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arxiv_dataset-178002301.08936 | Lead-free, luminescent perovskite nanocrystals obtained through ambient
condition synthesis
cond-mat.mtrl-sci
Heterovalent substitution of toxic lead is an increasingly popular design
strategy to obtain environmentally sustainable variants of the exciting
material class of halide perovskites. Perovskite nanocrystals (NCs) obtained
through solution-based methods exhibit exceedingly high optical quality.
Unfortunately, most of these synthesis routes still require reaction under
inert gas and at very high temperatures. Herein we present a novel synthesis
routine for lead-free double perovskite NCs. We combine hot injection and
ligand-assisted reprecipitation (LARP) methods to achieve a low-temperature and
ambient atmosphere-based synthesis for manganese-doped Cs_{2}NaBiCl_{6} NCs. Mn
incorporation is critical for the otherwise non-emissive material, with a 9:1
Bi:Mn precursor ratio maximizing the bright orange photoluminescence (PL) and
quantum yield (QY). Higher temperatures slightly increased the material's
performance, yet NCs synthesized at room temperature were still emissive,
highlighting the versatility of the synthetic approach. Furthermore, the NCs
show excellent long-term stability in ambient conditions, facilitating
additional investigations and energy-related applications.
| arxiv topic:cond-mat.mtrl-sci |
arxiv_dataset-178012301.09036 | Number of Matchings of Low Order in (4,6)-Fullerene Graphs
math.CO
We obtain the formulae for the numbers of 4-matchings and 5-matchings in
terms of the number of hexagonal faces in (4, 6)-fullerene graphs by studying
structural classification of 6-cycles and some local structural properties,
which correct the corresponding wrong results published. Furthermore, we obtain
a formula for the number of 6-matchings in tubular (4, 6)-fullerenes in terms
of the number of hexagonal faces, and a formula for the number of 6-matchings
in the other (4,6)-fullerenes in terms of the numbers of hexagonal faces and
dual-squares.
| arxiv topic:math.CO |
arxiv_dataset-178022301.09136 | Reconciling airborne disease transmission concerns with energy saving
requirements: the potential of UV-C pathogen deactivation and air
distribution optimization
physics.soc-ph
The COVID-19 pandemic caused a paradigm shift in our way of using heating,
ventilation, and air-conditioning (HVAC) systems in buildings. In the early
stages of the pandemic, it was indeed advised to reduce the reuse and thus the
recirculation of indoor air to minimize the risk of contamination through
inhalation of virus-laden aerosol particles emitted by humans when coughing,
sneezing, speaking or breathing. However, such recommendations are not
compatible with energy saving requirements stemming from climate change and
energy price increase concerns, especially in winter and summer when the
fraction of outdoor air supplied to the building needs to be significantly
heated or cooled down. In this experimental study, we aim at providing low-cost
and low-energy solutions to modify the ventilation strategies currently used in
many buildings to reduce the risk of respiratory disease transmission. We find
that ultraviolet germicidal irradiation (UVGI) modules added to the HVAC system
are very efficient at inactivating pathogens present in aerosols, leading to
good indoor air quality even with significant indoor air recirculation.
Moreover, we show that an optimal placement of the air exhaust vents relative
to air supply vents can improve the ventilation efficiency, with significant
consequences in terms of energy savings.
| arxiv topic:physics.soc-ph |
arxiv_dataset-178032301.09236 | On the (Im)plausibility of Public-Key Quantum Money from
Collision-Resistant Hash Functions
quant-ph cs.CC cs.CR
Public-key quantum money is a cryptographic proposal for using highly
entangled quantum states as currency that is publicly verifiable yet resistant
to counterfeiting due to the laws of physics. Despite significant interest,
constructing provably-secure public-key quantum money schemes based on standard
cryptographic assumptions has remained an elusive goal. Even proposing
plausibly-secure candidate schemes has been a challenge.
These difficulties call for a deeper and systematic study of the structure of
public-key quantum money schemes and the assumptions they can be based on.
Motivated by this, we present the first black-box separation of quantum money
and cryptographic primitives. Specifically, we show that collision-resistant
hash functions cannot be used as a black-box to construct public-key quantum
money schemes where the banknote verification makes classical queries to the
hash function. Our result involves a novel combination of state synthesis
techniques from quantum complexity theory and simulation techniques, including
Zhandry's compressed oracle technique.
| arxiv topic:quant-ph cs.CC cs.CR |
arxiv_dataset-178042301.09336 | Uphill inflation
astro-ph.CO gr-qc hep-th
Primordial black holes (PBH) may form from large cosmological perturbations,
produced during inflation when the inflaton's velocity is sufficiently slowed
down. This usually requires very flat regions in the inflationary potential. In
this paper we investigate another possibility, namely that the inflaton climbs
up its potential. When it turns back, its velocity crosses zero, which triggers
a short phase of ``uphill inflation'' during which cosmological perturbations
grow at a very fast rate. This naturally occurs in double-well potentials if
the width of the well is close to the Planck scale. We include the effect of
quantum diffusion in this scenario, which plays a crucial role, by means of the
stochastic-$\delta N$ formalism. We find that ultra-light black holes are
produced with very high abundances, which do not depend on the energy scale at
which uphill inflation occurs, and which suffer from substantially less fine
tuning than in alternative PBH-production models. They are such that PBHs later
drive a phase of PBH domination.
| arxiv topic:astro-ph.CO gr-qc hep-th |
arxiv_dataset-178052301.09436 | Light Curve and Hardness Tests for Millilensing in GRB 950830, GRB
090717A, and GRB 200716C
astro-ph.HE
To show an internal signature of gravitational lensing, two different
temporal sections of a single gamma-ray burst (GRB) must be statistically
similar. Here two straightforward gravitational lensing tests are defined and
applied: a light-curve similarity test and a hardness similarity test.
Gravitational millilensing has been claimed to be detected within several
individual GRBs that contain two emission episodes separated by a time delay.
However, our analyses indicate that none of those claims clearly satisfy both
tests. The hardness similarity test performed on GRB 950830 and GRB 090717A
found that the ratio between the second and the first emission episodes in each
energy channel differed from the same ratio averaged over all detected energy
channels at above the 90 percent confidence level. Also, a light curve
similarity test performed on GRB 950830, GRB 090717A, and GRB 200716C
separately, found that it is unlikely that the two emission episodes in each
GRB were drawn from a single parent emission episode for that GRB, with
differences at the 3.0 sigma, 8.3 sigma, and 8.3 sigma confidence levels
respectively.
| arxiv topic:astro-ph.HE |
arxiv_dataset-178062301.09536 | Twist-4 T-even proton TMDs in the light-front quark-diquark model
hep-ph
We have dealt with the twist-4 T-even transverse momentum-dependent parton
distributions (TMDs) for the case of proton in the light-front quark-diquark
model (LFQDM). By decoding the unintegrated quark-quark correlator for the
semi-inclusive deep inelastic scattering (SIDIS), we have specifically obtained
the overlap form for the unpolarized \bigg($f_{3}^{\nu}(x, {\bf
p_\perp^2})$\bigg), longitudinally polarized \bigg($ g_{3L}^{\nu}(x, {\bf
p_\perp^2}),~h_{3L}^{\perp\nu}(x, {\bf p_\perp^2})$\bigg) and transversely
polarized \bigg( ${g}^{\nu }_{3T}(x, {\bf p_\perp^2}),~{h}^{\nu }_{3T}(x, {\bf
p_\perp^2})$ and ${h}^{\nu\perp}_{3T}(x, {\bf p_\perp^2})$\bigg) proton TMDs.
We have provided the explicit expressions for both the cases of the diquark
being a scalar or a vector. Average transverse momenta and the average square
transverse momenta for the TMDs have been calculated and the results have been
tabulated with corresponding leading twist TMDs. In addition, the value of
average transverse momentum and average square transverse momentum for TMD
${f}^{\nu }_3(x, {\bf p_\perp^2})$ has been compared with the available
light-front constituent quark model (LFCQM) results. From TMDs, we have also
obtained and discussed the transverse momentum-dependent parton distribution
functions (TMDPDFs). The model relations of the twist-4 T-even TMDs with the
available leading twist T-even TMDs have also been obtained.
| arxiv topic:hep-ph |
arxiv_dataset-178072301.09636 | Scalable Spin Squeezing from Finite Temperature Easy-plane Magnetism
quant-ph cond-mat.quant-gas cond-mat.str-el physics.atom-ph
Spin squeezing is a form of entanglement that reshapes the quantum projection
noise to improve measurement precision. Here, we provide numerical and analytic
evidence for the following conjecture: any Hamiltonian exhibiting finite
temperature, easy-plane ferromagnetism can be used to generate scalable spin
squeezing, thereby enabling quantum-enhanced sensing. Our conjecture is guided
by a connection between the quantum Fisher information of pure states and the
spontaneous breaking of a continuous symmetry. We demonstrate that
spin-squeezing exhibits a phase diagram with a sharp transition between
scalable squeezing and non-squeezing. This transition coincides with the
equilibrium phase boundary for XY order at a finite temperature. In the
scalable squeezing phase, we predict a sensitivity scaling that lies in between
the standard quantum limit and the scaling achieved in all-to-all coupled
one-axis twisting models. A corollary of our conjecture is that short-ranged
versions of two-axis twisting cannot yield scalable metrological gain. Our
results provide insights into the landscape of Hamiltonians that can be used to
generate metrologically useful quantum states.
| arxiv topic:quant-ph cond-mat.quant-gas cond-mat.str-el physics.atom-ph |
arxiv_dataset-178082301.09736 | Poisson Limit Theorems for Systems with Product Structure
math.DS
We obtain a Poisson Limit for return times to small sets for product systems.
Only one factor is required to be hyperbolic while the second factor is only
required to satisfy polynomial deviation bounds for ergodic sums. In
particular, the second fact can be either elliptic or parabolic. As an
application of our main result, several maps of the form Anosov map $\times$
another map are shown to satisfy a Poisson Limit Theorem at typical points,
some even at all points. The methods can be extended to certain types of skew
products, including $T,T^{-1}$-maps of high rank.
| arxiv topic:math.DS |
arxiv_dataset-178092301.09836 | Optimal stopping problem under random horizon
math.PR
This paper considers a pair $(\mathbb{F},\tau)$, where $\mathbb{F}$ is a
filtration representing the "public" flow of information which is available to
all agents overtime, and $\tau$ is a random time which might not be an
$\mathbb{F}$-stopping time. This setting covers the case of credit risk
framework where $\tau$ models the default time of a firm or client, and the
setting of life insurance where $\tau$ is the death time of an agent. It is
clear that random times can not be observed before their occurrence. Thus the
larger filtration $\mathbb{G}$, which incorporates $\mathbb{F}$ and makes
$\tau$ observable, results from the progressive enlargement of $\mathbb{F}$
with $\tau$. For this informational setting, governed by $\mathbb{G}$, we
analyze the optimal stopping problem in three main directions. The first
direction consists of characterizing the existence of the solution to this
problem in terms of $\mathbb{F}$-observable processes. The second direction
lies in deriving the {\it mathematical structures} of the value process of this
control problem, while the third direction singles out the associated optimal
stopping problem under $\mathbb{F}$. These three aspects allow us to quantify
deeply how $\tau$ impact the optimal stopping problem, while they are also
vital for studying reflected backward stochastic differential equations which
arise {\it naturally} from pricing and hedging of vulnerable claims.
| arxiv topic:math.PR |
arxiv_dataset-178102301.09936 | Efficient learning of large sets of locally optimal classification rules
cs.LG
Conventional rule learning algorithms aim at finding a set of simple rules,
where each rule covers as many examples as possible. In this paper, we argue
that the rules found in this way may not be the optimal explanations for each
of the examples they cover. Instead, we propose an efficient algorithm that
aims at finding the best rule covering each training example in a greedy
optimization consisting of one specialization and one generalization loop.
These locally optimal rules are collected and then filtered for a final rule
set, which is much larger than the sets learned by conventional rule learning
algorithms. A new example is classified by selecting the best among the rules
that cover this example. In our experiments on small to very large datasets,
the approach's average classification accuracy is higher than that of
state-of-the-art rule learning algorithms. Moreover, the algorithm is highly
efficient and can inherently be processed in parallel without affecting the
learned rule set and so the classification accuracy. We thus believe that it
closes an important gap for large-scale classification rule induction.
| arxiv topic:cs.LG |
arxiv_dataset-178112301.10036 | Multiscale structure of the gravitational wave signal from GW150914
based on the nonextensivity $q$-triplet
gr-qc astro-ph.HE
We study the first gravitational wave, GW150914, detected by advanced LIGO
and constructed from the data of measurement of strain relative deformation of
the fabric of spacetime. We show that the time series from the gravitational
wave obeys Tsallis's $q$-Gaussian distribution as a probability density and its
dynamics evolve of the three associated Tsallis' indices named $q$-triplet.
