  In a multiuser context, the Bluetooth data from the smartphone could give an
approximation of the distance between users. Meanwhile, the Wi-Fi data can be
used to calculate the user's position directly. However, both the Wi-Fi-based
position outputs and Bluetooth-based distances are affected by some degree of
noise. In our work, we propose several approaches to combine the two types of
outputs for improving the tracking accuracy in the context of collaborative
positioning. The two proposed approaches attempt to build a model for measuring
the errors of the Bluetooth output and Wi-Fi output. In a non-temporal
approach, the model establishes the relationship in a specific interval of the
Bluetooth output and Wi-Fi output. In a temporal approach, the error
measurement model is expanded to include the time component between users'
movement. To evaluate the performance of the two approaches, we collected the
data from several multiuser scenarios in indoor environment. The results show
that the proposed approaches could reach a distance error around 3.0m for 75
percent of time, which outperforms the positioning results of the standard
Wi-Fi fingerprinting model.


  We discuss the extension of gauge-invariant electric and magnetic screening
masses in the Quark-Gluon Plasma to the case of a finite baryon density,
defining them in terms of a matrix of Polyakov loop correlators. We present
lattice results for $N_f=2+1$ QCD with physical quark masses, obtained using
the imaginary chemical potential approach, which indicate that the screening
masses increase as a function of $\mu_B$. A separate analysis is carried out
for the theoretically interesting case $\mu_B/T=3 i \pi$, where charge
conjugation is not explicitly broken and the usual definition of the screening
masses can be used for temperatures below the Roberge-Weiss transition.
Finally, we investigate the dependence of the static quark free energy on the
baryon chemical potential, showing that it is a decreasing function of $\mu_B$
which displays a peculiar behavior as the pseudocritical transition temperature
at $\mu_B=0$ is approached.


  A field algebra is a ``non-commutative'' generalization of a vertex algebra.
In this paper we develop foundations of the theory of field algebras.


  Community or modular structure is considered to be a significant property of
large scale real-world graphs such as social or information networks. Detecting
influential clusters or communities in these graphs is a problem of
considerable interest as it often accounts for the functionality of the system.
We aim to provide a thorough exposition of the topic, including the main
elements of the problem, a brief introduction of the existing research for both
disjoint and overlapping community search, the idea of influential communities,
its implications and the current state of the art and finally provide some
insight on possible directions for future research.


  Amorphous oxide semiconductors, especially indium oxide-based (InOx)
thin-films, have been major candidates for high mobility with easy-to-use
device processability. As one of the dopants in InOx semiconductors, we
proposed Si to design a thin-film transistor (TFT) channel. Because the
suppression of unstable oxygen vacancies in InOx is crucial to maintaining the
semiconducting behavior, Si was selected as a strong oxygen binder that is
reasonably available for large production. In this review, we focus on the
overall properties observed in Si-incorporated amorphous InOx TFTs in terms of
bond-dissociation energy, Gibbs free energy, Si-concentration dependence of TFT
properties, carrier transport mechanism, and bias stress instability. In
comparing low and high doping densities, we found that the activation energy
and density of states decreased at a high Si concentration in InOx TFTs,
implying that the trap density was reduced. As a result, stable operation under
bias stresses could be realized. Furthermore, the inverse Meyer-Neldel rule was
observed in the highly Si-doped InOx TFT, indicating reasonable ohmic contact.
Based on our fundamental knowledge of the Si-doped InOx film, we developed a
high-mobility bilayer TFT with a homogeneous stacked channel that was different
from a TFT with an etch stop layer structure. The TFT showed remarkably stable
operation. With simple element components based on InOx, it is possible to
systematically discuss vacancy engineering in terms of conduction properties.


  In this work we study maximal hypersurfaces in spatially open Generalized
Robertson-Walker spacetimes with Ricci-flat fiber by means of a generalized
maximum principle. In particular, under natural geometric and physical
assumptions we provide new uniqueness and non-existence results for complete
maximal hypersurfaces in spatially open Robertson-Walker spacetimes with flat
fiber. Moreover, our results are applied to relevant spacetimes as the steady
state spacetime, Einstein-de Sitter spacetime and radiation models.


  We study the problem of learning classification functions from noiseless
training samples, under the assumption that the decision boundary is of a
certain regularity. We establish universal lower bounds for this estimation
problem, for general classes of continuous decision boundaries. For the class
of locally Barron-regular decision boundaries, we find that the optimal
estimation rates are essentially independent of the underlying dimension and
can be realized by empirical risk minimization methods over a suitable class of
deep neural networks. These results are based on novel estimates of the $L^1$
and $L^\infty$ entropies of the class of Barron-regular functions.


  We prove that bilinear forms associated to the rough homogeneous singular
integrals $T_\Omega$ on $\mathbb R^d$, where the angular part $\Omega \in L^q
(S^{d-1})$ has vanishing average and $1<q\leq \infty$, and to Bochner-Riesz
means at the critical index in $\mathbb R^d$ are dominated by sparse forms
involving $(1,p)$ averages. This domination is stronger than the weak-$L^1$
estimates for $T_\Omega$ and for Bochner-Riesz means, respectively due to
Seeger and Christ. Furthermore, our domination theorems entail as a corollary
new sharp quantitative $A_p$-weighted estimates for Bochner-Riesz means and for
homogeneous singular integrals with unbounded angular part, extending previous
results of Hyt\"onen-Roncal-Tapiola for $T_\Omega$. Our results follow from a
new abstract sparse domination principle which does not rely on weak endpoint
estimates for maximal truncations.


  This paper studies mean-risk portfolio optimization models using the
conditional value-at-risk (CVaR) as a risk measure. We also employ a
cardinality constraint for limiting the number of invested assets. Solving such
a cardinality-constrained mean-CVaR model is computationally challenging for
two main reasons. First, this model is formulated as a mixed-integer
optimization (MIO) problem because of the cardinality constraint, so solving it
exactly is very hard when the number of investable assets is large. Second, the
problem size depends on the number of asset return scenarios, and the
computational efficiency decreases when the number of scenarios is large. To
overcome these challenges, we propose a high-performance algorithm named the
\emph{bilevel cutting-plane algorithm} for exactly solving the
cardinality-constrained mean-CVaR portfolio optimization problem. We begin by
reformulating the problem as a bilevel optimization problem and then develop a
cutting-plane algorithm for solving the upper-level problem. To speed up
computations for cut generation, we apply to the lower-level problem another
cutting-plane algorithm for efficiently minimizing CVaR with a large number of
scenarios. Moreover, we prove the convergence properties of our bilevel
cutting-plane algorithm. Numerical experiments demonstrate that, compared with
other MIO approaches, our algorithm can provide optimal solutions to large
problem instances faster.


  Clustering is an essential problem in machine learning and data mining. One
vital factor that impacts clustering performance is how to learn or design the
data representation (or features). Fortunately, recent advances in deep
learning can learn unsupervised features effectively, and have yielded state of
the art performance in many classification problems, such as character
recognition, object recognition and document categorization. However, little
attention has been paid to the potential of deep learning for unsupervised
clustering problems. In this paper, we propose a deep belief network with
nonparametric clustering. As an unsupervised method, our model first leverages
the advantages of deep learning for feature representation and dimension
reduction. Then, it performs nonparametric clustering under a maximum margin
framework -- a discriminative clustering model and can be trained online
efficiently in the code space. Lastly model parameters are refined in the deep
belief network. Thus, this model can learn features for clustering and infer
model complexity in an unified framework. The experimental results show the
advantage of our approach over competitive baselines.


