
inform new heuristics of a size and complexity similar to those human-designed
heuristics currently commonly used in symbolic computation.
| arxiv topic:cs.SC cs.LG |
arxiv_dataset-183032304.12254 | Active coherent beam combining and beam steering using a spatial mode
multiplexer
physics.optics
Coherent beam combination is one promising way to overcome the power limit of
one single laser. In this paper, we use a Multi-Plane Light Converter to
combine 12 fibers at 1.03 micron with a phase locking setup. The overall loss
measurement gives a combination efficiency in the fundamental Hermite-Gaussian
mode as high as 70%. This setup can generate the fundamental and higher-order
Hermite-Gaussian modes and has beam steering capabilities.
| arxiv topic:physics.optics |
arxiv_dataset-183042304.12354 | Hydrodynamics in long-range interacting systems with center-of-mass
conservation
cond-mat.stat-mech cond-mat.str-el quant-ph
In systems with a conserved density, the additional conservation of the
center of mass (dipole moment) has been shown to slow down the associated
hydrodynamics. At the same time, long-range interactions generally lead to
faster transport and information propagation. Here, we explore the competition
of these two effects and develop a hydrodynamic theory for long-range
center-of-mass-conserving systems. We demonstrate that these systems can
exhibit a rich dynamical phase diagram containing subdiffusive, diffusive, and
superdiffusive behaviors, with continuously varying dynamical exponents. We
corroborate our theory by studying quantum lattice models whose emergent
hydrodynamics exhibit these phenomena.
| arxiv topic:cond-mat.stat-mech cond-mat.str-el quant-ph |
arxiv_dataset-183052304.12454 | Benchmark tasks for Quality-Diversity applied to Uncertain domains
cs.NE
While standard approaches to optimisation focus on producing a single
high-performing solution, Quality-Diversity (QD) algorithms allow large diverse
collections of such solutions to be found. If QD has proven promising across a
large variety of domains, it still struggles when faced with uncertain domains,
where quantification of performance and diversity are non-deterministic.
Previous work in Uncertain Quality-Diversity (UQD) has proposed methods and
metrics designed for such uncertain domains. In this paper, we propose a first
set of benchmark tasks to analyse and estimate the performance of UQD
algorithms. We identify the key uncertainty properties to easily define UQD
benchmark tasks: the uncertainty location, the type of distribution and its
parameters. By varying the nature of those key UQD components, we introduce a
set of 8 easy-to-implement and lightweight tasks, split into 3 main categories.
All our tasks build on the Redundant Arm: a common QD environment that is
lightweight and easily replicable. Each one of these tasks highlights one
specific limitation that arises when considering UQD domains. With this first
benchmark, we hope to facilitate later advances in UQD.
| arxiv topic:cs.NE |
arxiv_dataset-183062304.12554 | The Ordinary Least Eigenvalues Estimator
econ.EM
We propose a rate optimal estimator for the linear regression model on
network data with interacted (unobservable) individual effects. The estimator
achieves a faster rate of convergence $N$ compared to the standard estimators'
$\sqrt{N}$ rate and is efficient in cases that we discuss. We observe that the
individual effects alter the eigenvalue distribution of the data's matrix
representation in significant and distinctive ways. We subsequently offer a
correction for the \textit{ordinary least squares}' objective function to
attenuate the statistical noise that arises due to the individual effects, and
in some cases, completely eliminate it. The new estimator is asymptotically
normal and we provide a valid estimator for its asymptotic covariance matrix.
While this paper only considers models accounting for first-order interactions
between individual effects, our estimation procedure is naturally extendable to
higher-order interactions and more general specifications of the error terms.
| arxiv topic:econ.EM |
arxiv_dataset-183072304.12654 | CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular
Synthesis
cs.LG cs.AI
With growing attention to tabular data these days, the attempt to apply a
synthetic table to various tasks has been expanded toward various scenarios.
Owing to the recent advances in generative modeling, fake data generated by
tabular data synthesis models become sophisticated and realistic. However,
there still exists a difficulty in modeling discrete variables (columns) of
tabular data. In this work, we propose to process continuous and discrete
variables separately (but being conditioned on each other) by two diffusion
models. The two diffusion models are co-evolved during training by reading
conditions from each other. In order to further bind the diffusion models,
moreover, we introduce a contrastive learning method with a negative sampling
method. In our experiments with 11 real-world tabular datasets and 8 baseline
methods, we prove the efficacy of the proposed method, called CoDi.
| arxiv topic:cs.LG cs.AI |
arxiv_dataset-183082304.12754 | Minimal-time trajectories of a linear control system on a homogeneous
space of the 2D Lie group
math.OC math.DS
Through the Pontryagin maximum principle, we solve a minimal-time problem for
a linear control system on a cylinder, considered as a homogeneous space of the
solvable Lie group of dimension two. The main result explicitly shows the
existence of an optimal trajectory connecting every couple of arbitrary states
on the manifold. It also gives a way to calculate the corresponding minimal
time. Finally, the system admits points with two distinct minimal-time
trajectories connecting them.
| arxiv topic:math.OC math.DS |
arxiv_dataset-183092304.12854 | Coherent Optical Spin Hall Transport for Spin-optronics at Room
Temperature
cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.quant-gas physics.optics
Spin or valley degrees of freedom in condensed matter have been proposed as
efficient information carriers towards next generation spintronics. It is
therefore crucial to develop effective strategies to generate and control spin
or valley-locked spin currents, e.g., by exploiting the spin Hall or valley
Hall effects. However, the scattering, and rapid dephasing of electrons pose
major challenges to achieve macroscopic coherent spin currents and realistic
spintronic or valleytronic devices, specifically at room temperature, where
strong thermal fluctuations could further obscure the spin flow. Exciton
polaritons in semiconductor microcavities being the quantum superposition of
excitons and photons, are believed to be promising platforms for spin-dependent
optoelectronic or, in short, spin-optronic devices. Long-range spin current
flows of exciton polaritons may be controlled through the optical spin Hall
effect. However, this effect could neither be unequivocally observed at room
temperature nor be exploited for realistic polariton spintronic devices due to
the presence of strong thermal fluctuations or large linear spin splittings.
Here, we report the observation of room temperature optical spin Hall effect of
exciton polaritons with the spin current flow over a distance as large as 60 um
in a hybrid organic-inorganic FAPbBr3 perovskite microcavity. We show direct
evidence of the long-range coherence at room temperature in the flow of exciton
polaritons, and the spin current carried by them. By harnessing the long-range
spin-Hall transport of exciton polaritons, we have demonstrated two novel room
temperature polaritonic devices, namely the NOT gate and the spin-polarized
beam splitter, advancing the frontier of room-temperature polaritonics in
perovskite microcavities.
| arxiv topic:cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.quant-gas physics.optics |
arxiv_dataset-183102304.12954 | Cofibrantly generated model structures for functor calculus
math.AT math.CT
Model structures for many different kinds of functor calculus can be obtained
by applying a theorem of Bousfield to a suitable category of functors. In this
paper, we give a general criterion for when model categories obtained via this
approach are cofibrantly generated. Our examples recover the homotopy functor
and $n$-excisive model structures of Biedermann and R\"ondigs, with different
proofs, but also include a model structure for the discrete functor calculus of
Bauer, Johnson, and McCarthy.
| arxiv topic:math.AT math.CT |
arxiv_dataset-183112304.13054 | Boson Star Normal Modes
astro-ph.CO hep-ph hep-th
Boson stars are gravitationally bound objects that arise in ultralight dark
matter models and form in the centers of galactic halos or axion miniclusters.
We systematically study the excitations of a boson star, taking into account
the mixing between positive and negative frequencies introduced by gravity. We
show that the spectrum contains zero-energy modes in the monopole and dipole
sectors resulting from spontaneous symmetry breaking by the boson star
background. We analyze the general properties of the eigenmodes and derive
their orthogonality and completeness conditions which have non-standard form
due to the positive-negative frequency mixing. The eigenvalue problem is solved
numerically for the first few energy levels in different multipole sectors and
the results are compared to the solutions of the Schr\"odinger equation in
fixed boson star gravitational potential. The two solutions differ
significantly for the lowest modes, but get close for higher levels. We further
confirm the normal mode spectrum in 3D wave simulations where we inject
perturbations with different multipoles. As an application of the normal mode
solutions, we compute the matrix element entering the evaporation rate of a
boson star immersed in a hot axion gas. The computation combines the use of
exact wavefunctions for the low-lying bound states and of the Schr\"odinger
approximation for the high-energy excitations.
| arxiv topic:astro-ph.CO hep-ph hep-th |
arxiv_dataset-183122304.13154 | Electrically Controlled Reversible Strain Modulation in MoS$_2$
Field-effect Transistors via an Electro-mechanically Coupled Piezoelectric
Thin Film
physics.app-ph
Strain can efficiently modulate the bandgap and carrier mobilities in
two-dimensional (2D) materials. Conventional mechanical strain-application
methodologies that rely on flexible, patterned or nano-indented substrates are
severely limited by low thermal tolerance, lack of tunability and/or poor
scalability. Here, we leverage the converse piezoelectric effect to
electrically generate and control strain transfer from a piezoelectric thin
film to electro-mechanically coupled ultra-thin 2D MoS$_2$. Electrical bias
polarity change across the piezoelectric film tunes the nature of strain
transferred to MoS$_2$ from compressive $\sim$0.23% to tensile $\sim$0.14% as
verified through peak shifts in Raman and photoluminescence spectroscopies and
substantiated by density functional theory calculations. The device
architecture, built on a silicon substrate, uniquely integrates an MoS$_2$
field-effect transistor on top of a metal-piezoelectric-metal stack enabling
strain modulation of transistor drain current 130$\times$, on/off current ratio
150$\times$, and mobility 1.19$\times$ with high precision, reversibility and
resolution. Large, tunable tensile (1056) and compressive (-1498) strain gauge
factors, easy electrical strain modulation, high thermal tolerance and
substrate compatibility make this technique promising for integration with
silicon-based CMOS and micro-electro-mechanical systems.
| arxiv topic:physics.app-ph |
arxiv_dataset-183132304.13254 | The directional isotropy of LIGO-Virgo binaries
gr-qc astro-ph.HE
We demonstrate how to constrain the degree of absolute alignment of the total
angular momenta of LIGO-Virgo binary black holes, looking for a special
direction in space that would break isotropy. We also allow for inhomogeneities
in the distribution of black holes over the sky. Making use of dipolar models
for the spatial distribution and orientation of the sources, we analyze 57
signals with false-alarm rates < 1/yr from the third LIGO-Virgo observing run.
Accounting for selection biases, we find the population of LIGO-Virgo black
holes to be fully consistent with both homogeneity and isotropy. We
additionally find the data to constrain some directions of alignment more than
others, and produce posteriors for the directions of total angular momentum of
all binaries in our set. All code and data are made publicly available in
https://github.com/maxisi/gwisotropy/.
| arxiv topic:gr-qc astro-ph.HE |
arxiv_dataset-183142304.13354 | Exploring the formation dynamics of affective polarization by
considering a coupled feedback
physics.soc-ph
Polarization issue is generally subject to ideological polarization and
affective polarization. In particular, affective polarization usually
accelerates the polarization process and transform social interactions into a
zero-sum game. Yet, a wide array of existing literature have not provided valid
ways to make distinction between them. Therefore, the mechanism contributing to
the rise of affective polarization still remain unclear, as well as its unique
emergent dynamics. To address this issue, this study introduces the coupled
feedback between opinions and response susceptibility to a attraction-repulsion
model which takes account into three parameters: interaction strength, response
susceptibility and tolerance to others. The model features phase diagrams of
global consensus, affective polarization, and ``harmony with diversity" states.
