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2022-06-26
|
Impact of Channel Memory on the Data Freshness
|
In this letter, we investigate the impact of channel memory on the average
age of information (AoI) for networks with various packet arrival models under
first-come-first-served (FCFS) and preemptive last-generated-first-served
(pLGFS) policies over Gilbert-Elliott (GE) erasure channel. For networks with
Bernoulli arrival model, we first derive the average AoI under the pLGFS
queuing policy, and then characterize the AoI gap between the FCFS and pLGFS
policies. For networks with Bernoulli arrival and generate-at-will arrival
models, the AoI performances under the FCFS and pLGFS policies are derived
explicitly. For networks with periodic arrival model, we derive the closed-form
expression for the average AoI under pLGFS over a general GE channel and
propose a numerical algorithm for calculating that under FCFS efficiently. It
is revealed that for pLGFS policy, the average AoI increases monotonically with
channel memory $\eta$ at $\frac{\eta}{1-\eta}$ over the symmetric GE channel.
For FCFS, the average AoI increases even faster due to the queuing delay, with
an additional term related to the packet arrival rate.
|
2206.12797v3
|
2022-07-06
|
Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
|
Accurate and consistent predictions of echocardiography parameters are
important for cardiovascular diagnosis and treatment. In particular,
segmentations of the left ventricle can be used to derive ventricular volume,
ejection fraction (EF) and other relevant measurements. In this paper we
propose a new automated method called EchoGraphs for predicting ejection
fraction and segmenting the left ventricle by detecting anatomical keypoints.
Models for direct coordinate regression based on Graph Convolutional Networks
(GCNs) are used to detect the keypoints. GCNs can learn to represent the
cardiac shape based on local appearance of each keypoint, as well as global
spatial and temporal structures of all keypoints combined. We evaluate our
EchoGraphs model on the EchoNet benchmark dataset. Compared to semantic
segmentation, GCNs show accurate segmentation and improvements in robustness
and inference runtime. EF is computed simultaneously to segmentations and our
method also obtains state-of-the-art ejection fraction estimation. Source code
is available online: https://github.com/guybenyosef/EchoGraphs.
|
2207.02549v1
|
2022-07-15
|
Probing helium reionization with kinetic Sunyaev Zel'dovich tomography
|
Reionization of helium is expected to occur at redshifts $z\sim3$ and have
important consequences for quasar populations, galaxy formation, and the
morphology of the intergalactic medium, but there is little known empirically
about the process. Here we show that kinetic Sunyaev-Zeldovich (kSZ)
tomography, based on the combination of CMB measurements and galaxy surveys,
can be used to infer the primordial helium abundance as well as the time and
duration of helium reionization. We find a high-significance detection at
${\sim10\sigma}$ can be expected from Vera Rubin Observatory and CMB-S4 in the
near future. A more robust characterization of helium reionization will require
next-generation experiments like MegaMapper (a proposed successor to DESI) and
CMB-HD.
|
2207.07660v1
|
2022-07-18
|
Optimal and tight Bell inequalities for state-independent contextuality sets
|
Two fundamental quantum resources, nonlocality and contextuality, can be
connected through Bell inequalities that are violated by state-independent
contextuality (SI-C) sets. These Bell inequalities allow for applications that
require simultaneous nonlocality and contextuality. However, for existing Bell
inequalities, the nonlocality produced by SI-C sets is very sensitive to noise.
This precludes experimental implementation. Here we identify the Bell
inequalities for which the nonlocality produced by SI-C sets is optimal, i.e.,
maximally robust to either noise or detection inefficiency, for the simplest
SI-C [S. Yu and C. H. Oh, Phys. Rev. Lett. 108, 030402 (2012)] and
Kochen-Specker sets [A. Cabello et al., Phys. Lett. A 212, 183 (1996)] and show
that, in both cases, nonlocality is sufficiently resistant for experiments. Our
work enables experiments that combine nonlocality and contextuality and
therefore paves the way for applications that take advantage of their synergy.
|
2207.08850v3
|
2022-07-25
|
Minimax Rates for High-dimensional Double Sparse Structure over $\ell_u(\ell_q)$-balls
|
In this paper, we focus on the high-dimensional double sparse structure,
where the parameter of interest simultaneously encourages group-wise sparsity
and element-wise sparsity in each group. By combining the Gilbert-Varshamov
bound and its variants, we develop a novel lower bound technique for the metric
entropy of the parameter space, specifically tailored for the double sparse
structure over $\ell_u(\ell_q)$-balls with $u,q \in [0,1]$. We prove lower
bounds on the estimation error using an information-theoretic approach,
leveraging our proposed lower bound technique and Fano's inequality. To
complement the lower bounds, we establish matching upper bounds through a
direct analysis of constrained least-squares estimators and utilize results
from empirical processes. A significant finding of our study is the discovery
of a phase transition phenomenon in the minimax rates for $u,q \in (0, 1]$.
Furthermore, we extend the theoretical results to the double sparse regression
model and determine its minimax rate for estimation error. To tackle double
sparse linear regression, we develop the DSIHT (Double Sparse Iterative Hard
Thresholding) algorithm, demonstrating its optimality in the minimax sense.
Finally, we demonstrate the superiority of our method through numerical
experiments.
|
2207.11888v2
|
2022-08-02
|
Two-Stream Transformer Architecture for Long Video Understanding
|
Pure vision transformer architectures are highly effective for short video
classification and action recognition tasks. However, due to the quadratic
complexity of self attention and lack of inductive bias, transformers are
resource intensive and suffer from data inefficiencies. Long form video
understanding tasks amplify data and memory efficiency problems in transformers
making current approaches unfeasible to implement on data or memory restricted
domains. This paper introduces an efficient Spatio-Temporal Attention Network
(STAN) which uses a two-stream transformer architecture to model dependencies
between static image features and temporal contextual features. Our proposed
approach can classify videos up to two minutes in length on a single GPU, is
data efficient, and achieves SOTA performance on several long video
understanding tasks.
|
2208.01753v1
|
2022-08-03
|
Mass and generalized Thiele equation of the magnetic skyrmion
|
An analytical expression is obtained for the mass of an isolated magnetic
skyrmion and its linearized equation of motion. The magnetic skyrmion is viewed
as a topologically protected spin-wave soliton in the magnetic ultrathin films
stabilized by the interfacial-Dzyaloshinskii-Moriya interaction. The equations
of motion are derived from the Landau-Lifshitz-Gilbert equation for both the
skyrmion charge and magnetization centers. They are generalized Thiele
equations, including the gyro-term, dissipation term, external force,
acceleration term with the tensorial mass, and time derivatives of the external
forces. The equation of motion of the center of the skyrmion charge essentially
shows the massless nature of the skyrmion. In contrast, the equation of motion
for the magnetization center results in a finite mass that is in the same order
as the Doring mass density for the linear domain wall. Furthermore, the time
derivative of the external force predominantly contributes to the immediate
response of the skyrmion motion, i.e., the mass-less property remains even
after the skyrmion acquires its kinetic mass.
A micromagnetic simulation based on the LLG equation was performed for
various magnetic parameters. Obtained trajectories at 0 K are compared with the
theoretical predictions.
|
2208.01835v2
|
2022-08-07
|
Transition state theory characterizes thin film macrospin dynamics driven by an oscillatory magnetic field: Inertial effects
|
Understanding the magnetization switching process in ferromagnetic thin films
is essential for many technological applications. We investigate the effects of
periodic driving via magnetic fields on a macrospin system under explicit
consideration of inertial dynamics. This is usually achieved by extending the
Landau-Lifshitz-Gilbert equation with a term including the second time
derivative of the magnetization. The dynamics of the magnetization switching
can then be characterized by its switching rate. We apply methods from
transition state theory for driven systems to resolve the rate of magnetization
switching in this general case. In doing so, we find that magnetization
exhibits resonance-like behavior under certain driving conditions, and it can
be affected strongly by the system's relaxation rate.
|
2208.03613v1
|
2022-08-09
|
HyperNST: Hyper-Networks for Neural Style Transfer
|
We present HyperNST; a neural style transfer (NST) technique for the artistic
stylization of images, based on Hyper-networks and the StyleGAN2 architecture.
Our contribution is a novel method for inducing style transfer parameterized by
a metric space, pre-trained for style-based visual search (SBVS). We show for
the first time that such space may be used to drive NST, enabling the
application and interpolation of styles from an SBVS system. The technical
contribution is a hyper-network that predicts weight updates to a StyleGAN2
pre-trained over a diverse gamut of artistic content (portraits), tailoring the
style parameterization on a per-region basis using a semantic map of the facial
regions. We show HyperNST to exceed state of the art in content preservation
for our stylized content while retaining good style transfer performance.
|
2208.04807v1
|
2022-08-19
|
Byzantine Consensus is Θ(n^2): The Dolev-Reischuk Bound is Tight even in Partial Synchrony! [Extended Version]
|
The Dolev-Reischuk bound says that any deterministic Byzantine consensus
protocol has (at least) quadratic communication complexity in the worst case.
While it has been shown that the bound is tight in synchronous environments, it
is still unknown whether a consensus protocol with quadratic communication
complexity can be obtained in partial synchrony. Until now, the most efficient
known solutions for Byzantine consensus in partially synchronous settings had
cubic communication complexity (e.g., HotStuff, binary DBFT).
This paper closes the existing gap by introducing SQuad, a partially
synchronous Byzantine consensus protocol with quadratic worst-case
communication complexity. In addition, SQuad is optimally-resilient and
achieves linear worst-case latency complexity. The key technical contribution
underlying SQuad lies in the way we solve view synchronization, the problem of
bringing all correct processes to the same view with a correct leader for
sufficiently long. Concretely, we present RareSync, a view synchronization
protocol with quadratic communication complexity and linear latency complexity,
which we utilize in order to obtain SQuad.
|
2208.09262v2
|
2022-08-26
|
Randomised Composition and Small-Bias Minimax
|
We prove two results about randomised query complexity $\mathrm{R}(f)$.
First, we introduce a "linearised" complexity measure $\mathrm{LR}$ and show
that it satisfies an inner-optimal composition theorem: $\mathrm{R}(f\circ g)
\geq \Omega(\mathrm{R}(f) \mathrm{LR}(g))$ for all partial $f$ and $g$, and
moreover, $\mathrm{LR}$ is the largest possible measure with this property. In
particular, $\mathrm{LR}$ can be polynomially larger than previous measures
that satisfy an inner composition theorem, such as the max-conflict complexity
of Gavinsky, Lee, Santha, and Sanyal (ICALP 2019).
Our second result addresses a question of Yao (FOCS 1977). He asked if
$\epsilon$-error expected query complexity $\bar{\mathrm{R}}_{\epsilon}(f)$
admits a distributional characterisation relative to some hard input
distribution. Vereshchagin (TCS 1998) answered this question affirmatively in
the bounded-error case. We show that an analogous theorem fails in the
small-bias case $\epsilon=1/2-o(1)$.
|
2208.12896v1
|
2022-09-04
|
Lévy flights as an emergent phenomenon in a spatially extended system
|
Anomalous diffusion and L\'evy flights, which are characterized by the
occurrence of random discrete jumps of all scales, have been observed in a
plethora of natural and engineered systems, ranging from the motion of
molecules to climate signals. Mathematicians have recently unveiled mechanisms
to generate anomalous diffusion, both stochastically and deterministically.
However, there exists to the best of our knowledge no explicit example of a
spatially extended system which exhibits anomalous diffusion without being
explicitly driven by L\'evy noise. We show here that the
Landau-Lifshitz-Gilbert equation, a stochastic partial differential equation
(SPDE), despite only driven by Gaussian white noise, exhibits superdiffusive
behaviour. The anomalous diffusion is an entirely emergent behaviour and
manifests itself in jumps in the location of its travelling front solution.