  The formalism of the exact six polarization modes of gravitational waves is
constructed in terms of both the small metric perturbations and the
Newman-Penrose scalars. The obtained formulae are applicable to any
metric-compatible gravity theories whose gravitational waves propagate along
either the null or non-null geodesics. Once a gravity theory, specifically its
linearized wave equation, is written, comparison to the observed data of the
laser interferometer experiments is direct.


  We present the Databox application development environment or SDK as a means
of enabling trusted IoT app development at the network edge. The Databox
platform is a dedicated domestic platform that stores IoT, mobile and cloud
data and executes local data processing by third party apps to provide end-user
control over data flow and enable data minimisation. Key challenges for
building apps in edge environments concern i. the complexity of IoT devices and
user requirements, and ii. supporting privacy preserving features that meet new
data protection regulations. We show how the Databox SDK can ease the burden of
regulatory compliance and be used to sensitize developers to privacy related
issues in the very course of building apps. We present feedback on the SDK's
exposure to over 3000 people across a range of developer and industry events.


  We revise the 'no-signaling' condition for the supraluminal communication
between two spatially separated finite quantum systems of arbitrary dimensions,
thus generalizing a similar preceding approach for two-qubits: non-linear
evolution does not necessarily imply the possibility of supraluminal
communication between any sort of finite quantum systems.


  We prove the existence of cluster characters for Hom-infinite cluster
categories. For this purpose, we introduce a suitable mutation-invariant
subcategory of the cluster category. We sketch how to apply our results in
order to categorify any skew-symmetric cluster algebra. More applications and a
comparison to Derksen-Weyman-Zelevinsky's results will be given in a future
paper.


  The rate coefficient for radiative charge transfer between the helium ion and
an argon atom is calculated. The rate coefficient is about $10^{-14}$
cm${}^3$/s at 300 K in agreement with earlier experimental data.


  Given a surjective mapping $f : E \to F$ between Banach spaces, we
investigate the existence of a subspace $G$ of $E$, with the same density
character as $F$, such that the restriction of $f$ to $G$ remains surjective.
We obtain a positive answer whenever $f$ is continuous and uniformly open. In
the smooth case, we deduce a positive answer when $f$ is a $C^1$-smooth
surjection whose set of critical values is countable. Finally we show that,
when $f$ takes values in the Euclidean space $\mathbb R^n$, in order to obtain
this result it is not sufficient to assume that the set of critical values of
$f$ has zero-measure.


  Social norms are shared rules that govern and facilitate social interaction.
Violating such social norms via teasing and insults may serve to upend power
imbalances or, on the contrary reinforce solidarity and rapport in
conversation, rapport which is highly situated and context-dependent. In this
work, we investigate the task of automatically identifying the phenomena of
social norm violation in discourse. Towards this goal, we leverage the power of
recurrent neural networks and multimodal information present in the
interaction, and propose a predictive model to recognize social norm violation.
Using long-term temporal and contextual information, our model achieves an F1
score of 0.705. Implications of our work regarding developing a social-aware
agent are discussed.


  We aim to overcome the lack of diversity in responses of current dialogue
systems and to develop a dialogue system that is engaging as a conversational
partner. We propose a generator-evaluator model that evaluates multiple
responses generated by a response generator and selects the best response by an
evaluator. By generating multiple responses, we obtain diverse responses. We
conduct human evaluations to compare the output of the proposed system with
that of a baseline system. The results of the human evaluations showed that the
proposed system's responses were often judged to be better than the baseline
system's, and indicated the effectiveness of the proposed method.


  The Bindlib library for OCaml provides a set of tools for the manipulation of
data structures with variable binding. It is very well suited for the
representation of abstract syntax trees, and has already been used for the
implementation of half a dozen languages and proof assistants (including a new
version of the logical framework Dedukti). Bindlib is optimised for fast
substitution, and it supports variable renaming. Since the representation of
binders is based on higher-order abstract syntax, variable capture cannot arise
during substitution. As a consequence, variable names are not updated at
substitution time. They can however be explicitly recomputed to avoid "visual
capture" (i.e., distinct variables with the same apparent name) when a data
structure is displayed.


  Web requests are growing exponentially since the 90s due to the rapid
development of the Internet. This process was further accelerated by the
introduction of cloud services. It has been observed statistically that memory
or web requests generally follow power-law distribution, Breslau et al.
INFOCOM'99. That is, the $i^{\text{th}}$ most popular web page is requested
with a probability proportional to $1 / i^{\alpha}$ ($\alpha > 0$ is a
constant). Furthermore, this study, which was performed more than 20 years ago,
indicated Zipf-like behavior, i.e., that $\alpha \le 1$. Surprisingly, the
memory access traces coming from petabyte-size modern cloud systems not only
show that $\alpha$ can be bigger than one but also illustrate a shifted
power-law distribution -- called Pareto type II or Lomax. These previously not
reported phenomenon calls for statistical explanation.
  Our first contribution is a new statistical {\it multi-core power-law} model
indicating that double-power law can be attributed to the presence of multiple
cores running many virtual machines in parallel on such systems. We verify
experimentally the applicability of this model using the Kolmogorov-Smirnov
test (K-S test).
  The second contribution of this paper is a theoretical analysis indicating
why LRU and LFU-based algorithms perform well in practice on data satisfying
power-law or multi-core assumptions. We provide an explanation by studying the
online paging problem in the stochastic input model, i.e., the input is a
random sequence with each request independently drawn from a page set according
to a distribution $\pi$. We derive formulas (as a function of the page
probabilities in $\pi$) to upper bound their ratio-of-expectations, which help
in establishing O(1) performance ratio given the random sequence following
power-law and multi-core power-law distributions.


  The phenomenon of fully coherent energy loss (FCEL) in the collisions of
protons on light ions affects the physics of cosmic ray air showers. As an
illustration, we address two closely related observables: hadron production in
forthcoming proton-oxygen collisions at the LHC, and the atmospheric neutrino
fluxes induced by the semileptonic decays of hadrons produced in proton-air
collisions. In both cases, a significant nuclear suppression due to FCEL is
predicted. The conventional and prompt neutrino fluxes are suppressed by $\sim
10...25\%$ in their relevant neutrino energy ranges. Previous estimates of
atmospheric neutrino fluxes should be scaled down accordingly to account for
FCEL.


  This study aims to build a theoretical model to describe the ignition process
of spherical spray flame laden with fuel droplets under overall fuel-lean
condition. Two characteristic fronts are introduced to reveal where the
evaporation starts and ends in the ignition process. The results demonstrate
that the flame has been strengthened with extra fuel droplet adding while the
minimum ignition energy (MIE) is decreased. The evolution of the two
evaporation fronts in the flame kernel and propagating branches has also be
revealed coupled with the evaporation zone length.


  The aim of this note is to exhibit explicit sufficient criteria ensuring
bigness of globally generated, rank-$r$ vector bundles, $r \geqslant 2$, on
smooth, projective varieties of even dimension $d \leqslant 4$. We also discuss
connections of our general criteria to some recent results of other authors, as
well as applications to tangent bundles of Fano varieties, to suitable
Lazarsfeld-Mukai bundles on four-folds, etcetera.