The simulations on time-varying and static social networks show that
intermediate parameter ranges yield a global convergence, as one integrated
cluster collapsing and converging towards a uncertain moderate position after
long-time persistence. Overall, the simulations reveal that the feedback
essentially offers a counterforce to establish an inversion between global
convergence and ``harmony with diversity". Remarkably, strengthening feedback
may facilitate polarization by driving the system priorly self-organize into
one integrated cluster which then gradually approaching polarization,
especially for low tolerance and strong interactions, and the step-like dynamic
behaviors of opinion entropy suggest the occurrence of dynamic equilibrium. For
the first time, this study attempts to offer a useful approach to the micro
foundations of affective polarization, and the results guide us how to avoid
the dilemmas from this polarization.
| arxiv topic:physics.soc-ph |
arxiv_dataset-183152304.13454 | Some aspects of anisotropic curvature flow of planar partitions
math.DG math.AP math.CA math.FA
We consider the geometric evolution of a network in the plane, flowing by
anisotropic curvature. We discuss local existence of a classical solution in
the presence of several smooth anisotropies. Next, we discuss some aspects of
the polycrystalline case.
| arxiv topic:math.DG math.AP math.CA math.FA |
arxiv_dataset-183162304.13554 | A Statistical Investigation of Decayless Oscillations in Small-scale
Coronal Loops Observed by Solar Orbiter/EUI
astro-ph.SR
Decayless kink oscillations are omnipresent in the solar atmosphere and a
viable candidate for coronal heating. Though there have been extensive studies
of decayless oscillations in coronal loops with a few hundred Mm lengths, the
properties of these oscillations in small-scale ($\sim$10 Mm) loops are yet to
be explored. In this study, we present the properties of decayless oscillations
in small loops embedded in the quiet corona and coronal holes. We use high
resolution observations from the Extreme Ultraviolet Imager onboard Solar
Orbiter with pixel scales of 210 km and 5 s cadence or better. We find 42
oscillations in 33 coronal loops with loop lengths varying between 3 to 23 Mm.
The average displacement amplitude is found to be 136 km. The oscillations
period has a range of 27 to 276 s, and the velocity amplitudes range from 2.2
to 19.3 km s$^{-1}$. The observed kink speeds are lower than those observed in
active region coronal loops. The variation of loop length with the period does
not indicate a strong correlation. Coronal seismology technique indicated an
average magnetic field value of 2.1 G. We estimate the energy flux with a broad
range of 0.6-314 W m$^{-2}$. Moreover, we note that the short-period decayless
oscillations are not prevalent in the quiet Sun and coronal holes. Therefore,
our study suggests that decayless oscillations in small-scale coronal loops are
unlikely to provide enough energy to heat the quiet Sun and accelerate solar
wind in the coronal holes.
| arxiv topic:astro-ph.SR |
arxiv_dataset-183172304.13654 | A Personalized Dense Retrieval Framework for Unified Information Access
cs.IR
Developing a universal model that can efficiently and effectively respond to
a wide range of information access requests -- from retrieval to recommendation
to question answering -- has been a long-lasting goal in the information
retrieval community. This paper argues that the flexibility, efficiency, and
effectiveness brought by the recent development in dense retrieval and
approximate nearest neighbor search have smoothed the path towards achieving
this goal. We develop a generic and extensible dense retrieval framework,
called \framework, that can handle a wide range of (personalized) information
access requests, such as keyword search, query by example, and complementary
item recommendation. Our proposed approach extends the capabilities of dense
retrieval models for ad-hoc retrieval tasks by incorporating user-specific
preferences through the development of a personalized attentive network. This
allows for a more tailored and accurate personalized information access
experience. Our experiments on real-world e-commerce data suggest the
feasibility of developing universal information access models by demonstrating
significant improvements even compared to competitive baselines specifically
developed for each of these individual information access tasks. This work
opens up a number of fundamental research directions for future exploration.
| arxiv topic:cs.IR |
arxiv_dataset-183182304.13754 | Finding the effective dynamics to make rare events typical in chaotic
maps
cond-mat.stat-mech math-ph math.DS math.MP nlin.CD quant-ph
Dynamical fluctuations or rare events associated with atypical trajectories
in chaotic maps due to specific initial conditions can crucially determine
their fate, as the may lead to stability islands or regions in phase space
otherwise displaying unusual behavior. Yet, finding such initial conditions is
a daunting task precisely because of the chaotic nature of the system. In this
work, we circumvent this problem by proposing a framework for finding an
effective topologically-conjugate map whose typical trajectories correspond to
atypical ones of the original map. This is illustrated by means of examples
which focus on counterbalancing the instability of fixed points and periodic
orbits, as well as on the characterization of a dynamical phase transition
involving the finite-time Lyapunov exponent. The procedure parallels that of
the application of the generalized Doob transform in the stochastic dynamics of
Markov chains, diffusive processes and open quantum systems, which in each case
results in a new process having the prescribed statistics in its stationary
state. This work thus brings chaotic maps into the growing family of systems
whose rare fluctuations -- sustaining prescribed statistics of dynamical
observables -- can be characterized and controlled by means of a
large-deviation formalism.
| arxiv topic:cond-mat.stat-mech math-ph math.DS math.MP nlin.CD quant-ph |
arxiv_dataset-183192304.13854 | Understand the Dynamic World: An End-to-End Knowledge Informed Framework
for Open Domain Entity State Tracking
cs.AI
Open domain entity state tracking aims to predict reasonable state changes of
entities (i.e., [attribute] of [entity] was [before_state] and [after_state]
afterwards) given the action descriptions. It's important to many reasoning
tasks to support human everyday activities. However, it's challenging as the
model needs to predict an arbitrary number of entity state changes caused by
the action while most of the entities are implicitly relevant to the actions
and their attributes as well as states are from open vocabularies. To tackle
these challenges, we propose a novel end-to-end Knowledge Informed framework
for open domain Entity State Tracking, namely KIEST, which explicitly retrieves
the relevant entities and attributes from external knowledge graph (i.e.,
ConceptNet) and incorporates them to autoregressively generate all the entity
state changes with a novel dynamic knowledge grained encoder-decoder framework.
To enforce the logical coherence among the predicted entities, attributes, and
states, we design a new constraint decoding strategy and employ a coherence
reward to improve the decoding process. Experimental results show that our
proposed KIEST framework significantly outperforms the strong baselines on the
public benchmark dataset OpenPI.
| arxiv topic:cs.AI |
arxiv_dataset-183202304.13954 | Why magnetic monopole becomes dyon in topological insulators
cond-mat.mes-hall cond-mat.str-el hep-lat hep-th
The Witten effect predicts that a magnetic monopole acquires a fractional
electric charge inside topological insulators.
In this work, we give a microscopic description of this phenomenon, as well
as an analogous two-dimensional system with a vortex. We solve the Dirac
equation of electron field both analytically in continuum and numerically on a
lattice, by adding the Wilson term and smearing the gauge field within a finite
range to regularize the short-distance behavior of the system. Our results
reveal that the Wilson term induces a strong positive mass shift, creating a
domain-wall around the monopole/vortex. This small, yet finite-sized
domain-wall localizes the chiral zero modes and ensures their stability through
the Atiyah-Singer index theorem, whose cobordism invariance is crucial in
explaining why the electric charge is fractional.
| arxiv topic:cond-mat.mes-hall cond-mat.str-el hep-lat hep-th |
arxiv_dataset-183212304.14054 | Unification of Lagrangian staggered-grid hydrodynamics and cell-centered
hydrodynamics in one dimension
math.NA cs.NA math-ph math.MP
This paper focuses on the novel scheme to unify both Lagrangian
staggered-grid and cell-centered hydrodynamic methods in one dimension. The
scheme neither contains empirical parameters nor solves the Riemann problem. It
includes two key points: one is the relationship between pressure and velocity,
and the other is Newton's second law. The two methods that make use of this
scheme satisfy the entropy condition and are conservative in total mass,
momentum, and energy. Numerical results show the robustness and accuracy of
both methods.
| arxiv topic:math.NA cs.NA math-ph math.MP |
arxiv_dataset-183222304.14154 | Traced Types for Safe Strategic Rewriting
cs.PL
Strategy languages enable programmers to compose rewrite rules into
strategies and control their application. This is useful in programming
languages, e.g., for describing program transformations compositionally, but
also in automated theorem proving, where related ideas have been studies with
tactics languages. Clearly, not all compositions of rewrites are correct, but
how can we assist programmers in writing correct strategies?
In this paper, we present a static type system for strategy languages. We
combine a structural type system capturing how rewrite strategies transform the
shape of the rewritten syntax with a novel tracing system that keeps track of
all possible legal strategy execution paths. Our type system raises warnings
when parts of a composition are guaranteed to fail at runtime, and errors when
no legal execution for a strategy is possible. We present a formalization of
our strategy language and novel tracing type system, and formally prove its
type soundness.
We present formal results, showing that ill-traced strategies are guaranteed
to fail at runtime and that well-traced strategy executions "can't go wrong",
meaning that they are guaranteed to have a possible successful execution path.
| arxiv topic:cs.PL |
arxiv_dataset-183232304.14254 | Charge Stripe Manipulation of Superconducting Pairing Symmetry
Transition
cond-mat.str-el cond-mat.supr-con
Charge stripes have been widely observed in many different types of
unconventional superconductors, holding varying periods ($\mathcal{P}$) and
intensities. However, a general understanding on the interplay between charge
stripes and superconducting properties is still incomplete. Here, using
large-scale unbiased numerical simulations on a general inhomogeneous Hubbard
model, we discover that the charge-stripe period $\mathcal{P}$, which is
variable in different real material systems, could dictate the pairing
symmetries -- $d$ wave for $\mathcal{P} \ge 4$, $s$ and $d$ waves for
$\mathcal{P} \le 3$. In the latter, tuning hole doping and charge-stripe
amplitude can trigger a $d$-$s$ wave transition and magnetic-correlation shift,
where the $d$-wave state converts to a pairing-density wave state, competing
with the $s$ wave. These interesting phenomena arise from an unusual
stripe-induced selection rule of pairing symmetries around on-stripe region and
within inter-stripe region, giving rise to a critical point of $\mathcal{P}=3$
for the phase transition. In general, our findings offer new insights into the
differences in the superconducting pairing mechanisms across many
$\mathcal{P}$-dependent superconducting systems, highlighting the decisive role
of charge stripe.
| arxiv topic:cond-mat.str-el cond-mat.supr-con |
arxiv_dataset-183242304.14354 | Industrial Engineering with Large Language Models: A case study of
ChatGPT's performance on Oil & Gas problems
cs.CL
Large Language Models (LLMs) have shown great potential in solving complex
problems in various fields, including oil and gas engineering and other
industrial engineering disciplines like factory automation, PLC programming
etc. However, automatic identification of strong and weak solutions to
fundamental physics equations governing several industrial processes remain a
challenging task. This paper identifies the limitation of current LLM
approaches, particularly ChatGPT in selected practical problems native to oil
and gas engineering but not exclusively. The performance of ChatGPT in solving
complex problems in oil and gas engineering is discussed and the areas where
LLMs are most effective are presented.
| arxiv topic:cs.CL |
arxiv_dataset-183252304.14454 | PMC-LLaMA: Towards Building Open-source Language Models for Medicine
cs.CL
Recently, Large Language Models (LLMs) have showcased remarkable capabilities
in natural language understanding. While demonstrating proficiency in everyday
conversations and question-answering situations, these models frequently
struggle in domains that require precision, such as medical applications, due
to their lack of domain-specific knowledge. In this paper, we describe the
procedure for building a powerful, open-source language model specifically
designed for medicine applications, termed as PMC-LLaMA. Our contributions are
threefold: (i) we systematically investigate the process of adapting a
general-purpose foundation language model towards medical domain, this involves
data-centric knowledge injection through the integration of 4.8M biomedical
academic papers and 30K medical textbooks, as well as comprehensive fine-tuning
for alignment with domain-specific instructions; (ii) we contribute a
large-scale, comprehensive dataset for instruction tuning. This dataset
encompasses medical question-answering (QA), rationale for reasoning, and
conversational dialogues, comprising a total of 202M tokens; (iii) we conduct
thorough ablation studies to demonstrate the effectiveness of each proposed
component. While evaluating on various public medical question-answering
benchmarks, our lightweight PMCLLaMA, which consists of only 13 billion
parameters, exhibits superior performance, even surpassing ChatGPT. All models,
codes, datasets can be found in https://github.com/chaoyi-wu/PMC-LLaMA.
| arxiv topic:cs.CL |
arxiv_dataset-183262304.14554 | AI-aided Geometric Design of Anti-infection Catheters
physics.med-ph cond-mat.soft physics.bio-ph physics.flu-dyn
Bacteria can swim upstream due to hydrodynamic interactions with the fluid
flow in a narrow tube, and pose a clinical threat of urinary tract infection to
patients implanted with catheters. Coatings and structured surfaces have been
proposed as a way to suppress bacterial contamination in catheters. However,
there is no surface structuring or coating approach to date that thoroughly
addresses the contamination problem. Here, based on the physical mechanism of
upstream swimming, we propose a novel geometric design, optimized by an AI
model predicting in-flow bacterial dynamics. The AI method, based on Fourier
neural operator, offers significant speedups over traditional simulation
methods. Using Escherichia coli, we demonstrate the anti-infection mechanism in
quasi-2D micro-fluidic experiments and evaluate the effectiveness of the design
in 3Dprinted prototype catheters under clinical flow rates. Our catheter design
shows 1-2 orders of magnitude improved suppression of bacterial contamination
at the upstream end of the catheter, potentially prolonging the in-dwelling
time for catheter use and reducing the overall risk of catheter-associated
urinary tract infections.
| arxiv topic:physics.med-ph cond-mat.soft physics.bio-ph physics.flu-dyn |
arxiv_dataset-183272304.14654 | Effective Data Aggregation in WSN for Enhanced Security and Data Privacy
cs.CR
The two biggest problems with wireless sensor networks are security and
energy usage. In sensing devices, malicious nodes could be found in large
numbers. The researchers have proposed several methods to find these rogue
nodes. To prevent assaults on these networks and data transmission, the data
must be secured. Data aggregation aids in reducing the number of messages
transmitted within the network, which in turn lowers total network energy
consumption. Additionally, when decrypting the aggregated data, the base
station can distinguish between encrypted and consolidated analysis based on
top of the cryptographic keys. By examining the effectiveness of the data
aggregation in this research. To solve the above problem, the system provides a
method in which an efficient cluster agent is preferred pedestal on its
location at the access point and energy availability. The sensor network's
energy consumption is reduced by selecting an effective cluster agent,
extending the network's lifespan. The cluster's agent is in indict of compiling
data for each member node. The clustering agent validates the data and tosses
any errors before aggregation. The clustering agent only aggregates confirmed
data. To provide end-to-end anonymity, ElGamal elliptic curve (ECE) encryption
is used to secure the client data and reassign the encrypted information en
route for the cluster agent. Only the base station (BS) can decrypt the data.