This fact strongly suggests that these black hole merger systems behave in a
non-extensive manner. Furthermore, our results point out that the entropic
indexes obtained as a function of frequency are useful statistical parameters
to determine the dominant frequency when black hole coalescence is achieved.
| arxiv topic:gr-qc astro-ph.HE |
arxiv_dataset-178122301.10136 | A note on the Hasse norm principle
math.NT
Let $A$ be a finite, abelian group. We show that the density of
$A$-extensions satisfying the Hasse norm principle exists, when the extensions
are ordered by discriminant. This strengthens earlier work of
Frei--Loughran--Newton \cite{FLN}, who obtained a density result under the
additional assumption that $A/A[\ell]$ is cyclic with $\ell$ denoting the
smallest prime divisor of $\# A$.
| arxiv topic:math.NT |
arxiv_dataset-178132301.10236 | Engaging with Researchers and Raising Awareness of FAIR and Open Science
through the FAIR+ Implementation Survey Tool (FAIRIST)
cs.CY cs.DL
Six years after the seminal paper on FAIR was published, researchers still
struggle to understand how to implement FAIR. For many researchers FAIR
promises long-term benefits for near-term effort, requires skills not yet
acquired, and is one more thing in a long list of unfunded mandates and onerous
requirements on scientists. Even for those required to or who are convinced
they must make time for FAIR research practices, the preference is for
just-in-time advice properly sized to the scientific artifacts and process.
Because of the generality of most FAIR implementation guidance, it is difficult
for a researcher to adjust the advice to their situation. Technological
advances, especially in the area of artificial intelligence (AI) and machine
learning (ML), complicate FAIR adoption as researchers and data stewards ponder
how to make software, workflows, and models FAIR and reproducible. The FAIR+
Implementation Survey Tool (FAIRIST) mitigates the problem by integrating
research requirements with research proposals in a systematic way. FAIRIST
factors in new scholarly outputs such as nanopublications and notebooks, and
the various research artifacts related to AI research (data, models, workflows,
and benchmarks). Researchers step through a self-serve survey process and
receive a table ready for use in their DMP and/or work plan while gaining
awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model
that uses part of the proposal process as a way to do outreach, raise awareness
of FAIR dimensions and considerations, while providing just-in-time assistance
for competitive proposals.
| arxiv topic:cs.CY cs.DL |
arxiv_dataset-178142301.10336 | A survey of Digital Manufacturing Hardware and Software Trojans
cs.CR
Digital Manufacturing (DM) refers to the on-going adoption of smarter, more
agile manufacturing processes and cyber-physical systems. This includes modern
techniques and technologies such as Additive Manufacturing (AM)/3D printing, as
well as the Industrial Internet of Things (IIoT) and the broader trend toward
Industry 4.0. However, this adoption is not without risks: with a growing
complexity and connectivity, so too grows the cyber-physical attack surface.
Here, malicious actors might seek to steal sensitive information or sabotage
products or production lines, causing financial and reputational loss. Of
particular concern are where such malicious attacks may enter the complex
supply chains of DM systems as Trojans -- malicious modifications that may
trigger their payloads at later times or stages of the product lifecycle.
In this work, we thus present a comprehensive overview of the threats posed
by Trojans in Digital Manufacturing. We cover both hardware and software
Trojans which may exist in products or their production and supply lines. From
this, we produce a novel taxonomy for classifying and analyzing these threats,
and elaborate on how different side channels (e.g. visual, thermal, acoustic,
power, and magnetic) may be used to either enhance the impact of a given Trojan
or utilized as part of a defensive strategy. Other defenses are also presented
-- including hardware, web-, and software-related. To conclude, we discuss
seven different case studies and elaborate how they fit into our taxonomy.
Overall, this paper presents a detailed survey of the Trojan landscape for
Digital Manufacturing: threats, defenses, and the importance of implementing
secure practices.
| arxiv topic:cs.CR |
arxiv_dataset-178152301.10436 | Lithium abundances in giants as a function of stellar mass: An evidence
for He-flash as the source of Li enhancement in low mass giants
astro-ph.SR astro-ph.GA
In this work, we studied the distribution of lithium abundances in giants as
a function of stellar mass. We used a sample of 1240 giants common among Kepler
photometric and LAMOST medium resolution (R $\approx$ 7500) spectroscopic
survey fields. The asteroseismic $\Delta$P - $\Delta \nu$ diagram is used to
define core He-burning red clump giants and red giant branch stars with inert
He-core. Li abundances have been derived using spectral synthesis for the
entire sample stars. Directly measured values of asteroseismic parameters
$\Delta$P(or $\Delta \Pi_1$) and $\Delta \nu$ are either taken from the
literature or measured in this study. Of the 777 identified red clump giants,
we found 668 low mass ($\leq$ 2~M$_{\odot}$) primary red clump giants and 109
high mass ($>$ 2~M$_{\odot}$) secondary red clump giants. Observed Li
abundances in secondary red clump giants agree with the theoretical model
predictions. The lack of Li-rich giants among secondary red clump giants and
the presence of Li-rich, including super Li-rich giants, among primary red
clump stars reinforces the idea that Helium-flash holds the key for Li
enrichment among low-mass giants. The results will further constrain
theoretical models searching for a physical mechanism for Li enhancement among
low-mass red clump giants. Results also serve as observational evidence that
only giants with mass less than $\approx$ 2~M$_{\odot}$ develop degenerate
He-core and undergo He-flash.
| arxiv topic:astro-ph.SR astro-ph.GA |
arxiv_dataset-178162301.10536 | Understanding and Improving Deep Graph Neural Networks: A Probabilistic
Graphical Model Perspective
cs.LG
Recently, graph-based models designed for downstream tasks have significantly
advanced research on graph neural networks (GNNs). GNN baselines based on
neural message-passing mechanisms such as GCN and GAT perform worse as the
network deepens. Therefore, numerous GNN variants have been proposed to tackle
this performance degradation problem, including many deep GNNs. However, a
unified framework is still lacking to connect these existing models and
interpret their effectiveness at a high level. In this work, we focus on deep
GNNs and propose a novel view for understanding them. We establish a
theoretical framework via inference on a probabilistic graphical model. Given
the fixed point equation (FPE) derived from the variational inference on the
Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the
classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different
approximations of the FPE. Moreover, given this framework, more accurate
approximations of FPE are brought, guiding us to design a more powerful GNN:
coupling graph neural network (CoGNet). Extensive experiments are carried out
on citation networks and natural language processing downstream tasks. The
results demonstrate that the CoGNet outperforms the SOTA models.
| arxiv topic:cs.LG |
arxiv_dataset-178172301.10636 | Equations for formal toric degenerations
math.AG
Let $R$ be a complete equicharacteristic noetherian local domain and $\nu$ a
valuation of its field of fractions whose valuation ring dominates $R$ with
trivial residue field extension. The semigroup of values of $\nu$ on
$R\setminus \{0\}$ is not finitely generated in general. We produce equations
in an appropriate generalized power series ring for the algebra encoding the
degeneration of $R$ to the toric graded algebra ${\rm gr}_\nu R$ associated to
the filtration defined by $\nu$. We apply this to represent $\nu$ as the limit
of a sequence of Abhyankar semivaluations (valuations on quotients) of $R$ with
finitely generated semigroups.
| arxiv topic:math.AG |
arxiv_dataset-178182301.10736 | Generating large-scale network analyses of scientific landscapes in
seconds using Dimensions on Google BigQuery
cs.DL physics.soc-ph
The growth of large, programatically accessible bibliometrics databases
presents new opportunities for complex analyses of publication metadata. In
addition to providing a wealth of information about authors and institutions,
databases such as those provided by Dimensions also provide conceptual
information and links to entities such as grants, funders and patents. However,
data is not the only challenge in evaluating patterns in scholarly work: These
large datasets can be challenging to integrate, particularly for those
unfamiliar with the complex schemas necessary for accommodating such
heterogeneous information, and those most comfortable with data mining may not
be as experienced in data visualisation. Here, we present an open-source Python
library that streamlines the process accessing and diagramming subsets of the
Dimensions on Google BigQuery database and demonstrate its use on the freely
available Dimensions COVID-19 dataset. We are optimistic that this tool will
expand access to this valuable information by streamlining what would otherwise
be multiple complex technical tasks, enabling more researchers to examine
patterns in research focus and collaboration over time.
| arxiv topic:cs.DL physics.soc-ph |
arxiv_dataset-178192301.10836 | A Pseudo Plane-wave Gravitational Calibrator for Gravitational Wave
Observatories
gr-qc physics.ins-det
The precisions of existing gravitational calibrators for gravitational wave
observatories are limited by their dependence on the relative position between
the calibrators and the observatory's test masses. Here we present a novel
geometry consisting of four quadrupole rotors placed at the vertices of a
rectangle centered on the test mass. The phases and rotation directions are
selected to produce a pseudo plane-wave sinusoidal gravitational acceleration
with amplitude of ~ 100 fm/s^2. We show that this acceleration only has minimal
dependence on the test mass position relative to the rotor array and can yield
0.15% acceleration amplitude uncertainty while tolerating a 1-cm test mass
position uncertainty. The acceleration can be directed precisely along the
optical axis of the interferometer arm and applies no torque on the test mass.
In addition, the small size of the rotors has significant engineering and
safety benefits.
| arxiv topic:gr-qc physics.ins-det |
arxiv_dataset-178202301.10936 | PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation
Invariant Transformation
cs.LG cs.NE
Dynamic sparsity, where the sparsity patterns are unknown until runtime,
poses a significant challenge to deep learning. The state-of-the-art
sparsity-aware deep learning solutions are restricted to pre-defined, static
sparsity patterns due to significant overheads associated with preprocessing.
Efficient execution of dynamic sparse computation often faces the misalignment
between the GPU-friendly tile configuration for efficient execution and the
sparsity-aware tile shape that minimizes coverage wastes (non-zero values in
tensor).
In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity.
PIT proposes a novel tiling mechanism that leverages Permutation Invariant
Transformation (PIT), a mathematically proven property, to transform multiple
sparsely located micro-tiles into a GPU-efficient dense tile without changing
the computation results, thus achieving both high GPU utilization and low
coverage waste. Given a model, PIT first finds feasible PIT rules for all its
operators and generates efficient GPU kernels accordingly. At runtime, with the
novel SRead and SWrite primitives, PIT rules can be executed extremely fast to
support dynamic sparsity in an online manner. Extensive evaluation on diverse
models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x
(average 2.43x) over state-of-the-art compilers.
| arxiv topic:cs.LG cs.NE |
arxiv_dataset-178212301.11036 | Design and Assessment of a Bimanual Haptic Epidural Needle Insertion
Simulator
cs.HC cs.RO
The case experience of anesthesiologists is one of the leading causes of
accidental dural punctures and failed epidurals - the most common complications
of epidural analgesia used for pain relief during delivery. We designed a
bimanual haptic simulator to train anesthesiologists and optimize epidural
analgesia skill acquisition. We present an assessment study conducted with 22
anesthesiologists of different competency levels from several Israeli
hospitals. Our simulator emulates the forces applied to the epidural (Touhy)
needle, held by one hand, and those applied to the Loss of Resistance (LOR)
syringe, held by the other one. The resistance is calculated based on a model
of the epidural region layers parameterized by the weight of the patient. We
measured the movements of both haptic devices and quantified the results' rate
(success, failed epidurals, and dural punctures), insertion strategies, and the
participants' answers to questionnaires about their perception of the
simulation realism. We demonstrated good construct validity by showing that the
simulator can distinguish between real-life novices and experts. Face and
content validity were examined by studying users' impressions regarding the
simulator's realism and fulfillment of purpose. We found differences in
strategies between different level anesthesiologists, and suggest trainee-based
instruction in advanced training stages.
| arxiv topic:cs.HC cs.RO |
arxiv_dataset-178222301.11136 | Conformal Prediction for Trustworthy Detection of Railway Signals
stat.ML
We present an application of conformal prediction, a form of uncertainty
quantification with guarantees, to the detection of railway signals.
State-of-the-art architectures are tested and the most promising one undergoes
the process of conformalization, where a correction is applied to the predicted
bounding boxes (i.e. to their height and width) such that they comply with a
predefined probability of success. We work with a novel exploratory dataset of
images taken from the perspective of a train operator, as a first step to build
and validate future trustworthy machine learning models for the detection of
railway signals.
| arxiv topic:stat.ML |
arxiv_dataset-178232301.11236 | Quantum exploration of high-dimensional canyon landscapes
cond-mat.dis-nn hep-th
Canyon landscapes in high dimension can be described as manifolds of small,
but extensive dimension, immersed in a higher dimensional ambient space and
characterized by a zero potential energy on the manifold. Here we consider the
problem of a quantum particle exploring a prototype of a high-dimensional
random canyon landscape. We characterize the thermal partition function and
show that around the point where the classical phase space has a satisfiability
transition so that zero potential energy canyons disappear, moderate quantum
fluctuations have a deleterious effect and induce glassy phases at temperature
where classical thermal fluctuations alone would thermalize the system.