  Square-root topology is one of the newest additions to the ever expanding
field of topological insulators (TIs). It characterizes systems that relate to
their parent TI through the squaring of their Hamiltonians. Extensions to
$2^n$-root topology, where $n$ is the number of squaring operations involved in
retrieving the parent TI, were quick to follow. Here, we go one step further
and develop the framework for designing general $n$-root TIs, with $n$ any
positive integer, using the Su-Schrieffer-Heeger (SSH) model as the parent TI
from which the higher-root versions are constructed. The method relies on using
loops of unidirectional couplings as building blocks, such that the resulting
model is non-Hermitian and embedded with a generalized chiral symmetry. Edge
states are observed at the $n$ branches of the complex energy spectrum,
appearing within what we designate as a ring gap, shown to be irreducible to
the usual point or line gaps. We further detail on how such an $n$-root model
can be realistically implemented in photonic ring systems. Near perfect
unidirectional effective couplings between the main rings can be generated via
mediating auxiliary rings with modulated gains and losses. These induce high
imaginary gauge fields that strongly supress couplings in one direction, while
enhancing them in the other. We use these photonic lattices to validate and
benchmark the analytical predictions. Our results introduce a new class of
high-root topological models, as well as a route for their experimental
realization.


  The 'dyon' system of D'Hoker and Vinet consisting of a spin 1/2 particle with
anomalous gyromagnetic ratio 4 in the combined field of a Dirac monopole plus a
Coulomb plus a suitable $1/r^2$ potential (which arises in the long-range limit
of a self-dual monopole) is studied following Biedenharn's approach to the
Dirac-Coulomb problem: the explicit solution is obtained using the
`Biedenharn-Temple operator', $\Gamma$, and the extra two-fold degeneracy is
explained by the subtle supersymmetry generated by the 'Dyon Helicity' or
generalized `Biedenharn-Johnson-Lippmann' operator ${\cal R}$. The new SUSY
anticommutes with the chiral SUSY discussed previously.


  Pose estimation is a basic module in many robot manipulation pipelines.
Estimating the pose of objects in the environment can be useful for grasping,
motion planning, or manipulation. However, current state-of-the-art methods for
pose estimation either rely on large annotated training sets or simulated data.
Further, the long training times for these methods prohibit quick interaction
with novel objects. To address these issues, we introduce a novel method for
zero-shot object pose estimation in clutter. Our approach uses a hypothesis
generation and scoring framework, with a focus on learning a scoring function
that generalizes to objects not used for training. We achieve zero-shot
generalization by rating hypotheses as a function of unordered point
differences. We evaluate our method on challenging datasets with both textured
and untextured objects in cluttered scenes and demonstrate that our method
significantly outperforms previous methods on this task. We also demonstrate
how our system can be used by quickly scanning and building a model of a novel
object, which can immediately be used by our method for pose estimation. Our
work allows users to estimate the pose of novel objects without requiring any
retraining. Additional information can be found on our website
https://bokorn.github.io/zephyr/


  Research on Digital Nudging has become increasingly popular in the
Information Systems (IS) community. This paper presents an overview of the
current progress, a critical reflection and an outlook to further research
regarding Digital Nudging in IS. For this purpose, we conducted a comprehensive
literature review as well as an interview with Markus Weinmann from Rotterdam
School of Management at Erasmus University, one of the first scholars who
introduced Digital Nudging to the IS community, and Alexey Voinov, director of
the Centre on Persuasive Systems for Wise Adaptive Living at University of
Technology Sydney. The findings uncover a gap between what we know about what
constitutes Digital Nudging and how consequent requirements can actually be put
into practice. In this context, the original concept of Nudging bears inherent
challenges, e.g. regarding the focus on the individuals' welfare, which hence
also apply to Digital Nudging. Moreover, we need a better understanding of how
Nudging in digital choice environments differs from that in the offline world.
To further distinguish itself from other disciplines that already tested
various nudges in many different domains, Digital Nudging Research in IS may
benefit from a strong Design Science perspective, going beyond the test of
effectiveness and providing specific design principles for the different types
of digital nudges.


  We extend the classical phase-space distribution function to include the spin
and electromagnetic fields coupling and derive the modified constitutive
relations for charge current, energy-momentum tensor, and spin tensor. Because
of the coupling, the new tensors receive corrections to their perfect fluid
counterparts and make the background and spin fluid equations of motion
communicate with each other. We investigate special cases which are relevant in
high-energy heavy-ion collisions, including baryon free matter and large mass
limit. Using Bjorken symmetries, we find that spin polarization increases with
increasing magnetic field for an initially positive baryon chemical potential.
The corrections derived in this framework may help to explain the splitting
observed in Lambda hyperons spin polarization measurements.


  Recently it was shown that high density QCD supports of number of topological
defects. In particular, there are U(1)_Y strings that arise due to K^0
condensation that occurs when the strange quark mass is relatively large. The
unique feature of these strings is that they possess a nonzero K^+ condensate
that is trapped on the core. In the following we will show that these strings
(with nontrivial core structure) can form closed loops with conserved charge
and currents trapped on the string worldsheet. The presence of conserved
charges allows these topological defects, called vortons, to carry angular
momentum, which makes them classically stable objects. We also give arguments
demonstrating that vortons carry angular momentum very efficiently (in terms of
energy per unit angular momentum) such that they might be the important degrees
of freedom in the cores of neutron stars.


  We prove the equivariant Gromov-Witten theory of a nonsingular toric 3-fold X
with primary insertions is equivalent to the equivariant Donaldson-Thomas
theory of X. As a corollary, the topological vertex calculations by Agangic,
Klemm, Marino, and Vafa of the Gromov-Witten theory of local Calabi-Yau toric
3-folds are proven to be correct in the full 3-leg setting.


  Carbon nanotubes are of potential interest as heterogeneous catalysis
supports, in part because they offer a high surface area hexagonal array of
carbon atoms for columnar or epitaxial attachment. Fringe visibility modeling
of electron microscope lattice images allows one to investigate the
relationship between individual nanoparticles and such nanotube supports. We
show specifically how (111) columnar or epitaxial growth of FCC metal lattices,
on carbon nanotubes viewed side-on, results in well-defined patterns of
(111)-fringe orientations with respect to the tube axis. In the epitaxial case,
the observations also provide information on chirality of the nanotube's
outermost graphene sheet.


  Chern insulators, which are the lattice analogs of the quantum Hall states,
can potentially manifest high-temperature topological orders at zero magnetic
field to enable next-generation topological quantum devices. To date, integer
Chern insulators have been experimentally demonstrated in several systems at
zero magnetic field, but fractional Chern insulators have been reported only in
graphene-based systems under a finite magnetic field. The emergence of
semiconductor moir\'e materials, which support tunable topological flat bands,
opens a new opportunity to realize fractional Chern insulators. Here, we report
the observation of both integer and fractional Chern insulators at zero
magnetic field in small-angle twisted bilayer MoTe2 by combining the local
electronic compressibility and magneto-optical measurements. At hole filling
factor {\nu}=1 and 2/3, the system is incompressible and spontaneously breaks
time reversal symmetry. We determine the Chern number to be 1 and 2/3 for the
{\nu}=1 and {\nu}=2/3 gaps, respectively, from their dispersion in filling
factor with applied magnetic field using the Streda formula. We further
demonstrate electric-field-tuned topological phase transitions involving the
Chern insulators. Our findings pave the way for demonstration of quantized
fractional Hall conductance and anyonic excitation and braiding in
semiconductor moir\'e materials.


  We present a quantum heat engine based on a cavity with two oscillating
mirrors that confine a quantum field as the working substance. The engine
performs an Otto cycle during which the walls and a field mode interact via a
nonlinear Hamiltonian. Resonances between the frequencies of the cavity mode
and the walls allow to transfer heat from the hot and the cold bath by
exploiting the conversion between phononic and photonic excitations. We study
the time evolution of the system and show that net work can be extracted after
a full cycle. We evaluate the efficiency of the process.