Furthermore, an ID-based signature system is utilized to enable authenticity.
This research presents a technique for recuperating lost data. The access point
employs a cache-based backup system to search for lost data.
| arxiv topic:cs.CR |
arxiv_dataset-183282304.14754 | Shadows and quasinormal modes of the Bardeen black hole in cloud of
strings
gr-qc
We investigate the black hole (BH) solution of the Einstein's gravity coupled
with non-linear electrodynamics (NED) source in the background of a cloud of
strings. We analyze the horizon structure of the obtained BH solution. The
optical features of the BH are explored. The photon radius and shadows of the
BH are obtained as a function of black hole parameters. We observe that the
size of the shadow image is bigger than its horizon radius and photon sphere.
We also study the Quasinormal modes (QNM) using WKB formula for this black
hole. The dependence of shadow radius and QN modes on black hole parameters
reflects that they are mimicker to each other.
| arxiv topic:gr-qc |
arxiv_dataset-183292304.14854 | Scaling regimes in rapidly rotating thermal convection at extreme
Rayleigh numbers
physics.flu-dyn
The geostrophic turbulence in rapidly rotating thermal convection exhibits
characteristics shared by many highly turbulent geophysical and astrophysical
flows. In this regime, the convective length and velocity scales, heat flux,
and kinetic and thermal dissipation rates are all diffusion-free, meaning that
they are independent of the viscosity and thermal diffusivity. Our direct
numerical simulations (DNS) of rotating Rayleigh--B\'enard convection in
domains with no-slip top and bottom and periodic lateral boundary conditions
for a fluid with the Prandtl number $Pr=1$ and extreme buoyancy and rotation
parameters (the Rayleigh number up to $Ra=3\times10^{13}$ and the Ekman number
down to $Ek=5\times10^{-9}$) indeed demonstrate these diffusion-free scaling
relations, in particular, that the dimensionless convective heat transport
scales with the supercriticality parameter $\widetilde{Ra}\equiv Ra\,Ek^{4/3}$
as $Nu-1\propto \widetilde{Ra}^{3/2}$, where $Nu$ is the Nusselt number. We
further derive and verify in the DNS that with the decreasing $\widetilde{Ra}$
the geostrophic turbulence regime undergoes a transition into another
geostrophic regime where the convective heat transport scales as $Nu-1\propto
\widetilde{Ra}^{3}$.
| arxiv topic:physics.flu-dyn |
arxiv_dataset-183302304.14954 | A Class of Dependent Random Distributions Based on Atom Skipping
stat.ME stat.ML
We propose the Plaid Atoms Model (PAM), a novel Bayesian nonparametric model
for grouped data. Founded on an idea of `atom skipping', PAM is part of a
well-established category of models that generate dependent random
distributions and clusters across multiple groups. Atom skipping referrs to
stochastically assigning 0 weights to atoms in an infinite mixture. Deploying
atom skipping across groups, PAM produces a dependent clustering pattern with
overlapping and non-overlapping clusters across groups. As a result,
interpretable posterior inference is possible such as reporting the posterior
probability of a cluster being exclusive to a single group or shared among a
subset of groups. We discuss the theoretical properties of the proposed and
related models. Minor extensions of the proposed model for multivariate or
count data are presented. Simulation studies and applications using real-world
datasets illustrate the performance of the new models with comparison to
existing models.
| arxiv topic:stat.ME stat.ML |
arxiv_dataset-183312305.00044 | Hedonic Prices and Quality Adjusted Price Indices Powered by AI
econ.GN cs.LG q-fin.EC
We develop empirical models that efficiently process large amounts of
unstructured product data (text, images, prices, quantities) to produce
accurate hedonic price estimates and derived indices. To achieve this, we
generate abstract product attributes (or ``features'') from descriptions and
images using deep neural networks. These attributes are then used to estimate
the hedonic price function. To demonstrate the effectiveness of this approach,
we apply the models to Amazon's data for first-party apparel sales, and
estimate hedonic prices. The resulting models have a very high out-of-sample
predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we
construct the AI-based hedonic Fisher price index, chained at the
year-over-year frequency, and contrast it with the CPI and other electronic
indices.
| arxiv topic:econ.GN cs.LG q-fin.EC |
arxiv_dataset-183322305.00144 | Integrating Across Application, Model, Algorithm, Compilation, and Error
Correction Chasms With Quantum Type Theory
quant-ph cond-mat.mes-hall
We briefly discuss the current state, and future computational implications,
of quantum type theory.
| arxiv topic:quant-ph cond-mat.mes-hall |
arxiv_dataset-183332305.00244 | A Critical Analysis of the Limitation of Deep Learning based 3D Dental
Mesh Segmentation Methods in Segmenting Partial Scans
cs.CV cs.LG
Tooth segmentation from intraoral scans is a crucial part of digital
dentistry. Many Deep Learning based tooth segmentation algorithms have been
developed for this task. In most of the cases, high accuracy has been achieved,
although, most of the available tooth segmentation techniques make an implicit
restrictive assumption of full jaw model and they report accuracy based on full
jaw models. Medically, however, in certain cases, full jaw tooth scan is not
required or may not be available. Given this practical issue, it is important
to understand the robustness of currently available widely used Deep Learning
based tooth segmentation techniques. For this purpose, we applied available
segmentation techniques on partial intraoral scans and we discovered that the
available deep Learning techniques under-perform drastically. The analysis and
comparison presented in this work would help us in understanding the severity
of the problem and allow us to develop robust tooth segmentation technique
without strong assumption of full jaw model.
| arxiv topic:cs.CV cs.LG |
arxiv_dataset-183342305.00344 | Ricci flow from spaces with edge type conical singularities
math.DG math.AP
We study the Ricci flow out of spaces with edge type conical singularities
along a closed, embedded curve. Under the additional assumption that for each
point of the curve, our space is locally modelled on the product of a fixed
positively curved cone and a line, we show existence of a solution to Ricci
flow $(M,g(t))$ for $t\in (0,T],$ which converges back to the singular space as
$t\searrow 0$ in the pointed Gromov-Hausdorff topology. We also prove curvature
estimates for the solution and, for edge points, we show that the tangent flow
at these points is a positively curved expanding Ricci soliton solution crossed
with a line.
| arxiv topic:math.DG math.AP |
arxiv_dataset-183352305.00444 | Superconductivity in graphite intercalation compounds with sodium
cond-mat.supr-con physics.comp-ph
The discovery of superconductivity in CaC6 with a critical temperature (Tc)
of 11.5 K reignites much interest in exploring high-temperature
superconductivity in graphite intercalation compounds (GICs). Here we identify
a GIC NaC4, discovered by ab initio evolutionary structure search, as a
superconductor with a computed Tc of 41.2 K at 5 GPa. This value is eight times
higher than that of the synthesized GIC NaC2 and possesses the highest Tc among
available GICs. The remarkable superconductivity of GIC NaC4 mainly arises from
the coupling of {\pi} electrons in graphene with the low-frequency vibrations
involving both Na and C atoms. These findings suggest that Na-GICs may hold
great promise as high-Tc superconductors.
| arxiv topic:cond-mat.supr-con physics.comp-ph |
arxiv_dataset-183362305.00544 | On the State Estimation Error of "Beam-Pointing'' Channels: The Binary
Case
cs.IT math.IT
Sensing capabilities as an integral part of the network have been identified
as a novel feature of sixth-generation (6G) wireless networks. As a key driver,
millimeterwave (mmWave) communication largely boosts speed, capacities, and
connectivity. In order to maximize the potential of mmWave communication,
precise and fast beam acquisition (BA) is crucial, since it compensates for a
high pathloss and provides a large beamforming gain. Practically, the
angle-of-departure (AoD) remains almost constant over numerous consecutive time
slots, the backscatter signal experiences some delay, and the hardware is
restricted under the peak power constraint. This work captures these main
features by a simple binary beam-pointing (BBP) channel model with in-block
memory (iBM) [1], peak cost constraint, and one unit-delayed feedback. In
particular, we focus on the sensing capabilities of such a model and
characterize the performance of the BA process in terms of the Hamming
distortion of the estimated channel state. We encode the position of the AoD
and derive the minimum distortion of the BBP channel under the peak cost
constraint with no communication constraint. Our previous work [2] proposed a
joint communication and sensing (JCAS) algorithm, which achieves the capacity
of the same channel model. Herein, we show that by employing this JCAS
transmission strategy, optimal data communication and channel estimation can be
accomplished simultaneously. This yields the complete characterization of the
capacity-distortion tradeoff for this model.
| arxiv topic:cs.IT math.IT |
arxiv_dataset-183372305.00644 | Procedural Content Generation via Knowledge Transformation (PCG-KT)
cs.AI
We introduce the concept of Procedural Content Generation via Knowledge
Transformation (PCG-KT), a new lens and framework for characterizing PCG
methods and approaches in which content generation is enabled by the process of
knowledge transformation -- transforming knowledge derived from one domain in
order to apply it in another. Our work is motivated by a substantial number of
recent PCG works that focus on generating novel content via repurposing derived
knowledge. Such works have involved, for example, performing transfer learning
on models trained on one game's content to adapt to another game's content, as
well as recombining different generative distributions to blend the content of
two or more games. Such approaches arose in part due to limitations in PCG via
Machine Learning (PCGML) such as producing generative models for games lacking
training data and generating content for entirely new games. In this paper, we
categorize such approaches under this new lens of PCG-KT by offering a
definition and framework for describing such methods and surveying existing
works using this framework. Finally, we conclude by highlighting open problems
and directions for future research in this area.
| arxiv topic:cs.AI |
arxiv_dataset-183382305.00744 | Entropy production in the nonreciprocal Cahn-Hilliard model
cond-mat.soft cond-mat.stat-mech physics.flu-dyn
We study the nonreciprocal Cahn-Hilliard model with thermal noise as a
prototypical example of a generic class of non-Hermitian stochastic field
theories, analyzed in two companion papers [Suchanek, Kroy, Loos,
ArXiv:2303.16701 (2023); Suchanek, Kroy, Loos, ArXiv:2305.05633 (2023)]. Due to
the nonreciprocal coupling between two field components, the model is
inherently out of equilibrium and can be regarded as an active field theory.
Beyond the conventional homogeneous and static-demixed phases, it exhibits a
traveling-wave phase, which can be entered via either an oscillatory
instability or a critical exceptional point. By means of a Fourier
decomposition of the entropy production rate, we quantify the associated
scale-resolved time-reversal symmetry breaking, in all phases and across the
transitions, in the low-noise regime. Our perturbative calculation reveals its
dependence on the strength of the nonreciprocal coupling. Surging entropy
production near the static-dynamic transitions can be attributed to
entropy-generating fluctuations in the longest wavelength mode and heralds the
emerging traveling wave. Its translational dynamics can be mapped on the
dissipative ballistic motion of an active (quasi)particle.
| arxiv topic:cond-mat.soft cond-mat.stat-mech physics.flu-dyn |
arxiv_dataset-183392305.00844 | Automated Paper Screening for Clinical Reviews Using Large Language
Models
cs.CL cs.AI
Objective: To assess the performance of the OpenAI GPT API in accurately and
efficiently identifying relevant titles and abstracts from real-world clinical
review datasets and compare its performance against ground truth labelling by
two independent human reviewers.