Surprisingly we show that even when, classically, diffusion is expected to be
unbounded in space, the interplay between quantum fluctuations and the
randomness of the canyon landscape conspire to have a confining effect.
| arxiv topic:cond-mat.dis-nn hep-th |
arxiv_dataset-178242301.11336 | Causal Structural Learning from Time Series: A Convex Optimization
Approach
cs.LG stat.ME
Structural learning, which aims to learn directed acyclic graphs (DAGs) from
observational data, is foundational to causal reasoning and scientific
discovery. Recent advancements formulate structural learning into a continuous
optimization problem; however, DAG learning remains a highly non-convex
problem, and there has not been much work on leveraging well-developed convex
optimization techniques for causal structural learning. We fill this gap by
proposing a data-adaptive linear approach for causal structural learning from
time series data, which can be conveniently cast into a convex optimization
problem using a recently developed monotone operator variational inequality
(VI) formulation. Furthermore, we establish non-asymptotic recovery guarantee
of the VI-based approach and show the superior performance of our proposed
method on structure recovery over existing methods via extensive numerical
experiments.
| arxiv topic:cs.LG stat.ME |
arxiv_dataset-178252301.11436 | Synesthetic Dice: Sensors, Actuators, And Mappings
cs.HC
How bright can you cry? How loud does the sun shine? We developed a
multisensory and multimodal tool, the Loaded Dice, for use in co-design
workshops to research the design space of IoT usage scenarios. The Loaded Dice
incorporate the principle of a technical synesthesia, being able to map any of
the included sensors to any of the included actuators. With just a turn of one
of the cubical devices it is possible to create a new combination. We discuss
the core principles of the Loaded Dice, what sensors and actuators are
included, how they relate to human senses, and how we realized a meaningful
mapping between sensors and actuators. We further discuss where we see
additional potential in the Loaded Dice to support synesthetic exploration - as
Synesthetic Dice - so that you can eventually find out who cries brighter.
| arxiv topic:cs.HC |
arxiv_dataset-178262301.11536 | $2$-reflective lattices of signature $(n,2)$ with $n\geq 8$
math.NT math.AG
An even lattice $M$ of signature $(n,2)$ is called $2$-reflective if there is
a non-constant modular form for the orthogonal group of $M$ which vanishes only
on quadratic divisors orthogonal to $2$-roots of $M$. In [Amer. J. Math. 2017]
Shouhei Ma proved that there are only finitely many $2$-reflective lattices of
signature $(n,2)$ with $n\geq 7$. In this paper we extend the finiteness result
of Ma to $n\geq 5$ and show that there are exactly forty-two $2$-reflective
lattices of signature $(n,2)$ with $n\geq 8$.
| arxiv topic:math.NT math.AG |
arxiv_dataset-178272301.11636 | Chern-Ricci flat balanced metrics on small resolutions of Calabi-Yau
threefolds
math.DG math.AG
Given a (smoothable) projective nodal K\"ahler Calabi-Yau threefold, we show,
via a gluing construction, that all its - possibly non-K\"ahler - small
resolutions admit Chern-Ricci flat balanced metrics, which among other things
solve the dilatino equation appearing in the Hull-Strominger system.
| arxiv topic:math.DG math.AG |
arxiv_dataset-178282301.11736 | Post-blowup dynamics for the nonlinear Schr\"odinger equation
nlin.PS math-ph math.MP
In this work we present a systematic numerical study of the post-blowup
dynamics of singular solutions of the 1D focusing critical NLS equation in the
framework of a nonlinear damped perturbation. The first part of this study
shows that initially the post-blowup is described by the adiabatic
approximation, in which the collapsing core approaches an universal profile and
the solution width is governed by a system of ODEs (reduced system). After
that, a non-adiabatic regime is observed soon after the maximum of the
solution, in which our direct numerical simulations show a clear deviation from
the dynamics based on the reduced system. Our study suggests that such
non-adiabatic regime is caused by the increasing influx of mass into the
collapsing core of the solution, which is not considered in the derivation of
the reduced system. Also, adiabatic theoretical predictions related to the
wave-maximum and wave-dissipation are compared with our numerical simulations.
The second part of this work describes the non-adiabatic dynamics. Here,
numerical simulations reveal a dominant quasi linear regime, caused by the
rapid defocusing process. The collapsing core approaches the universal profile,
after removing some oscillations resulting from the interference with the tail.
Finally, our numerical study suggests that in the limit of vanishing
dissipation, and in a free-space domain, the critical mass is radiated to
infinity instantly at the collapse time.
| arxiv topic:nlin.PS math-ph math.MP |
arxiv_dataset-178292301.11836 | Global 3D radiation-hydrodynamical models of AGB stars with dust-driven
winds
astro-ph.SR astro-ph.GA
Convection and mass loss by stellar winds are two dynamical processes that
shape asymptotic giant branch (AGB) stars and their evolution. Observations and
earlier 3D models indicate that giant convection cells cause high-contrast
surface intensity patterns, and contribute to the origin of clumpy dust clouds.
We study the formation and resulting properties of dust-driven winds from AGB
stars, using new global 3D simulations. The dynamical stellar interiors,
atmospheres, and wind acceleration zones of two M-type AGB stars were modeled
with the CO5BOLD code. These first global 3D simulations are based on
frequency-dependent gas opacities, and they feature time-dependent condensation
and evaporation of silicate grains. Convection and pulsations emerge
self-consistently, allowing us to derive wind properties (e.g., mass-loss rates
and outflow velocities), without relying on parameterized descriptions of these
processes. In contrast to 1D models with purely radial pulsations, the shocks
induced by convection and pulsation in the 3D models cover large parts, but not
the entirety, of the sphere, leading to a patchy, nonspherical structure of the
atmosphere. Since dust condensation critically depends on gas density, new dust
clouds form mostly in the dense wakes of atmospheric shocks, where the grains
can grow efficiently. The resulting clumpy distribution of newly formed dust
leads to a complex 3D morphology of the extended atmosphere and
wind-acceleration zone, with simultaneous infall and outflow regions close to
the star. Highly nonspherical isotherms and short-lived cool pockets of gas in
the stellar vicinity are prominent features. Efficient dust formation sets in
closer to the star than spherical averages of the temperature indicate, in
dense regions where grain growth rates are higher than average.
| arxiv topic:astro-ph.SR astro-ph.GA |
arxiv_dataset-178302301.11936 | Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks
with Quantum Computation
quant-ph cs.LG stat.ML
A significant challenge in the field of quantum machine learning (QML) is to
establish applications of quantum computation to accelerate common tasks in
machine learning such as those for neural networks. Ridgelet transform has been
a fundamental mathematical tool in the theoretical studies of neural networks,
but the practical applicability of ridgelet transform to conducting learning
tasks was limited since its numerical implementation by conventional classical
computation requires an exponential runtime $\exp(O(D))$ as data dimension $D$
increases. To address this problem, we develop a quantum ridgelet transform
(QRT), which implements the ridgelet transform of a quantum state within a
linear runtime $O(D)$ of quantum computation. As an application, we also show
that one can use QRT as a fundamental subroutine for QML to efficiently find a
sparse trainable subnetwork of large shallow wide neural networks without
conducting large-scale optimization of the original network. This application
discovers an efficient way in this regime to demonstrate the lottery ticket
hypothesis on finding such a sparse trainable neural network. These results
open an avenue of QML for accelerating learning tasks with commonly used
classical neural networks.
| arxiv topic:quant-ph cs.LG stat.ML |
arxiv_dataset-178312301.12036 | Analyzing Robustness of the Deep Reinforcement Learning Algorithm in
Ramp Metering Applications Considering False Data Injection Attack and
Defense
cs.LG
Ramp metering is the act of controlling on-going vehicles to the highway
mainlines. Decades of practices of ramp metering have proved that ramp metering
can decrease total travel time, mitigate shockwaves, decrease rear-end
collisions by smoothing the traffic interweaving process, etc. Besides
traditional control algorithm like ALINEA, Deep Reinforcement Learning (DRL)
algorithms have been introduced to build a finer control. However, two
remaining challenges still hinder DRL from being implemented in the real world:
(1) some assumptions of algorithms are hard to be matched in the real world;
(2) the rich input states may make the model vulnerable to attacks and data
noises. To investigate these issues, we propose a Deep Q-Learning algorithm
using only loop detectors information as inputs in this study. Then, a set of
False Data Injection attacks and random noise attack are designed to
investigate the robustness of the model. The major benefit of the model is that
it can be applied to almost any ramp metering sites regardless of the road
geometries and layouts. Besides outcompeting the ALINEA method, the Deep
Q-Learning method also shows a good robustness through training among very
different demands and geometries. For example, during the testing case in I-24
near Murfreesboro, TN, the model shows its robustness as it still outperforms
ALINEA algorithm under Fast Gradient Sign Method attacks. Unlike many previous
studies, the model is trained and tested in completely different environments
to show the capabilities of the model.
| arxiv topic:cs.LG |
arxiv_dataset-178322301.12136 | $R^p$ Attractors Static Neutron Star Phenomenology
gr-qc
In this work we study the neutron star phenomenology of $R^p$ attractor
theories in the Einstein frame. The Einstein frame $R^p$ attractor theories
have the attractor property that they originate from a large class of Jordan
frame scalar theories with arbitrary non-minimal coupling. These theories in
the Einstein frame provide a viable class of inflationary models, and in this
work we investigate their implications on static neutron stars. We numerically
solve the Tolman-Oppenheimer-Volkoff equations in the Einstein frame, for three
distinct equations of state, and we provide the mass-radius diagrams for
several cases of interest of the $R^p$ attractor theories. We confront the
results with several timely constraints on the radii of specific mass neutron
stars, and as we show, only a few cases corresponding to specific equations of
state pass the stringent tests on neutron stars phenomenology.
| arxiv topic:gr-qc |
arxiv_dataset-178332301.12236 | Ideal Bose Gas and Blackbody Radiation in the Dunkl Formalism
cond-mat.stat-mech
Recently, deformed quantum systems gather lots of attention in the
literature. Dunkl formalism differs from others by containing the
difference-differential and reflection operator. It is one of the most
interesting deformations since it let us discuss the solutions according to the
even and odd solutions. In this work, we studied the ideal Bose gas and the
blackbody radiation via the Dunkl formalism. To this end, we made a liaison
between the coordinate and momentum operators with the creation and
annihilation operators which allowed us to obtain the expressions of the
partition function, the condensation temperature, and the ground state
population of the Bose gas. We found that Dunkl-condensation temperature
increases with increasing {\theta} value. In the blackbody radiation phenomena,
we found how the Dunkl formalism modifies total radiated energy. Then, we
examined the thermal quantities of the system. We found that the Dunkl
deformation causes an increase in entropy and specific heat functions as well
as in the total radiation energy. However, we observed a decrease in the
Dunk-corrected Helmholtz free energy in this scenario. Finally, we found that
the equation of state is invariant even in the considered formalism.
| arxiv topic:cond-mat.stat-mech |
arxiv_dataset-178342301.12336 | A Comparative Study of Solar Active Region 12371 with Data-constrained
and Data-driven MHD Simulations
astro-ph.SR
We performed two data-based magnetohydrodynamic (MHD) simulations for solar
active region 12371 which produced an M6.5 flare. The first simulation is a
full data-driven simulation where the initial condition is given by a nonlinear
force-free field (NLFFF). This NLFFF was extrapolated from photospheric
magnetograms approximately 1 hour prior to the flare, and then a time-varying
photospheric magnetic field is imposed at the bottom surface. The second
simulation is also a data-driven simulation, but it stops driving at the bottom
before the time of flare onset and then switches to the data-constrained
simulation, where the horizontal component of the magnetic field varies
according to an induction equation while the normal component is fixed with
time. Both simulations lead to an eruption, with both simulations producing
highly twisted field lines before the eruption which were not found in the
NLFFF alone. After the eruption, the first simulation based on the time-varying
photospheric magneitic field, continues to produce sheared field lines after
the flare without reproducing phenomena such as post-flare loops. The second
simulation reproduces the phenomena associated with flares well. However in
this case the evolution of the bottom magnetic field is inconsistent with the
evolution of the observed magnetic field. In this letter, we report potential
advantages and disadvantages in data-constrained and data-driven MHD
simulations that need to be taken into consideration by future studies.
| arxiv topic:astro-ph.SR |
arxiv_dataset-178352301.12436 | Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain
Adaptation Challenge for Action Recognition 2022
cs.CV
In this report, we present the technical details of our submission to the
EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action
Recognition 2022. This task aims to adapt an action recognition model trained
on a labeled source domain to an unlabeled target domain. To achieve this goal,
we propose an action-aware domain adaptation framework that leverages the prior
knowledge induced from the action recognition task during the adaptation.
Specifically, we disentangle the source features into action-relevant features
and action-irrelevant features using the learned action classifier and then
align the target features with the action-relevant features. To further improve
the action prediction performance, we exploit the verb-noun co-occurrence
matrix to constrain and refine the action predictions. Our final submission
achieved the first place in terms of top-1 action recognition accuracy.
| arxiv topic:cs.CV |
arxiv_dataset-178362301.12536 | Random points are good for universal discretization
math.FA cs.NA math.CA math.NA
There has been significant progress in the study of sampling discretization
of integral norms for both a designated finite-dimensional function space and a
finite collection of such function spaces (universal discretization). Sampling
discretization results turn out to be very useful in various applications,
particularly in sampling recovery.
Recent sampling discretization results typically provide existence of good
sampling points for discretization.
In this paper, we show that independent and identically distributed random
points provide good universal discretization with high probability.