  We prove two density theorems for quadrature domains in $\mathbb{C}^n$, $n
\geq 2$. It is shown that quadrature domains are dense in the class of all
product domains of the form $D \times \Omega$, where $D \subset
\mathbb{C}^{n-1}$ is a smoothly bounded domain satisfying Bell's Condition R
and $\Omega \subset \mathbb{C}$ is a smoothly bounded domain and also in the
class of all smoothly bounded complete Hartogs domains in $\mathbb{C}^2$.


  The Panoramic Search for Extraterrestrial Intelligence (PANOSETI) experiment
is designed to detect pulsed optical signals on nanosecond timescales. PANOSETI
is therefore sensitive to Cherenkov radiation generated by extensive air
showers, and can be used for gamma-ray astronomy. Each PANOSETI telescope uses
a 0.5 m Fresnel lens to focus light onto a 1024 pixel silicon photomultiplier
camera that images a 9.9$^\circ\times$9.9$^\circ$ square field of view. Recent
detections of PeV gamma-rays from extended sources in the Galactic Plane
motivate constructing an array with effective area and angular resolution
surpassing current observatories. The PANOSETI telescopes are much smaller and
far more affordable than traditional imaging atmospheric Cherenkov telescopes
(IACT), making them ideal instruments to construct such an array. We present
the results of coincident observations between two PANOSETI telescopes and the
gamma-ray observatory VERITAS, along with simulations characterizing the
performance of a PANOSETI IACT array.


  In what has become a standard eternal inflation picture of the string
landscape there are many problematic consequences and a difficulty defining
probabilities for the occurrence of each type of universe. One feature in
particular that might be philosophically disconcerting is the infinite cloning
of each individual and each civilization in infinite numbers of separated
regions of the multiverse. Even if this is not ruled out due to causal
separation one should ask whether the infinite cloning is a universal
prediction of string landscape models or whether there are scenarios in which
it is avoided. If a viable alternative cosmology can be constructed one might
search for predictions that might allow one to discriminate experimentally
between the models. We present one such scenario although, in doing so, we are
forced to give up several popular presuppositions including the absence of a
preferred frame and the homogeneity of matter in the universe. The model also
has several ancillary advantages. We also consider the future lifetime of the
current universe before becoming a light trapping region.


  We study the nuclear symmetry energy S(rho) and related quantities of nuclear
physics and nuclear astrophysics predicted generically by relativistic
mean-field (RMF) and Skyrme-Hartree-Fock (SHF) models. We establish a simple
prescription for preparing equivalent RMF and SHF parametrizations starting
from a minimal set of empirical constraints on symmetric nuclear matter,
nuclear binding energy and charge radii, enforcing equivalence of their Lorenz
effective masses, and then using the pure neutron matter (PNM) equation of
state (EoS) obtained from ab-initio calculations to optimize the pure isovector
parameters in the RMF and SHF models. We find the resulting RMF and SHF
parametrizations give broadly consistent predictions of the symmetry energy J
and its slope parameter L at saturation density within a tight range of <~2 MeV
and <~6 MeV respectively, but that clear model dependence shows up in the
predictions of higher-order symmetry energy parameters, leading to important
differences in (a) the slope of the correlation between J and L from the
confidence ellipse, (b) the isospin-dependent part of the incompressibility of
nuclear matter K_tau, (c) the symmetry energy at supra-saturation densities,
and (d) the predicted neutron star radii. The model dependence can lead to
about 1-2 km difference in predictions of the neutron star radius given
identical predicted values of J, L and symmetric nuclear matter (SNM)
saturation properties. Allowing the full freedom in the effective masses in
both models leads to constraints of 30<~J<~31.5 MeV, 35<~L<~60 MeV,
-330<~K_tau<~-216 MeV for the RMF model as a whole and 30<~J<~33 MeV, 28<~L<~65
MeV, -420<~K_tau<~-325 MeV for the SHF model as a whole. Notably, given PNM
constraints, these results place RMF and SHF models as a whole at odds with
some constraints on K_tau inferred from giant monopole resonance and neutron
skin experimental results.


  Ferroelectric memristors have attracted much attention as a type of
nonvolatile resistance switching memories in neuromorphic computing, image
recognition, and information storage. Their resistance switching mechanisms
have been studied several times in perovskite and complicated materials
systems. It was interpreted as the modulation of carrier transport by
polarization control over Schottky barriers. Here, we experimentally report the
isothermal resistive switching across a CoPt/MgZnO Schottky barrier using a
simple binary semiconductor. The crystal and texture properties showed
high-quality and single-crystal Co$_{0.30}$Pt$_{0.70}$/Mg$_{0.20}$Zn$_{0.80}$O
hetero-junctions. The resistive switching was examined by an electric-field
cooling method that exhibited a ferroelectric T$_C$ of MgZnO close to the bulk
value. The resistive switching across CoPt/MgZnO Schottky barrier was
accompanied by a change in the Schottky barrier height of 26.5 meV due to an
interfacial charge increase and/or orbital hybridization induced reversal of
MgZnO polarization. The magnitude of the reversed polarization was estimated to
be a reasonable value of 3.0 (8.25) $\mu$ C/cm$^2$ at 300 K (2 K). These
findings demonstrated the utilities of CoPt/MgZnO interface as a potential
candidate for ferroelectric memristors and can be extended to probe the
resistive switching of other hexagonal ferroelectric materials.


  In this letter, following an extensive experimental validation, we perform
constant-volume shearing simulations of non-Brownian granular suspensions using
the discrete element method coupled with the lattice Boltzmann method. We
choose a wide range of solid fractions, shear rates, fluid viscosities,
particle sizes, and inter-particle frictional coefficients to obtain a scaling
solution for the viscous behavior of suspensions in both dilute and dense
regimes. This letter demonstrates that, with a proposed dilute-dense
transitional solid fraction, $\phi_d$, there exists a strong correlation
between the inverse relative viscosity and the shear stress. This work
incorporates both the $\phi$-dependence and the $\dot{\gamma}$-dependence of
suspension viscosity in a universal framework, which provides a scaling
solution for granular suspensions across dilute and dense regimes and sheds
light on the dilute-dense transition mechanisms.


  The central regions of star-forming barred spiral galaxies can be devoid of
star formation because of the redistribution of gas along the length of the
bar. However, there can be gas outside the length of the bar that can host star
formation. We study a sample of barred disc galaxies in the local Universe with
an aim to discriminate between centrally quenched and globally quenched
galaxies based on their positions on star-formation-rate(SFR)--stellar mass
plots and to find a connection between the SFR of quenched galaxies and the
length of their bar. We classified barred galaxies as centrally quenched and
globally quenched based on their position on SFR--stellar mass plots, with SFRs
derived from H$\alpha$ flux and spectral energy distribution fits on combined
ultraviolet and optical flux. We selected galaxies as passive based on the
distance from the main sequence relation. From a total 2514 barred galaxies
studied here, we present 651 with suppressed star formation in their central
region but hosting star formation outside. We also find a possible correlation
between bar length and SFR for the galaxies that are fully quenched because of
the stellar bar.


  In this article we give an upper bound for the number of cusps on a cuspidal
curve on a Hirzebruch surface. We adapt the results that have been found for a
similar question asked for cuspidal curves on the projective plane, and restate
the results in this new setting.


  We introduce a novel method for real-time animation control and generation on
rigged models using natural language input. First, we embed a large language
model (LLM) in Unity to output structured texts that can be parsed into diverse
and realistic animations. Second, we illustrate LLM's potential to enable
flexible state transition between existing animations. We showcase the
robustness of our approach through qualitative results on various rigged models
and motions.