Using a collective coordinate approach we reduce the SPDE to a set of
stochastic differential equations (SDEs) driven by Gaussian white noise. This
allows us to identify the mechanism giving rise to the anomalous diffusion as
random widening events of the front interface.
|
2209.01520v3
|
2022-08-29
|
Probably Something: A Multi-Layer Taxonomy of Non-Fungible Tokens
|
Purpose: This paper aims to establish a fundamental and comprehensive
understanding of Non-Fungible Tokens (NFTs) by identifying and structuring
common characteristics within a taxonomy. NFTs are hyped and increasingly
marketed as essential building blocks of the Metaverse. However, the dynamic
evolution of the NFT space has posed challenges for those seeking to develop a
deep and comprehensive understanding of NFTs, their features, and capabilities.
Design/methodology/approach: Utilizing common guidelines for the creation of
taxonomies, we developed (over three iterations), a multi-layer taxonomy based
on workshops and interviews with 11 academic and 15 industry experts. Through
an evaluation of 25 NFTs, we demonstrate the usefulness of our taxonomy.
Findings: The taxonomy has four layers, 14 dimensions and 42 characteristics,
which describe NFTs in terms of reference object, token properties, token
distribution, and realizable value.
Originality: Our framework is the first to systematically cover the emerging
NFT phenomenon. It is concise yet extendible and presents many avenues for
future research in a plethora of disciplines. The characteristics identified in
our taxonomy are useful for NFT and Metaverse related research in Finance,
Marketing, Law, and Information Systems. Additionally, the taxonomy can serve
as an information source for policymakers as they consider NFT regulation.
|
2209.05456v1
|
2022-09-19
|
Introducing the step Monte Carlo method for simulating dynamic properties
|
In this work, we introduce a simple modification of the Monte Carlo
algorithm, which we call step Monte Carlo (sMC). The sMC approach allows to
simulate processes far from equilibrium and obtain information about the
dynamic properties of the system under investigation. In the approach proposed
here the probability of accepting the final (trial) state depends on the
activation energy, not on the relative energy between the final and initial
state. This barrier height is probed on an ongoing basis, by generating
intermediate states along the path connecting the initial and trial positions.
Importantly, to calculate the activation energy, our model only requires
knowledge of the Hamiltonian without having to introduce additional input
parameters such as transition rates etc. The details of sMC are explained for
the case of a simple spin model. The comparison of its results with the ones
obtained within the frame of stochastic Landau-Lifshitz-Gilbert indicates the
correctness of sMC. In our opinion, the proposed here method can be applied to
simulate other processes, for example dynamics of classical atoms and complex
fluids, diffusion, nucleation, surface adsorption and crystal growth processes.
|
2209.08961v3
|
2022-09-23
|
Logarithmically larger deletion codes of all distances
|
The deletion distance between two binary words $u,v \in \{0,1\}^n$ is the
smallest $k$ such that $u$ and $v$ share a common subsequence of length $n-k$.
A set $C$ of binary words of length $n$ is called a $k$-deletion code if every
pair of distinct words in $C$ has deletion distance greater than $k$. In 1965,
Levenshtein initiated the study of deletion codes by showing that, for $k\ge 1$
fixed and $n$ going to infinity, a $k$-deletion code $C\subseteq \{0,1\}^n$ of
maximum size satisfies $\Omega_k(2^n/n^{2k}) \leq |C| \leq O_k( 2^n/n^k)$. We
make the first asymptotic improvement to these bounds by showing that there
exist $k$-deletion codes with size at least $\Omega_k(2^n \log n/n^{2k})$. Our
proof is inspired by Jiang and Vardy's improvement to the classical
Gilbert--Varshamov bounds. We also establish several related results on the
number of longest common subsequences and shortest common supersequences of a
pair of words with given length and deletion distance.
|
2209.11882v2
|
2022-10-19
|
Generalised form of the magnetic anisotropy field in micromagnetic and atomistic spin models
|
We present a general approach to the derivation of the effective anisotropy
field which determines the dynamical behaviour of magnetic spins according to
the Landau-Lifshitz-Gilbert equation. The approach is based on the gradient in
spherical polar coordinates with the final results being expressed in Cartesian
coordinates as usually applied in atomistic and micromagnetic model
calculations. The approach is generally valid for all orders of anisotropies
including higher order combinations of azimuthal and rotational anisotropies
often found in functional magnetic materials such as permanent magnets and an
emerging class of antiferromagnetic materials with applications in spintronics.
Anisotropies are represented in terms of spherical harmonics which have the
important property of rational temperature scaling. Effective field vectors are
given for anisotropies up to sixth order, presenting a unified framework for
implementing higher order magnetic anisotropies in numerical simulations.
|
2210.10916v4
|
2022-10-27
|
Formal Semantics for the Halide Language
|
We present the first formalization and metatheory of language soundness for a
user-schedulable language, the widely used array processing language Halide.
User-schedulable languages strike a balance between abstraction and control in
high-performance computing by separating the specification of what a program
should compute from a schedule for how to compute it. In the process, they make
a novel language soundness claim: the result of a program should always be the
same, regardless of how it is scheduled. This soundness guarantee is tricky to
provide in the presence of schedules that introduce redundant recomputation and
computation on uninitialized data, rather than simply reordering statements. In
addition, Halide ensures memory safety through a compile-time bounds inference
engine that determines safe sizes for every buffer and loop in the generated
code, presenting a novel challenge: formalizing and analyzing a language
specification that depends on the results of unreliable program synthesis
algorithms. Our formalization has revealed flaws and led to improvements in the
practical Halide system, and we believe it provides a foundation for the design
of new languages and tools that apply programmer-controlled scheduling to other
domains.
|
2210.15740v1
|
2022-11-08
|
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
|
We present SLATE, a sequence labeling approach for extracting tasks from
free-form content such as digitally handwritten (or "inked") notes on a virtual
whiteboard. Our approach allows us to create a single, low-latency model to
simultaneously perform sentence segmentation and classification of these
sentences into task/non-task sentences. SLATE greatly outperforms a baseline
two-model (sentence segmentation followed by classification model) approach,
achieving a task F1 score of 84.4%, a sentence segmentation (boundary
similarity) score of 88.4% and three times lower latency compared to the
baseline. Furthermore, we provide insights into tackling challenges of
performing NLP on the inking domain. We release both our code and dataset for
this novel task.
|
2211.04454v2
|
2022-11-10
|
Unifying the communicable disease spreading paradigm with Gompertzian growth
|
A number of studies have shown that cumulative mortality followed a Gompertz
curve in the initial Covid pandemic period, March-April 2020. We show that the
Gompertz curve is incompatible with expected initial logistic growth curves as
predicted by traditional Susceptible-Infected-Recovered (SIR) models, and
propose a new theory which better explains the nature of the mortality
characteristics based on a global biosphere disturbance. Second, we show that
for the Gompertz curve to emerge, the disturbance has to act on everyone
simultaneously, rejecting the possibility of a disease propagation stage.
Third, we connect logistic growth with Gompertzian growth by augmenting the
logistic growth equation with higher order interaction terms, and show that the
SIR model family is compatible with Gompertzian growth only when all nodes in
the transmission network communicate with infinite speed and interaction.
Crucially, this augmentation must be accompanied by a causality-reversal where
the source of growth is not the pool of infected but the pool of susceptible
people. We thus find a novel bridge between logistic and Gompertzian growth,
separate from the existing Richards model (also called $\theta$-logistic
growth).
|
2211.05653v2
|
2022-11-12
|
Helio2024 Science White Paper: ngGONG -- Future Ground-based Facilities for Research in Heliophysics and Space Weather Operational Forecast
|
Long-term synoptic observations of the Sun are critical for advancing our
understanding of Sun as an astrophysical object, understanding the solar
irradiance and its role in solar-terrestrial climate, for developing predictive
capabilities of solar eruptive phenomena and their impact on our home planet,
and heliosphere in general, and as a data provider for the operational space
weather forecast. We advocate for the development of a ground-based network of
instruments provisionally called ngGONG to maintain critical observing
capabilities for synoptic research in solar physics and for the operational
space weather forecast.
|
2211.06712v1
|
2022-11-14
|
SVS: Adversarial refinement for sparse novel view synthesis
|
This paper proposes Sparse View Synthesis. This is a view synthesis problem
where the number of reference views is limited, and the baseline between target
and reference view is significant. Under these conditions, current radiance
field methods fail catastrophically due to inescapable artifacts such 3D
floating blobs, blurring and structural duplication, whenever the number of
reference views is limited, or the target view diverges significantly from the
reference views.
Advances in network architecture and loss regularisation are unable to
satisfactorily remove these artifacts. The occlusions within the scene ensure
that the true contents of these regions is simply not available to the model.
In this work, we instead focus on hallucinating plausible scene contents within
such regions. To this end we unify radiance field models with adversarial
learning and perceptual losses. The resulting system provides up to 60%
improvement in perceptual accuracy compared to current state-of-the-art
radiance field models on this problem.
|
2211.07301v1
|
2022-11-15
|
Viscosity of pure-glue QCD from the lattice
|
We calculate shear viscosity and bulk viscosity in SU(3) gauge theory on the
lattice at $1.5 \,T_c$. The viscosities are extracted via a Kubo formula from
the reconstructed spectral function which we determine from the Euclidean-time
dependence of the corresponding channel of the energy-momentum tensor
correlators. We obtain unprecedented precision for the correlators by applying
gradient flow and blocking methods. The correlators are extrapolated to the
continuum and then to zero flow time. To extract the viscosities we fit
theoretically inspired models to the lattice data and crosscheck the fit
results using the Backus Gilbert method. The final estimates for shear and bulk
viscosity are $\eta/s = 0.15-0.48$ and $\zeta/s = 0.017-0.059$.
|
2211.08230v2
|
2022-11-15
|
Nonlinear chiral photocurrent in parity-violating magnetic Weyl semimetals
|
The strong correlation between the non-trivial band topology and the magnetic
texture makes magnetic Weyl semimetals excellent candidates for the
manipulation and detection of magnetization dynamics. The parity violation
together with the Pauli blocking cause only one Weyl node to contribute to the
photocurrent response, which in turn affects the magnetic texture due to the
spin transfer torque. Utilizing the Landau-Lifshitz-Gilbert equation and the
spin-transfer torque in non-centrosymmetric Weyl magnets, we show that the
chiral photocurrent rotates the magnetization from the easy c axis to the a or
b axis, which leads to an exotic current next to the photocurrent response. The
chiral photocurrent is calculated in the context of quantum kinetic theory and
it has a strong resonance on the order of mA/W near the Weyl nodes, the
magnitude of which is controlled by the momentum relaxation time. Remarkably,
we study the influence of magnetic texture dynamics on the topological
nonlinear photocurrent response, including shift and injection currents along
with the new chiral photocurrent, and show that both the magnitude and the
in-plane orientation of the chiral photocurrent are strongly correlated with
the direction of the magnetic moments.
|
2211.08521v1
|
2022-11-17
|
3D Interconnected Magnetic Nanowire Networks as Potential Integrated Multistate Memristors
|
Interconnected magnetic nanowire (NW) networks offer a promising platform for
3-dimensional (3D) information storage and integrated neuromorphic computing.