Furthermore, we demonstrate that a simple greedy algorithm based on those
points that are good for universal discretization provides excellent sparse
recovery results in the square norm.
| arxiv topic:math.FA cs.NA math.CA math.NA |
arxiv_dataset-178372301.12636 | Exploring Image Augmentations for Siamese Representation Learning with
Chest X-Rays
eess.IV cs.AI cs.CV cs.LG
Image augmentations are quintessential for effective visual representation
learning across self-supervised learning techniques. While augmentation
strategies for natural imaging have been studied extensively, medical images
are vastly different from their natural counterparts. Thus, it is unknown
whether common augmentation strategies employed in Siamese representation
learning generalize to medical images and to what extent. To address this
challenge, in this study, we systematically assess the effect of various
augmentations on the quality and robustness of the learned representations. We
train and evaluate Siamese Networks for abnormality detection on chest X-Rays
across three large datasets (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate
the efficacy of the learned representations through experiments involving
linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally,
we identify a set of augmentations that yield robust representations that
generalize well to both out-of-distribution data and diseases, while
outperforming supervised baselines using just zero-shot transfer and linear
probes by up to 20%. Our code is available at
https://github.com/StanfordMIMI/siaug.
| arxiv topic:eess.IV cs.AI cs.CV cs.LG |
arxiv_dataset-178382301.12736 | On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
cs.LG stat.ML
It is well known that accurate probabilistic predictors can be trained
through empirical risk minimisation with proper scoring rules as loss
functions. While such learners capture so-called aleatoric uncertainty of
predictions, various machine learning methods have recently been developed with
the goal to let the learner also represent its epistemic uncertainty, i.e., the
uncertainty caused by a lack of knowledge and data. An emerging branch of the
literature proposes the use of a second-order learner that provides predictions
in terms of distributions on probability distributions. However, recent work
has revealed serious theoretical shortcomings for second-order predictors based
on loss minimisation. In this paper, we generalise these findings and prove a
more fundamental result: There seems to be no loss function that provides an
incentive for a second-order learner to faithfully represent its epistemic
uncertainty in the same manner as proper scoring rules do for standard
(first-order) learners. As a main mathematical tool to prove this result, we
introduce the generalised notion of second-order scoring rules.
| arxiv topic:cs.LG stat.ML |
arxiv_dataset-178392301.12836 | Integral equation method for microseismic wavefield modelling in
anisotropic elastic media
physics.geo-ph
In this paper, we present a frequency-domain volume integral method to model
the microseismic wavefield in heterogeneous anisotropic-elastic media. The
elastic wave equation is written as an integral equation of the
Lippmann-Schwinger type, and the seismic source is represented as a general
moment tensor. The displacement field due to a moment tensor source can be
computed using the spatial derivative of the elastodynamic Green's function.
The existing matrix-based implementation of the integral equation is
computationally inefficient to model the wavefield in a three-dimensional
earth. An integral equation for the particle displacement is, hence, formulated
in a matrix-free manner through the application of the Fourier transform. The
biconjugate gradient stabilized method is used to iteratively obtain the
solution of this equation. We apply the numerical scheme to three different
models in order of increasing geological complexity and obtain the elastic
displacement fields corresponding to the different types of moment tensor
sources. The volume integral method has an advantage over the time domain
methods in regard to adding multiple sources since it can work with discrete
frequencies, one by one, and limit the computational cost. The generated
synthetic data can be useful in inversion for the microseismic source and model
parameters.
| arxiv topic:physics.geo-ph |
arxiv_dataset-178402301.12936 | Clausius Implies That Nearly Anything Can Be A Thermometer
cond-mat.stat-mech
There are three types of thermometries. One is a proxy, such as the purely
phenomenological resistivity. More fundamental are those based on
thermodynamics, as in the Carnot cycle, and those based on statistical
mechanics, such as the ideal gas law. With heat flow $Q$ and temperature $T$, a
temperature scale in principle (but not in practice) can be based on the simple
Carnot cycle relation $Q/T+Q'/T'=0$, with a temperature $T_{0}(p_{0},V_{0})$
specified. More generally, a thermodynamics based temperature scale may be
determined by the Clausius condition $\oint dQ/T=0$ for every closed path in a
given region $\Omega$ of $p$-$V$ space. Taking a discretized grid $i$ (from
which such closed paths can be composed), for some parametrized model
temperature function $T_{n}$ a root-mean-square minimization of
$\sum_{i}(\oint_{i}dQ/T_{n})^{2}$ yields the best set of model $T_{n}$'s
parameters. Thus any stable material -- even one not described by a known
statistical mechanical model -- can be used as a thermometer. If, because of
inaccuracy of $dQ$ measurement, the Clausius condition method gives a
temperature scale of lower accuracy than the best proxy temperature scale, then
that proxy temperature scale can be employed with the rms Clausius condition
method to improve the accuracy of (i.e., raise the standards for) the $dQ$
measurements to the accuracy of the proxy-based temperature scale.
| arxiv topic:cond-mat.stat-mech |
arxiv_dataset-178412301.13036 | Federated Learning for Water Consumption Forecasting in Smart Cities
cs.LG
Water consumption remains a major concern among the world's future
challenges. For applications like load monitoring and demand response, deep
learning models are trained using enormous volumes of consumption data in smart
cities. On the one hand, the information used is private. For instance, the
precise information gathered by a smart meter that is a part of the system's
IoT architecture at a consumer's residence may give details about the
appliances and, consequently, the consumer's behavior at home. On the other
hand, enormous data volumes with sufficient variation are needed for the deep
learning models to be trained properly. This paper introduces a novel model for
water consumption prediction in smart cities while preserving privacy regarding
monthly consumption. The proposed approach leverages federated learning (FL) as
a machine learning paradigm designed to train a machine learning model in a
distributed manner while avoiding sharing the users data with a central
training facility. In addition, this approach is promising to reduce the
overhead utilization through decreasing the frequency of data transmission
between the users and the central entity. Extensive simulation illustrate that
the proposed approach shows an enhancement in predicting water consumption for
different households.
| arxiv topic:cs.LG |
arxiv_dataset-178422301.13136 | Contrastive Meta-Learning for Partially Observable Few-Shot Learning
cs.LG
Many contrastive and meta-learning approaches learn representations by
identifying common features in multiple views. However, the formalism for these
approaches generally assumes features to be shared across views to be captured
coherently. We consider the problem of learning a unified representation from
partial observations, where useful features may be present in only some of the
views. We approach this through a probabilistic formalism enabling views to map
to representations with different levels of uncertainty in different
components; these views can then be integrated with one another through
marginalisation over that uncertainty. Our approach, Partial Observation
Experts Modelling (POEM), then enables us to meta-learn consistent
representations from partial observations. We evaluate our approach on an
adaptation of a comprehensive few-shot learning benchmark, Meta-Dataset, and
demonstrate the benefits of POEM over other meta-learning methods at
representation learning from partial observations. We further demonstrate the
utility of POEM by meta-learning to represent an environment from partial views
observed by an agent exploring the environment.
| arxiv topic:cs.LG |
arxiv_dataset-178432301.13236 | SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree
Search
cs.LG cs.AI
Despite the popularity of policy gradient methods, they are known to suffer
from large variance and high sample complexity. To mitigate this, we introduce
SoftTreeMax -- a generalization of softmax that takes planning into account. In
SoftTreeMax, we extend the traditional logits with the multi-step discounted
cumulative reward, topped with the logits of future states. We consider two
variants of SoftTreeMax, one for cumulative reward and one for exponentiated
reward. For both, we analyze the gradient variance and reveal for the first
time the role of a tree expansion policy in mitigating this variance. We prove
that the resulting variance decays exponentially with the planning horizon as a
function of the expansion policy. Specifically, we show that the closer the
resulting state transitions are to uniform, the faster the decay. In a
practical implementation, we utilize a parallelized GPU-based simulator for
fast and efficient tree search. Our differentiable tree-based policy leverages
all gradients at the tree leaves in each environment step instead of the
traditional single-sample-based gradient. We then show in simulation how the
variance of the gradient is reduced by three orders of magnitude, leading to
better sample complexity compared to the standard policy gradient. On Atari,
SoftTreeMax demonstrates up to 5x better performance in a faster run time
compared to distributed PPO. Lastly, we demonstrate that high reward correlates
with lower variance.
| arxiv topic:cs.LG cs.AI |
arxiv_dataset-178442301.13336 | The Fair Value of Data Under Heterogeneous Privacy Constraints in
Federated Learning
cs.LG cs.CR cs.GT
Modern data aggregation often involves a platform collecting data from a
network of users with various privacy options. Platforms must solve the problem
of how to allocate incentives to users to convince them to share their data.
This paper puts forth an idea for a \textit{fair} amount to compensate users
for their data at a given privacy level based on an axiomatic definition of
fairness, along the lines of the celebrated Shapley value. To the best of our
knowledge, these are the first fairness concepts for data that explicitly
consider privacy constraints. We also formulate a heterogeneous federated
learning problem for the platform with privacy level options for users. By
studying this problem, we investigate the amount of compensation users receive
under fair allocations with different privacy levels, amounts of data, and
degrees of heterogeneity. We also discuss what happens when the platform is
forced to design fair incentives. Under certain conditions we find that when
privacy sensitivity is low, the platform will set incentives to ensure that it
collects all the data with the lowest privacy options. When the privacy
sensitivity is above a given threshold, the platform will provide no incentives
to users. Between these two extremes, the platform will set the incentives so
some fraction of the users chooses the higher privacy option and the others
chooses the lower privacy option.
| arxiv topic:cs.LG cs.CR cs.GT |
arxiv_dataset-178452301.13436 | Closed Form Expressions for Certain Improper Integrals of Mathematical
Physics
math-ph math.MP
We present new closed-form expressions for certain improper integrals of
Mathematical Physics such as certain Ising, Box, and Associated integrals. The
techniques we employ here include (a) the Method of Brackets and its
modifications and suitable extensions to obtain the Mellin-Barnes
representation. (b) The evaluation of the resulting Mellin-Barnes
representations via the recently discovered Conic Hull method via the automated
package $\textit{MBConichulls.wl}$. Finally, the analytic continuations of
these series solutions are then produced using the automated package
\texttt{Olsson.wl}, based on the method of Olsson. Thus, combining all these
recent advances allows for closed-form evaluation of the hitherto unknown
$B_3(s)$, $B_4(s)$, and related integrals in terms of multivariable
hypergeometric functions. Along the way, we also discuss certain complications
while using the Original Method of Brackets for these evaluations and how to
rectify them. The interesting cases of $C_{5,k}$ are also studied. It is not
yet fully resolved for the reasons we discuss in this paper.
| arxiv topic:math-ph math.MP |
arxiv_dataset-178462301.13536 | Low Complexity Adaptive Machine Learning Approaches for End-to-End
Latency Prediction
cs.NI cs.LG
Software Defined Networks have opened the door to statistical and AI-based
techniques to improve efficiency of networking. Especially to ensure a certain
Quality of Service (QoS) for specific applications by routing packets with
awareness on content nature (VoIP, video, files, etc.) and its needs (latency,
bandwidth, etc.) to use efficiently resources of a network. Monitoring and
predicting various Key Performance Indicators (KPIs) at any level may handle
such problems while preserving network bandwidth. The question addressed in
this work is the design of efficient, low-cost adaptive algorithms for KPI
estimation, monitoring and prediction. We focus on end-to-end latency
prediction, for which we illustrate our approaches and results on data obtained
from a public generator provided after the recent international challenge on
GNN [12]. In this paper, we improve our previously proposed low-cost estimators
[6] by adding the adaptive dimension, and show that the performances are
minimally modified while gaining the ability to track varying networks.
| arxiv topic:cs.NI cs.LG |
arxiv_dataset-178472301.13636 | Transport with Support: Data-Conditional Diffusion Bridges
cs.LG
The dynamic Schr\"odinger bridge problem provides an appealing setting for
solving constrained time-series data generation tasks posed as optimal
transport problems. It consists of learning non-linear diffusion processes
using efficient iterative solvers. Recent works have demonstrated
state-of-the-art results (eg. in modelling single-cell embryo RNA sequences or
sampling from complex posteriors) but are limited to learning bridges with only
initial and terminal constraints. Our work extends this paradigm by proposing
the Iterative Smoothing Bridge (ISB). We integrate Bayesian filtering and
optimal control into learning the diffusion process, enabling the generation of
constrained stochastic processes governed by sparse observations at
intermediate stages and terminal constraints. We assess the effectiveness of
our method on synthetic and real-world data generation tasks and we show that
the ISB generalises well to high-dimensional data, is computationally
efficient, and provides accurate estimates of the marginals at intermediate and
terminal times.
| arxiv topic:cs.LG |
arxiv_dataset-178482301.13736 | Approximate Functional Differencing
econ.EM
Inference on common parameters in panel data models with individual-specific
fixed effects is a classic example of Neyman and Scott's (1948) incidental
parameter problem (IPP). One solution to this IPP is functional differencing
(Bonhomme 2012), which works when the number of time periods T is fixed (and
may be small), but this solution is not applicable to all panel data models of
interest. Another solution, which applies to a larger class of models, is
"large-T" bias correction (pioneered by Hahn and Kuersteiner 2002 and Hahn and
Newey 2004), but this is only guaranteed to work well when T is sufficiently
large. This paper provides a unified approach that connects those two seemingly
disparate solutions to the IPP. In doing so, we provide an approximate version
of functional differencing, that is, an approximate solution to the IPP that is
applicable to a large class of panel data models even when T is relatively
small.