Methods: We introduce a novel workflow using the OpenAI GPT API for screening
titles and abstracts in clinical reviews. A Python script was created to make
calls to the GPT API with the screening criteria in natural language and a
corpus of title and abstract datasets that have been filtered by a minimum of
two human reviewers. We compared the performance of our model against
human-reviewed papers across six review papers, screening over 24,000 titles
and abstracts.
Results: Our results show an accuracy of 0.91, a sensitivity of excluded
papers of 0.91, and a sensitivity of included papers of 0.76. On a randomly
selected subset of papers, the GPT API demonstrated the ability to provide
reasoning for its decisions and corrected its initial decision upon being asked
to explain its reasoning for a subset of incorrect classifications.
Conclusion: The GPT API has the potential to streamline the clinical review
process, save valuable time and effort for researchers, and contribute to the
overall quality of clinical reviews. By prioritizing the workflow and acting as
an aid rather than a replacement for researchers and reviewers, the GPT API can
enhance efficiency and lead to more accurate and reliable conclusions in
medical research.
| arxiv topic:cs.CL cs.AI |
arxiv_dataset-183402305.00944 | Poisoning Language Models During Instruction Tuning
cs.CL cs.CR cs.LG
Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetuned on
datasets that contain user-submitted examples, e.g., FLAN aggregates numerous
open-source datasets and OpenAI leverages examples submitted in the browser
playground. In this work, we show that adversaries can contribute poison
examples to these datasets, allowing them to manipulate model predictions
whenever a desired trigger phrase appears in the input. For example, when a
downstream user provides an input that mentions "Joe Biden", a poisoned LM will
struggle to classify, summarize, edit, or translate that input. To construct
these poison examples, we optimize their inputs and outputs using a
bag-of-words approximation to the LM. We evaluate our method on open-source
instruction-tuned LMs. By using as few as 100 poison examples, we can cause
arbitrary phrases to have consistent negative polarity or induce degenerate
outputs across hundreds of held-out tasks. Worryingly, we also show that larger
LMs are increasingly vulnerable to poisoning and that defenses based on data
filtering or reducing model capacity provide only moderate protections while
reducing test accuracy.
| arxiv topic:cs.CL cs.CR cs.LG |
arxiv_dataset-183412305.01044 | Venn Diagram Multi-label Class Interpretation of Diabetic Foot Ulcer
with Color and Sharpness Enhancement
cs.CV cs.AI
DFU is a severe complication of diabetes that can lead to amputation of the
lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer
Grand Challenge, researchers designed automated multi-class classification of
DFU, including infection, ischaemia, both of these conditions, and none of
these conditions. However, it remains a challenge as classification accuracy is
still not satisfactory. This paper proposes a Venn Diagram interpretation of
multi-label CNN-based method, utilizing different image enhancement strategies,
to improve the multi-class DFU classification. We propose to reduce the four
classes into two since both class wounds can be interpreted as the simultaneous
occurrence of infection and ischaemia and none class wounds as the absence of
infection and ischaemia. We introduce a novel Venn Diagram representation block
in the classifier to interpret all four classes from these two classes. To make
our model more resilient, we propose enhancing the perceptual quality of DFU
images, particularly blurry or inconsistently lit DFU images, by performing
color and sharpness enhancements on them. We also employ a fine-tuned
optimization technique, adaptive sharpness aware minimization, to improve the
CNN model generalization performance. The proposed method is evaluated on the
test dataset of DFUC2021, containing 5,734 images and the results are compared
with the top-3 winning entries of DFUC2021. Our proposed approach outperforms
these existing approaches and achieves Macro-Average F1, Recall and Precision
scores of 0.6592, 0.6593, and 0.6652, respectively.Additionally, We perform
ablation studies and image quality measurements to further interpret our
proposed method. This proposed method will benefit patients with DFUs since it
tackles the inconsistencies in captured images and can be employed for a more
robust remote DFU wound classification.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-183422305.01144 | Increasing trust in new data sources: crowdsourcing image classification
for ecology
stat.AP
Crowdsourcing methods facilitate the production of scientific information by
non-experts. This form of citizen science (CS) is becoming a key source of
complementary data in many fields to inform data-driven decisions and study
challenging problems. However, concerns about the validity of these data often
constrain their utility. In this paper, we focus on the use of citizen science
data in addressing complex challenges in environmental conservation. We
consider this issue from three perspectives. First, we present a literature
scan of papers that have employed Bayesian models with citizen science in
ecology. Second, we compare several popular majority vote algorithms and
introduce a Bayesian item response model that estimates and accounts for
participants' abilities after adjusting for the difficulty of the images they
have classified. The model also enables participants to be clustered into
groups based on ability. Third, we apply the model in a case study involving
the classification of corals from underwater images from the Great Barrier
Reef, Australia. We show that the model achieved superior results in general
and, for difficult tasks, a weighted consensus method that uses only groups of
experts and experienced participants produced better performance measures.
Moreover, we found that participants learn as they have more classification
opportunities, which substantially increases their abilities over time.
Overall, the paper demonstrates the feasibility of CS for answering complex and
challenging ecological questions when these data are appropriately analysed.
This serves as motivation for future work to increase the efficacy and
trustworthiness of this emerging source of data.
| arxiv topic:stat.AP |
arxiv_dataset-183432305.01244 | On boundedness and moduli spaces of K-stable Calabi-Yau fibrations over
curves
math.AG math.DG
We show boundedness of polarized Calabi--Yau fibrations over curves only with
fixed volumes of general fibers and Iitaka volumes. As its application, we
construct a separated coarse moduli space of K-stable Calabi-Yau fibrations
over curves in an adiabatic sense [Hat22b] and show that all members (resp.
smooth members) of the moduli are simultaneously uniformly K-stable (resp. have
cscK metrics) for a certain choice of polarizations.
| arxiv topic:math.AG math.DG |
arxiv_dataset-183442305.01344 | Quantum calculation of axion-photon transition in electromagnetodynamics
for cavity haloscope
hep-ph hep-ex
The Witten effect implies the presence of electric charge of magnetic monople
and possible relationship between axion and dyon. The axion-dyon dynamics can
be reliably built based on the quantum electromagnetodynamics (QEMD) which was
developed by Schwinger and Zwanziger in 1960's. A generic low-energy
axion-photon effective field theory can also be realized in the language of
``generalized symmetries'' with higher-form symmetries and background gauge
fields. In this work, we implement the quantum calculation of axion-single
photon transition rate inside a homogeneous electromagnetic field in terms of
the new axion interaction Hamiltonian in QEMD. This quantum calculation can
clearly imply the enhancement of conversion rate through resonant cavity in
axion haloscope experiments. We also show the promising potentials on the
cavity search of new axion-photon couplings in QEMD.
| arxiv topic:hep-ph hep-ex |
arxiv_dataset-183452305.01444 | On reversing arcs to improve arc-connectivity
math.CO cs.DM
We show that if the arc-connectivity of a directed graph $D$ is at most
$\lfloor\frac{k+1}{2}\rfloor$ and the reorientation of an arc set $F$ in $D$
results in a $k$-arc-connected directed graph then we can reorient one arc of
$F$ without decreasing the arc-connectivity of $D.$ This improves a result of
Fukuda, Prodon, Sakuma and one of Ito et al. for $k\in\{2,3\}$.
| arxiv topic:math.CO cs.DM |
arxiv_dataset-183462305.01544 | Analytical Fitting of Gamma-ray Photopeaks in Germanium Cross Strip
Detectors
astro-ph.IM nucl-ex
In an ideal germanium detector, fully-absorbed monoenergetic gamma-rays will
appear in the measured spectrum as a narrow peak, broadened into a Gaussian of
width determined only by the statistical properties of charge cloud generation
and the electronic noise of the readout electronics. Multielectrode detectors
complicate this picture. Broadening of the charge clouds as they drift through
the detector will lead to charge sharing between neighboring electrodes and,
inevitably, low-energy tails on the photopeak spectra. We simulate charge
sharing in our germanium cross strip detectors in order to reproduce the
low-energy tails due to charge sharing. Our goal is to utilize these simulated
spectra to develop an analytical fit (shape function) for the spectral lines
that provides a robust and high-quality fit to the spectral profile, reliably
reproduces the interaction energy, noise width, and the number of counts in
both the true photopeak and the low-energy tail, and minimizes the number of
additional parameters. Accurate modeling of the detailed line profiles is
crucial for both calibration of the detectors as well as scientific
interpretation of measured spectra.
| arxiv topic:astro-ph.IM nucl-ex |
arxiv_dataset-183472305.01644 | Key-Locked Rank One Editing for Text-to-Image Personalization
cs.CV cs.AI cs.GR
Text-to-image models (T2I) offer a new level of flexibility by allowing users
to guide the creative process through natural language. However, personalizing
these models to align with user-provided visual concepts remains a challenging
problem. The task of T2I personalization poses multiple hard challenges, such
as maintaining high visual fidelity while allowing creative control, combining
multiple personalized concepts in a single image, and keeping a small model
size. We present Perfusion, a T2I personalization method that addresses these
challenges using dynamic rank-1 updates to the underlying T2I model. Perfusion
avoids overfitting by introducing a new mechanism that "locks" new concepts'
cross-attention Keys to their superordinate category. Additionally, we develop
a gated rank-1 approach that enables us to control the influence of a learned
concept during inference time and to combine multiple concepts. This allows
runtime-efficient balancing of visual-fidelity and textual-alignment with a
single 100KB trained model, which is five orders of magnitude smaller than the
current state of the art. Moreover, it can span different operating points
across the Pareto front without additional training. Finally, we show that
Perfusion outperforms strong baselines in both qualitative and quantitative
terms. Importantly, key-locking leads to novel results compared to traditional
approaches, allowing to portray personalized object interactions in
unprecedented ways, even in one-shot settings.
| arxiv topic:cs.CV cs.AI cs.GR |
arxiv_dataset-183482305.01744 | A Novel Mechanism for the Formation of Dislocation Cell Patterns in BCC
Metal
cond-mat.mtrl-sci hep-th
In this study, we present the first simulation results of the formation of
dislocation cell wall microstructures in tantalum subjected to shock loading.
Dislocation patterns and cell wall formation are important to understanding the
mechanical properties of the materials in which they spontaneously arise, and
yet the processing and self-assembly mechanisms leading to their formation are
poorly understood. By employing transmission electron microscopy and discrete
dislocation dynamics, we propose a new mechanism involving coplanar
dislocations and pseudo-dipole mixed dislocation arrays that is essential to
the pattern formation process. Our large-scale 3D DDD simulations demonstrate
the self-organization of dislocation networks into cell walls in deformed BCC
metal (tantalum) persisting at the strain 20%. The simulation analysis captures
several crucial aspects of how the dislocation cell pattern affects metal
plasticity, as observed in experiments. Although experimental evidence is
inconclusive regarding whether cell wall formation takes place at the shock
front, after the shock, during release, or when the sample has had enough time
to relax post-recovery, our simulations indicate cell wall formation occurs
after the shock and before release. The extended Taylor hardening composite
model effectively considers the non-uniform dislocation density when cell walls
form and accurately describes the corresponding flow stress.
| arxiv topic:cond-mat.mtrl-sci hep-th |
arxiv_dataset-183492305.01844 | Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A
Minimalist Approach
cs.CV eess.IV
In this study, we explore the potential of using a straightforward neural
network inspired by the retina model to efficiently restore low-light images.
The retina model imitates the neurophysiological principles and dynamics of
various optical neurons. Our proposed neural network model reduces the
computational overhead compared to traditional signal-processing models while
achieving results similar to complex deep learning models from a subjective
perceptual perspective. By directly simulating retinal neuron functionalities
with neural networks, we not only avoid manual parameter optimization but also
lay the groundwork for constructing artificial versions of specific
neurobiological organizations.
| arxiv topic:cs.CV eess.IV |
arxiv_dataset-183502305.01944 | Signs of criticality in social explosions
physics.soc-ph
The success of an on-line movement could be defined in terms of the shift to
large-scale and the later off-line massive street actions of protests. The role
of social media in this process is to facilitate the transformation from small
or local feelings of disagreement into large-scale social actions. The way how
social media achieves that effect is by growing clusters of people and groups
with similar effervescent feelings, which in another case would never be in
communication. It is natural to think that these kinds of macro social actions,
as a consequence of the spontaneous and massive interactions, will attain the
growth and divergence of the correlation length, giving rise to important
simplifications on several statistics. In this work, we report the presence of
signs of criticality in social demonstrations. Namely, the same power-law
exponents are found whenever the distributions are calculated, either
considering the same windows-time or the same number of hashtags. The exponents
for the distributions during the event were found to be smaller than before
(and after) the event. The latter also happens whenever the hashtags are
counted only once per user or if all their usages are considered. By means of
network representations, we show that the systems present two kinds of high
correlations, characterised by either high or low values of modularity. The
temporal points of high modularity are characterised by a sustained correlation
while the ones of low modularity are characterised by a punctual correlation.