Here we report discrete propagation of magnetic states in interconnected Co
nanowire networks driven by magnetic field and current, manifested in distinct
magnetoresistance (MR) features. In these networks, when only a few
interconnected NWs were measured, multiple MR kinks and local minima were
observed, including a significant minimum at a positive field during the
descending field sweep. Micromagnetic simulations showed that this unusual
feature was due to domain wall (DW) pinning at the NW intersections, which was
confirmed by off-axis electron holography imaging. In a complex network with
many intersections, sequential switching of nanowire sections separated by
interconnects was observed, along with stochastic characteristics. The
pinning/depinning of the DWs can be further controlled by the driving current
density. These results illustrate the promise of such interconnected networks
as integrated multistate memristors.
|
2211.09687v2
|
2022-11-22
|
Enabling On-Demand Cyber-Physical Control Applications with UAV Access Points
|
Achieving cyber-physical control over a wireless channel requires satisfying
both the timeliness of a single packet and preserving the latency reliability
across several consecutive packets. To satisfy those requirements as an
ubiquitous service requires big infrastructural developments, or flexible
on-demand equipment such as UAVs. To avoid the upfront cost in terms of finance
and energy, this paper analyzes the capability of UAV access points (UAVAPs) to
satisfy the requirements for cyber-physical traffic. To investigate this, we
perform a Gilbert-Eliott burst-error analysis that is analytically derived as a
combination of two separate latency measurement campaigns and provide an
upper-bound analysis of the UAVAP system. The analysis is centered around a
UAVAP that uses its LTE connection to reach the backhaul, while providing
service to ground nodes (GNs) with a Wi-Fi access point (AP). Thus, we combine
both measurement campaigns to analyze the plausibility of the described setup
in casual, crowded or mixed network settings.
|
2211.12249v1
|
2022-11-30
|
SuSpect3: A C++ Code for the Supersymmetric and Higgs Particle Spectrum of the MSSM
|
We present the program SuSpect3 that calculates the masses and couplings of
the Higgs and supersymmetric particles predicted by the Minimal Supersymmetric
Standard Model (MSSM). The model is implemented in both its non-constrained
version, the MSSM, and its constrained versions, such as the minimal
supergravity and the gauge or anomaly mediated supersymmetry breaking models,
in which the soft supersymmetry-breaking parameters obey certain universal
boundary conditions at the high energy scale. The low energy parameters are
then obtained using renormalization group equations and electroweak symmetry
breaking, and all the dominant radiative corrections have been consistently
implemented. SuSpect3 is a major rewrite, in C++ object oriented programming,
of the FORTRAN code SuSpect. It includes all the features of the earlier code
in an improved and updated manner, and involves new options such as compressed
SUSY scenarios, an MSSM-inflation model and the possibility of using the
observed Higgs mass as an input. The main features and the use of the program
are explained.
|
2211.16956v2
|
2022-12-06
|
Ground-based Synoptic Studies of the Sun
|
Ground-based synoptic solar observations provide critical contextual data
used to model the large-scale state of the heliosphere. The next decade will
see a combination of ground-based telescopes and space missions that will study
our Sun's atmosphere microscopic processes with unprecedented detail. This
white paper describes contextual observations from a ground-based network
needed to fully exploit this new knowledge of the underlying physics that leads
to the magnetic linkages between the heliosphere and the Sun. This combination
of a better understanding of small-scale processes and the appropriate global
context will enable a physics-based approach to Space Weather comparable to
Terrestrial Weather forecasting.
|
2212.03247v2
|
2022-12-14
|
Non-uniform Superlattice Magnetic Tunnel Junctions
|
We propose a new class of non-uniform superlattice magnetic tunnel junctions
(Nu-SLTJs) with the Linear, Gaussian, Lorentzian, and P\"oschl-teller width and
height based profiles manifesting a sizable enhancement in the TMR($\approx
10^4-10^6\%$) with a significant suppression in the switching bias($\approx$9
folds) owing to the physics of broad-band spin filtering. By exploring the
negative differential resistance region in the current-voltage characteristics
of the various Nu-SLTJs, we predict the Nu-SLTJs offer the fastest spin
transfer torque switching in the order of a few hundred picoseconds. We
self-consistently employ the atomistic non-equilibrium Green's function
formalism coupled with the Landau-Lifshitz-Gilbert-Slonczewski equation to
evaluate the device performance of the various Nu-SLTJs. We also present the
design of minimal three-barrier Nu-SLTJs having significant TMR($\approx
10^4\%$) and large spin current for ease of device fabrication. We hope that
the class of Nu-SLTJs proposed in this work may lay the bedrock to embark on
the exhilarating voyage of exploring various non-uniform superlattices for the
next generation of spintronic devices.
|
2212.07202v2
|
2022-12-20
|
A combinatorial proof of a tantalizing symmetry on Catalan objects
|
We investigate a tantalizing symmetry on Catalan objects. In terms of Dyck
paths, this symmetry is interpreted in the following way: if $w_{n,k,m}$ is the
number of Dyck paths of semilength $n$ with $k$ occurrences of $UD$ and $m$
occurrences of $UUD$, then $w_{2k+1,k,m}=w_{2k+1,k,k+1-m}$. We give two proofs
of this symmetry: an algebraic proof using generating functions, and a
combinatorial proof which makes heavy use of the cycle lemma and an alternate
interpretation of the numbers $w_{n,k,m}$ using plane trees. In particular, our
combinatorial proof expresses the numbers $w_{2k+1,k,m}$ in terms of Narayana
numbers, and we generalize this to a relationship between the numbers
$w_{n,k,m}$ and a family of generalized Narayana numbers due to Callan. Some
further generalizations and applications of our combinatorial proofs are
explored. Finally, we investigate properties of the polynomials $W_{n,k}(t)=
\sum_{m=0}^k w_{n,k,m} t^m$, including real-rootedness, $\gamma$-positivity,
and a symmetric decomposition.
|
2212.10586v1
|
2022-12-30
|
Asymptotic Analysis of Harmonic Maps With Prescribed Singularities
|
Motivated by stationary vacuum solutions of the Einstein field equations, we
study singular harmonic maps from domains of 3-dimensional Euclidean space to
the hyperbolic plane having bounded hyperbolic distance to Kerr harmonic maps.
In the degenerate case, we prove that every such harmonic map admits a unique
tangent harmonic map at the extreme black hole horizon. The possible tangent
maps are classified and shown to be shifted 'extreme Kerr' geodesics in the
hyperbolic plane that depend on two parameters, one determined by angular
momentum and another by conical singularities. In addition, rates of
convergence to the tangent map are established. Similarly, expansions in the
asymptotically flat end are presented. These results, together with those of
Li-Tian and Weinstein, provide a complete regularity theory for harmonic maps
from $\mathbb R^3\setminus z\text{-axis}$ to $\mathbb H^2$ with prescribed
singularities. Lastly, the analysis is utilized to prove existence of the so
called near horizon limit, and to compute the associated near horizon
geometries of extreme black holes.
|
2212.14826v1
|
2023-01-06
|
Measuring a Priori Voting Power -- Taking Delegations Seriously
|
We introduce new power indices to measure the a priori voting power of voters
in liquid democracy elections where an underlying network restricts
delegations. We argue that our power indices are natural extensions of the
standard Penrose-Banzhaf index in simple voting games. We show that computing
the criticality of a voter is #P-hard even when voting weights are
polynomially-bounded in the size of the instance. However, for specific
settings, such as when the underlying network is a bipartite or complete graph,
recursive formulas can compute these indices for weighted voting games in
pseudo-polynomial time. We highlight their theoretical properties and provide
numerical results to illustrate how restricting the possible delegations can
alter voters' voting power.
|
2301.02462v4
|
2023-01-10
|
The spectral reconstruction of inclusive rates
|
A recently re-discovered variant of the Backus-Gilbert algorithm for spectral
reconstruction enables the controlled determination of smeared spectral
densities from lattice field theory correlation functions. A particular
advantage of this approach is the \emph{a priori} specification of the kernel
with which the underlying spectral density is smeared, allowing for variation
of its peak position, smearing width, and functional form. If the unsmeared
spectral density is sufficiently smooth in the neighborhood of a particular
energy, it can be obtained from an extrapolation to zero smearing-kernel width
at fixed peak position. A natural application for this approach is scattering
processes summed over all hadronic final states. As a proof-of-principle test,
an inclusive rate is computed in the two-dimensional O(3) sigma model from a
two-point correlation function of conserved currents. The results at finite and
zero smearing radius are in good agreement with the known analytic form up to
energies at which 40-particle states contribute, and are sensitive to the
4-particle contribution to the inclusive rate. The straight-forward adaptation
to compute the $R$-ratio in lattice QCD from two-point functions of the
electromagnetic current is briefly discussed.
|
2301.04072v1
|
2023-01-12
|
Redundancy of Codes with Graph Constraints
|
In this paper, we study the redundancy of linear codes with graph
constraints. First we consider linear parity check codes based on bipartite
graphs with diversity and with generalized graph constraints. We describe
sufficient conditions on the constraint probabilities and use the probabilistic
method to obtain linear codes that achieve the Gilbert-Varshamov redundancy
bound in addition to satisfying the constraints and the diversity index. In the
second part we consider a generalization of graph capacity which we call as the
fractional graph capacity and use the probabilistic method to determine bounds
on the fractional capacity for arbitrary graphs. Specifically, we establish an
upper bound in terms of the full graph capacity and a lower bound in terms of
the average and maximum vertex degree of the graph.
|
2301.04808v1
|
2023-01-12
|
Magnetic-field-free nonreciprocal transport in graphene multi-terminal Josephson junctions
|
Nonreciprocal superconducting devices have attracted growing interest in
recent years as they potentially enable directional charge transport for
applications in superconducting quantum circuits. Specifically, the
superconducting diode effect has been explored in two-terminal devices that
exhibit superconducting transport in one current direction while showing
dissipative transport in the opposite direction. Here, we exploit
multi-terminal Josephson junctions (MTJJs) to engineer magnetic-field-free
nonreciprocity in multi-port networks. We show that when treated as a two-port
electrical network, a three-terminal Josephson junction (JJ) with an asymmetric
graphene region exhibits reconfigurable two-port nonreciprocity. We observe
nonreciprocal (reciprocal) transport between superconducting terminals with
broken (preserved) spatial mirror symmetry. We explain our observations by
considering a circuit-network of JJs with different critical currents.
|
2301.05081v3
|
2023-01-24
|
Recent Results from the FASTSUM Collaboration
|
The FASTSUM Collaboration has developed a comprehensive research programme in
thermal QCD using 2+1 flavour, anisotropic ensembles. In this talk, we
summarise some of our recent results including thermal hadron spectrum
calculations using our ``Generation 2L'' ensembles which have pion masses of
239(1) MeV. These include open charm mesons and charm baryons. We also
summarise our work using the Backus Gilbert approach to determining the
spectral function of the NRQCD bottomonium system. Finally, we review our
determination of the interquark potential in the same system, but using our
``Generation 2'' ensembles which have heavier pion masses of 384(4) MeV.
|
2301.10282v1
|
2023-01-27
|
Women's Perspectives on Harm and Justice after Online Harassment
|
Social media platforms aspire to create online experiences where users can
participate safely and equitably. However, women around the world experience
widespread online harassment, including insults, stalking, aggression, threats,
and non-consensual sharing of sexual photos. This article describes women's
perceptions of harm associated with online harassment and preferred platform
responses to that harm. We conducted a survey in 14 geographic regions around
the world (N = 3,993), focusing on regions whose perspectives have been
insufficiently elevated in social media governance decisions (e.g. Mongolia,
Cameroon). {Results show} that, on average, women perceive greater harm
associated with online harassment than men, especially for non-consensual image
sharing. Women also prefer most platform responses compared to men, especially
removing content and banning users; however, women are less favorable towards
payment as a response. Addressing global gender-based violence online requires
understanding how women experience online harms and how they wish for it to be
addressed. This is especially important given that the people who build and
govern technology are not typically those who are most likely to experience
online harms.
|
2301.11733v1
|
2023-02-02
|
Thermal and atomic effects on coupled-channels heavy-ion fusion
|
Stellar nuclear fusion reactions take place in a hot, dense plasma within
stars. To account for the effect of these environments, the theory of open
quantum systems is used to conduct pioneering studies of thermal and atomic
effects on fusion probability at a broad range of temperatures and densities.