| arxiv topic:econ.EM |
arxiv_dataset-178492301.13836 | A New Definition of Exoplanet Habitability: Introducing the
Photosynthetic Habitable Zone
astro-ph.EP
It may be possible to detect biosignatures of photosynthesis in an
exoplanet's atmosphere. However, such a detection would likely require a
dedicated study, occupying a large amount of telescope time. It is therefore
prudent, while searching for signs of life that we may recognise, to pick the
best target possible. In this work, we present a new region, the
``photosynthetic habitable zone'' \textemdash the distance from a star where
both liquid water and oxygenic photosynthesis can occur. It is therefore the
region where detectable biosignatures of oxygenic photosynthesis are most
likely to occur. Our analysis indicates that in the most ideal conditions for
life and no atmospheric effects, the photosynthetic habitable zone is almost as
broad as the habitable zone. On the other hand, if conditions for life are
anything less than excellent and atmospheric effects are even moderate, the
photosynthetic habitable zone is concentrated at larger separations around more
massive stars. Such cases are also not tidally locked to their host star, which
could result in planetary rotation periods similar to the Earth's. We identify
five planets, Kepler-452 b, Kepler-1638 b, Kepler-1544 b and Kepler-62 e and
Kepler-62 f, that are consistently in the photosynthetic habitable zone for a
variety of conditions, and we predict their day lengths to be between 9 and 11
hours. We conclude that the parameter space in which we should search for signs
of life is much narrower than the standard habitable zone.
| arxiv topic:astro-ph.EP |
arxiv_dataset-178502302.00066 | Over 100% Light Extraction Enhancement of Organic Light Emitting Diodes
Using Flat Moire Micro-Lens Array Fabricated by Double Nanoimprint
Lithography Over a Large Area
physics.optics physics.app-ph
To improve the light extraction efficiency of organic light emitting diodes
(OLEDs), we developed a novel substrate, i.e., a metamaterial based flat Moire
micro-lens array formed using double nanoimprint, termed Mlens-array,
consisting of a hexagonal moir\'e pattern pillar array. By choosing a low
refractive index dielectric material for the pillar array and a high refractive
index dielectric material for the layer capped on top of the pillar array, we
fabricated the Mlens-array behaving as a conventional convex optical micro-lens
array. The Mlens-array was fabricated on a 4-inch wafer-size glass substrate by
double-cycle compositional nanoimprint lithography (NIL) which is easy for
achieving high throughput fabrication in large-scale. Applying the Mlens-array
substrate in a typical green-emitting OLED, the light extraction efficiency was
enhanced by over 100% (2.08-fold) compared to a control device fabricated on
the conventional planar glass substrate.
| arxiv topic:physics.optics physics.app-ph |
arxiv_dataset-178512302.00166 | Market-Based Coordination of Price-Responsive Demand Using Dantzig-Wolfe
Decomposition Method
eess.SY cs.SY math.OC
With the increased share of Distributed Generation (DG) and Demand Responsive
(DR) loads in the power systems, new approaches based on the game theory
framework have been proposed to tackle the problem of coordination of Price
Responsive Devices (PRD). The PRDs are modeled as self-benefiting players who
try to optimize their consumption based on the price. In this paper, for the
first time, a new algorithm based on the Dantzig-Wolfe (DW) Decomposition
method to solve the coordination problem of self-benefiting PRDs in a
distributed fashion has been proposed. By utilizing the distributed nature of
Dantzig-Wolfe, the PRD's self-benefiting algorithms are modeled as sub-problems
of the DW, and the coordinator (or the grid operator) who collects energy
consumption of PRDs (their energy bid), solves the master problem of the DW and
calculate the price signal accordingly. The proposed algorithm is fast since
the subproblem in DW (which could be millions of PRDs) can be solved
simultaneously. Furthermore, based on the DW theory, if the PRDs subproblems
are convex, reaching the optimal point (Equal to Nash Equilibrium) in limited
iterations is guaranteed. A simulation with 8 participant households has been
conducted to evaluate the model. Each house is equipped with two types of
loads: an Electric Vehicle (EV) as a sample of interruptible loads and an
Electric Water Heater (EWH) as a sample of Thermostatically Control Loads
(TCA). The results show that when the algorithm reaches the optimal point, the
generation cost and the user payment (based on the marginal cost of generation)
decrease. Furthermore, the aggregate load's Peak to Average (PAR) reduces
significantly.
| arxiv topic:eess.SY cs.SY math.OC |
arxiv_dataset-178522302.00266 | Electron as a Tiny Mirror: Radiation From a Worldline With Asymptotic
Inertia
gr-qc
We present a moving mirror analog of the electron, whose worldline possesses
asymptotic constant velocity with corresponding beta Bogolubov coefficients
that are consistent with finite total emitted energy. Furthermore, the quantum
analog model is in agreement with the total energy obtained by integrating the
classical Larmor power.
| arxiv topic:gr-qc |
arxiv_dataset-178532302.00366 | CzSL: Learning from citizen science, experts and unlabelled data in
astronomical image classification
astro-ph.GA astro-ph.IM eess.IV
Citizen science is gaining popularity as a valuable tool for labelling large
collections of astronomical images by the general public. This is often
achieved at the cost of poorer quality classifications made by amateur
participants, which are usually verified by employing smaller data sets
labelled by professional astronomers. Despite its success, citizen science
alone will not be able to handle the classification of current and upcoming
surveys. To alleviate this issue, citizen science projects have been coupled
with machine learning techniques in pursuit of a more robust automated
classification. However, existing approaches have neglected the fact that,
apart from the data labelled by amateurs, (limited) expert knowledge of the
problem is also available along with vast amounts of unlabelled data that have
not yet been exploited within a unified learning framework. This paper presents
an innovative learning methodology for citizen science capable of taking
advantage of expert- and amateur-labelled data, featuring a transfer of labels
between experts and amateurs. The proposed approach first learns from
unlabelled data with a convolutional autoencoder and then exploits amateur and
expert labels via the pre-training and fine-tuning of a convolutional neural
network, respectively. We focus on the classification of galaxy images from the
Galaxy Zoo project, from which we test binary, multi-class, and imbalanced
classification scenarios. The results demonstrate that our solution is able to
improve classification performance compared to a set of baseline approaches,
deploying a promising methodology for learning from different confidence levels
in data labelling.
| arxiv topic:astro-ph.GA astro-ph.IM eess.IV |
arxiv_dataset-178542302.00466 | Hypersurfaces of $\mathbb{S}^2\times\mathbb{S}^2$ with constant
sectional curvature
math.DG
In this paper, we classify the hypersurfaces of
$\mathbb{S}^2\times\mathbb{S}^2$ with constant sectional curvature. By applying
the so-called Tsinghua principle, which was first discovered by the first three
authors in 2013 at Tsinghua University, we prove that the constant sectional
curvature can only be $\frac{1}{2}$ and the product angle function $C$ defined
by Urbano is identically zero. We show that any such hypersurface is a parallel
hypersurface of a minimal hypersurface in $\mathbb{S}^2\times\mathbb{S}^2$ with
$C=0$, and we establish a one-to-one correspondence between the involving
minimal hypersurface and the famous ``sinh-Gordon equation'' $$
(\frac{\partial^2}{\partial u^2}+\frac{\partial^2}{\partial v^2})h
=-\tfrac{1}{\sqrt{2}}\sinh(\sqrt{2}h). $$ As a byproduct, we give a complete
classification of the hypersurfaces of $\mathbb{S}^2\times\mathbb{S}^2$ with
constant mean curvature and constant product angle function $C$.
| arxiv topic:math.DG |
arxiv_dataset-178552302.00566 | NISQ-friendly measurement-based quantum clustering algorithms
quant-ph
Two novel measurement-based, quantum clustering algorithms are proposed based
on quantum parallelism and entanglement. The first algorithm follows a divisive
approach. The second algorithm is based on unsharp measurements, where we
construct an effect operator with a Gaussian probability distribution to
cluster similar data points. A major advantage of both algorithms is that they
are simplistic in nature, easy to implement, and well suited for noisy
intermediate scale quantum computers. We have successfully applied the first
algorithm on a concentric circle data set, where the classical clustering
approach fails, as well as on the Churrtiz data set of $130$ cities, where we
show that the algorithm succeeds with very low quantum resources. We applied
the second algorithm on the labeled Wisconsin breast cancer dataset, and found
that it is able to classify the dataset with high accuracy using only
$O(log(D))$ qubits and polynomial measurements, where $D$ is the maximal
distance within any two points in the dataset. We also show that this algorithm
works better with an assumed measurement error in the quantum system, making it
extremely well-suited for NISQ devices.
| arxiv topic:quant-ph |
arxiv_dataset-178562302.00666 | Rapid Identification and Classification of Eccentric Gravitational Wave
Inspirals with Machine Learning
gr-qc astro-ph.HE
Current templated searches for gravitational waves (GWs) emanated from
compact binary coalescences (CBCs) assume that the binaries have circularized
by the time they enter the sensitivity band of the LIGO-Virgo-KAGRA (LVK)
network. However, certain formation channels predict that in future observing
runs (O4 and beyond), a fraction of detectable binaries could enter the
sensitivity band with a measurable eccentricity $e$. Constraining $e$ for each
GW event with Bayesian parameter estimation methods is computationally
expensive and time-consuming. This motivates the need for a machine learning
based identification and classification scheme, which could weed out the
majority of GW events as non-eccentric and drastically reduce the set of
candidate eccentric GWs. As a proof of principle, we train a
separable-convolutional neural network (SCNN) with spectrograms of synthetic
GWs added to Gaussian noise characterized by O4 representative \texttt{PSD}s.
We use the trained network to (i) segregate candidates as either eccentric or
non-eccentric (henceforth called the detection problem) and (ii) classify the
events as non-eccentric $(e = 0)$, moderately eccentric $(e \in (0, 0.2])$, and
highly eccentric $(e \in (0.2, 0.5])$. On the detection problem, our best
performing network detects eccentricity with $0.914$ accuracy and true and
false positive rates of $0.862$ and $0.138$, respectively. On the
classification problem, the best performing network classifies signals with
$0.853$ accuracy. We find that our trained detector displays close to ideal
behavior for the data we consider.
| arxiv topic:gr-qc astro-ph.HE |
arxiv_dataset-178572302.00766 | Privacy Risk for anisotropic Langevin dynamics using relative entropy
bounds
cs.LG
The privacy preserving properties of Langevin dynamics with additive
isotropic noise have been extensively studied. However, the isotropic noise
assumption is very restrictive: (a) when adding noise to existing learning
algorithms to preserve privacy and maintain the best possible accuracy one
should take into account the relative magnitude of the outputs and their
correlations; (b) popular algorithms such as stochastic gradient descent (and
their continuous time limits) appear to possess anisotropic covariance
properties. To study the privacy risks for the anisotropic noise case, one
requires general results on the relative entropy between the laws of two
Stochastic Differential Equations with different drifts and diffusion
coefficients. Our main contribution is to establish such a bound using
stability estimates for solutions to the Fokker-Planck equations via functional
inequalities. With additional assumptions, the relative entropy bound implies
an $(\epsilon,\delta)$-differential privacy bound or translates to bounds on
the membership inference attack success and we show how anisotropic noise can
lead to better privacy-accuracy trade-offs. Finally, the benefits of
anisotropic noise are illustrated using numerical results in quadratic loss and
neural network setups.
| arxiv topic:cs.LG |
arxiv_dataset-178582302.00866 | Exploring the Relationship Between Softness and Excess Entropy in
Glass-forming Systems
cond-mat.soft
We explore the relationship between a machine-learned structural quantity
(softness) and excess entropy in simulations of supercooled liquids. Excess
entropy is known to scale well the dynamical properties of liquids, but this
quasi-universal scaling is known to breakdown in the supercooled and glassy
regimes. Using numerical simulations, we test whether a local form of the
excess entropy can lead to predictions that derive from softness, which has
been shown to correlate well with the tendency for individual particles to
rearrange. To that end, we explore leveraging softness to compute excess
entropy in the traditional fashion over softness groupings. Our results show
that by computing the excess entropy over softness-binned groupings, we can
build a strong quantitative relationship between the rearrangement barriers
across the explored systems.
| arxiv topic:cond-mat.soft |
arxiv_dataset-178592302.00966 | Coupling of Yu-Shiba-Rusinov states in 1D chains of Fe atoms on Nb(110)
cond-mat.supr-con cond-mat.mes-hall
The hybridization of Yu-Shiba-Rusinov states in dimers of magnetic impurities
leads to molecular-like bonding and antibonding modes. In many-impurity
systems, the interaction gives rise to YSR bands and can even result in the
formation of a topologically non-trivial superconducting state, characterized
by Majorana fermions at the edges of the system. To obtain a more detailed
understanding of these interactions, we investigate the coupling of YSR states
in short one-dimensional Fe chains on clean Nb(110). We observe a splitting of
the single-atom YSR peaks into multiple states with even or odd spatial
symmetry and identify a peculiar dependence of the even and odd states' energy
position on the chain length.