  We study a class of partial differential equations (PDEs) in the family of
the so-called Euler-Poincar\'e differential systems, with the aim of developing
a foundation for numerical algorithms of their solutions. This requires
particular attention to the mathematical properties of this system when the
associated class of elliptic operators possesses non-smooth kernels. By casting
the system in its Lagrangian (or characteristics) form, we first formulate a
particles system algorithm in free space with homogeneous Dirichlet boundary
conditions for the evolving fields. We next examine the deformation of the
system when non-homogeneous "constant stream" boundary conditions are assumed.
We show how this simple change at the boundary deeply affects the nature of the
evolution, from hyperbolic-like to dispersive with a non-trivial dispersion
relation, and examine the potentially regularizing properties of singular
kernels offered by this deformation. From the particle algorithm viewpoint,
kernel singularities affect the existence and uniqueness of solutions to the
corresponding ordinary differential equations systems. We illustrate this with
the case when the operator kernel assumes a conical shape over the spatial
variables, and examine in detail two-particle dynamics under the resulting lack
of Lipschitz-continuity. Curiously, we find that for the conically-shaped
kernels the motion of the related two-dimensional waves can become completely
integrable under appropriate initial data. This reduction projects the
two-dimensional system to the one-dimensional completely integrable
Shallow-Water equation [Camassa, R. and Holm, D. D., Phys. Rev. Lett., 71,
1961-1964, 1993], while retaining the full dependence on two spatial dimensions
for the single channel solutions.


  Clinical text provides essential information to estimate the acuity of a
patient during hospital stays in addition to structured clinical data. In this
study, we explore how clinical text can complement a clinical predictive
learning task. We leverage an internal medical natural language processing
service to perform named entity extraction and negation detection on clinical
notes and compose selected entities into a new text corpus to train document
representations. We then propose a multimodal neural network to jointly train
time series signals and unstructured clinical text representations to predict
the in-hospital mortality risk for ICU patients. Our model outperforms the
benchmark by 2% AUC.


  Electron vortex beams have been predicted to enable atomic scale magnetic
information measurement, via transfer of orbital angular momentum. Research so
far has focussed on developing production techniques and applications of these
beams. However, methods to measure the outgoing orbital angular momentum
distribution are also a crucial requirement towards this goal. Here, we use a
method to obtain the orbital angular momentum decomposition of an electron
beam, using a multi-pinhole interferometer. We demonstrate both its ability to
accurately measure orbital angular momentum distribution, and its experimental
limitations when used in a transmission electron microscope.


  The breakdown of the SU(6) global symmetry to its SU(5) subgroup, that
contains the standard model (SM) gauge group, in the E6 inspired composite
Higgs model (E6CHM) results in a set of pseudo-Nambu-Goldstone bosons (pNGBs).
This set, in particular, involves the SM-like Higgs doublet and a SM singlet
boson. In the limit when CP is conserved the SM singlet scalar A is a CP-odd
state that does not mix with the SM-like Higgs. The interactions of A with
exotic matter beyond the SM, which ensures anomaly cancellation and approximate
gauge coupling unification, can induce couplings of this pseudoscalar to the SM
gauge bosons. We specify the interactions of the SM singlet pNGB state with the
exotic vector-like fermions, top quark and SM gauge bosons. Also we explore the
dependence of the branching ratios of the pseudoscalar A and its LHC production
cross section on the parameters of the E6CHM.


  In this paper we describe an experiment of efficient cooling of $^{87}$Rb
atoms in two-frequency diffuse laser lights. Compared with single frequency
diffuse light, two-frequency diffuse lights have wider velocity capture range
and thus can cool more atoms. In our experiment, the maximum number of cooled
atoms can reach up to $3.9\times10^9$. Such a result is quite useful in
building a compact cold atom clock.


  Small scale renewable energy harvesting is an attractive solution to the
growing need for power in remote technological applications. For this purpose,
localized thermal gradients on-chip--created via radiative cooling--could be
exploited to create microscale renewable heat engines running on environmental
heat. This could allow self-powering in small scale portable applications, thus
reducing the need for non-renewable sources of electricity and hazardous
batteries. In this work, we demonstrate the creation of a local thermal
gradient on-chip by radiative cooling of a 90 nm thick freestanding silicon
nitride nanomechanical resonator integrated on a silicon substrate that remains
at ambient temperature. The reduction in temperature of the thin film is
inferred by tracking its mechanical resonance frequency, under high vacuum,
using an optical fiber interferometer. Experiments were conducted on 15
different days during fall and summer months, resulting in successful radiative
cooling of the membrane in each case. Maximum temperature drops of 9.3 K and
7.1 K are demonstrated during the day and night, respectively, in close
correspondence with our heat transfer model. Future improvements to the
experimental setup could improve the temperature reduction to 48 K for the same
membrane, while emissivity engineering potentially yields a maximum theoretical
cooling of 67 K with an ideal emitter.


  Long-duration gamma-ray bursts (LGRBs) associated with supernovae (SNe) are
possibly born out of the death of a massive star. After the star collapses, a
stellar-mass black hole (BH) is formed, surrounded by a hyperaccretion disk
with outflows. Blandford-Znajek jets can be launched and then break out from
the envelope to power LGRBs. The jet luminosity depends on the net inflow
accretion rate at the inner radius of the disk. Furthermore, $\rm ^{56}Ni$
synthesis should occur in the strong outflows from the accretion disk. The
decay of $\rm ^{56}Ni$ is considered to be the possible origin of SN bumps in
the subsequent optical afterglows of LGRBs. If $\rm ^{56}Ni$ originates
entirely from the outflows, there is competition between the luminosities of
LGRBs and those of the corresponding $\rm ^{56}Ni$ bumps because of the
material distribution between the disk inflows and outflows. In this paper, we
investigated these two luminosities based on 15 cases of LGRB-SN in the
framework of the BH hyperaccretion inflow-outflow model. Then, one can
constrain the characteristics of the progenitor stars of these LGRBs. The
results indicate that these LGRBs may originate from the low-metallicity
($Z\lesssim 10^{-2}Z_{\odot}$, where $Z$ and $Z_{\odot}$ are the metallicities
of the stars and the Sun, respectively) stars or some massive solar-metallicity
stars. For ultra-LGRBs (ULGRBs), such as GRB 111209A, most of the massive
low-metallicity stars with $Z \lesssim 10^{-2}Z_{\odot}$ could be progenitors
only if very strong outflows are launched from the disks. When the
contributions of nucleosynthesis in the disk outflows are considered, there is
no shortage of $\rm ^{56}Ni$ mass for luminous SNe associated with ULGRBs.


  The open problem of calculating the limiting spectrum (or its Shannon
transform) of increasingly large random Hermitian finite-band matrices is
described. In general, these matrices include a finite number of non-zero
diagonals around their main diagonal regardless of their size. Two different
communication setups which may be modeled using such matrices are presented: a
simple cellular uplink channel, and a time varying inter-symbol interference
channel. Selected recent information-theoretic works dealing directly with such
channels are reviewed. Finally, several characteristics of the still unknown
limiting spectrum of such matrices are listed, and some reflections are touched
upon.


  We show that the latest and very precise dispersive data analyses require a
large and very unnat- ural fine-tuning of the 1/Nc expansion at Nc = 3 if the
f_0(600) and K(800) light scalar mesons are to be considered predominantly
quark-antiquark states, which is not needed for light vector mesons. For this,
we use scattering observables whose 1/Nc corrections are suppressed further
than one power of 1/Nc for quark-antiquark or glueball states, thus enhancing
contributions of other nature. This is achieved without using unitarized ChPT,
but if it is used we can also show that it is not just that the coefficients of
the 1/Nc expansion are unnatural, but that the expansion itself does not even
follow the expected 1/Nc scaling of a glueball or a quark-antiquark meson.