Since low-lying excited states are more likely to be populated at stellar
temperatures and increase nuclear plasma interaction rates, a 188Os nucleus was
used as a target that interacts with an inert 16O projectile. Key results
showed thermal effects yield an average increase in fusion probability of 15.5%
and 36.9% for our test nuclei at temperatures of 0.1 and 0.5 MeV respectively,
compared to calculations at zero temperature. Thermal effects could be tested
in a laboratory using targets prepared in excited states as envisaged in
facilities exploiting laser-nucleus interactions.
|
2302.01272v2
|
2023-02-02
|
Topological data analysis reveals differences between simulated galaxies and dark matter haloes
|
We use topological summaries based on Betti curves to characterize the
large-scale spatial distribution of simulated dark matter haloes and galaxies.
Using the IllustrisTNG and CAMELS-SAM simulations, we show that the topology of
the galaxy distribution is significantly different from the topology of the
dark matter halo distribution. Further, there are significant differences
between the distributions of star-forming and quiescent galaxies. These
topological differences are broadly consistent across all simulations, while at
the same time there are noticeable differences when comparing between different
models. Finally, using the CAMELS-SAM simulations, we show that the topology of
the quiescent galaxies in particular depends strongly on the amount of
supernova feedback. These results suggest that topological summary statistics
could be used to help better understand the processes of galaxy formation and
evolution.
|
2302.01363v2
|
2023-02-06
|
Landau theory for ferro-paramagnetic phase transition in finitely-strained viscoelastic magnets
|
The thermodynamic model of visco-elastic deformable magnetic materials at
finite strains is formulated in a fully Eulerian way in rates. The Landau
theory applies for ferro-to-para-magnetic phase transition, the gradient theory
(leading exchange energy) for magnetization with general mechanically dependent
coefficient, hysteresis in magnetization evolution by Landau-Lifshitz-Gilbert
equation involving objective corotational time derivative of magnetization, and
demagnetizing field are considered in the model. The Kelvin-Voigt viscoelastic
rheology with a higher-order viscosity (exploiting the concept of multipolar
materials) is used, allowing for physically relevant frame-indifferent stored
energies and for local invertibility of deformation. The model complies with
energy conservation and Clausius-Duhem entropy inequality. Existence and a
certain regularity of weak solutions is proved by a Faedo-Galerkin
semi-discretization and a suitable regularization.
|
2302.02850v1
|
2023-02-13
|
Zero-frequency chiral magnonic edge states protected by non-equilibrium topology
|
Topological bosonic excitations must, in contrast to their fermionic
counterparts, appear at finite energies. This is a key challenge for magnons,
as it prevents straightforward excitation and detection of
topologically-protected magnonic edge states and their use in magnonic devices.
In this work, we show that in a non-equilibrium state, in which the
magnetization is pointing against the external magnetic field, the
topologically-protected chiral edge states in a magnon Chern insulator can be
lowered to zero frequency, making them directly accessible by existing
experimental techniques. We discuss the spin-orbit torque required to stabilize
this non-equilibrium state, and show explicitly using numerical
Landau-Lifshitz-Gilbert simulations that the edge states can be excited with a
microwave field. Finally, we consider a propagating spin wave spectroscopy
experiment, and demonstrate that the edge states can be directly detected.
|
2302.06597v3
|
2023-02-15
|
Reliable optimization of arbitrary functions over quantum measurements
|
As the connection between classical and quantum worlds, quantum measurements
play a unique role in the era of quantum information processing. Given an
arbitrary function of quantum measurements, how to obtain its optimal value is
often considered as a basic yet important problem in various applications.
Typical examples include but not limited to optimizing the likelihood functions
in quantum measurement tomography, searching the Bell parameters in Bell-test
experiments, and calculating the capacities of quantum channels. In this work,
we propose reliable algorithms for optimizing arbitrary functions over the
space of quantum measurements by combining the so-called Gilbert's algorithm
for convex optimization with certain gradient algorithms. With extensive
applications, we demonstrate the efficacy of our algorithms with both convex
and nonconvex functions.
|
2302.07534v1
|
2023-02-18
|
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits
|
Reactive power sharing and containment of voltages within limits for
inverter-based resources (IBRs) are two important, yet coupled objectives in ac
networks. In this article, we propose a distributed control technique to
simultaneously achieve these objectives. Our controller consists of two
components: a purely local nonlinear integral controller which adjusts the IBR
voltage setpoint, and a distributed primal-dual optimizer that coordinates
reactive power sharing between the IBRs. The controller prioritizes the voltage
containment objective over reactive power sharing at all points in time;
excluding the IBRs with saturated voltages, it provides reactive power sharing
among all the IBRs. Considering the voltage saturation and the coupling between
voltage and angle dynamics, a formal closed-loop stability analysis based on
singular perturbation theory is provided, yielding practical tuning guidance
for the overall control system. To validate the effectiveness of the proposed
controller for different case studies, we apply it to a low-voltage microgrid
and a microgrid adapted from the CIGRE medium-voltage network benchmark, both
simulated in the MATLAB/Simulink environment.
|
2302.09241v2
|
2023-02-21
|
Micromagnetic study of inertial spin waves in ferromagnetic nanodots
|
Here we report the possibility to excite ultra-short spin waves in
ferromagnetic thin-films by using time-harmonic electromagnetic fields with
terahertz frequency. Such ultra-fast excitation requires to include inertial
effects in the description of magnetization dynamics. In this respect, we
consider the inertial Landau-Lifshitz-Gilbert (iLLG) equation and develop
analytical theory for exchange-dominated inertial spin waves. The theory
predicts a finite limit for inertial spin wave propagation velocity, as well as
spin wave spatial decay and lifetime as function of material parameters. Then,
guided by the theory, we perform numerical micromagnetic simulations that
demonstrate the excitation of ultra-short inertial spin waves (20 nm long)
propagating at finite speed in a confined magnetic nanodot. The results are in
agreement with the theory and provide the order of magnitude of quantities
observable in realistic ultra-fast dynamics experiments.
|
2302.10759v2
|
2023-03-04
|
Dynamic Modeling and Validation of Soft Robotic Snake Locomotion
|
Soft robotic snakes made of compliant materials can continuously deform their
bodies and, therefore, mimic the biological snakes' flexible and agile
locomotion gaits better than their rigid-bodied counterparts. Without wheel
support, to date, soft robotic snakes are limited to emulating planar
locomotion gaits, which are derived via kinematic modeling and tested on
robotic prototypes. Given that the snake locomotion results from the reaction
forces due to the distributed contact between their skin and the ground, it is
essential to investigate the locomotion gaits through efficient dynamic models
capable of accommodating distributed contact forces. We present a complete
spatial dynamic model that utilizes a floating-base kinematic model with
distributed contact dynamics for a pneumatically powered soft robotic snake. We
numerically evaluate the feasibility of the planar and spatial rolling gaits
utilizing the proposed model and experimentally validate the corresponding
locomotion gait trajectories on a soft robotic snake prototype. We
qualitatively and quantitatively compare the numerical and experimental results
which confirm the validity of the proposed dynamic model.
|
2303.02291v1
|
2023-03-20
|
Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
|
The aim of this manuscript is to explore semiparametric methods for inferring
subgroup-specific relative vaccine efficacy in a partially vaccinated
population against multiple strains of a virus. We consider methods for
observational case-only studies with informative missingness in viral strain
type due to vaccination status, pre-vaccination variables, and also
post-vaccination factors such as viral load. We establish general causal
conditions under which the relative conditional vaccine efficacy between
strains can be identified nonparametrically from the observed data-generating
distribution. Assuming that the relative strain-specific conditional vaccine
efficacy has a known parametric form, we propose semiparametric asymptotically
linear estimators of the parameters based on targeted (debiased) machine
learning estimators for partially linear logistic regression models. Finally,
we apply our methods to estimate the relative strain-specific conditional
vaccine efficacy in the ENSEMBLE COVID-19 vaccine trial.
|
2303.11462v1
|
2023-03-16
|
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
|
Intersectionality is a critical framework that, through inquiry and praxis,
allows us to examine how social inequalities persist through domains of
structure and discipline. Given AI fairness' raison d'etre of "fairness", we
argue that adopting intersectionality as an analytical framework is pivotal to
effectively operationalizing fairness. Through a critical review of how
intersectionality is discussed in 30 papers from the AI fairness literature, we
deductively and inductively: 1) map how intersectionality tenets operate within
the AI fairness paradigm and 2) uncover gaps between the conceptualization and
operationalization of intersectionality. We find that researchers
overwhelmingly reduce intersectionality to optimizing for fairness metrics over
demographic subgroups. They also fail to discuss their social context and when
mentioning power, they mostly situate it only within the AI pipeline. We: 3)
outline and assess the implications of these gaps for critical inquiry and
praxis, and 4) provide actionable recommendations for AI fairness researchers
to engage with intersectionality in their work by grounding it in AI
epistemology.
|
2303.17555v2
|
2023-04-04
|
Direct in situ determination of the surface area and structure of deposited metallic lithium within lithium metal batteries using ultra small and small angle neutron scattering
|
Despite being the major cause of battery safety issues and detrimental
performance, a comprehensive growth mechanism for metallic lithium deposited at
electrode surfaces in lithium metal batteries remains elusive. While lithium
surface morphology is often derived indirectly, here, detailed information is
directly obtained using in situ small and ultra-small angle neutron scattering,
in bulk and non-destructively. Features of 1-10 um and 100-300 nm are
identified; the latter contribute to most of the surface area and their size
inversely correlates to applied current density. Surface area per unit volume
increases continuously during charging from 1-4 h at 2 mA/cm2 but more slowly
during discharge. Comparatively higher values are reached after just 1 h at 20
mA/cm2 which remain constant in subsequent cycles. Such quantitative insight
into the processes of metallic lithium growth within batteries may enable the
development of safer high performance lithium metal batteries.
|
2304.01557v1
|
2023-04-10
|
EKILA: Synthetic Media Provenance and Attribution for Generative Art
|
We present EKILA; a decentralized framework that enables creatives to receive
recognition and reward for their contributions to generative AI (GenAI). EKILA
proposes a robust visual attribution technique and combines this with an
emerging content provenance standard (C2PA) to address the problem of synthetic
image provenance -- determining the generative model and training data
responsible for an AI-generated image. Furthermore, EKILA extends the
non-fungible token (NFT) ecosystem to introduce a tokenized representation for
rights, enabling a triangular relationship between the asset's Ownership,
Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators
to express agency over training consent and, through our attribution model, to
receive apportioned credit, including royalty payments for the use of their
assets in GenAI.
|
2304.04639v1
|
2023-04-11
|
NeAT: Neural Artistic Tracing for Beautiful Style Transfer
|
Style transfer is the task of reproducing the semantic contents of a source
image in the artistic style of a second target image. In this paper, we present
NeAT, a new state-of-the art feed-forward style transfer method. We
re-formulate feed-forward style transfer as image editing, rather than image
generation, resulting in a model which improves over the state-of-the-art in
both preserving the source content and matching the target style. An important
component of our model's success is identifying and fixing "style halos", a
commonly occurring artefact across many style transfer techniques. In addition
to training and testing on standard datasets, we introduce the BBST-4M dataset,
a new, large scale, high resolution dataset of 4M images. As a component of
curating this data, we present a novel model able to classify if an image is
stylistic. We use BBST-4M to improve and measure the generalization of NeAT
across a huge variety of styles. Not only does NeAT offer state-of-the-art
quality and generalization, it is designed and trained for fast inference at
high resolution.
|
2304.05139v1
|
2023-04-12
|
ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer
|
Representation learning aims to discover individual salient features of a
domain in a compact and descriptive form that strongly identifies the unique
characteristics of a given sample respective to its domain. Existing works in
visual style representation literature have tried to disentangle style from
content during training explicitly. A complete separation between these has yet
to be fully achieved. Our paper aims to learn a representation of visual
artistic style more strongly disentangled from the semantic content depicted in
an image. We use Neural Style Transfer (NST) to measure and drive the learning
signal and achieve state-of-the-art representation learning on explicitly
disentangled metrics. We show that strongly addressing the disentanglement of
style and content leads to large gains in style-specific metrics, encoding far
less semantic information and achieving state-of-the-art accuracy in downstream
multimodal applications.
|
2304.05755v2
|
2023-04-18
|
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer
|
Text-to-image models (T2I) such as StableDiffusion have been used to generate
high quality images of people. However, due to the random nature of the
generation process, the person has a different appearance e.g. pose, face, and
clothing, despite using the same text prompt. The appearance inconsistency
makes T2I unsuitable for pose transfer. We address this by proposing a
multimodal diffusion model that accepts text, pose, and visual prompting. Our
model is the first unified method to perform all person image tasks -
generation, pose transfer, and mask-less edit. We also pioneer using small
dimensional 3D body model parameters directly to demonstrate new capability -
simultaneous pose and camera view interpolation while maintaining the person's
appearance.
|
2304.08870v2
|
2023-05-02
|
The Pseudoinverse of $A=CR$ is $A^+=R^+C^+$ (?)
|
This paper gives three formulas for the pseudoinverse of a matrix product $A
= CR$. The first is sometimes correct, the second is always correct, and the
third is almost never correct. But that third randomized pseudoinverse $A^+_r$
may be very useful when $A$ is a very large matrix.