| arxiv topic:cond-mat.supr-con cond-mat.mes-hall |
arxiv_dataset-178602302.01066 | Approximate Reversible Circuits for NISQ-Era Quantum Computers
cs.ET
The synthesis approaches for quantum circuits typically aim at minimizing the
number of lines or gates. Given the tight restrictions on those logical
resources in physical implementations, we propose to view the problem
fundamentally different: Given noisy gates and a fixed number of lines, how can
we use them to perform a computation as precisely as possible? In this paper we
show approximate circuits can be deployed for computations with limited
resources. Performing experiments on a QC simulator, we show that under the
influence of noise, approximate circuits can have lower error-rates than exact
circuits.
| arxiv topic:cs.ET |
arxiv_dataset-178612302.01166 | Faithful guiding-center orbits in an axisymmetric magnetic field
physics.plasm-ph
The problem of the charged-particle motion in an axisymmetric magnetic
geometry is used to assess the validity of higher-order Hamiltonian
guiding-center theory, which includes higher-order corrections associated with
gyrogauge invariance as well as guiding-center polarization induced by
magnetic-field non-uniformity. Two axisymmetric magnetic geometries are
considered: a magnetic mirror geometry and a simple tokamak geometry. When a
magnetically-confined charged-particle orbit is regular (i.e., its
guiding-center magnetic moment is adiabatically invariant), the guiding-center
approximation, which conserves both energy and azimuthal canonical angular
momentum, is shown to be faithful to the particle orbit when higher-order
corrections are taken into account.
| arxiv topic:physics.plasm-ph |
arxiv_dataset-178622302.01266 | A room-temperature moir\'e interlayer exciton laser
physics.optics cond-mat.mes-hall
Moir\'e superlattices in van der Waals heterostructures offer highly tunable
quantum systems with emergent electronic and excitonic properties such as
superconductivity, topological edge states, and moir\'e-trapped excitons.
Theoretical calculations predicted the existence of the moir\'e potential at
elevated temperatures; however, its impact on the optical properties of
interlayer excitons (IXs) at room temperature is lacking, and the benefits of
the moir\'e effects for lasing applications remain unexplored. We report that
the moir\'e potential in a molybdenum disulfide/tungsten diselenide (MoS2/WSe2)
heterobilayer system can significantly enhance light emission, elongate the IX
lifetime, and modulate the IX emission energy at room temperature. By
integrating a moir\'e superlattice with a silicon topological nanocavity, we
achieve ultra-low-threshold lasing at the technologically important
telecommunication O-band thanks to the significant moir\'e modulation.
Moreover, the high-quality topological nanocavities facilitate the highest
spectral coherence of < 0.1 nm linewidth among all reported two-dimensional
material-based laser systems. Our findings not only open a new avenue for
studying correlated states at elevated temperatures, but also enable novel
architectures for integrated on-chip photonics and optoelectronics.
| arxiv topic:physics.optics cond-mat.mes-hall |
arxiv_dataset-178632302.01366 | Exploiting Extensive-Form Structure in Empirical Game-Theoretic Analysis
cs.GT
Empirical game-theoretic analysis (EGTA) is a general framework for reasoning
about complex games using agent-based simulation. Data from simulating select
strategy profiles is employed to estimate a cogent and tractable game model
approximating the underlying game. To date, EGTA methodology has focused on
game models in normal form; though the simulations play out in sequential
observations and decisions over time, the game model abstracts away this
temporal structure. Richer models of \textit{extensive-form games} (EFGs)
provide a means to capture temporal patterns in action and information, using
tree representations. We propose \textit{tree-exploiting EGTA} (TE-EGTA), an
approach to incorporate EFG models into EGTA\@. TE-EGTA constructs game models
that express observations and temporal organization of activity, albeit at a
coarser grain than the underlying agent-based simulation model. The idea is to
exploit key structure while maintaining tractability. We establish
theoretically and experimentally that exploiting even a little temporal
structure can vastly reduce estimation error in strategy-profile payoffs
compared to the normal-form model. Further, we explore the implications of EFG
models for iterative approaches to EGTA, where strategy spaces are extended
incrementally. Our experiments on several game instances demonstrate that
TE-EGTA can also improve performance in the iterative setting, as measured by
the quality of equilibrium approximation as the strategy spaces are expanded.
| arxiv topic:cs.GT |
arxiv_dataset-178642302.01466 | Semi-dilute rheology of particle suspensions: derivation of Doi-type
models
math.AP
This work is devoted to the large-scale rheology of suspensions of
non-Brownian inertialess rigid particles, possibly self-propelling, suspended
in Stokes flow. Starting from a hydrodynamic model, we derive a semi-dilute
mean-field description in form of a Doi-type model, which is given by a
'macroscopic' effective Stokes equation coupled with a 'microscopic' Vlasov
equation for the statistical distribution of particle positions and
orientations. This accounts for some non-Newtonian effects since the viscosity
in the effective Stokes equation depends on the local distribution of particle
orientations via Einstein's formula. The main difficulty is the detailed
analysis of multibody hydrodynamic interactions between the particles, which we
perform by means of a cluster expansion combined with a multipole expansion in
a suitable dilute regime.
| arxiv topic:math.AP |
arxiv_dataset-178652302.01566 | Measured and projected beam backgrounds in the Belle II experiment at
the SuperKEKB collider
hep-ex physics.ins-det
The Belle II experiment at the SuperKEKB electron-positron collider aims to
collect an unprecedented data set of $50~{\rm ab}^{-1}$ to study $CP$-violation
in the $B$-meson system and to search for Physics beyond the Standard Model.
SuperKEKB is already the world's highest-luminosity collider. In order to
collect the planned data set within approximately one decade, the target is to
reach a peak luminosity of $\rm 6 \times 10^{35}~cm^{-2}s^{-1}$ by further
increasing the beam currents and reducing the beam size at the interaction
point by squeezing the betatron function down to $\beta^{*}_{\rm y}=\rm
0.3~mm$. To ensure detector longevity and maintain good reconstruction
performance, beam backgrounds must remain well controlled. We report on current
background rates in Belle II and compare these against simulation. We find that
a number of recent refinements have significantly improved the background
simulation accuracy. Finally, we estimate the safety margins going forward. We
predict that backgrounds should remain high but acceptable until a luminosity
of at least $\rm 2.8 \times 10^{35}~cm^{-2}s^{-1}$ is reached for
$\beta^{*}_{\rm y}=\rm 0.6~mm$. At this point, the most vulnerable Belle II
detectors, the Time-of-Propagation (TOP) particle identification system and the
Central Drift Chamber (CDC), have predicted background hit rates from
single-beam and luminosity backgrounds that add up to approximately half of the
maximum acceptable rates.
| arxiv topic:hep-ex physics.ins-det |
arxiv_dataset-178662302.01666 | The Analytical Method algorithm for trigger primitives generation at the
LHC Drift Tubes detector
hep-ex physics.ins-det
The Compact Muon Solenoid (CMS) experiment prepares its Phase-2 upgrade for
the high-luminosity era of the LHC operation (HL-LHC). Due to the increase of
occupancy, trigger latency and rates, the full electronics of the CMS Drift
Tube (DT) chambers will need to be replaced. In the new design, the time bin
for the digitisation of the chamber signals will be of around 1~ns, and the
totality of the signals will be forwarded asynchronously to the service cavern
at full resolution. The new backend system will be in charge of building the
trigger primitives of each chamber. These trigger primitives contain the
information at chamber level about the muon candidates position, direction, and
collision time, and are used as input in the L1 CMS trigger. The added
functionalities will improve the robustness of the system against ageing. An
algorithm based on analytical solutions for reconstructing the DT trigger
primitives, called Analytical Method, has been implemented both as a software
C++ emulator and in firmware. Its performance has been estimated using the
software emulator with simulated and real data samples, and through hardware
implementation tests. Measured efficiencies are 96 to 98\% for all qualities
and time and spatial resolutions are close to the ultimate performance of the
DT chambers. A prototype chain of the HL-LHC electronics using the Analytical
Method for trigger primitive generation has been installed during Long Shutdown
2 of the LHC and operated in CMS cosmic data taking campaigns in 2020 and 2021.
Results from this validation step, the so-called Slice Test, are presented.
| arxiv topic:hep-ex physics.ins-det |
arxiv_dataset-178672302.01766 | Avalanche: A PyTorch Library for Deep Continual Learning
cs.LG
Continual learning is the problem of learning from a nonstationary stream of
data, a fundamental issue for sustainable and efficient training of deep neural
networks over time. Unfortunately, deep learning libraries only provide
primitives for offline training, assuming that model's architecture and data
are fixed. Avalanche is an open source library maintained by the ContinualAI
non-profit organization that extends PyTorch by providing first-class support
for dynamic architectures, streams of datasets, and incremental training and
evaluation methods. Avalanche provides a large set of predefined benchmarks and
training algorithms and it is easy to extend and modular while supporting a
wide range of continual learning scenarios. Documentation is available at
\url{https://avalanche.continualai.org}.
| arxiv topic:cs.LG |
arxiv_dataset-178682302.01866 | Coxeter quiver representations in fusion categories and Gabriel's
theorem
math.RT math.CT math.QA
We introduce a notion of representation for a class of generalised quivers
known as Coxeter quivers. These representations are built using fusion
categories associated to $U_q(\mathfrak{s}\mathfrak{l}_2)$ at roots of unity
and we show that many of the classical results on representations of quivers
can be generalised to this setting. Namely, we prove a generalised Gabriel's
theorem for Coxeter quivers that encompasses all Coxeter--Dynkin diagrams --
including the non-crystallographic types $H$ and $I$. Moreover, a similar
relation between reflection functors and Coxeter theory is used to show that
the indecomposable representations correspond bijectively to the positive roots
of Coxeter root systems over fusion rings.
| arxiv topic:math.RT math.CT math.QA |
arxiv_dataset-178692302.01966 | Towards an Understanding of Distributed Asymmetric Collaborative
Visualization on Problem-solving
cs.HC
This paper provided empirical knowledge of the user experience for using
collaborative visualization in a distributed asymmetrical setting through
controlled user studies. With the ability to access various computing devices,
such as Virtual Reality (VR) head-mounted displays, scenarios emerge when
collaborators have to or prefer to use different computing environments in
different places. However, we still lack an understanding of using VR in an
asymmetric setting for collaborative visualization. To get an initial
understanding and better inform the designs for asymmetric systems, we first
conducted a formative study with 12 pairs of participants. All participants
collaborated in asymmetric (PC-VR) and symmetric settings (PC-PC and VR-VR). We
then improved our asymmetric design based on the key findings and observations
from the first study. Another ten pairs of participants collaborated with
enhanced PC-VR and PC-PC conditions in a follow-up study. We found that a
well-designed asymmetric collaboration system could be as effective as a
symmetric system. Surprisingly, participants using PC perceived less mental
demand and effort in the asymmetric setting (PC-VR) compared to the symmetric
setting (PC-PC). We provided fine-grained discussions about the trade-offs
between different collaboration settings.
| arxiv topic:cs.HC |
arxiv_dataset-178702302.02066 | Calculating spin-lattice interactions in ferro- and antiferromagnets:
the role of symmetry, dimension and frustration
cond-mat.mtrl-sci
Recently, the interplay between spin and lattice degrees of freedom has
gained a lot of attention due to its importance for various fundamental
phenomena as well as for spintronic and magnonic applications. Examples are
ultrafast angular momentum transfer between the spin and lattice subsystems
during ultrafast demagnetization, frustration driven by structural distortions
in transition metal oxides, or in acoustically driven spin-wave resonances. In
this work, we provide a systematic analysis of spin-lattice interactions for
ferro- and antiferromagnetic materials and focus on the role of lattice
symmetries and dimensions, magnetic order, and the relevance of spin-lattice
interactions for angular momentum transfer as well as magnetic frustration. For
this purpose, we use a recently developed scheme which allows an efficient
calculation of spin-lattice interaction tensors from first principles. In
addition to that, we provide a more accurate and self consistent scheme to
calculate ab initio spin lattice interactions by using embedded clusters which
allows to benchmark the performance of the scheme introduced previously.
| arxiv topic:cond-mat.mtrl-sci |
arxiv_dataset-178712302.02166 | The cradle of nonlinear asteroseismology: observations of oscillation
mode variability in compact pulsating stars
astro-ph.SR
We briefly review progress in developing a pathway to nonlinear
astereoseismology, both from theoretical and observational aspects. As
predicted by the theory of weak nonlinear interactions between resonant modes,
their amplitude and frequency can be modulated according to various kinds of
patterns. However, those subtle modulations could hardly be well characterized
from ground-based photometric monitoring. The {\sl Kepler} spacecraft offered a
new window to find clear-cut evidence of well-determined amplitude and
frequency modulations, leading to the first discoveries of such variations in
pulsating white dwarf and hot B subdwarf stars. Following that direction, a
systematic survey of oscillation mode properties in compact pulsators monitored
by {\sl Kepler} suggests that mode variability is likely a common phenomenon,
which remain unaccounted for by standard linear non-radial pulsation theory. To
reach this conclusion firmly, the survey has now been extended to a larger
context including compact stars observed by K2 and TESS. We expect that this
extended survey will help to constrain key parameters governing weak nonlinear
effects in stellar oscillations.
| arxiv topic:astro-ph.SR |
arxiv_dataset-178722302.02266 | Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning
cs.RO
Multi-agent motion planning (MAMP) is a critical challenge in applications
such as connected autonomous vehicles and multi-robot systems. In this paper,
we propose a space-time conflict resolution approach for MAMP. We formulate the
problem using a novel, flexible sphere-based discretization for trajectories.