The importance of analysing systems near a critical point is that any small
disturbance can escalate and induce large-scale -- nationwide -- chain
reactions.
| arxiv topic:physics.soc-ph |
arxiv_dataset-183512305.02044 | Estimating the error in CG-like algorithms for least-squares and
least-norm problems
math.NA cs.NA
In [Meurant, Pape\v{z}, Tich\'y; Numerical Algorithms 88, 2021], we presented
an adaptive estimate for the energy norm of the error in the conjugate gradient
(CG) method. In this paper, we extend the estimate to algorithms for solving
linear approximation problems with a general, possibly rectangular matrix that
are based on applying CG to a system with a positive (semi-)definite matrix
build from the original matrix. We show that the resulting estimate preserves
its key properties: it can be very cheaply evaluated, and it is numerically
reliable in finite-precision arithmetic under some mild assumptions. We discuss
algorithms based on Hestenes-Stiefel-like implementation (often called CGLS and
CGNE in the literature) as well as on bidiagonalization (LSQR and CRAIG), and
both unpreconditioned and preconditioned variants. The numerical experiments
confirm the robustness and very satisfactory behaviour of the estimate.
| arxiv topic:math.NA cs.NA |
arxiv_dataset-183522305.02144 | Ultra-delayed material failure via shear banding after straining an
amorphous material
cond-mat.soft cond-mat.stat-mech
We predict a phenomenon of catastrophic material failure arising suddenly
within an amorphous material, with an extremely long delay time since the
material was last deformed. By simulating a mesoscopic soft glassy rheology
model in one dimension (1D), a mesoscopic elastoplastic model in 1D and 2D, and
a continuum fluidity model in 1D, we demonstrate the basic physics to involve a
dramatic ultra-delayed shear banding instability, in which strain suddenly
strongly localises within the material and the stress drops precipitously. The
delay time after the long historical shear strain was applied before failure
occurs increases steeply with decreasing strain amplitude, decreasing working
temperature, and increasing sample annealing prior to shear. In demonstrating
the same physics -- which is directly testable experimentally and in particle
simulations -- to obtain within three different constitutive models, we suggest
it may be generic across amorphous materials. The counter-intuitive prediction
of catastrophic material failure long after any deformation was last applied
could have important consequences for material processing and performance.
| arxiv topic:cond-mat.soft cond-mat.stat-mech |
arxiv_dataset-183532305.02244 | NVMM cache design: Logging vs. Paging
cs.OS cs.AR
Modern NVMM is closing the gap between DRAM and persistent storage, both in
terms of performance and features. Having both byte addressability and
persistence on the same device gives NVMM an unprecedented set of features,
leading to the following question: How should we design an NVMM-based caching
system to fully exploit its potential? We build two caching mechanisms, NVPages
and NVLog, based on two radically different design approaches. NVPages stores
memory pages in NVMM, similar to the Linux page cache (LPC). NVLog uses NVMM to
store a log of pending write operations to be submitted to the LPC, while it
ensures reads with a small DRAM cache. Our study shows and quantifies
advantages and flaws for both designs.
| arxiv topic:cs.OS cs.AR |
arxiv_dataset-183542305.02344 | Inspiraling streams of enriched gas observed around a massive galaxy 11
billion years ago
astro-ph.GA astro-ph.CO
Stars form in galaxies, from gas that has been accreted from the
intergalactic medium. Simulations have shown that recycling of gas-the
reaccretion of gas that was previously ejected from a galaxy-could sustain star
formation in the early Universe. We observe the gas surrounding a massive
galaxy at redshift 2.3 and detect emission lines from neutral hydrogen, helium,
and ionized carbon that extend 100 kiloparsecs from the galaxy. The kinematics
of this circumgalactic gas is consistent with an inspiraling stream. The carbon
abundance indicates that the gas had already been enriched with elements
heavier than helium, previously ejected from a galaxy. We interpret the results
as evidence of gas recycling during high-redshift galaxy assembly.
| arxiv topic:astro-ph.GA astro-ph.CO |
arxiv_dataset-183552305.02444 | FT-GEMM: A Fault Tolerant High Performance GEMM Implementation on x86
CPUs
cs.DC cs.PF
General matrix/matrix multiplication (GEMM) is crucial for scientific
computing and machine learning. However, the increased scale of the computing
platforms raises concerns about hardware and software reliability. In this
poster, we present FT-GEMM, a high-performance GEMM being capable of tolerating
soft errors on-the-fly. We incorporate the fault tolerant functionality at
algorithmic level by fusing the memory-intensive operations into the GEMM
assembly kernels. We design a cache-friendly scheme for parallel FT-GEMM.
Experimental results on Intel Cascade Lake demonstrate that FT-GEMM offers high
reliability and performance -- faster than Intel MKL, OpenBLAS, and BLIS by
3.50\%$\sim$ 22.14\% for both serial and parallel GEMM, even under hundreds of
errors injected per minute.
| arxiv topic:cs.DC cs.PF |
arxiv_dataset-183562305.02544 | Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA
cs.LG cs.DS math.ST stat.ML stat.TH
We study principal component analysis (PCA), where given a dataset in
$\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that
approximately maximizes the variance of the distribution after being projected
along $v$. Despite being a classical task, standard estimators fail drastically
if the data contains even a small fraction of outliers, motivating the problem
of robust PCA. Recent work has developed computationally-efficient algorithms
for robust PCA that either take super-linear time or have sub-optimal error
guarantees. Our main contribution is to develop a nearly-linear time algorithm
for robust PCA with near-optimal error guarantees. We also develop a
single-pass streaming algorithm for robust PCA with memory usage nearly-linear
in the dimension.
| arxiv topic:cs.LG cs.DS math.ST stat.ML stat.TH |
arxiv_dataset-183572305.02644 | Neuralizer: General Neuroimage Analysis without Re-Training
eess.IV cs.CV
Neuroimage processing tasks like segmentation, reconstruction, and
registration are central to the study of neuroscience. Robust deep learning
strategies and architectures used to solve these tasks are often similar. Yet,
when presented with a new task or a dataset with different visual
characteristics, practitioners most often need to train a new model, or
fine-tune an existing one. This is a time-consuming process that poses a
substantial barrier for the thousands of neuroscientists and clinical
researchers who often lack the resources or machine-learning expertise to train
deep learning models. In practice, this leads to a lack of adoption of deep
learning, and neuroscience tools being dominated by classical frameworks.
We introduce Neuralizer, a single model that generalizes to previously unseen
neuroimaging tasks and modalities without the need for re-training or
fine-tuning. Tasks do not have to be known a priori, and generalization happens
in a single forward pass during inference. The model can solve processing tasks
across multiple image modalities, acquisition methods, and datasets, and
generalize to tasks and modalities it has not been trained on. Our experiments
on coronal slices show that when few annotated subjects are available, our
multi-task network outperforms task-specific baselines without training on the
task.
| arxiv topic:eess.IV cs.CV |
arxiv_dataset-183582305.02744 | Deep Learning Aided Beamforming for Downlink Non-Orthogonal Multiple
Access Systems
eess.SP
In this work, we investigate the optimal beamformer design for the downlink
of Multiple-Input Single-Output (MISO) Non-Orthogonal Multiple Access (NOMA),
mainly focusing on a two-user scenario. We derive novel closed-form expressions
for the Bit Error Rate (BER) experienced by both users when Quadrature
Amplitude Modulation (QAM) is employed. Using these expressions, we formulate a
fairness-based optimal beamforming problem aiming to minimize the maximum BER
encountered by the users. Due to the complexity of this problem and the
time-consuming nature of Constraint Optimization (CO) algorithms for real-time
telecommunication systems, we propose a deep learning (DL) approach for its
solution. The proposed DL architecture possesses specific input and output
characteristics that enable the simultaneous training and use of the system by
multiple different antenna schemes. By conducting extensive simulations, we
demonstrate that our proposed approach outperforms existing beamforming
solutions and achieves BER performance close to that given by CO algorithms
while significantly reducing the computational time needed. Finally, we conduct
simulations to examine the robustness and efficiency of our system in different
test scenarios.
| arxiv topic:eess.SP |
arxiv_dataset-183592305.02844 | Open-Closed String Field Theory in the Large $N$ Limit
hep-th
We use the new nilpotent formulation of open-closed string field theory to
explore the limit where the number $N$ of identical D-branes of the starting
background is large. By reformulating the theory in terms of the 't Hooft
coupling $\lambda=\kappa N$, where $\kappa$ is the string coupling constant, we
explicitly see that at large $N$ only genus zero vertices with arbitrary number
of boundaries survive. After discussing the homotopy structure of the obtained
large $N$ open-closed theory we discuss the possibility of integrating out the
open string sector with a quantum but planar homotopy transfer. As a result we
end up with a classical closed string field theory described by a weak
$L_\infty$-algebra, containing a tree-level tadpole which, to first order in
$\lambda$, is given by the initial boundary state. We discuss the possibility
of removing the tadpole with a closed string vacuum shift solution, to end up
with a new classical closed string background, where the initial D-branes have
been turned into pure closed-string backreaction.
| arxiv topic:hep-th |
arxiv_dataset-183602305.02944 | A cold-atom Ramsey clock with a low volume physics package
physics.atom-ph
We demonstrate a Ramsey-type microwave clock interrogating the 6.835~GHz
ground-state transition in cold \textsuperscript{87}Rb atoms loaded from a
grating magneto-optical trap (GMOT) enclosed in an additively manufactured
loop-gap resonator microwave cavity. A short-term stability of $1.5
\times10^{-11} $~$\tau^{-1/2}$ is demonstrated, in reasonable agreement with
predictions from the signal-to-noise ratio of the measured Ramsey fringes. The
cavity-grating package has a volume of $\approx$67~cm\textsuperscript{3},
ensuring an inherently compact system while the use of a GMOT drastically
simplifies the optical requirements for laser cooled atoms. This work is
another step towards the realisation of highly compact portable cold-atom
frequency standards.
| arxiv topic:physics.atom-ph |
arxiv_dataset-183612305.03044 | Electronic Excited States from a Variance-Based Contracted Quantum
Eigensolver
quant-ph physics.chem-ph physics.comp-ph
Electronic excited states of molecules are central to many physical and
chemical processes, and yet they are typically more difficult to compute than
ground states. In this paper we leverage the advantages of quantum computers to
develop an algorithm for the highly accurate calculation of excited states. We
solve a contracted Schr\"odinger equation (CSE) -- a contraction (projection)
of the Schr\"odinger equation onto the space of two electrons -- whose
solutions correspond identically to the ground and excited states of the
Schr\"odinger equation. While recent quantum algorithms for solving the CSE,
known as contracted quantum eigensolvers (CQE), have focused on ground states,
we develop a CQE based on the variance that is designed to optimize rapidly to
a ground or excited state. We apply the algorithm in a classical simulation
without noise to computing the ground and excited states of H$_{4}$ and BH.
| arxiv topic:quant-ph physics.chem-ph physics.comp-ph |
arxiv_dataset-183622305.03144 | Influence of various text embeddings on clustering performance in NLP
cs.LG cs.CL cs.IR
With the advent of e-commerce platforms, reviews are crucial for customers to
assess the credibility of a product. The star ratings do not always match the
review text written by the customer. For example, a three star rating (out of
five) may be incongruous with the review text, which may be more suitable for a
five star review. A clustering approach can be used to relabel the correct star
ratings by grouping the text reviews into individual groups. In this work, we
explore the task of choosing different text embeddings to represent these
reviews and also explore the impact the embedding choice has on the performance
of various classes of clustering algorithms. We use contextual (BERT) and
non-contextual (Word2Vec) text embeddings to represent the text and measure
their impact of three classes on clustering algorithms - partitioning based
(KMeans), single linkage agglomerative hierarchical, and density based (DBSCAN
and HDBSCAN), each with various experimental settings. We use the silhouette
score, adjusted rand index score, and cluster purity score metrics to evaluate
the performance of the algorithms and discuss the impact of different
embeddings on the clustering performance. Our results indicate that the type of
embedding chosen drastically affects the performance of the algorithm, the
performance varies greatly across different types of clustering algorithms, no
embedding type is better than the other, and DBSCAN outperforms KMeans and
single linkage agglomerative clustering but also labels more data points as
outliers. We provide a thorough comparison of the performances of different
algorithms and provide numerous ideas to foster further research in the domain
of text clustering.
| arxiv topic:cs.LG cs.CL cs.IR |
arxiv_dataset-183632305.03244 | Plasmonic-enhanced bright single spin defects in silicon carbide
membranes
physics.optics quant-ph
Optically addressable spin defects in silicon carbide (SiC) have emerged as
attractable platforms for various quantum technologies. However, the low photon
count rate significantly limits their applications. We strongly enhanced the
brightness by 7 times and spin-control strength by 14 times of single divacancy
defects in 4H-SiC membranes using surface plasmon generated by gold film
coplanar waveguides. The mechanism of the plasmonic-enhanced effect is further
studied by tuning the distance between single defects and the surface of the
gold film. A three-energy-level model is used to determine the corresponding
transition rates consistent with the enhanced brightness of single defects.