1. $A^+ = R^+C^+$ when $A = CR$ and $C$ has independent columns and $R$ has
independent rows.
2. $A^+ = (C^+CR)^+(CRR^+)^+$ is always correct.
3. $A^+_r = (P^TCR)^+P^TCRQ(CRQ)^+ = A^+$ only when $\mathrm{rank}(P^TA) =
\mathrm{rank}(AQ) = \mathrm{rank}(A)$ with $A = CR$.
|
2305.01716v3
|
2023-05-10
|
Symmetry and nonlinearity of spin wave resonance excited by focused surface acoustic waves
|
The use of a complex ferromagnetic system to manipulate GHz surface acoustic
waves is a rich current topic under investigation, but the high-power nonlinear
regime is under-explored. We introduce focused surface acoustic waves, which
provide a way to access this regime with modest equipment. Symmetry of the
magneto-acoustic interaction can be tuned by interdigitated transducer design
which can introduce additional strain components. Here, we compare the impact
of focused acoustic waves versus standard unidirectional acoustic waves in
significantly enhancing the magnon-phonon coupling behavior. Analytical
simulation results based on modified Landau-Lifshitz-Gilbert theory show good
agreement with experimental findings. We also report nonlinear input power
dependence of the transmission through the device. This experimental
observation is supported by the micromagnetic simulation using mumax3 to model
the nonlinear dependence. These results pave the way for extending the
understanding and design of acoustic wave devices for exploration of
acoustically driven spin wave resonance physics.
|
2305.06259v1
|
2023-05-16
|
Phase locking in voltage-controlled parametric oscillator
|
A recent experimental demonstration of a parametric magnetization oscillation
excited by applying a microwave voltage to a ferromagnetic metal will be
applicable not only to a new magnetization switching method but also to
bio-inspired computing. It should be, however, noted that a phase of the
parametric magnetization oscillation is not uniquely locked, related to the
fact that a frequency of the microwave voltage is twice the value of the
magnetization oscillation. There are two possible phases in the parametric
oscillation state, and which of the two is realized depends on the initial
condition of the magnetization. Here, we examine two approaches to lock the
phase uniquely. One is to suppress the distribution of the initial state by
enhancing the perpendicular magnetic anisotropy before applying microwave
voltage, and the other is to use a sweeping frequency. Through numerical
simulation of the Landau-Lifshitz-Gilbert equation and quantification of locked
rate, we find that the sweeping frequency is more effective to lock the phase
of the parametric magnetization oscillation.
|
2305.09143v1
|
2023-05-16
|
Non-periodic input-driven magnetization dynamics in voltage-controlled parametric oscillator
|
Input-driven dynamical systems have attracted attention because their
dynamics can be used as resources for brain-inspired computing. The recent
achievement of human-voice recognition by spintronic oscillator also utilizes
an input-driven magnetization dynamics. Here, we investigate an excitation of
input-driven chaos in magnetization dynamics by voltage controlled magnetic
anisotropy effect. The study focuses on the parametric magnetization
oscillation induced by a microwave voltage and investigates the effect of
random-pulse input on the oscillation behavior. Solving the
Landau-Lifshitz-Gilbert equation, temporal dynamics of the magnetization and
its statistical character are evaluated. In a weak perturbation limit, the
temporal dynamics of the magnetization are mainly determined by the input
signal, which is classified as input-driven synchronization. In a large
perturbation limit, on the other hand, chaotic dynamics are observed, where the
dynamical response is sensitive to the initial state. The existence of chaos is
also identified by the evaluation of the Lyapunov exponent.
|
2305.09151v1
|
2023-05-23
|
Approaches to inclusive semileptonic $B_{(s)}$-meson decays from Lattice QCD
|
We address the nonperturbative calculation of the inclusive decay rate of
semileptonic $B_{(s)}$-meson decays from lattice QCD. Precise Standard-Model
predictions are key ingredients in searches for new physics, and this type of
computation may eventually provide new insight into the long-standing tension
between the inclusive and exclusive determinations of the
Cabibbo-Kobayashi-Maskawa (CKM) matrix elements $|V_{cb}|$ and $|V_{ub}|$. We
present results from a pilot lattice computation for $B_s \rightarrow X_c\, l
\nu_l$, where the initial $b$ quark described by the relativistic-heavy-quark
(RHQ) formalism on the lattice and the other valence quarks discretised with
domain-wall fermions are simulated approximately at their physical quark
masses. We compare two different methods for computing the decay rate from
lattice data of Euclidean $n$-point functions, namely Chebyshev and
Backus-Gilbert approaches. We further study how much the ground-state meson
dominates the inclusive decay rate and indicate our strategy towards a
computation with a more comprehensive systematic error budget.
|
2305.14092v2
|
2023-05-25
|
Crystallization dynamics of magnetic skyrmions in a frustrated itinerant magnet
|
We investigate the phase ordering kinetics of skyrmion lattice (SkL) in a
metallic magnet. The SkL can be viewed as a superposition of magnetic stripes
whose periods are determined by the quasi-nesting wave vectors of the
underlying Fermi surface. An effective magnetic Hamiltonian that describes the
electron-mediated spin-spin interaction is obtained for a two-dimensional s-d
model with the Rashba spin-orbit coupling. Large-scale Landau-Lifshitz-Gilbert
dynamics simulations based on the effective spin Hamiltonian reveal a two-stage
phase ordering of the SkL phase after a thermal quench. The initial fast
crystallization of skyrmions is followed by a slow relaxation dominated by the
annihilation dynamics of dislocations, which are topological defects of the
constituent magnetic stripe orders. The late-stage phase ordering also exhibits
a dynamical scaling symmetry. We further show that the annihilation of
dislocations follows a power-law time dependence with a logarithmic correction
that depends on magnetic fields. Implications of our results for SkL phases in
magnetic materials are also discussed.
|
2305.16182v1
|
2023-05-31
|
Magnetization dynamics in a three-dimensional interconnected nanowire array
|
Three-dimensional magnetic nanostructures have recently emerged as artificial
magnetic material types with unique properties bearing potential for
applications, including magnonic devices. Interconnected magnetic nanowires are
a sub-category within this class of materials that is attracting particular
interest. We investigate the high-frequency magnetization dynamics in a cubic
array of cylindrical magnetic nanowires through micromagnetic simulations based
on a frequency-domain formulation of the linearized Landau-Lifshitz-Gilbert
equation. The small-angle high-frequency magnetization dynamics excited by an
external oscillatory field displays clear resonances at distinct frequencies.
These resonances are identified as oscillations connected to specific geometric
features and micromagnetic configurations. The geometry- and
configuration-dependence of the nanowire array's absorption spectrum
demonstrates the potential of such magnetic systems for tuneable and
reprogrammable magnonic applications.
|
2306.00174v1
|
2023-06-12
|
Continuum Limit of Spin Dynamics on Hexagonal Lattice
|
Compared to their three-dimensional counterparts, two-dimensional materials
exhibit intriguing electronic and magnetic properties. Notable examples include
twisted graphene's superconducting states and chromium trichloride's meron spin
textures. Understanding nontrivial topological spin textures is crucial for
magnetization dynamics and spintronic technologies. In this study, we analyze
the full model of discrete spin dynamics on a two-dimensional hexagonal lattice
used in experiments with chromium trichloride. We prove its convergence to the
continuum Landau-Lifshitz-Gilbert equation in the weak sense, despite
difficulties arising from the absence of central symmetry when constructing
difference quotient and interpolation operators on hexagonal lattices. To
overcome these challenges, we introduce multi-step difference quotient and
interpolation operators that possess an isometric property as a generalization
of Ladysenskaya's interpolation operator. This result not only establishes a
precise connection between parameters in atomistic models and those in
continuum models but also provides necessary tools for analyzing weak
convergence in other nonlinear problems on hexagonal lattices at microscopic
and macroscopic scales seamlessly.
|
2306.06958v1
|
2023-06-23
|
Molecular Insights into Chemical Reactions at Aqueous Aerosol Interfaces
|
Atmospheric aerosols facilitate reactions between ambient gases and dissolved
species. Here, we review our efforts to interrogate the uptake of these gases
and the mechanisms of their reactions both theoretically and experimentally. We
highlight the fascinating behavior of $\mathrm{N}_2\mathrm{O}_5$ in solutions
ranging from pure water to complex mixtures, chosen because its
aerosol-mediated reactions significantly impact global ozone, hydroxyl, and
methane concentrations. As a hydrophobic, weakly soluble, and highly reactive
species, $\mathrm{N}_2\mathrm{O}_5$ is a sensitive probe of the chemical and
physical properties of aerosol interfaces. We employ contemporary theory to
disentangle the fate of $\mathrm{N}_2\mathrm{O}_5$ as it approaches pure and
salty water, starting with adsorption and ending with hydrolysis to HNO$_3$,
chlorination to $\mathrm{ClNO}_2$, or evaporation. Flow reactor and gas-liquid
scattering experiments probe even greater complexity as added ions, organic
molecules, and surfactants alter interfacial composition and reaction rates.
Together, we reveal a new perspective on multiphase chemistry in the
atmosphere.
|
2306.13811v1
|
2023-07-09
|
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer
|
Neural Style Transfer (NST) is the field of study applying neural techniques
to modify the artistic appearance of a content image to match the style of a
reference style image. Traditionally, NST methods have focused on texture-based
image edits, affecting mostly low level information and keeping most image
structures the same. However, style-based deformation of the content is
desirable for some styles, especially in cases where the style is abstract or
the primary concept of the style is in its deformed rendition of some content.
With the recent introduction of diffusion models, such as Stable Diffusion, we
can access far more powerful image generation techniques, enabling new
possibilities. In our work, we propose using this new class of models to
perform style transfer while enabling deformable style transfer, an elusive
capability in previous models. We show how leveraging the priors of these
models can expose new artistic controls at inference time, and we document our
findings in exploring this new direction for the field of style transfer.
|
2307.04157v2
|
2023-07-11
|
Charge conservation in spin torque oscillators leads to a self-induced torque
|
Spin torque oscillators are conventionally described by the
Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation. However, at the onset of
oscillations, the predictions of the conventional LLGS equation differ
qualitatively from experimental results and thus appear to be incomplete. In
this work we show that taking charge conservation into account leads to a
previously-overlooked self-induced torque, which modifies the LLGS equation. We
show that the self-induced torque originates from the pumping current that a
precessing magnetization drives through a magnetic tunnel junction. To
illustrate the importance of the self-induced torque, we consider an in-plane
magnetized nanopillar, where it gives clear qualitative corrections to the
conventional LLGS description.
|
2307.05105v3
|
2023-07-13
|
Magnon-magnon coupling in synthetic ferrimagnets
|
Magnetic multilayers with interlayer exchange coupling have been widely
studied for both static and dynamic regimes. Their dynamical responses depend
on the exchange coupling strength and magnetic properties of individual layers.