  We study boundary value problems for some differential operators on Euclidean
space and the Heisenberg group which are invariant under the conformal group of
a Euclidean subspace resp. Heisenberg subgroup. These operators are shown to be
self-adjoint in certain Sobolev type spaces and the related boundary value
problems are proven to have unique solutions in these spaces. We further find
the corresponding Poisson transforms explicitly in terms of their integral
kernels and show that they are isometric between Sobolev spaces and extend to
bounded operators between certain $L^p$-spaces.
  The conformal invariance of the differential operators allows us to apply
unitary representation theory of reductive Lie groups, in particular recently
developed methods for restriction problems.


  With the growth of comparative exoplanetology, it is increasingly clear that
investigating the relationships between inner and outer planets plays a key
role in discriminating between competing formation and evolution models. To do
so, it is important to probe the inner region of systems hosting long-period
giants in search for undetected lower-mass planetary companions. In this work
we present our results on the K-dwarf star BD-11~4672, already known to host a
long-period giant planet, as the first output of a subsample of the GAPS
programme specifically aimed at assessing the impact of inefficient migration
of planets formed beyond the snowline by searching for Neptune-mass and
super-Earths planetary companions of known longer-period giants. The
high-precision HARPS-N observations of BD-11~4672 are used in conjunction with
literature time series in order to search for additional inner planetary
signals to be fitted using differential evolution Markov chain Monte Carlo. The
long-term stability of the new orbital solutions is tested by N-body dynamical
simulations. We report the detection of BD-11~4672~c, a new Neptune-mass planet
with an orbital period of $74.20_{-0.08}^{+0.06}\,\rm{d}$, eccentricity
$0.40_{-0.15}^{+0.13}$, semimajor axis $0.30\pm0.01\,\rm{au}$ and minimum mass
$15.37_{-2.81}^{+2.97}\,M_\oplus$ orbiting slightly outside the inner edge of
the optimistic circumstellar habitable zone. In order to assess its impact on
the dynamical stability of the habitable zone we compute the angular momentum
deficit of the system, showing that planet c has a severe negative impact on
the stability of possible additional lower-mass temperate planets. The
BD-11~4672 system is notable for its architecture, hosting both a long-period
giant planet and an inner lower-mass planet, the latter being also among the
most eccentric Neptune-mass planets known at similar periods.


  We investigate the necessary and sufficient condition for a convex cone of
positive semidefinite operators to be fixed by a unital quantum operation
$\phi$ acting on finite-dimensional quantum states. By reducing this problem to
the problem of simultaneous diagonalization of the Kraus operators associated
with $\phi$, we can completely characterize the kind of quantum states that are
fixed by $\phi$. Our work has several applications. It gives a simple proof of
the structural characterization of a unital quantum operation that acts on
finite-dimensional quantum states --- a result not explicitly mentioned in
earlier studies. It also provides a necessary and sufficient condition for what
kind of measurement statistics is preserved by a unital quantum operation.
Finally, our result clarifies and extends the work of St{\o}rmer by giving a
proof of a reduction theorem on the unassisted and entanglement-assisted
classical capacities, coherent information, and minimal output Renyi entropy of
a unital channel acting on finite-dimensional quantum state.


  We discuss the problem of the electron mass in the framework of Deformed
Special Relativity (DSR), a generalization of Special Relativity based on a
deformed Minkowski space (i.e. a four-dimensional space-time with metric
coefficients depending on the energy). We show that, by such a formalism, it is
possible to derive the value of the electron mass from the space-time geometry
via the experimental knowledge of the parameter of local Lorentz invariance
breakdown, and of the Minkowskian threshold energy $E_{0,em}$ for the
electromagnetic interaction. We put forward the suggestion that mass generation
can be related, in DSR, to the possible dependence of mass on the metric
background (relativity of mass).


  The recent success of Large Language Models (LLMs) has gained significant
attention in both academia and industry. Substantial efforts have been made to
enhance the zero- and few-shot generalization capabilities of open-source LLMs
through finetuning. Currently, the prevailing approach is instruction-tuning,
which trains LLMs to complete real-world tasks by generating responses guided
by natural language instructions. It is worth noticing that such an approach
may underperform in sequence and token classification tasks. Unlike text
generation tasks, classification tasks have a limited label space, where
precise label prediction is more appreciated than generating diverse and
human-like responses. Prior research has unveiled that instruction-tuned LLMs
cannot outperform BERT, prompting us to explore the potential of leveraging
latent representations from LLMs for supervised label prediction. In this
paper, we introduce a label-supervised adaptation for LLMs, which aims to
finetuning the model with discriminant labels. We evaluate this approach with
Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively
small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU. We
extract latent representations from the final LLaMA layer and project them into
the label space to compute the cross-entropy loss. The model is finetuned by
Low-Rank Adaptation (LoRA) to minimize this loss. Remarkably, without intricate
prompt engineering or external knowledge, LS-LLaMA substantially outperforms
LLMs ten times its size in scale and demonstrates consistent improvements
compared to robust baselines like BERT-Large and RoBERTa-Large in text
classification. Moreover, by removing the causal mask from decoders, LS-unLLaMA
achieves the state-of-the-art performance in named entity recognition (NER).
Our work will shed light on a novel approach to adapting LLMs for various
downstream tasks.


  The thermalization of a gas towards a Maxwellian velocity distribution with
the background temperature is described by a kinetic relaxation model. The sum
of the kinetic energy of the gas and the thermal energy of the background are
conserved, and the heat flow in the background is governed by the Fourier law.
  For the coupled nonlinear system of the kinetic and the heat equation,
existence of solutions is proved on the one-dimensional torus. Spectral
stability of the equilibrium is shown on the torus in arbitrary dimensions by
hypocoercivity methods. The macroscopic limit towards a nonlinear
cross-diffusion problem is carried out formally.


  We theoretically study the electronic band structure and the Hall effect in
the negatively-curved three-dimensional (3D) graphene network in magnetic
fields. We found that special energy regions appear above and below the
zero-energy Landau level, where the 3D Hall conductivity is quantized even
though the spectrum is not fully gapped. The energy regions are dominated by
the chiral snake states traveling along the zero magnetic field contour on the
3D surface, and the quantization of the Hall conductivity crucially depends on
the topology of the contour. The exclusive energy region for the chiral states
is found to be a universal feature of 3D graphene systems.


  We study the dependence of the entanglement entropy with a magnetic flux, and
show that the former quantity witnesses an Aharonov Bohm-like effect. In
particular, we consider free charged scalar and Dirac fields living on a two
dimensional cylinder and study how the entanglement entropy for a strip-like
region on the surface of the cylinder is affected by a magnetic field enclosed
by it.


  Gene expression has a stochastic component owing to the single molecule
nature of the gene and the small number of copies of individual DNA binding
proteins in the cell. We show how the statistics of such systems can be mapped
on to quantum many body problems. The dynamics of a single gene switch
resembles the spin boson model of a two site polaron or an electron transfer
reaction. Networks of switches can be approximately described as quantum spin
systems by using an appropriate variational principle. In this way the concept
of frustration for magnetic systems can be taken over into gene networks. The
landscape of stable attractors depends on the degree and style of frustration,
much as for neural networks. We show the number of attractors, which may
represent cell types, is much smaller for appropriately designed, weakly
frustrated stochastic networks than for randomly connected networks.


  We present a variety of dissipative solitons and breathing modes in a medium
with localized gain and homogeneous linear dissipation. The system posses a
number of unusual properties, like exponentially localized modes in both
focusing and defocusing media, the existence of modes in focusing media at
negative propagation constant values, the simultaneous existence of stable
symmetric and anti-symmetric localized modes when the gain landscape possesses
two local maxima, as well as the existence of stable breathing solutions.