Lifetime measurements also verified the coupling between defects and surface
plasmons. Our scheme is low-cost, without complicated microfabrication and
delicate structures, which is applicable for other spin defects in different
materials. This work would promote developing spin defect-based quantum
applications in mature SiC materials.
| arxiv topic:physics.optics quant-ph |
arxiv_dataset-183642305.03344 | A Multi-Marginal C-Convex Duality Theorem for Martingale Optimal
Transport
math.PR
A convex duality result for martingale optimal transport problems with two
marginals was established in Beiglb\"ock et al. (2013). In this paper we
provide a generalization of this result to the multi-period setting.
| arxiv topic:math.PR |
arxiv_dataset-183652305.03444 | Local Gaussian Modifiers (LGMs): UAV dynamic trajectory generation for
onboard computation
cs.RO
Agile autonomous drones are becoming increasingly popular in research due to
the challenges they represent in fields like control, state estimation, or
perception at high speeds. When all algorithms are computed onboard the uav,
the computational limitations make the task of agile and robust flight even
more difficult. One of the most computationally expensive tasks in agile flight
is the generation of optimal trajectories that tackles the problem of planning
a minimum time trajectory for a quadrotor over a sequence of specified
waypoints. When these trajectories must be updated online due to changes in the
environment or uncertainties, this high computational cost can leverage to not
reach the desired waypoints or even crash in cluttered environments. In this
paper, a fast lightweight dynamic trajectory modification approach is presented
to allow modifying computational heavy trajectories using Local Gaussian
Modifiers (LGMs), when recalculating a trajectory is not possible due to the
time of computation.
Our approach was validated in simulation, being able to pass through a race
circuit with dynamic gates with top speeds up to 16.0 m/s, and was also
validated in real flight reaching speeds up to 4.0 m/s in a fully autonomous
onboard computing condition.
| arxiv topic:cs.RO |
arxiv_dataset-183662305.03544 | Deformations of Fano manifolds with weighted solitons
math.DG math.AG math.CV
We consider weighted solitons on Fano manifolds which include Kaehler-Ricci
solitons, Mabuchi solitons and base metrics which induce Calabi-Yau cone
metrics outside the zero sections of the canonical line bundles
(Sasaki-Einstein metrics on the associated $U(1)$-bundles). In this paper, we
give a condition for a weighted soliton on a Fano manifold $M_0$ to extend to
weighted solitons on small deformations $M_t$ of the Fano manifold $M_0$. More
precisely, we show that all the members $M_t$ of the Kuranishi family of a Fano
manifold $M_0$ with a weighted soliton have weighted solitons if and only if
the dimensions of $T$-equivariant automorphism groups of $M_t$ are equal to
that of $M_0$, and also if and only if the $T$-equivariant automorphism groups
of $M_t$ are all isomorphic to that of $M_0$, where the weight functions are
defined on the moment polytope of the Hamiltonian $T$-action. This generalizes
a result of Cao-Sun-Yau-Zhang for Kaehler-Einstein metrics.
| arxiv topic:math.DG math.AG math.CV |
arxiv_dataset-183672305.03644 | Rankings-Dependent Preferences: A Real Goods Matching Experiment
econ.GN q-fin.EC
We investigate whether preferences for objects received via a matching
mechanism are influenced by how highly agents rank them in their reported rank
order list. We hypothesize that all else equal, agents receive greater utility
for the same object when they rank it higher. The addition of
rankings-dependent utility implies that it may not be a dominant strategy to
submit truthful preferences to a strategyproof mechanism, and that
non-strategyproof mechanisms that give more agents objects they \emph{report}
as higher ranked may increase market welfare. We test these hypotheses with a
matching experiment in a strategyproof mechanism, the random serial
dictatorship, and a non-strategyproof mechanism, the Boston mechanism. A novel
feature of our experimental design is that the objects allocated in the
matching markets are real goods, which allows us to directly measure
rankings-dependence by eliciting values for goods both inside and outside of
the mechanism. The experimental results are mixed, with stronger evidence for
rankings-dependence in the RSD treatment than the Boston treatment. We find no
differences between the two mechanisms for the rates of truth-telling and the
final welfare.
| arxiv topic:econ.GN q-fin.EC |
arxiv_dataset-183682305.03744 | Plebanski-Demianski goes NUTs (to remove the Misner string)
gr-qc hep-th
We present a general procedure, based on the Ehlers transformation of the
Ernst equations, to add the gravitomagnetic mass to the whole
Plebanski-Demianski family of solutions. We can efficiently generate a large
class of accelerating black holes, such as Reissner-Nordstrom or Kerr-Newman,
endowed with the NUT parameter. The full rotating version carries a couple of
independent NUT charges, one associated to the black hole and the other to the
accelerating Rindler background. The two NUT parameters can be coupled to
remove the axial irregularity which causes the Misner string, still remaining
with a Lorentzian spacetime, without the need to impose periodic time. All the
metrics we build are not of D-type according to the Petrov classification, but
type-I, so they belong to a more general category with respect to C-metrics and
the Plebanski-Demianski seed. A convenient form of the most general type D
black hole solution in general relativity, coupled with Maxwell
electromagnetism, is obtained when switching off one of the two NUT parameters.
| arxiv topic:gr-qc hep-th |
arxiv_dataset-183692305.03844 | Physics-based network fine-tuning for robust quantitative susceptibility
mapping from high-pass filtered phase
eess.IV cs.CV
Purpose: To improve the generalization ability of convolutional neural
network (CNN) based prediction of quantitative susceptibility mapping (QSM)
from high-pass filtered phase (HPFP) image. Methods: The proposed network
addresses two common generalization issues that arise when using a pre-trained
network to predict QSM from HPFP: a) data with unseen voxel sizes, and b) data
with unknown high-pass filter parameters. A network fine-tuning step based on a
high-pass filtering dipole convolution forward model is proposed to reduce the
generalization error of the pre-trained network. A progressive Unet
architecture is proposed to improve prediction accuracy without increasing
fine-tuning computational cost. Results: In retrospective studies using RMSE,
PSNR, SSIM and HFEN as quality metrics, the performance of both Unet and
progressive Unet was improved after physics-based fine-tuning at all voxel
sizes and most high-pass filtering cutoff frequencies tested in the experiment.
Progressive Unet slightly outperformed Unet both before and after fine-tuning.
In a prospective study, image sharpness was improved after physics-based
fine-tuning for both Unet and progressive Unet. Compared to Unet, progressive
Unet had better agreement of regional susceptibility values with reference QSM.
Conclusion: The proposed method shows improved robustness compared to the
pre-trained network without fine-tuning when the test dataset deviates from
training. Our code is available at https://github.com/Jinwei1209/SWI_to_QSM/
| arxiv topic:eess.IV cs.CV |
arxiv_dataset-183702305.03944 | Structural and Statistical Texture Knowledge Distillation for Semantic
Segmentation
cs.CV
Existing knowledge distillation works for semantic segmentation mainly focus
on transferring high-level contextual knowledge from teacher to student.
However, low-level texture knowledge is also of vital importance for
characterizing the local structural pattern and global statistical property,
such as boundary, smoothness, regularity and color contrast, which may not be
well addressed by high-level deep features. In this paper, we are intended to
take full advantage of both structural and statistical texture knowledge and
propose a novel Structural and Statistical Texture Knowledge Distillation
(SSTKD) framework for semantic segmentation. Specifically, for structural
texture knowledge, we introduce a Contourlet Decomposition Module (CDM) that
decomposes low-level features with iterative Laplacian pyramid and directional
filter bank to mine the structural texture knowledge. For statistical
knowledge, we propose a Denoised Texture Intensity Equalization Module (DTIEM)
to adaptively extract and enhance statistical texture knowledge through
heuristics iterative quantization and denoised operation. Finally, each
knowledge learning is supervised by an individual loss function, forcing the
student network to mimic the teacher better from a broader perspective.
Experiments show that the proposed method achieves state-of-the-art performance
on Cityscapes, Pascal VOC 2012 and ADE20K datasets.
| arxiv topic:cs.CV |
arxiv_dataset-183712305.04044 | Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive
Text Generation
cs.CL
Recently, continuous diffusion models (CDM) have been introduced into
non-autoregressive (NAR) text-to-text generation. However, the discrete nature
of text increases the difficulty of CDM to generate coherent and fluent texts,
and also causes the incompatibility problem between CDM and advanced NLP
techniques, especially the popular pre-trained language models~(PLMs). To solve
it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM)
into NAR text-to-text generation and integrates BART to improve the
performance. By revising the decoding process of BART and the typical settings
of DDM, we unify the inference process of BART and the denoising process of DDM
into the same NAR masked tokens recovering task. In this way, DDM can rely on
BART to perform denoising, which can benefit from both the rich pre-learned
knowledge of BART and the iterative refining paradigm of DDM. Besides, we also
propose the iterative self-prompting strategy to further improve the generation
quality. Experimental results on 7 datasets show that our approach can
outperform competitive NAR methods, and even surpass autoregressive methods.
Our code and data will be publicly released.
| arxiv topic:cs.CL |
arxiv_dataset-183722305.04144 | Representations of polynomial covariance type commutation relations by
linear integral operators with separable kernels in $L_p$
math.FA
Representations of polynomial covariance type commutation relations by linear
integral operators on $L_p$ over measures spaces are investigated. Necessary
and sufficient conditions for integral operators to satisfy polynomial
covariance type commutation relations are obtained in terms of their kernels.
For important classes of polynomial covariance commutation relations associated
to arbitrary monomials and to affine functions, these conditions on the kernels
are specified in terms of the coefficients of the monomials and affine
functions. By applying these conditions, examples of integral operators on
$L_p$ spaces, with separable kernels representing covariance commutation
relations associated to monomials, are constructed for the kernels involving
multi-parameter trigonometric functions, polynomials, and Laurent polynomials
on bounded intervals. Commutators of these operators are computed and exact
conditions for commutativity of these operators in terms of the parameters are
obtained.
| arxiv topic:math.FA |
arxiv_dataset-183732305.04244 | The Spectra of transitive models
math.LO
In this paper we study the spectrum of heights of transitive models of
theories extending $V = L[A]$, under various definitions. In particular, we
investigate the consistency strength of making those spectra as simple as
possible.
| arxiv topic:math.LO |
arxiv_dataset-183742305.04344 | Empowering Language Model with Guided Knowledge Fusion for Biomedical
Document Re-ranking
cs.CL
Pre-trained language models (PLMs) have proven to be effective for document
re-ranking task. However, they lack the ability to fully interpret the
semantics of biomedical and health-care queries and often rely on simplistic
patterns for retrieving documents. To address this challenge, we propose an
approach that integrates knowledge and the PLMs to guide the model toward
effectively capturing information from external sources and retrieving the
correct documents. We performed comprehensive experiments on two biomedical and
open-domain datasets that show that our approach significantly improves vanilla
PLMs and other existing approaches for document re-ranking task.
| arxiv topic:cs.CL |
arxiv_dataset-183752305.04444 | Derived categories of character sheaves II: canonical
induction/restriction functors
math.RT
We give a combinatorial description of the dg category of character sheaves
on a complex reductive group $G$, extending results of [Li] for $G$
simply-connected. We also explicitly identify the parabolic
induction/restriction functors.
| arxiv topic:math.RT |
arxiv_dataset-183762305.04544 | Maximal Arrangement of Dominos in the Diamond
math.CO
"Dominos" are special entities consisting of a hard dimer-like kernel
surrounded by a soft hull and governed by local interactions. "Soft hull" and
"hard kernel" mean that the hulls can overlap while the kernel acts under a
repulsive potential. Unlike the dimer problem in statistical physics, which
lists the number of all possible configurations for a given n x n lattice, the
more modest goal herein is to provide lower and upper bounds for the maximum
allowed number of dominos in the diamond. In this NP problem, a deterministic
construction rule is proposed and leads to a suboptimal solution {\psi}_n as a
lower bound. A certain disorder is then injected and leads to an upper bound
{\psi}_n_upper reachable or not. In some cases, the lower and upper bounds
coincide, so {\psi}_n = {\psi}_n_upper becomes the exact number of dominos for
a maximum configuration.