Magnetic resonance spectra in such systems are conveniently discussed in terms
of coupling of acoustic and optical modes. At a certain value of applied
magnetic field, the two modes come close to being degenerate and the spectral
gap indicates the strength of mode hybridisation. In this work, we
theoretically and experimentally study the mode hybridisation of
interlayer-exchange-coupled moments with dissimilar magnetisation and thickness
of two ferromagnetic layers. In agreement with symmetry analysis for
eigenmodes, our low-symmetry multilayers exhibit sizable spectral gaps for all
experimental conditions. The spectra agree well with the predictions from the
Landau-Lifshitz-Gilbert equation at the macrospin limit whose parameters are
independently fixed by static measurements.
|
2307.06888v2
|
2023-07-14
|
Mod $\ell$ gamma factors and a converse theorem for finite general linear groups
|
For $q$ a power of a prime $p$, we study gamma factors of representations of
$GL_n(\mathbb{F}_q)$ over an algebraically closed field $k$ of positive
characteristic $\ell \neq p$. We show that the reduction mod $\ell$ of the
gamma factor defined in characteristic zero fails to satisfy the analogue of
the local converse theorem of Piatetski-Shapiro. To remedy this, we construct
gamma factors valued in arbitrary $\mathbb{Z}[1/p, \zeta_p]$-algebras $A$,
where $\zeta_p$ is a primitive $p$-th root of unity, for Whittaker-type
representations $\rho$ and $\pi$ of $GL_n(\mathbb{F}_q)$ and
$GL_m(\mathbb{F}_q)$ over $A$. We let $P(\pi)$ be the projective envelope of
$\pi$ and let $R(\pi)$ be its endomorphism ring and define new gamma factors
$\widetilde\gamma(\rho \times \pi) = \gamma((\rho\otimes_kR(\pi)) \times
P(\pi))$, which take values in the local Artinian $k$-algebra $R(\pi)$. We
prove a converse theorem for cuspidal representations using the new gamma
factors. When $n=2$ and $m=1$ we construct a different ``new'' gamma factor
$\gamma^{\ell}(\rho,\pi)$, which takes values in $k$ and satisfies a converse
theorem.
|
2307.07593v1
|
2023-07-20
|
Pathwise central limit theorem and moderate deviations via rough paths for SPDEs with multiplicative noise
|
We put forward a general framework for the study of a pathwise central limit
theorem (CLT) and a moderate deviation principle (MDP) for stochastic partial
differential equations perturbed with a small multiplicative linear noise by
means of the theory of rough paths. The CLT can be interpreted as the
convergence to a pathwise derivative of the It\^o-Lyons map. The result follows
by applying a pathwise Malliavin-like calculus for rough paths and from
compactness methods. The convergence in the CLT is quantified by an optimal
speed of convergence. From the exponential equivalence principle and the
knowledge of the speed of convergence, we can derive easily a MDP. In
particular, we do not apply the weak convergence approach usually employed in
this framework. We derive a pathwise CLT and a MDP for the stochastic
Landau-Lifschitz-Gilbert equation in one dimension, for the heat equation and
for a stochastic reaction-diffusion equation. As a further application, we
derive a pathwise convergence to the CLT limit and a corresponding MDP for
equations driven by linear It\^o noise.
|
2307.10965v1
|
2023-07-26
|
Learning sources of variability from high-dimensional observational studies
|
Causal inference studies whether the presence of a variable influences an
observed outcome. As measured by quantities such as the "average treatment
effect," this paradigm is employed across numerous biological fields, from
vaccine and drug development to policy interventions. Unfortunately, the
majority of these methods are often limited to univariate outcomes. Our work
generalizes causal estimands to outcomes with any number of dimensions or any
measurable space, and formulates traditional causal estimands for nominal
variables as causal discrepancy tests. We propose a simple technique for
adjusting universally consistent conditional independence tests and prove that
these tests are universally consistent causal discrepancy tests. Numerical
experiments illustrate that our method, Causal CDcorr, leads to improvements in
both finite sample validity and power when compared to existing strategies. Our
methods are all open source and available at github.com/ebridge2/cdcorr.
|
2307.13868v2
|
2023-07-26
|
An Asynchronous and Low-Power True Random Number Generator using STT-MTJ
|
The emerging Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ)
technology exhibits interesting stochastic behavior combined with small area
and low operation energy. It is, therefore, a promising technology for security
applications, specifically the generation of random numbers. In this paper,
STT-MTJ is used to construct an asynchronous true random number generator
(TRNG) with low power and a high entropy rate. The asynchronous design enables
decoupling of the random number generation from the system clock, allowing it
to be embedded in low-power devices. The proposed TRNG is evaluated by a
numerical simulation, using the Landau-Lifshitz-Gilbert (LLG) equation as the
model of the STT-MTJ devices. Design considerations, attack analysis, and
process variation are discussed and evaluated. We show that our design is
robust to process variation, achieving an entropy generating rate between
99.7Mbps and 127.8Mbps with 6-7.7 pJ per bit for 90% of the instances.
|
2307.14476v1
|
2023-07-31
|
Evidence of Pseudogravitational Distortions of the Fermi Surface Geometry in the Antiferromagnetic Metal FeRh
|
The confluence between high-energy physics and condensed matter has produced
groundbreaking results via unexpected connections between the two traditionally
disparate areas. In this work, we elucidate additional connectivity between
high-energy and condensed matter physics by examining the interplay between
spin-orbit interactions and local symmetry-breaking magnetic order in the
magnetotransport of thin-film magnetic semimetal FeRh. We show that the change
in sign of the normalized longitudinal magnetoresistance observed as a function
of increasing in-plane magnetic field results from changes in the Fermi surface
morphology. We demonstrate that the geometric distortions in the Fermi surface
morphology are more clearly understood via the presence of pseudogravitational
fields in the low-energy theory. The pseudogravitational connection provides
additional insights into the origins of a ubiquitous phenomenon observed in
many common magnetic materials and points to an alternative methodology for
understanding phenomena in locally-ordered materials with strong spin-orbit
interactions.
|
2308.00192v1
|
2023-08-02
|
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
|
Breast cancer is a major cause of cancer death among women, emphasising the
importance of early detection for improved treatment outcomes and quality of
life. Mammography, the primary diagnostic imaging test, poses challenges due to
the high variability and patterns in mammograms. Double reading of mammograms
is recommended in many screening programs to improve diagnostic accuracy but
increases radiologists' workload. Researchers explore Machine Learning models
to support expert decision-making. Stand-alone models have shown comparable or
superior performance to radiologists, but some studies note decreased
sensitivity with multiple datasets, indicating the need for high generalisation
and robustness models. This work devises MammoDG, a novel deep-learning
framework for generalisable and reliable analysis of cross-domain multi-center
mammography data. MammoDG leverages multi-view mammograms and a novel
contrastive mechanism to enhance generalisation capabilities. Extensive
validation demonstrates MammoDG's superiority, highlighting the critical
importance of domain generalisation for trustworthy mammography analysis in
imaging protocol variations.
|
2308.01057v1
|
2023-08-02
|
Sphaleron rate of $N_f=2+1$ QCD
|
We compute the sphaleron rate of $N_f=2+1$ QCD at the physical point for a
range of temperatures $200$ MeV $\lesssim T \lesssim 600$ MeV. We adopt a
strategy recently applied in the quenched case, based on the extraction of the
rate via a modified version of the Backus-Gilbert method from
finite-lattice-spacing and finite-smoothing-radius Euclidean topological charge
density correlators. The physical sphaleron rate is finally computed by
performing a continuum limit at fixed physical smoothing radius, followed by a
zero-smoothing extrapolation. Dynamical fermions were discretized using the
staggered formulation, which is known to yield large lattice artifacts for the
topological susceptibility. However, we find them to be rather mild for the
sphaleron rate.
|
2308.01287v3
|
2023-07-07
|
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI
|
As AI technology advances rapidly, concerns over the risks of bigness in
digital markets are also growing. The EU's Digital Markets Act (DMA) aims to
address these risks. Still, the current framework may not adequately cover
generative AI systems that could become gateways for AI-based services. This
paper argues for integrating certain AI software as core platform services and
classifying certain developers as gatekeepers under the DMA. We also propose an
assessment of gatekeeper obligations to ensure they cover generative AI
services. As the EU considers generative AI-specific rules and possible DMA
amendments, this paper provides insights towards diversity and openness in
generative AI services.
|
2308.02033v1
|
2023-08-04
|
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies
|
Restrictive rules for data sharing in many industries have led to the
development of federated learning. Federated learning is a machine-learning
technique that allows distributed clients to train models collaboratively
without the need to share their respective training data with others. In this
paper, we first explore the technical foundations of federated learning and its
organizational opportunities. Second, we present a conceptual framework for the
adoption of federated learning, mapping four types of organizations by their
artificial intelligence capabilities and limits to data sharing. We then
discuss why exemplary organizations in different contexts - including public
authorities, financial service providers, manufacturing companies, as well as
research and development consortia - might consider different approaches to
federated learning. To conclude, we argue that federated learning presents
organizational challenges with ample interdisciplinary opportunities for
information systems researchers.
|
2308.02219v2
|
2023-08-04
|
Algorithm for evaluating distance-based entanglement measures
|
Quantifying entanglement in quantum systems is an important yet challenging
task due to its NP-hard nature. In this work, we propose an efficient algorithm
for evaluating distance-based entanglement measures. Our approach builds on
Gilbert's algorithm for convex optimization, providing a reliable upper bound
on the entanglement of a given arbitrary state. We demonstrate the
effectiveness of our algorithm by applying it to various examples, such as
calculating the squared Bures metric of entanglement as well as the relative
entropy of entanglement for GHZ states, $W$ states, Horodecki states, and
chessboard states. These results demonstrate that our algorithm is a versatile
and accurate tool that can quickly provide reliable upper bounds for
entanglement measures.
|
2308.02326v1
|
2023-08-07
|
Robust Ordinal Regression for Subsets Comparisons with Interactions
|
This paper is dedicated to a robust ordinal method for learning the
preferences of a decision maker between subsets. The decision model, derived
from Fishburn and LaValle (1996) and whose parameters we learn, is general
enough to be compatible with any strict weak order on subsets, thanks to the
consideration of possible interactions between elements. Moreover, we accept
not to predict some preferences if the available preference data are not
compatible with a reliable prediction. A predicted preference is considered
reliable if all the simplest models (Occam's razor) explaining the preference
data agree on it. Following the robust ordinal regression methodology, our
predictions are based on an uncertainty set encompassing the possible values of
the model parameters. We define a robust ordinal dominance relation between
subsets and we design a procedure to determine whether this dominance relation
holds. Numerical tests are provided on synthetic and real-world data to
evaluate the richness and reliability of the preference predictions made.
|
2308.03376v1
|
2023-08-07
|
Strong Byzantine Agreement with Adaptive Word Complexity
|
The strong Byzantine agreement (SBA) problem is defined among n processes,
out of which t < n can be faulty and behave arbitrarily. SBA allows correct
(non-faulty) processes to agree on a common value. Moreover, if all correct
processes have proposed the same value, only that value can be agreed upon. It
has been known for a long time that any solution to the SBA problem incurs
quadratic worst-case word complexity; additionally, the bound was known to be
tight. However, no existing protocol achieves adaptive word complexity, where
the number of exchanged words depends on the actual number of faults, and not
on the upper bound. Therefore, it is still unknown whether SBA with adaptive
word complexity exists. This paper answers the question in the affirmative.