  Metric Temporal Logic can express temporally evolving properties with
time-critical constraints or time-triggered constraints for real-time systems.
This paper extends the Metric Interval Temporal Logic with a distribution
eventuality operator to express time-sensitive missions for a system
interacting with a dynamic, probabilistic environment. This formalism enables
us to describe the probabilistic occurrences of random external events as part
of the task specification and event-triggered temporal constraints for the
intended system's behavior. The main contributions of this paper are two folds:
First, we propose a procedure to translate a specification into a stochastic
timed automaton. Second, we develop an approximate-optimal probabilistic
planning problem for synthesizing the control policy that maximizes the
probability for the planning agent to achieve the task, provided that the
external events satisfy the specification. The planning algorithm employs a
truncation in the clocks for the timed automaton to reduce the planning in a
countably infinite state space to a finite state space with a bounded error
guarantee. We illustrate the method with a robot motion planning example.


  In this paper, we present our solution, which placed 5th in the kaggle Google
Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder
of CLIP from the openclip repository as a backbone and train a head model
composed of BatchNormalization and Linear layers using ArcFace. The dataset
used was a subset of products10K, GLDv2, GPR1200, and Food101. And applying TTA
for part of images also improves the score. With this method, we achieve a
score of 0.684 on the public and 0.688 on the private leaderboard. Our code is
available.
https://github.com/riron1206/kaggle-Google-Universal-Image-Embedding-Competition-5th-Place-Solution


  Biomedical research papers use significantly different language and jargon
when compared to typical English text, which reduces the utility of pre-trained
NLP models in this domain. Meanwhile Medline, a database of biomedical
abstracts, introduces nearly a million new documents per-year. Applications
that could benefit from understanding this wealth of publicly available
information, such as scientific writing assistants, chat-bots, or descriptive
hypothesis generation systems, require new domain-centered approaches. A
conditional language model, one that learns the probability of words given some
a priori criteria, is a fundamental building block in many such applications.
We propose a transformer-based conditional language model with a shallow
encoder "condition" stack, and a deep "language model" stack of multi-headed
attention blocks. The condition stack encodes metadata used to alter the output
probability distribution of the language model stack. We sample this
distribution in order to generate biomedical abstracts given only a proposed
title, an intended publication year, and a set of keywords. Using typical
natural language generation metrics, we demonstrate that this proposed approach
is more capable of producing non-trivial relevant entities within the abstract
body than the 1.5B parameter GPT-2 language model.


  We give new bounds on the cosystolic expansion constants of several families
of high dimensional expanders, and the known coboundary expansion constants of
order complexes of homogeneous geometric lattices, including the spherical
building of $SL_n(F_q)$. The improvement applies to the high dimensional
expanders constructed by Lubotzky, Samuels and Vishne, and by Kaufman and
Oppenheim.
  Our new expansion constants do not depend on the degree of the complex nor on
its dimension, nor on the group of coefficients. This implies improved bounds
on Gromov's topological overlap constant, and on Dinur and Meshulam's cover
stability, which may have applications for agreement testing. In comparison,
existing bounds decay exponentially with the ambient dimension (for spherical
buildings) and in addition decay linearly with the degree (for all known
bounded-degree high dimensional expanders). Our results are based on several
new techniques:
  * We develop a new "color-restriction" technique which enables proving
dimension-free expansion by restricting a multi-partite complex to small random
subsets of its color classes.
  * We give a new "spectral" proof for Evra and Kaufman's local-to-global
theorem, deriving better bounds and getting rid of the dependence on the
degree. This theorem bounds the cosystolic expansion of a complex using
coboundary expansion and spectral expansion of the links.
  * We derive absolute bounds on the coboundary expansion of the spherical
building (and any order complex of a homogeneous geometric lattice) by
constructing a novel family of very short cones.


  Digitally engaged communities can be described as communities created and
evolved within Web 2.0 Websites such as Facebook, Bebo, and Twitter. The
growing importance of digitally engaged communities calls for the need to
efficiently manage the building blocks of sustaining a healthy community. The
initial operation of any digitally-engaged community depends on the existence
of its own members, the beneficial values created and exchanged, and the
relationships interlinking both. However, the level of contribution and
involvement might vary depending on the benefits being gratified from engaging
in such communities. In other words, motivations for participating and getting
involved are purposive; individuals are driven into joining and /or taking part
in any digitallyengaged network for capturing and purtaining certain beneficial
values. Accordingly, this paper proposes a framework that classifies the values
created and exchanged within these communities as well as the roles adopted and
played by users of these communities. Utilizing ethnography as the primay
methodological strategy to study Bebo digitally-engaged community, this
research identifies five different roles of users: Newbie, Lurker, Novice,
Insider, and Leader. Moreover, the research also identifies five value elements
that could be captured by different users: Social, Hedonic, Epistemic, Gift,
and Utilitarian. The results of this study provides insights for decision and
policy makers, service providers, and developers; as it inspires them in
knowing and meeting the needs and values of participants based on the roles
adopted by users.


  Owing to Vaidya metric including, the model of Expansive Nondecelerative
Universe (ENU) is able to localize gravitational energy. Based on the
quantification of gravitational field of the Earth, ENU allows to rationalize
and quantify the effects of a superconductor-based high voltage impulse gravity
generator constructed by Podkletnov. The differences in energy effect observed
for two generators used are explained and improvement of the experiment
arrangement is proposed.


  AI developers often apply safety alignment procedures to prevent the misuse
of their AI systems. For example, before Meta released Llama 2-Chat - a
collection of instruction fine-tuned large language models - they invested
heavily in safety training, incorporating extensive red-teaming and
reinforcement learning from human feedback. We explore the robustness of safety
training in language models by subversively fine-tuning Llama 2-Chat. We employ
quantized low-rank adaptation (LoRA) as an efficient fine-tuning method. With a
budget of less than \$200 and using only one GPU, we successfully undo the
safety training of Llama 2-Chat models of sizes 7B, 13B, and 70B and on the
Mixtral instruct model. Specifically, our fine-tuning technique significantly
reduces the rate at which the model refuses to follow harmful instructions. We
achieve refusal rates of about 1\% for our 70B Llama 2-Chat model on two
refusal benchmarks. Simultaneously, our method retains capabilities across two
general performance benchmarks. We show that subversive fine-tuning is
practical and effective, and hence argue that evaluating risks from fine-tuning
should be a core part of risk assessments for releasing model weights. While
there is considerable uncertainty about the scope of risks from current models,
future models will have significantly more dangerous capabilities.


  We discuss selected aspects of classical relativistic scalar field theories
with nonzero chemical potential. First, we offer a review of classical field
theory at nonzero density within the Lagrangian formalism. The aspects covered
include the question of equivalence of descriptions of finite-density states
using a chemical potential or time-dependent field configurations, the choice
of Hamiltonian whose minimization yields the finite-density equilibrium state,
and the issue of breaking of Lorentz invariance. Second, we demonstrate how the
low-energy effective field theory for Nambu-Goldstone (NG) modes arising from
the spontaneous breakdown of global internal symmetries can be worked out
explicitly by integrating out the heavy (Higgs) fields. This makes it possible
to analyze the spectrum of NG modes and their interactions without having to
deal with mixing of NG and Higgs fields, ubiquitous in the linear-sigma-model
description of spontaneous symmetry breaking.