| arxiv topic:math.CO |
arxiv_dataset-183772305.04644 | The Baire category method for intermittent convex integration
math.AP
We use a convex integration construction from \cite{ModenaSattig2020} in a
Baire category argument to show that weak solutions to the transport equation
with incompressible vector fields with Sobolev regularity are generic in the
Baire category sense. Using the construction of
\cite{BurczakModenaSzekelyhidi20} we prove an analog statement for the 3D
Navier-Stokes equations.
| arxiv topic:math.AP |
arxiv_dataset-183782305.04744 | Large magnetocaloric effect in the kagome ferromagnet
Li$_9$Cr$_3$(P$_2$O$_7$)$_3$(PO$_4$)$_2$
cond-mat.mtrl-sci cond-mat.str-el
Single-crystal growth, magnetic properties, and magnetocaloric effect of the
$S = 3/2$ kagome ferromagnet Li$_9$Cr$_3$(P$_2$O$_7$)$_3$(PO$_4$)$_2$
(trigonal, space group: $P\bar{3}c1$) are reported. Magnetization data suggest
dominant ferromagnetic intra-plane coupling with a weak anisotropy and the
onset of ferromagnetic ordering at $T_{\rm C} \simeq 2.6$ K. Microscopic
analysis reveals a very small ratio of interlayer to intralayer ferromagnetic
couplings ($J_{\perp}/J \simeq 0.02$). Electron spin resonance data suggest the
presence of short-range correlations above $T_{\rm C}$ and confirms
quasi-two-dimensional character of the spin system. A large magnetocaloric
effect characterized by isothermal entropy change of $-\Delta S_{\rm m}\simeq
31$ J kg$^{-1}$ K$^{-1}$ and adiabatic temperature change of $-\Delta T_{\rm
ad}\simeq 9$ K upon a field sweep of 7 T is observed around $T_{\rm C}$. This
leads to a large relative cooling power of $RCP \simeq 284$ J kg$^{-1}$. The
large magnetocaloric effect, together with negligible hysteresis render
Li$_9$Cr$_3$(P$_2$O$_7$)$_3$(PO$_4$)$_2$ a promising material for magnetic
refrigeration at low temperatures. The magnetocrystalline anisotropy constant
$K \simeq -7.42 \times 10^4$ erg cm$^{-3}$ implies that the compound is an
easy-plane type ferromagnet with the hard axis normal to the $ab$-plane,
consistent with the magnetization data.
| arxiv topic:cond-mat.mtrl-sci cond-mat.str-el |
arxiv_dataset-183792305.04844 | SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate
Reduction
eess.IV cs.CV
In recent years, there has been significant interest in Super-Resolution
(SR), which focuses on generating a high-resolution image from a low-resolution
input. Deep learning-based methods for super-resolution have been particularly
popular and have shown impressive results on various benchmarks. However,
research indicates that these methods may not perform as well on strongly
compressed videos. We developed a super-resolution benchmark to analyze SR's
capacity to upscale compressed videos. Our dataset employed video codecs based
on five widely-used compression standards: H.264, H.265, H.266, AV1, and AVS3.
We assessed 19 popular SR models using our benchmark and evaluated their
ability to restore details and their susceptibility to compression artifacts.
To get an accurate perceptual ranking of SR models, we conducted a
crowd-sourced side-by-side comparison of their outputs. We found that some SR
models, combined with compression, allow us to reduce the video bitrate without
significant loss of quality. We also compared a range of image and video
quality metrics with subjective scores to evaluate their accuracy on
super-resolved compressed videos. The benchmark is publicly available at
https://videoprocessing.ai/benchmarks/super-resolution-for-video-compression.html
| arxiv topic:eess.IV cs.CV |
arxiv_dataset-183802305.04944 | Exploring the nature of UV-bright $z \gtrsim 10$ galaxies detected by
JWST: star formation, black hole accretion, or a non-universal IMF?
astro-ph.GA
We use the Cosmic Archaeology Tool (CAT) semi-analytical model to explore the
contribution of Population (Pop) III/II stars and active galactic nuclei (AGNs)
to the galaxy UV luminosity function (LF) evolution at $4 \leq z \leq 20$. We
compare in particular with recent JWST data in order to explore the apparent
tension between observations and theoretical models in the number density of
bright galaxies at $z \gtrsim 10$. The model predicts a star formation history
dominated by UV faint ($M_{\rm UV} > - 18$) galaxies, with a Pop III
contribution of $\lesssim 10\%$ ($\lesssim 0.5\%$) at $z \simeq 20$ ($z \simeq
10$). Stars are the primary sources of cosmic reionization, with $5 - 10 \%$ of
ionizing photons escaping into the intergalatic medium at $5 \leq z \leq 10$,
while the contribution of unobscured AGNs becomes dominant only at $z \lesssim
5$. The predicted stellar and AGN UV LFs reproduce the observational data at $5
\lesssim z \lesssim 9 - 10$. At higher redshift, CAT predicts a steeper
evolution in the faint-end slope ($M_{\rm UV} > - 18$), and a number density of
bright galaxies ($M_{\rm UV} \simeq -20$) consistent with data at $z \sim 10 -
11$, but smaller by 0.8 dex at $z \sim 12 - 13$, and 1.2 dex at $z \sim 14 -
16$, when compared to the values estimated by recent studies. Including the AGN
emission does not affect the above findings, as AGNs contribute at most to
$\lesssim 10 \%$ of the total UV luminosity at $M_{\rm UV} < - 19$ and $z
\gtrsim 10$. Interestingly, considering a gradual transition in the stellar
IMF, modulated by metallicity and redshift as suggested by recent simulations,
the model agrees with JWST data at $z \sim 12 - 13$, and the disagreement at $z
\sim 14 - 16$ is reduced to 0.5 dex.
| arxiv topic:astro-ph.GA |
arxiv_dataset-183812305.05044 | Some properties of affine $\mathcal C$-semigroups
math.RA math.AC
Numerical semigroups have been extensively studied throughout the literature,
and many of their invariants have been characterized. In this work, we
generalize some of the most important results about symmetry, pseudo-symmetry,
or fundamental gaps, to affine $\mathcal C$-semigroups. In addition, we give
algorithms to compute the tree of irreducible $\mathcal C$-semigroups and
$\mathcal C$-semigroups with a given Frobenius vector.
| arxiv topic:math.RA math.AC |
arxiv_dataset-183822305.05144 | Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval
cs.CV cs.AI
Zero-shot sketch-based image retrieval (ZS-SBIR) is challenging due to the
cross-domain nature of sketches and photos, as well as the semantic gap between
seen and unseen image distributions. Previous methods fine-tune pre-trained
models with various side information and learning strategies to learn a compact
feature space that is shared between the sketch and photo domains and bridges
seen and unseen classes. However, these efforts are inadequate in adapting
domains and transferring knowledge from seen to unseen classes. In this paper,
we present an effective ``Adapt and Align'' approach to address the key
challenges. Specifically, we insert simple and lightweight domain adapters to
learn new abstract concepts of the sketch domain and improve cross-domain
representation capabilities. Inspired by recent advances in image-text
foundation models (e.g., CLIP) on zero-shot scenarios, we explicitly align the
learned image embedding with a more semantic text embedding to achieve the
desired knowledge transfer from seen to unseen classes. Extensive experiments
on three benchmark datasets and two popular backbones demonstrate the
superiority of our method in terms of retrieval accuracy and flexibility.
| arxiv topic:cs.CV cs.AI |
arxiv_dataset-183832305.05244 | Rigidity properties of holomorphic isometries into homogeneous
K\"{a}hler manifolds
math.DG math.CV
We prove two rigidity results on holomorphic isometries into homogeneous
K\"{a}hler manifolds. The first shows that a K\"{a}hler-Ricci soliton induced
by the homogeneous metric of the K\"{a}hler product of a special flag manifold
(i.e. a flag of classical type or integral type) with a bounded homogeneous
domain is trivial, i.e. K\"{a}hler-Einstein. In the second one we prove that:
(i) a flat space is not relative to the K\"{a}hler product of a special flag
manifold with a homogeneous bounded domain, (ii) a special flag manifold is not
relative to the K\"{a}hler product of a flat space with a homogeneous bounded
domain and (iii) a homogeneous bounded domain is not relative to the K\"{a}hler
product of a flat space with a special flag manifold. Our theorems strongly
extend the results in [4], [5], [12], [13] and [22].
| arxiv topic:math.DG math.CV |
arxiv_dataset-183842305.05344 | Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based
Uncertainty
eess.IV cs.CV
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey
the complementary multi-phase information for liver tumor segmentation (LiTS),
which are crucial to assist the diagnosis of liver cancer clinically. However,
the performances of existing multi-phase liver tumor segmentation
(MPLiTS)-based methods suffer from redundancy and weak interpretability, % of
the fused result, resulting in the implicit unreliability of clinical
applications. In this paper, we propose a novel trustworthy multi-phase liver
tumor segmentation (TMPLiTS), which is a unified framework jointly conducting
segmentation and uncertainty estimation. The trustworthy results could assist
the clinicians to make a reliable diagnosis. Specifically, Dempster-Shafer
Evidence Theory (DST) is introduced to parameterize the segmentation and
uncertainty as evidence following Dirichlet distribution. The reliability of
segmentation results among multi-phase CECT images is quantified explicitly.
Meanwhile, a multi-expert mixture scheme (MEMS) is proposed to fuse the
multi-phase evidences, which can guarantee the effect of fusion procedure based
on theoretical analysis. Experimental results demonstrate the superiority of
TMPLiTS compared with the state-of-the-art methods. Meanwhile, the robustness
of TMPLiTS is verified, where the reliable performance can be guaranteed
against the perturbations.
| arxiv topic:eess.IV cs.CV |
arxiv_dataset-183852305.05444 | A Pexider equation containing the aritmetic mean
math.CA
In this paper we determine the solutions $(\varphi,f_1,f_2)$ of the Pexider
functional equation
\[\varphi\Big(\frac{x+y}2\Big)\big(f_1(x)-f_2(y)\big)=0,\qquad (x,y)\in
I_1\times I_2,\] where $I_1$ and $I_2$ are nonempty open subintervals. Special
cases of the above equation regularly arise in problems with two-variable
means. We show that, under a rather weak regularity condition, the
coordinate-functions of a typical solution of the equation are constant over
several subintervals of their domain. The regularity condition in question will
be that the set of zeros of $\varphi$ is closed. We also discuss particular
solutions where this condition is not met.
| arxiv topic:math.CA |
arxiv_dataset-183862305.05544 | Implementation of a Channel Model for Non-Terrestrial Networks in ns-3
cs.NI eess.SP
While the 5th generation (5G) of mobile networks has landed in the commercial
area, the research community is exploring new functionalities for 6th
generation (6G) networks, for example non-terrestrial networks (NTNs) via
space/air nodes such as Unmanned Aerial Vehicles (UAVs), High Altitute
Platforms (HAPs) or satellites. Specifically, satellite-based communication
offers new opportunities for future wireless applications, such as providing
connectivity to remote or otherwise unconnected areas, complementing
terrestrial networks to reduce connection downtime, as well as increasing
traffic efficiency in hot spot areas. In this context, an accurate
characterization of the NTN channel is the first step towards proper protocol
design. Along these lines, this paper provides an ns-3 implementation of the
3rd Generation Partnership Project (3GPP) channel and antenna models for NTN
described in Technical Report 38.811. In particular, we extend the ns-3 code
base with new modules to implement the attenuation of the signal in air/space
due to atmospheric gases and scintillation, and new mobility and fading models
to account for the Geocentric Cartesian coordinate system of satellites.