Namely, we introduce STRONG, a synchronous protocol that solves SBA among n =
(2 + Omega(1))t + 1 processes and achieves adaptive word complexity. We show
that the fundamental challenge of adaptive SBA lies in efficiently solving
certification, the problem of obtaining a constant-sized, locally-verifiable
proof that a value can safely be decided.
|
2308.03524v1
|
2023-08-24
|
Methods for transverse and longitudinal spin-photon coupling in silicon quantum dots with intrinsic spin-orbit effect
|
In a full-scale quantum computer with a fault-tolerant architecture, having
scalable, long-range interaction between qubits is expected to be a highly
valuable resource. One promising method of achieving this is through the
light-matter interaction between spins in semiconductors and photons in
superconducting cavities. This paper examines the theory of both transverse and
longitudinal spin-photon coupling and their applications in the silicon
metal-oxide-semiconductor (SiMOS) platform. We propose a method of coupling
which uses the intrinsic spin-orbit interaction arising from orbital
degeneracies in SiMOS qubits. Using theoretical analysis and experimental data,
we show that the strong coupling regime is achievable in the transverse scheme.
We also evaluate the feasibility of a longitudinal coupling driven by an AC
modulation on the qubit. These coupling methods eschew the requirement for an
external micromagnet, enhancing prospects for scalability and integration into
a large-scale quantum computer.
|
2308.12626v1
|
2023-08-24
|
Object level footprint uncertainty quantification in infrastructure based sensing
|
We examine the problem of estimating footprint uncertainty of objects imaged
using the infrastructure based camera sensing. A closed form relationship is
established between the ground coordinates and the sources of the camera
errors. Using the error propagation equation, the covariance of a given ground
coordinate can be measured as a function of the camera errors. The uncertainty
of the footprint of the bounding box can then be given as the function of all
the extreme points of the object footprint. In order to calculate the
uncertainty of a ground point, the typical error sizes of the error sources are
required. We present a method of estimating the typical error sizes from an
experiment using a static, high-precision LiDAR as the ground truth. Finally,
we present a simulated case study of uncertainty quantification from
infrastructure based camera in CARLA to provide a sense of how the uncertainty
changes across a left turn maneuver.
|
2308.12846v1
|
2023-08-28
|
Data fusion using weakly aligned sources
|
We introduce a new data fusion method that utilizes multiple data sources to
estimate a smooth, finite-dimensional parameter. Most existing methods only
make use of fully aligned data sources that share common conditional
distributions of one or more variables of interest. However, in many settings,
the scarcity of fully aligned sources can make existing methods require unduly
large sample sizes to be useful. Our approach enables the incorporation of
weakly aligned data sources that are not perfectly aligned, provided their
degree of misalignment can be characterized by a prespecified density ratio
model. We describe gains in efficiency and provide a general means to construct
estimators achieving these gains. We illustrate our results by fusing data from
two harmonized HIV monoclonal antibody prevention efficacy trials to study how
a neutralizing antibody biomarker associates with HIV genotype.
|
2308.14836v1
|
2023-08-31
|
Bi-level iterative regularization for inverse problems in nonlinear PDEs
|
We investigate the ill-posed inverse problem of recovering unknown spatially
dependent parameters in nonlinear evolution PDEs. We propose a bi-level
Landweber scheme, where the upper-level parameter reconstruction embeds a
lower-level state approximation. This can be seen as combining the classical
reduced setting and the newer all-at-once setting, allowing us to,
respectively, utilize well-posedness of the parameter-to-state map, and to
bypass having to solve nonlinear PDEs exactly. Using this, we derive stopping
rules for lower- and upper-level iterations and convergence of the bi-level
method. We discuss application to parameter identification for the
Landau-Lifshitz-Gilbert equation in magnetic particle imaging.
|
2308.16617v2
|
2023-09-03
|
On Galois self-orthogonal algebraic geometry codes
|
Galois self-orthogonal (SO) codes are generalizations of Euclidean and
Hermitian SO codes. Algebraic geometry (AG) codes are the first known class of
linear codes exceeding the Gilbert-Varshamov bound. Both of them have attracted
much attention for their rich algebraic structures and wide applications in
these years. In this paper, we consider them together and study Galois SO AG
codes. A criterion for an AG code being Galois SO is presented. Based on this
criterion, we construct several new classes of maximum distance separable (MDS)
Galois SO AG codes from projective lines and several new classes of Galois SO
AG codes from projective elliptic curves, hyper-elliptic curves and hermitian
curves. In addition, we give an embedding method that allows us to obtain more
MDS Galois SO codes from known MDS Galois SO AG codes.
|
2309.01051v2
|
2023-09-17
|
Unleashing Quantum Simulation Advantages: Hamiltonian Subspace Encoding for Resource Efficient Quantum Simulations
|
Number-conserved subspace encoding for fermionic Hamiltonians, which
exponentially reduces qubit cost, is necessary for quantum advantages in
variational quantum eigensolver (VQE). However, optimizing the trade-off
between qubit compression and increased measurement cost poses a challenge. By
employing the Gilbert-Varshamov bound on linear code, we optimize qubit scaling
$\mathcal{O}(N\log_2M)$ and measurement cost $\mathcal{O}(M^4)$ for $M$ modes
$N$ electrons chemistry problems. The compression is implemented with the
Randomized Linear Encoding (RLE) algorithm on VQE for $\text{H}_2$ and LiH in
the 6-31G* and STO-3G/6-31G* basis respectively. The resulting subspace circuit
expressivity and trainability are enhanced with less circuit depth and higher
noise tolerance.
|
2309.09370v1
|
2023-09-20
|
Dimensions of splines of degree two
|
Splines are defined as piecewise polynomials on the faces of a polyhedral
complex that agree on the intersections of two faces. Splines are used in
approximation theory and numerical analysis, with applications in data
interpolation, to create smooth curves in computer graphics and to find
numerical solutions to partial differential equations. Gilbert, Tymoczko, and
Viel generalized the classical splines combinatorially and algebraically: a
generalized spline is a vertex labeling of a graph $G$ by elements of the ring
so that the difference between the labels of any two adjacent vertices lies in
the ideal generated by the corresponding edge label. We study the generalized
splines on the planar graphs whose edges are labeled by two-variable
polynomials of the form $(ax+by+c)^2$ and whose vertices are labeled by
polynomials of degree at most two. In this paper we address the upper-bound
conjecture for the dimension of degree-2 splines of smoothness 1 when the edge
labels are generic. The dimension is expressed in terms of the rank of the
extended cycle basis matrix. We also provide a combinatorial algorithm on
graphs to compute the rank.
|
2309.11650v1
|
2023-09-25
|
DECORAIT -- DECentralized Opt-in/out Registry for AI Training
|
We present DECORAIT; a decentralized registry through which content creators
may assert their right to opt in or out of AI training as well as receive
reward for their contributions. Generative AI (GenAI) enables images to be
synthesized using AI models trained on vast amounts of data scraped from public
sources. Model and content creators who may wish to share their work openly
without sanctioning its use for training are thus presented with a data
governance challenge. Further, establishing the provenance of GenAI training
data is important to creatives to ensure fair recognition and reward for their
such use. We report a prototype of DECORAIT, which explores hierarchical
clustering and a combination of on/off-chain storage to create a scalable
decentralized registry to trace the provenance of GenAI training data in order
to determine training consent and reward creatives who contribute that data.
DECORAIT combines distributed ledger technology (DLT) with visual
fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance
and Authenticity) standard to create a secure, open registry through which
creatives may express consent and data ownership for GenAI.
|
2309.14400v1
|
2023-10-05
|
Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization
|
Temporal Action Localization (TAL) aims to identify actions' start, end, and
class labels in untrimmed videos. While recent advancements using transformer
networks and Feature Pyramid Networks (FPN) have enhanced visual feature
recognition in TAL tasks, less progress has been made in the integration of
audio features into such frameworks. This paper introduces the Multi-Resolution
Audio-Visual Feature Fusion (MRAV-FF), an innovative method to merge
audio-visual data across different temporal resolutions. Central to our
approach is a hierarchical gated cross-attention mechanism, which discerningly
weighs the importance of audio information at diverse temporal scales. Such a
technique not only refines the precision of regression boundaries but also
bolsters classification confidence. Importantly, MRAV-FF is versatile, making
it compatible with existing FPN TAL architectures and offering a significant
enhancement in performance when audio data is available.
|
2310.03456v1
|
2023-10-20
|
The History and Risks of Reinforcement Learning and Human Feedback
|
Reinforcement learning from human feedback (RLHF) has emerged as a powerful
technique to make large language models (LLMs) easier to use and more
effective. A core piece of the RLHF process is the training and utilization of
a model of human preferences that acts as a reward function for optimization.
This approach, which operates at the intersection of many stakeholders and
academic disciplines, remains poorly understood. RLHF reward models are often
cited as being central to achieving performance, yet very few descriptors of
capabilities, evaluations, training methods, or open-source models exist. Given
this lack of information, further study and transparency is needed for learned
RLHF reward models. In this paper, we illustrate the complex history of
optimizing preferences, and articulate lines of inquiry to understand the
sociotechnical context of reward models. In particular, we highlight the
ontological differences between costs, rewards, and preferences at stake in
RLHF's foundations, related methodological tensions, and possible research
directions to improve general understanding of how reward models function.
|
2310.13595v2
|
2023-11-24
|
The quenched glueball spectrum from smeared spectral densities
|
The standard approach to compute the glueball spectrum on the lattice relies
on the evaluation of effective masses from two-point correlation functions of
operators with the quantum numbers of the desired state. In this work, we
propose an alternative procedure, based on the numerical computation of smeared
spectral densities. Even though the extraction of the latter from lattice
correlators is a notoriously ill-posed inverse problem, we show that a recently
developed numerical method, based on the Backus-Gilbert regularization,
provides a robust way to evaluate a smeared version of the spectral densities.
Fitting the latter to a combination of Gaussians, we extract the masses of the
lightest glueball and of its first excitation in the spectrum of the theory.
While the preliminary results presented in this contribution are restricted to
simulations at finite lattice spacing and finite volume, and for the purely
gluonic sector of QCD, they represent the first step in a systematic
investigation of glueballs using spectral-reconstruction methods.
|
2311.14806v1
|
2023-11-28
|
Data-efficient operator learning for solving high Mach number fluid flow problems
|
We consider the problem of using SciML to predict solutions of high Mach
fluid flows over irregular geometries. In this setting, data is limited, and so
it is desirable for models to perform well in the low-data setting. We show
that Neural Basis Functions (NBF), which learns a basis of behavior modes from
the data and then uses this basis to make predictions, is more effective than a
basis-unaware baseline model. In addition, we identify continuing challenges in
the space of predicting solutions for this type of problem.
|
2311.16860v2
|
2023-11-30
|
ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs
|
In the field of media production, video editing techniques play a pivotal
role. Recent approaches have had great success at performing novel view image
synthesis of static scenes. But adding temporal information adds an extra layer
of complexity. Previous models have focused on implicitly representing static
and dynamic scenes using NeRF. These models achieve impressive results but are
costly at training and inference time. They overfit an MLP to describe the
scene implicitly as a function of position. This paper proposes ZeST-NeRF, a
new approach that can produce temporal NeRFs for new scenes without retraining.