Our approach leverages a depth-first conflict search strategy to provide the
scalability of decoupled approaches while maintaining the computational
guarantees of coupled approaches. We compose procedures for evading
discretization error and adhering to kinematic constraints in generated
solutions. Theoretically, we prove the continuous-time feasibility and
formulation-space completeness of our algorithm. Experimentally, we demonstrate
that our algorithm matches the performance of the current state of the art with
respect to both runtime and solution quality, while expanding upon the
abilities of current work through accommodation for both static and dynamic
obstacles. We evaluate our algorithm in various unsignalized traffic
intersection scenarios using CARLA, an open-source vehicle simulator. Results
show significant success rate improvement in spatially constrained settings,
involving both connected and non-connected vehicles. Furthermore, we maintain a
reasonable suboptimality ratio that scales well among increasingly complex
scenarios.
| arxiv topic:cs.RO |
arxiv_dataset-178732302.02366 | Evolution of the spiral structure of galaxies from the HST COSMOS field
astro-ph.GA
We have investigated the pitch angle ($\psi$) of the spiral arms of galaxies
in the Hubble Space Telescope COSMOS field. The sample consists of 102 face-on
galaxies with a two-armed pattern at a mean redshift $\langle z \rangle \approx
0.5$. The typical values of $\psi$ in the spiral arms of distant galaxies are
shown to be close to those for nearby spiral galaxies. Within one galaxy the
scatter of $\psi$ for different arms is, on average, half the mean pitch angle.
In the $z$ range from 1 to 0 we have found a tendency for $\psi$ to decrease.
Our analysis of the $\psi$ distributions in galaxies at different redshifts is
consistent with the assumption that in most of the galaxies at $z \leq 0.5$ the
spiral arms are tidal in origin or they arose from transient recurrent
instabilities in their disks.
| arxiv topic:astro-ph.GA |
arxiv_dataset-178742302.02466 | An uncertainty principle for M\"obius inversion on posets
math.CO
We give conditions for a locally finite poset $P$ to have the property that
for any functions $f:P\to {\bf C}$ and $g:P\to {\bf C}$ not identically zero
and linked by the M\"obius inversion formula, the support of at least one of
$f$ and $g$ is infinite. This generalises and gives an entirely poset-theoretic
proof of a result of Pollack. Various examples and non-examples are discussed.
| arxiv topic:math.CO |
arxiv_dataset-178752302.02566 | Mobile Cell-Free Massive MIMO: Challenges, Solutions, and Future
Directions
cs.IT math.IT
Cell-free (CF) massive multiple-input multiple-output (MIMO) systems, which
exploit many geographically distributed access points to coherently serve user
equipments via spatial multiplexing on the same time-frequency resource, has
become a vital component of the next-generation mobile communication networks.
Theoretically, CF massive MIMO systems have many advantages, such as large
capacity, great coverage, and high reliability, but several obstacles must be
overcome. In this article, we study the paradigm of CF massive MIMO-aided
mobile communications, including the main application scenarios and associated
deployment architectures. Furthermore, we thoroughly investigate the challenges
of CF massive MIMO-aided mobile communications. We then exploit a novel
predictor antenna, hierarchical cancellation, rate-splitting and dynamic
clustering system for CF massive MIMO. Finally, several important research
directions regarding CF massive MIMO for mobile communications are presented to
facilitate further investigation.
| arxiv topic:cs.IT math.IT |
arxiv_dataset-178762302.02666 | Symmetry Resolved Entanglement of Excited States in Quantum Field Theory
III: Bosonic and Fermionic Negativity
hep-th cond-mat.stat-mech quant-ph
In two recent works, we studied the symmetry resolved R\'enyi entropies of
quasi-particle excited states in quantum field theory. We found that the
entropies display many model-independent features which we discussed and
analytically characterised. In this paper we extend this line of investigation
by providing analytical and numerical evidence that a similar universal
behavior arises for the symmetry resolved negativity. In particular, we compute
the ratio of charged moments of the partially transposed reduced density matrix
as an expectation value of twist operators. These are ``fused" versions of the
more traditionally used branch point twist fields and were introduced in a
previous work. The use of twist operators allows us to perform the computation
in an arbitrary number of spacial dimensions. We show that, in the large-volume
limit, only the commutation relations between the twist operators and local
fields matter, and computations reduce to a purely combinatorial problem. We
address some specific issues regarding fermionic excitations, whose treatment
requires the notion of partial time-reversal transformation, and we discuss the
differences and analogies with their bosonic counterpart. We find that although
the operation of partial transposition requires a redefinition for fermionic
theories, the ratio of the negativity moments between an excited state and the
ground state is universal and identical for fermions and bosons as well as for
a large variety of very different states, ranging from simple qubit states to
the excited states of free quantum field theories. Our predictions are tested
numerically on a 1D Fermi chain.
| arxiv topic:hep-th cond-mat.stat-mech quant-ph |
arxiv_dataset-178772302.02766 | Generalization Bounds with Data-dependent Fractal Dimensions
stat.ML cs.LG
Providing generalization guarantees for modern neural networks has been a
crucial task in statistical learning. Recently, several studies have attempted
to analyze the generalization error in such settings by using tools from
fractal geometry. While these works have successfully introduced new
mathematical tools to apprehend generalization, they heavily rely on a
Lipschitz continuity assumption, which in general does not hold for neural
networks and might make the bounds vacuous. In this work, we address this issue
and prove fractal geometry-based generalization bounds without requiring any
Lipschitz assumption. To achieve this goal, we build up on a classical covering
argument in learning theory and introduce a data-dependent fractal dimension.
Despite introducing a significant amount of technical complications, this new
notion lets us control the generalization error (over either fixed or random
hypothesis spaces) along with certain mutual information (MI) terms. To provide
a clearer interpretation to the newly introduced MI terms, as a next step, we
introduce a notion of "geometric stability" and link our bounds to the prior
art. Finally, we make a rigorous connection between the proposed data-dependent
dimension and topological data analysis tools, which then enables us to compute
the dimension in a numerically efficient way. We support our theory with
experiments conducted on various settings.
| arxiv topic:stat.ML cs.LG |
arxiv_dataset-178782302.02866 | Out of Sample Predictability in Predictive Regressions with Many
Predictor Candidates
econ.EM
This paper is concerned with detecting the presence of out of sample
predictability in linear predictive regressions with a potentially large set of
candidate predictors. We propose a procedure based on out of sample MSE
comparisons that is implemented in a pairwise manner using one predictor at a
time and resulting in an aggregate test statistic that is standard normally
distributed under the global null hypothesis of no linear predictability.
Predictors can be highly persistent, purely stationary or a combination of
both. Upon rejection of the null hypothesis we subsequently introduce a
predictor screening procedure designed to identify the most active predictors.
An empirical application to key predictors of US economic activity illustrates
the usefulness of our methods and highlights the important forward looking role
played by the series of manufacturing new orders.
| arxiv topic:econ.EM |
arxiv_dataset-178792302.02966 | Universal quantum computing with qubits embedded in trapped-ion qudits
quant-ph
Recent developments in qudit-based quantum computing, in particular with
trapped ions, open interesting possibilities for scaling quantum processors
without increasing the number of physical information carriers. In this work,
we propose a method for compiling quantum circuits in the case, where qubits
are embedded into qudits of experimentally relevant dimensionalities,
$d=3,\ldots,8$, for the trapped-ion platform. In particular, we demonstrate how
single-qubit, two-qubit, and multiqubit gates can be realized using
single-qudit operations and the Molmer-Sorensen (MS) gate as a basic
two-particle operation. We expect that our findings are directly applicable to
trapped-ion-based qudit processors.
| arxiv topic:quant-ph |
arxiv_dataset-178802302.03066 | On the equivalence between the minimax theorem and strong duality of
conic linear programming
math.OC cs.GT
We prove the almost equivalence between two-player zero-sum games and conic
linear programming problems in reflexive Banach spaces. The previous
fundamental results of von Neumann, Dantzig, Adler, and von Stengel on the
equivalence between linear programming and finite games with strategy sets
defined over $\mathbb{R}^n$, are therefore extended to more general strategy
spaces. More specifically, we show that for every two-player zero-sum game with
a bilinear payoff function of the form $u(x,y)=\langle y,Ax\rangle$, for some
linear operator $A$, and strategy sets that represent bases of convex cones,
the minimax theorem holds, and its game value and Nash equilibria can be
computed by solving a primal-dual pair of conic linear problems. Conversely,
the minimax theorem for the same class of games "almost always" implies strong
duality of conic linear programming. The main results are applied to a number
of infinite zero-sum games, whose classes include those of semi-infinite,
semidefinite, time-continuous, quantum, polynomial, and homogeneous separable
games.
| arxiv topic:math.OC cs.GT |
arxiv_dataset-178812302.03166 | Topological Phases on Quantum Trees
cond-mat.mes-hall
In this work, we present a theory for topological phases for quantum systems
on tree graphs. Conventionally, topological phases of matter have been studied
in regular lattices, but also in quasicrystals and amorphous settings. We
consider specific generalizations of regular tree graphs, and explore their
topological properties. Unlike conventional systems, infinite quantum trees are
not finite-dimensional, allowing for novel phenomena. We find a proliferation
of topological zero modes present throughout the entire system, indicating that
the bulk also acts as a boundary. We then go on to show that only three
symmetry classes host stable topological phases in contrast to the usual five
symmetry classes per dimension. Finally, we introduce what we call the
Su-Schrieffer-Heeger tree which is topologically non-trivial even in the
absence of inner degrees of freedom and does not possess any gapped trivial
phases. We realize this system in an electronic circuit and show that our
theory matches with experiments.
| arxiv topic:cond-mat.mes-hall |
arxiv_dataset-178822302.03266 | Learning to Count Isomorphisms with Graph Neural Networks
cs.LG
Subgraph isomorphism counting is an important problem on graphs, as many
graph-based tasks exploit recurring subgraph patterns. Classical methods
usually boil down to a backtracking framework that needs to navigate a huge
search space with prohibitive computational costs. Some recent studies resort
to graph neural networks (GNNs) to learn a low-dimensional representation for
both the query and input graphs, in order to predict the number of subgraph
isomorphisms on the input graph. However, typical GNNs employ a node-centric
message passing scheme that receives and aggregates messages on nodes, which is
inadequate in complex structure matching for isomorphism counting. Moreover, on
an input graph, the space of possible query graphs is enormous, and different
parts of the input graph will be triggered to match different queries. Thus,
expecting a fixed representation of the input graph to match diversely
structured query graphs is unrealistic. In this paper, we propose a novel GNN
called Count-GNN for subgraph isomorphism counting, to deal with the above
challenges. At the edge level, given that an edge is an atomic unit of encoding
graph structures, we propose an edge-centric message passing scheme, where
messages on edges are propagated and aggregated based on the edge adjacency to
preserve fine-grained structural information. At the graph level, we modulate
the input graph representation conditioned on the query, so that the input
graph can be adapted to each query individually to improve their matching.
Finally, we conduct extensive experiments on a number of benchmark datasets to
demonstrate the superior performance of Count-GNN.
| arxiv topic:cs.LG |
arxiv_dataset-178832302.03366 | Optimal wire cutting with classical communication
quant-ph
Circuit knitting is the process of partitioning large quantum circuits into
smaller subcircuits such that the result of the original circuits can be
deduced by only running the subcircuits. Such techniques will be crucial for
near-term and early fault-tolerant quantum computers, as the limited number of
qubits is likely to be a major bottleneck for demonstrating quantum advantage.