Finally, we validate the accuracy of our ns-3 module via simulations against
3GPP calibration results
| arxiv topic:cs.NI eess.SP |
arxiv_dataset-183872305.05644 | Towards Building the Federated GPT: Federated Instruction Tuning
cs.CL cs.DC cs.SY eess.SY
While "instruction-tuned" generative large language models (LLMs) have
demonstrated an impressive ability to generalize to new tasks, the training
phases heavily rely on large amounts of diverse and high-quality instruction
data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data,
especially when it comes to human-written data, can pose significant challenges
both in terms of cost and accessibility. Moreover, concerns related to privacy
can further limit access to such data, making the process of obtaining it a
complex and nuanced undertaking. Consequently, this hinders the generality of
the tuned models and may restrict their effectiveness in certain contexts. To
tackle this issue, our study introduces a new approach called Federated
Instruction Tuning (FedIT), which leverages federated learning (FL) as the
learning framework for the instruction tuning of LLMs. This marks the first
exploration of FL-based instruction tuning for LLMs. This is especially
important since text data is predominantly generated by end users. Therefore,
it is imperative to design and adapt FL approaches to effectively leverage
these users' diverse instructions stored on local devices, while preserving
privacy and ensuring data security. In the current paper, by conducting widely
used GPT-4 auto-evaluation, we demonstrate that by exploiting the heterogeneous
and diverse sets of instructions on the client's end with the proposed
framework FedIT, we improved the performance of LLMs compared to centralized
training with only limited local instructions. Further, in this paper, we
developed a Github repository named Shepherd. This repository offers a
foundational framework for exploring federated fine-tuning of LLMs using
heterogeneous instructions across diverse categories.
| arxiv topic:cs.CL cs.DC cs.SY eess.SY |
arxiv_dataset-183882305.05744 | Neck pinch singularities and Joyce conjectures in Lagrangian mean
curvature flow with circle symmetry
math.DG math.AP math.SG
In this article we consider the Lagrangian mean curvature flow of compact,
circle-invariant, almost calibrated Lagrangian surfaces in hyperk\"ahler
4-manifolds with circle symmetry. We show that this Lagrangian mean curvature
flow can be continued for all time, through finite time singularities, and
converges to a chain of special Lagrangians, thus verifying various aspects of
Joyce's conjectures in this setting. We show that the singularities of the flow
are neck pinches in the sense conjectured by Joyce. We also give examples where
such finite time singularities are guaranteed to occur.
| arxiv topic:math.DG math.AP math.SG |
arxiv_dataset-183892305.05844 | Constraining gravitational wave amplitude birefringence with GWTC-3
gr-qc astro-ph.HE
The propagation of gravitational waves can reveal fundamental features of the
structure of spacetime. For instance, differences in the propagation of
gravitational-wave polarizations would be a smoking gun for parity violations
in the gravitational sector, as expected from birefringent theories like
Chern-Simons gravity. Here we look for evidence of amplitude birefringence in
the third catalog of detections by the Laser Interferometer Gravitational Wave
Observatory and Virgo through the use of birefringent templates inspired by
dynamical Chern-Simons gravity. From $71$ binary-black-hole signals, we obtain
the most precise constraints on gravitational-wave amplitude birefringence yet,
measuring a birefringent attenuation of $\kappa = -0.019^{+0.038}_{-0.029} \,
\mathrm{Gpc}^{-1}$ at $100 \, \mathrm{Hz}$ with $90\%$ credibility, equivalent
to a parity-violation energy scale of $M_{\rm PV} \gtrsim 6.8 \times 10^{-21}\,
{\rm GeV}$.
| arxiv topic:gr-qc astro-ph.HE |
arxiv_dataset-183902305.05944 | Stealth Shaper: Reflectivity Optimization as Surface Stylization
cs.GR
We present a technique to optimize the reflectivity of a surface while
preserving its overall shape. The naive optimization of the mesh vertices using
the gradients of reflectivity simulations results in undesirable distortion. In
contrast, our robust formulation optimizes the surface normal as an independent
variable that bridges the reflectivity term with differential rendering, and
the regularization term with as-rigid-as-possible elastic energy. We further
adaptively subdivide the input mesh to improve the convergence. Consequently,
our method can minimize the retroreflectivity of a wide range of input shapes,
resulting in sharply creased shapes ubiquitous among stealth aircraft and
Sci-Fi vehicles. Furthermore, by changing the reward for the direction of the
outgoing light directions, our method can be applied to other reflectivity
design tasks, such as the optimization of architectural walls to concentrate
light in a specific region. We have tested the proposed method using
light-transport simulations and real-world 3D-printed objects.
| arxiv topic:cs.GR |
arxiv_dataset-183912305.06044 | Correlation visualization under missing values: a comparison between
imputation and direct parameter estimation methods
cs.LG stat.ML
Correlation matrix visualization is essential for understanding the
relationships between variables in a dataset, but missing data can pose a
significant challenge in estimating correlation coefficients. In this paper, we
compare the effects of various missing data methods on the correlation plot,
focusing on two common missing patterns: random and monotone. We aim to provide
practical strategies and recommendations for researchers and practitioners in
creating and analyzing the correlation plot. Our experimental results suggest
that while imputation is commonly used for missing data, using imputed data for
plotting the correlation matrix may lead to a significantly misleading
inference of the relation between the features. We recommend using DPER, a
direct parameter estimation approach, for plotting the correlation matrix based
on its performance in the experiments.
| arxiv topic:cs.LG stat.ML |
arxiv_dataset-183922305.06144 | Learning Semi-supervised Gaussian Mixture Models for Generalized
Category Discovery
cs.CV
In this paper, we address the problem of generalized category discovery
(GCD), \ie, given a set of images where part of them are labelled and the rest
are not, the task is to automatically cluster the images in the unlabelled
data, leveraging the information from the labelled data, while the unlabelled
data contain images from the labelled classes and also new ones. GCD is similar
to semi-supervised learning (SSL) but is more realistic and challenging, as SSL
assumes all the unlabelled images are from the same classes as the labelled
ones. We also do not assume the class number in the unlabelled data is known
a-priori, making the GCD problem even harder. To tackle the problem of GCD
without knowing the class number, we propose an EM-like framework that
alternates between representation learning and class number estimation. We
propose a semi-supervised variant of the Gaussian Mixture Model (GMM) with a
stochastic splitting and merging mechanism to dynamically determine the
prototypes by examining the cluster compactness and separability. With these
prototypes, we leverage prototypical contrastive learning for representation
learning on the partially labelled data subject to the constraints imposed by
the labelled data. Our framework alternates between these two steps until
convergence. The cluster assignment for an unlabelled instance can then be
retrieved by identifying its nearest prototype. We comprehensively evaluate our
framework on both generic image classification datasets and challenging
fine-grained object recognition datasets, achieving state-of-the-art
performance.
| arxiv topic:cs.CV |
arxiv_dataset-183932305.06244 | Explainable Knowledge Distillation for On-device Chest X-Ray
Classification
cs.CV cs.LG
Automated multi-label chest X-rays (CXR) image classification has achieved
substantial progress in clinical diagnosis via utilizing sophisticated deep
learning approaches. However, most deep models have high computational demands,
which makes them less feasible for compact devices with low computational
requirements. To overcome this problem, we propose a knowledge distillation
(KD) strategy to create the compact deep learning model for the real-time
multi-label CXR image classification. We study different alternatives of CNNs
and Transforms as the teacher to distill the knowledge to a smaller student.
Then, we employed explainable artificial intelligence (XAI) to provide the
visual explanation for the model decision improved by the KD. Our results on
three benchmark CXR datasets show that our KD strategy provides the improved
performance on the compact student model, thus being the feasible choice for
many limited hardware platforms. For instance, when using DenseNet161 as the
teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the
ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer
parameters of 4.7 million and computational cost of 0.3 billion FLOPS.
| arxiv topic:cs.CV cs.LG |
arxiv_dataset-183942305.06344 | Orthogonal Transforms in Neural Networks Amount to Effective
Regularization
cs.LG cs.NE cs.SY eess.SY
We consider applications of neural networks in nonlinear system
identification and formulate a hypothesis that adjusting general network
structure by incorporating frequency information or other known orthogonal
transform, should result in an efficient neural network retaining its universal
properties. We show that such a structure is a universal approximator and that
using any orthogonal transform in a proposed way implies regularization during
training by adjusting the learning rate of each parameter individually. We
empirically show in particular, that such a structure, using the Fourier
transform, outperforms equivalent models without orthogonality support.
| arxiv topic:cs.LG cs.NE cs.SY eess.SY |
arxiv_dataset-183952305.06444 | \'Etale degree map and 0-cycles
math.AG
By using the triangulated category of \'etale motives over a field $k$, for a
smooth projective variety $X$ over $k$, we define the group
$\text{CH}^\text{\'et}_0(X)$ as an \'etale analogue of 0-cycles. We study the
properties of $\text{CH}^\text{\'et}_0(X)$, giving a description about the
birational invariance of such group. We define and present the \'etale degree
map by using Gysin morphisms in \'etale motivic cohomology and the \'etale
index as an analogue to the classical case. We give examples of smooth
projective varieties over a field $k$ without zero cycles of degree one but
with \'etale zero cycles of degree one, however, this property is not always
true as we present examples where the \'etale degree map is not surjective.
| arxiv topic:math.AG |
arxiv_dataset-183962305.06544 | Self-steepening-induced stabilization of nonlinear edge waves at
photonic valley-Hall interfaces
physics.optics cond-mat.mtrl-sci nlin.PS
Localized nonlinear modes at valley-Hall interfaces in staggered photonic
graphene can be described in the long-wavelength limit by a nonlinear
Dirac-like model including spatial dispersion terms. It leads to a modified
nonlinear Schr\"odinger equation for the wave field amplitude that remarkably
incorporates a nonlinear velocity term. We show that this nonlinear velocity
correction results in a counter-intuitive stabilization effect for relatively
high-amplitude plane-wave-like edge states, which we confirm by calculation of
complex-valued small-amplitude perturbation spectra and direct numerical
simulation of propagation dynamics in staggered honeycomb waveguide lattices
with on-site Kerr nonlinearity. Our findings are relevant to a variety of
nonlinear photonic systems described by Dirac-like Hamiltonians.
| arxiv topic:physics.optics cond-mat.mtrl-sci nlin.PS |
arxiv_dataset-183972305.06644 | Brightness and purity of a room-temperature single-photon source in the
blue-green range
physics.optics
Single-photon sources are crucial for developing secure telecommunications.
However, most systems operate at cryogenic temperatures. Here, we discuss a
promising solid-state system emitting single photons at room temperature in the
blue-green range, allowing for quantum communications in free space and
underwater. The active element is a core-shell ZnSe tapered nanowire embedding
a single CdSe quantum dot grown by molecular beam epitaxy. A patterned
substrate enables a thorough study of the one and same nanowire by different
methods. Our source exhibits anti-bunching with g(2)(0) < 0.3 near the centre
of the photoluminescence line and shows high brightness. This work paves the
way for developing single-photon sources operating at non-cryogenic
temperatures.
| arxiv topic:physics.optics |
arxiv_dataset-183982305.06744 | Laminar flow of charged quantum fluids of the Calogero-Sutherland
universality class
hep-th cond-mat.str-el
The effective field theory of the Calogero-Sutherland model represents a
universality class of quantum hydrodynamic fluids in one spatial dimension. It
describes quantum compressible fluids involving both chiralities in which the
chiral density field obeys the quantum Benjamin-Ono equation. An extension of
this theory to describe a laminar flow of the Calogero-Sutherland fluids in a
rectangular geometry with small transverse width and the topology of a ribbon,
is considered here. The physical picture is based on the edge states in the
hierarchical quantum Hall effect, which may be seen as a collection of parallel
one-dimensional quantum incompressible fluids moving along but confined within
the transverse microscopic width of the edge of the sample. The effective
theory is thus defined as the direct product of two one-dimensional theories of
the Calogero-Sutherland class so that one involves motion while the other is
confining. Charge transport may be induced by coupling the system to an
external electromagnetic field that yields a global translation of the ground
state. The effective theory describes quantum solitonic excitations along the
direction of the flow and possesses a two-dimensional electric current density
which shows a Wigner semicircle law profile in the transverse direction,
suggesting a Poiseuille-like behavior but without dissipative viscous effects
since the velocity of the fluid is not a well-defined quantum field. This
simple physical picture predicts interesting phenomena with distinctive
signatures that may be tested in real samples.
| arxiv topic:hep-th cond-mat.str-el |
arxiv_dataset-183992305.06844 | Partial separability and symplectic-Haantjes manifolds
math-ph math.MP math.SG
A theory of partial separability for classical Hamiltonian systems is
proposed in the context of Haantjes geometry.
As a general result, we show that the knowledge of a non-semisimple
symplectic-Haantjes manifold for a given Hamiltonian system is sufficient to
construct sets of coordinates (called Darboux-Haantjes coordinates) which allow
both the partial separability of the associated Hamilton-Jacobi equations and
the block-diagonalization of the operators of the corresponding Haantjes
algebra.
We also introduce a novel class of Hamiltonian systems, characterized by the
existence of a generalized St\"ackel matrix, which by construction are
partially separable. They widely generalize the known families of partially
separable Hamiltonian systems. Our systems can be described in terms of
semisimple but non-maximal-rank symplectic-Haantjes manifolds.
| arxiv topic:math-ph math.MP math.SG |
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