We can accurately reconstruct novel views using multi-view synthesis techniques
and scene flow-field estimation, trained only with unrelated scenes. We
demonstrate how existing state-of-the-art approaches from a range of fields
cannot adequately solve this new task and demonstrate the efficacy of our
solution. The resulting network improves quantitatively by 15% and produces
significantly better visual results.
|
2311.18491v1
|
2023-12-05
|
ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
|
This paper introduces ViscoNet, a novel method that enhances text-to-image
human generation models with visual prompting. Unlike existing methods that
rely on lengthy text descriptions to control the image structure, ViscoNet
allows users to specify the visual appearance of the target object with a
reference image. ViscoNet disentangles the object's appearance from the image
background and injects it into a pre-trained latent diffusion model (LDM) model
via a ControlNet branch. This way, ViscoNet mitigates the style mode collapse
problem and enables precise and flexible visual control. We demonstrate the
effectiveness of ViscoNet on human image generation, where it can manipulate
visual attributes and artistic styles with text and image prompts. We also show
that ViscoNet can learn visual conditioning from small and specific object
domains while preserving the generative power of the LDM backbone.
|
2312.03154v1
|
2023-12-08
|
Convergent finite element methods for antiferromagnetic and ferrimagnetic materials
|
We consider the numerical approximation of a continuum model of
antiferromagnetic and ferrimagnetic materials. The state of the material is
described in terms of two unit-length vector fields, which can be interpreted
as the magnetizations averaging the spins of two sublattices. For the static
setting, which requires the solution of a constrained energy minimization
problem, we introduce a discretization based on first-order finite elements and
prove its $\Gamma$-convergence. Then, we propose and analyze two iterative
algorithms for the computation of low-energy stationary points. The algorithms
are obtained from (semi-)implicit time discretizations of gradient flows of the
energy. Finally, we extend the algorithms to the dynamic setting, which
consists of a nonlinear system of two Landau-Lifshitz-Gilbert equations solved
by the two fields, and we prove unconditional stability and convergence of the
finite element approximations toward a weak solution of the problem. Numerical
experiments assess the performance of the algorithms and demonstrate their
applicability for the simulation of physical processes involving
antiferromagnetic and ferrimagnetic materials.
|
2312.04939v1
|
2023-12-18
|
Modelling the 3D spatiotemporal organisation of chromatin replication
|
We propose a polymer model for the dynamics of chromatin replication in three
dimensional space. Our simulations indicate that both immobile and tracking
replisomes may self-assemble during the process, reconciling previous
apparently discordant experimental evidence in favour of either scenario. Which
of the two morphologies appears in our model depends on the balance between
non-specific and origin-targeting interactions between chromatin and firing
factors -- polymerases and other components of the replisome. Non-specific
interactions are also necessary to yield clustering of factors and replication
forks, creating structures akin to the replication foci observed in mammalian
cells in vivo. We suggest that cluster formation provides an underappreciated
but robust pathway to avoid stalled or faulty forks, which would otherwise
diminish the efficiency of the replication process. Additionally, our
simulations allow us to predict different modes of cluster growth during
S-phase, which could be tested experimentally, and they show that the three
dimensional chromatin context is important to understand replication patterns
in fission yeast.
|
2312.11275v1
|
2024-01-09
|
Revealing dark exciton signatures in polariton spectra of 2D materials
|
Dark excitons in transition metal dichalcogenides (TMD) have been so far
neglected in the context of polariton physics due to their lack of oscillator
strength. However, in tungsten-based TMDs, dark excitons are known to be the
energetically lowest states and could thus provide important scattering
partners for polaritons. In this joint theory-experiment work, we investigate
the impact of the full exciton energy landscape on polariton absorption and
reflectance. By changing the cavity detuning, we vary the polariton energy
relative to the unaffected dark excitons in such a way that we open or close
specific phonon-driven scattering channels. We demonstrate both in theory and
experiment that this controlled switching of scattering channels manifests in
characteristic sharp changes in optical spectra of polaritons. These spectral
features can be exploited to extract the position of dark excitons. Our work
suggests new possibilities for exploiting polaritons for fingerprinting
nanomaterials via their unique exciton landscape.
|
2401.04588v1
|
2024-01-10
|
Electrical Non-Hermitian Control of Topological Magnon Spin Transport
|
Magnonic topological phases realize chiral edge spin waves that are protected
against backscattering, potentially enabling highly efficient spin transport.
Here we show that the spin transport through these magnonic chiral edge states
can be electrically manipulated by non-Hermitian control. We consider the
paradigmatic magnon Haldane model and show that it is transformed into an
effective non-Hermitian magnon Chern insulator by including a
sublattice-dependent spin-orbit torque. In linear spin-wave theory, this
electrically induced torque causes a lasing of the chiral edge magnons along
certain edge directions, leading to an enhancement of the spin-wave amplitude.
This prediction is confirmed by numerical simulations based on the
Landau-Lifshitz-Gilbert equation. For a spin-wave transport setup, in which
magnons are excited by a microwave field and detected with a normal metal
conductor, we find that the magnon amplification is remarkably robust against
disorder, establishing non-Hermitian control as a promising avenue for
topological magnonics.
|
2401.04967v2
|
2024-01-24
|
The Dynamics of (Not) Unfollowing Misinformation Spreaders
|
Many studies explore how people 'come into' misinformation exposure. But much
less is known about how people 'come out of' misinformation exposure. Do people
organically sever ties to misinformation spreaders? And what predicts doing so?
Over six months, we tracked the frequency and predictors of ~900K followers
unfollowing ~5K health misinformation spreaders on Twitter. We found that
misinformation ties are persistent. Monthly unfollowing rates are just 0.52%.
In other words, 99.5% of misinformation ties persist each month. Users are also
31% more likely to unfollow non-misinformation spreaders than they are to
unfollow misinformation spreaders. Although generally infrequent, the factors
most associated with unfollowing misinformation spreaders are (1) redundancy
and (2) ideology. First, users initially following many spreaders, or who
follow spreaders that tweet often, are most likely to unfollow later. Second,
liberals are more likely to unfollow than conservatives. Overall, we observe a
strong persistence of misinformation ties. The fact that users rarely unfollow
misinformation spreaders suggests a need for external nudges and the importance
of preventing exposure from arising in the first place.
|
2401.13480v2
|
2024-01-29
|
FPGA Technology Mapping Using Sketch-Guided Program Synthesis
|
FPGA technology mapping is the process of implementing a hardware design
expressed in high-level HDL (hardware design language) code using the
low-level, architecture-specific primitives of the target FPGA. As FPGAs become
increasingly heterogeneous, achieving high performance requires hardware
synthesis tools that better support mapping to complex, highly configurable
primitives like digital signal processors (DSPs). Current tools support DSP
mapping via handwritten special-case mapping rules, which are laborious to
write, error-prone, and often overlook mapping opportunities. We introduce
Lakeroad, a principled approach to technology mapping via sketch-guided program
synthesis. Lakeroad leverages two techniques -- architecture-independent sketch
templates and semantics extraction from HDL -- to provide extensible technology
mapping with stronger correctness guarantees and higher coverage of mapping
opportunities than state-of-the-art tools. Across representative
microbenchmarks, Lakeroad produces 2--3.5$\times$ the number of optimal
mappings compared to proprietary state-of-the-art tools and 6--44$\times$ the
number of optimal mappings compared to popular open-source tools, while also
providing correctness guarantees not given by any other tool.
|
2401.16526v1
|
2024-02-05
|
Cybersickness Detection through Head Movement Patterns: A Promising Approach
|
Despite the widespread adoption of Virtual Reality (VR) technology,
cybersickness remains a barrier for some users. This research investigates head
movement patterns as a novel physiological marker for cybersickness detection.
Unlike traditional markers, head movements provide a continuous, non-invasive
measure that can be easily captured through the sensors embedded in all
commercial VR headsets. We used a publicly available dataset from a VR
experiment involving 75 participants and analyzed head movements across six
axes. An extensive feature extraction process was then performed on the head
movement dataset and its derivatives, including velocity, acceleration, and
jerk. Three categories of features were extracted, encompassing statistical,
temporal, and spectral features. Subsequently, we employed the Recursive
Feature Elimination method to select the most important and effective features.
In a series of experiments, we trained a variety of machine learning
algorithms. The results demonstrate a 76% accuracy and 83% precision in
predicting cybersickness in the subjects based on the head movements. This
study contribution to the cybersickness literature lies in offering a
preliminary analysis of a new source of data and providing insight into the
relationship of head movements and cybersickness.
|
2402.02725v2
|
2024-02-05
|
Bifurcation to complex dynamics in largely modulated voltage-controlled parametric oscillator
|
An experimental demonstration of a parametric oscillation of a magnetization
in a ferromagnet was performed recently by applying a microwave voltage,
indicating the potential to be applied in a switching method in non-volatile
memories. In the previous works, the modulation of a perpendicular magnetic
anisotropy field produced by the microwave voltage was small compared with an
external magnetic field pointing in an in-plane direction. A recent trend is,
however, opposite, where an efficiency of the voltage controlled magnetic
anisotropy (VCMA) effect is increased significantly by material research and
thus, the modulated magnetic anisotropy field can be larger than the external
magnetic field. Here, we solved the Landau-Lifshitz-Gilbert equation
numerically and investigated the magnetization dynamics driven under a wide
range of the microwave VCMA effect. We evaluated bifurcation diagrams, which
summarize local maxima of the magnetization dynamics. For low modulation
amplitudes, the local maximum is a single point because the dynamics is the
periodic parametric oscillation. The bifurcation diagrams show distributions of
the local maxima when the microwave magnetic anisotropy field becomes larger
than the external magnetic field. The appearance of this broadened distribution
indicates complex dynamics such as chaotic and transient-chaotic behaviors,
which were confirmed from an analysis of temporal dynamics.
|
2402.02742v1
|
2024-02-12
|
Gravitational Lensing of Galaxy Clustering
|
We investigate lensing reconstruction using the clustered galaxy distribution
as a source field, using both the traditional cosmic microwave background
quadratic estimator and a shear-only estimator. We calculate the expected
signal-to-noise ratio of the cross power spectrum of such reconstructions with
cosmic shear measurements for an LSST-like galaxy survey. Modeling the galaxy
field as a Gaussian random field, we find that there is substantial clustering
signal in the source field at angular scales substantially smaller than those
typically used by CMB reconstructions. The expected signal-to-noise for
cross-correlations in LSST from cosmic shear is $\sim$60 in the presence of
shape noise, while cross correlating with a sample-variance limited mass map
would have signal-to-noise in the hundreds. This type of cross-correlation
could be used as a way to identify systematic errors in lensing studies and is
just one example of many possible higher order correlations in galaxy surveys
that may contain substantial cosmological information.
|
2402.07988v1
|
2024-03-05
|
Spintronic Implementation of UNet for Image Segmentation
|
Image segmentation plays a crucial role in computer vision applications like
self-driving cars, satellite imagery analysis, and medical diagnosis.
Implementing these complex deep neural networks on conventional hardware is
highly inefficient. In this work, we propose hardware implementation of UNet
for segmentation tasks, using spintronic devices. Our approach involves
designing hardware for convolution, deconvolution, ReLU, and max pooling layers
of the UNet architecture. We demonstrate the synaptic behavior of the domain
wall MTJ, and design convolution and deconvolution layers using the domain
wall-based crossbar array. We utilize the orthogonal current injected MTJ with
its continuous resistance change and showcase the ReLU and max pooling
functions. We employ a hybrid simulation setup by coupling micromagnetic
simulation, non-equilibrium Green's function,
Landau-Lifshitz-Gilbert-Slonczewski equations, and circuit simulation with
Python programming to incorporate the diverse physics of spin-transport,
magnetization dynamics, and CMOS elements in our proposed designs. We evaluate
our UNet design on the CamVid dataset and achieve segmentation accuracies that
are comparable to software implementation. During training, our design consumes
43.59pJ of energy for synaptic weight updates.
|
2403.02863v1
|
2024-03-06
|
A Survey on Adversarial Contention Resolution
|
Contention resolution addresses the challenge of coordinating access by
multiple processes to a shared resource such as memory, disk storage, or a
communication channel. Originally spurred by challenges in database systems and
bus networks, contention resolution has endured as an important abstraction for
resource sharing, despite decades of technological change. Here, we survey the
literature on resolving worst-case contention, where the number of processes
and the time at which each process may start seeking access to the resource is
dictated by an adversary. We highlight the evolution of contention resolution,
where new concerns -- such as security, quality of service, and energy
efficiency -- are motivated by modern systems. These efforts have yielded
insights into the limits of randomized and deterministic approaches, as well as
the impact of different model assumptions such as global clock synchronization,
knowledge of the number of processors, feedback from access attempts, and
attacks on the availability of the shared resource.
|
2403.03876v1
|
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