One typically distinguishes between gate cuts and wire cuts when partitioning a
circuit. The cost for any circuit knitting approach scales exponentially in the
number of cuts. One possibility to realize a cut is via the quasiprobability
simulation technique. In fact, we argue that all existing rigorous circuit
knitting techniques can be understood in this framework. Furthermore, we
characterize the optimal overhead for wire cuts where the subcircuits can
exchange classical information or not. We show that the optimal cost for
cutting $n$ wires without and with classical communication between the
subcircuits scales as $O(16^n)$ and $O(4^n)$, respectively.
| arxiv topic:quant-ph |
arxiv_dataset-178842302.03466 | Stand Up Indulgent Gathering
cs.DC cs.MA
We consider a swarm of mobile robots evolving in a bidimensional Euclidean
space. We study a variant of the crash-tolerant gathering problem: if no robot
crashes, robots have to meet at the same arbitrary location, not known
beforehand, in finite time; if one or several robots crash at the same
location, the remaining correct robots gather at the crash location to rescue
them. Motivated by impossibility results in the semi-synchronous setting, we
present the first solution to the problem for the fully synchronous setting
that operates in the vanilla Look-Compute-Move model with no additional
hypotheses: robots are oblivious, disoriented, have no multiplicity detection
capacity, and may start from arbitrary positions (including those with
multiplicity points). We furthermore show that robots gather in a time that is
proportional to the initial maximum distance between robots.
| arxiv topic:cs.DC cs.MA |
arxiv_dataset-178852302.03566 | Look Around and Learn: Self-Training Object Detection by Exploration
cs.CV
When an object detector is deployed in a novel setting it often experiences a
drop in performance. This paper studies how an embodied agent can automatically
fine-tune a pre-existing object detector while exploring and acquiring images
in a new environment without relying on human intervention, i.e., a fully
self-supervised approach. In our setting, an agent initially learns to explore
the environment using a pre-trained off-the-shelf detector to locate objects
and associate pseudo-labels. By assuming that pseudo-labels for the same object
must be consistent across different views, we learn the exploration policy Look
Around to mine hard samples, and we devise a novel mechanism called
Disagreement Reconciliation for producing refined pseudo-labels from the
consensus among observations. We implement a unified benchmark of the current
state-of-the-art and compare our approach with pre-existing exploration
policies and perception mechanisms. Our method is shown to outperform existing
approaches, improving the object detector by 6.2% in a simulated scenario, a
3.59% advancement over other state-of-the-art methods, and by 9.97% in the real
robotic test without relying on ground-truth. Code for the proposed approach
and baselines are available at
https://iit-pavis.github.io/Look_Around_And_Learn/.
| arxiv topic:cs.CV |
arxiv_dataset-178862302.03666 | Gravitational-Scalar Instability of a Cosmological Model Based on a
Two-Component System of Degenerate Scalarly Charged Fermions with Asymmetric
Higgs Interaction. I. Equations for Perturbations
gr-qc
A mathematical model is formulated for the evolution of plane perturbations
in a cosmological two-component statistical system of completely degenerate
scalarly charged fermions with an asymmetric scalar Higgs interaction. A
complete closed system of differential equations describing the unperturbed
state of a homogeneous and isotropic system and a system of self-consistent
evolution equations of small perturbations are constructed.
| arxiv topic:gr-qc |
arxiv_dataset-178872302.03766 | Newton iteration for lexicographic Gr\"obner bases in two variables
math.AC cs.SC math.AG
We present an $m$-adic Newton iteration with quadratic convergence for
lexicographic Gr\"obner basis of zero dimensional ideals in two variables. We
rely on a structural result about the syzygies in such a basis due to Conca and
Valla, that allowed them to explicitly describe these Gr\"obner bases by affine
parameters; our Newton iteration works directly with these parameters.
| arxiv topic:math.AC cs.SC math.AG |
arxiv_dataset-178882302.03866 | Thermodynamic instability of dark energy equation of state
gr-qc
We derive the dark energy fluid equation of state $P = -\epsilon = {\rm
const.}$ as an extremum of entropy, subject to the Hamiltonian constraint of
General Relativity. However, we identify perturbations that can render this
extremum an entropy minimum designating a thermodynamic instability and specify
the mathematical condition for this to occur.
| arxiv topic:gr-qc |
arxiv_dataset-178892302.03966 | Spanning trees in graphs without large bipartite holes
math.CO
We show that for any $\varepsilon>0$ and $\Delta\in\mathbb{N}$, there exists
$\alpha>0$ such that for sufficiently large $n$, every $n$-vertex graph $G$
satisfying that $\delta(G)\geq\varepsilon n$ and $e(X, Y)>0$ for every pair of
disjoint vertex sets $X, Y\subseteq V(G)$ of size $\alpha n$ contains all
spanning trees with maximum degree at most $\Delta$. This strengthens a result
of B\"ottcher et al.
| arxiv topic:math.CO |
arxiv_dataset-178902302.04066 | Time varying gratings model Hawking radiation
quant-ph physics.optics
Diffraction gratings synthetically moving at trans-luminal velocities contain
points where wave and grating velocities are equal. We show these points can be
understood as a series of optical event horizons where wave energy can be
trapped and amplified, leading to radiation from the quantum vacuum state. We
calculate the spectrum of this emitted radiation, finding a quasi-thermal
spectrum with features that depend on the grating profile, and an effective
temperature that scales exponentially with the length of the grating, emitting
a measurable flux even for very small grating contrast.
| arxiv topic:quant-ph physics.optics |
arxiv_dataset-178912302.04166 | GPTScore: Evaluate as You Desire
cs.CL
Generative Artificial Intelligence (AI) has enabled the development of
sophisticated models that are capable of producing high-caliber text, images,
and other outputs through the utilization of large pre-trained models.
Nevertheless, assessing the quality of the generation is an even more arduous
task than the generation itself, and this issue has not been given adequate
consideration recently. This paper proposes a novel evaluation framework,
GPTScore, which utilizes the emergent abilities (e.g., zero-shot instruction)
of generative pre-trained models to score generated texts. There are 19
pre-trained models explored in this paper, ranging in size from 80M (e.g.,
FLAN-T5-small) to 175B (e.g., GPT3). Experimental results on four text
generation tasks, 22 evaluation aspects, and corresponding 37 datasets
demonstrate that this approach can effectively allow us to achieve what one
desires to evaluate for texts simply by natural language instructions. This
nature helps us overcome several long-standing challenges in text
evaluation--how to achieve customized, multi-faceted evaluation without the
need for annotated samples. We make our code publicly available at
https://github.com/jinlanfu/GPTScore.
| arxiv topic:cs.CL |
arxiv_dataset-178922302.04266 | Large time behavior of fractional porous media equation
math.AP
Following the methodology of [Brasco and Volzone, Adv. Math. 2022], we study
the long-time behavior for the signed Fractional Porous Medium Equation in open
bounded sets with smooth boundary. Homogeneous exterior Dirichlet boundary
conditions are considered. We prove that if the initial datum has sufficiently
small energy, then the solution, once suitably rescaled, converges to a
nontrivial constant sign solution of a sublinear fractional Lane-Emden
equation. Furthermore, we give a nonlocal sufficient energetic criterion on the
initial datum, which is important to identify the exact limit profile, namely
the positive solution or the negative one.
| arxiv topic:math.AP |
arxiv_dataset-178932302.04366 | Machine learning detects multiplicity of the first stars in stellar
archaeology data
astro-ph.GA astro-ph.CO
In unveiling the nature of the first stars, the main astronomical clue is the
elemental compositions of the second generation of stars, observed as extremely
metal-poor (EMP) stars, in our Milky Way Galaxy. However, no observational
constraint was available on their multiplicity, which is crucial for
understanding early phases of galaxy formation. We develop a new data-driven
method to classify observed EMP stars into mono- or multi-enriched stars with
Support Vector Machines. We also use our own nucleosynthesis yields of
core-collapse supernovae with mixing-fallback that can explain many of observed
EMP stars. Our method predicts, for the first time, that $31.8\% \pm 2.3\%$ of
462 analyzed EMP stars are classified as mono-enriched. This means that the
majority of EMP stars are likely multi-enriched, suggesting that the first
stars were born in small clusters. Lower metallicity stars are more likely to
be enriched by a single supernova, most of which have high carbon enhancement.
We also find that Fe, Mg. Ca, and C are the most informative elements for this
classification. In addition, oxygen is very informative despite its low
observability. Our data-driven method sheds a new light on solving the mystery
of the first stars from the complex data set of Galactic archaeology surveys.
| arxiv topic:astro-ph.GA astro-ph.CO |
arxiv_dataset-178942302.04466 | A noncommutative weak type maximal inequality for modulated ergodic
averages with general weights
math.OA math.DS
In this article, we prove a weak type $(p,p)$ maximal inequality,
$1<p<\infty$, for weighted averages of a positive Dunford-Schwarz operator $T$
acting on a noncommutative $L_p$-space associated to a semifinite von Neumann
algebra $\mathcal{M}$, with weights in $W_q$, where
$\frac{1}{p}+\frac{1}{q}=1$. This result is then utilized to obtain modulated
individual ergodic theorems with $q$-Besicovitch and $q$-Hartman sequences as
weights. Multiparameter versions of these results are also investigated.
| arxiv topic:math.OA math.DS |
arxiv_dataset-178952302.04566 | Pointwise Kan extensions along 2-fibrations and the 2-category of
elements
math.CT math.LO
We study the 2-category of elements from an abstract point of view. We
generalize to dimension 2 the well-known result that the category of elements
can be captured by a comma object that also exhibits a pointwise left Kan
extension. For this, we propose an original definition of pointwise Kan
extension along a discrete 2-opfibration in the lax 3-category of 2-categories,
2-functors, lax natural transformations and modifications. Such definition uses
cartesian-marked lax limits, which are an alternative to weighted 2-limits. We
show that a pointwise Kan extension along a discrete 2-opfibration is always a
weak one as well. The proof is based on an original generalization of the
parametrized Yoneda lemma which is as lax as it can be.
| arxiv topic:math.CT math.LO |
arxiv_dataset-178962302.04666 | Understand Code Style: Efficient CNN-based Compiler Optimization
Recognition System
cs.PL
Compiler optimization level recognition can be applied to vulnerability
discovery and binary analysis. Due to the exists of many different compilation
optimization options, the difference in the contents of the binary file is very
complicated. There are thousands of compiler optimization algorithms and
multiple different processor architectures, so it is very difficult to manually
analyze binary files and recognize its compiler optimization level with rules.
This paper first proposes a CNN-based compiler optimization level recognition
model: BinEye. The system extracts semantic and structural differences and
automatically recognize the compiler optimization levels. The model is designed
to be very suitable for binary file processing and is easy to understand. We
built a dataset containing 80,028 binary files for the model training and
testing. Our proposed model achieves an accuracy of over 97%. At the same time,
BinEye is a fully CNN-based system and it has a faster forward calculation
speed, at least 8 times faster than the normal RNN-based model. Through our
analysis of the model output, we successfully found the difference in assembly
codes caused by the different compiler optimization level. This means that the
model we proposed is interpretable. Based on our model, we propose a method to
analyze the code differences caused by different compiler optimization levels,
which has great guiding significance for analyzing closed source compilers and
binary security analysis.
| arxiv topic:cs.PL |
arxiv_dataset-178972302.04766 | Interference and reflection from the event horizon of a quantum
corrected black hole
gr-qc hep-th
In this work, we calculate the Hawking temperature for a quantum corrected
black hole geometry using the $reflection$ $from$ $the$ $horizon$ method. We
observe that quantum gravity corrections indeed show up in the Hawking
temperature formula of the quantum corrected black hole. It is important to
notice that the quantum gravity corrections arise in the Hawking temperature
formula only due to the underlying quantum gravity corrections to the lapse
function of the black hole metric rather than the semi-classical methods used
in the analysis. We also substantiate our result by computing the Hawking
temperature using the tunneling approach.
| arxiv topic:gr-qc hep-th |
arxiv_dataset-178982302.04866 | RelightableHands: Efficient Neural Relighting of Articulated Hand Models
cs.CV cs.GR
We present the first neural relighting approach for rendering high-fidelity
personalized hands that can be animated in real-time under novel illumination.
Our approach adopts a teacher-student framework, where the teacher learns
appearance under a single point light from images captured in a light-stage,
allowing us to synthesize hands in arbitrary illuminations but with heavy
compute. Using images rendered by the teacher model as training data, an
efficient student model directly predicts appearance under natural
illuminations in real-time. To achieve generalization, we condition the student
model with physics-inspired illumination features such as visibility, diffuse
shading, and specular reflections computed on a coarse proxy geometry,
maintaining a small computational overhead. Our key insight is that these
features have strong correlation with subsequent global light transport
effects, which proves sufficient as conditioning data for the neural relighting
network. Moreover, in contrast to bottleneck illumination conditioning, these
features are spatially aligned based on underlying geometry, leading to better
generalization to unseen illuminations and poses. In our experiments, we
demonstrate the efficacy of our illumination feature representations,
outperforming baseline approaches. We also show that our approach can
photorealistically relight two interacting hands at real-time speeds.
https://sh8.io/#/relightable_hands
| arxiv topic:cs.CV cs.GR |
arxiv_dataset-178992302.04966 | Stability conditions and canonical metrics
math.DG math.AG
In this thesis we study the principle that extremal objects in differential
geometry correspond to stable objects in algebraic geometry. In our
introduction we survey the most famous instances of this principle with a view
towards the results and background needed in the later chapters. In Part I we
discuss the notion of a $Z$-critical metric recently introduced in joint work
with Ruadha\'i Dervan and Lars Martin Sektnan. We prove a correspondence for
existence with an analogue of Bridgeland stability in the large volume limit,
and study important properties of the subsolution condition away from this
limit, including identifying the analogues of the Donaldson and Yang-Mills
functionals for the equation. In Part II we study the recent theory of optimal
symplectic connections on K\"ahler fibrations in the isotrivial case. We prove
a correspondence with the existence of Hermite-Einstein metrics on holomorphic
principal bundles.
| arxiv topic:math.DG math.AG |
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