  High energy e+e- linear colliders are the next large scale project in
particle physics. They need intense sources to achieve the required luminosity.
In particular, the positron source must provide about 10E+14 positrons per
second. The positron source for the International Linear Collider (ILC) is
based on a helical undulator passed by the electron beam to create an intense
circularly polarized photon beam. With these photons a longitudinally polarized
positron beam is generated; the degree of polarization can be enhanced by
collimating the photon beam. However, the high photon beam intensity causes
huge thermal load in the collimator material. In this paper the thermal load in
the photon collimator is discussed and a flexible design solution is presented.


  Conventional 2D Convolutional Neural Networks (CNN) extract features from an
input image by applying linear filters. These filters compute the spatial
coherence by weighting the photometric information on a fixed neighborhood
without taking into account the geometric information. We tackle the problem of
improving the classical RGB CNN methods by using the depth information provided
by the RGB-D cameras. State-of-the-art approaches use depth as an additional
channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a
novel and generic procedure to articulate both photometric and geometric
information in CNN architecture. The depth data is represented as a 2D offset
to adapt spatial sampling locations. The new model presented is invariant to
scale and rotation around the X and the Y axis of the camera coordinate system.
Moreover, when depth data is constant, our model is equivalent to a regular
CNN. Experiments of benchmarks validate the effectiveness of our model.


  The 5-layer TCP and 7-layer OSI models are taught as high-level frameworks in
which the various protocols that are used in computer networks operate.
  These models provide valid insights in the organization of network
functionalities and protocols; however, the difficulties to fit some crucial
technologies within them hints that they don't provide a complete model for the
organization of -- and relationships between -- different mechanisms in a
computer network. Recently, a recursive model for computer networks was
proposed, which organizes networks in layers that conceptually provide the same
mechanisms through a common interface. Instead of defined by function, these
layers are distinguished by scope.
  We report our research on a model for computer networks. Following a rigorous
regime alternating design with the evaluation of its implications in an
implementation, we converged on a recursive architecture, named Ouroboros. One
of our main main objectives was to disentangle the fundamental mechanisms that
are found in computer networks as much as possible. Its distinguishing feature
is the separation of unicast and broadcast as different mechanisms, giving rise
to two different types of layers. These unicast and broadcast layers can easily
be spotted in today's networks.
  This article presents the concepts underpinning Ouroboros, details its
organization and interfaces, and introduces the free software prototype. We
hope the insights it provides can guide future network design and
implementation.


  We analyze topological charge contributions from classical SU(2) center
vortices with shapes of planes and spheres using different topological charge
definitions, namely the center vortex picture of topological charge, a discrete
version of F\~{F} in the plaquette and hypercube definitions and the lattice
index theorem. For the latter the zeromodes of the Dirac operator in the
fundamental and adjoint representations using both the overlap and asqtad
staggered fermion formulations are investigated. We find several problems for
the individual definitions and discuss the discrepancies between the different
topological charge definitions. Our results show that the interpretation of
topological charge in the background of center vortices is rather subtle.


  Methods of quantum mechanics promise information-theoretic security for
various protocols in cryptography. However, impossibility of some cryptographic
applications such as standard bit commitment, oblivious transfer, multiparty
secure computations and ideal coin tossing in quantum regime leaves an obvious
question on the completeness of quantum cryptography. Instead of using wide
range of rules and techniques for a variety of cryptographic applications, we
demonstrate here a unified structure for quantum cryptography based on quantum
non-local correlations. The unified framework achieves same goals in
information-theoretic way as classical cryptography does with computational
hardness. To cover the broad range of cryptographic applications, we show that
the framework (i) assures secrecy by providing encryption completely
unintelligible to eavesdroppers, (ii) guarantees that input from distant
parties is concealed unless they are willing to reveal, (iii) assures binding,
(iv) allows splitting information between several parties securely and more
generally, (v) evades both quantum and classical attacks from internal as well
as external eavesdropping.


  We show that if a connected, Hom-finite, Krull-Schmidt triangulated category
has an Auslander-Reiten quiver component with Dynkin tree class then the
category has Auslander-Reiten triangles and that component is the entire
quiver. This is an analogue for triangulated categories of a theorem of
Auslander, and extends a previous result of Scherotzke. We also show that if
there is a quiver component with extended Dynkin tree class, then other
components must also have extended Dynkin class or one of a small set of
infinite trees, provided there is a non-zero homomorphism between the
components. The proofs use the theory of additive functions.


  By studying the quantum Hall effect of stationary states with high values of
injected current using a von Neumann lattice representation, we found that
broadening of extended state bands due to a Hall electric field occurs and
causes the breakdown of the quantum Hall effect. The Hall conductance agrees
with a topological invariant that is quantized exactly below a critical field
and is not quantized above a critical field. The critical field is proportional
to $B^{3/2}$ and is enhanced substantially if the extended states occupy a
small fraction of the system.


  The compact dark objects with very large masses residing at the centres of
galaxies are believed to be black holes. Due to the gravitational lensing
effect, they would cast a shadow larger than their horizon size over the
background, whose shape and size can be calculated. For the supermassive black
hole candidate Sgr A*, this shadow spans an angular size of about 50 micro arc
second, which is under the resolution attainable with the current astronomical
instruments. Such a shadow image of Sgr A* will be observable at about 1 mm
wavelength, considering the scatter broadening by the interstellar medium. By
simulating the black hole shadow image of Sgr A* with the radiatively
inefficient accretion flow model, we demonstrate that analyzing the properties
of the visibility function can help us determine some parameters of the black
hole configuration, which is instructive to the sub-millimeter VLBI
observations of Sgr A* in the near future.


  Let $u_1,\ldots,u_n$ be unitary operators on a Hilbert space $H$. We study
the norm $$\left\|\sum^{i=n}_{i=1} u_i \otimes \bar u_i\right\|\leqno (1)$$ of
the operator $\sum u_i \otimes \bar u_i$ acting on the Hilbertian tensor
product $H\otimes_2 \overline H$. The main result of this note is
  Theorem 1. For any $n$-tuple $u_1,\ldots, u_n$ of unitary operators in
$B(H)$, we have $$2\sqrt{n-1} \le \left\|\sum^n_1 u_i \otimes \bar
u_i\right\|.\leqno (6)$$ In other words, the right side of (6) is minimal
exactly when $u_i = \lambda(g_i)$.


  We study the properties of photochemical hazes in super-Earths/mini-Neptunes
atmospheres with particular focus on GJ1214b. We evaluate photochemical haze
properties at different metallicities between solar and 10000$\times$solar.
Within the four orders of magnitude change in metallicity, we find that the
haze precursor mass fluxes change only by a factor of $\sim$3. This small
diversity occurs with a non-monotonic manner among the different metallicity
cases, reflecting the interaction of the main atmospheric gases with the
radiation field. Comparison with relative haze yields at different
metallicities from laboratory experiments reveals a qualitative similarity with
our theoretical calculations and highlights the contributions of different gas
precursors. Our haze simulations demonstrate that higher metallicity results
into smaller average particle sizes. Metallicities at and above
100$\times$solar with haze formation yields of $\sim$10$\%$ provide enough haze
opacity to satisfy transit observation at visible wavelengths and obscure
sufficiently the H$_2$O molecular absorption features between 1.1 $\mu$m and
1.7 $\mu$m. However, only the highest metallicity case considered
(10000$\times$solar) brings the simulated spectra into closer agreement with
transit depths at 3.6 $\mu$m and 4.5 $\mu$m indicating a high contribution of
CO/CO$_2$ in GJ1214b's atmosphere. We also evaluate the impact of aggregate
growth in our simulations, in contrast to spherical growth, and find that the
two growth modes provide similar transit signatures (for D$_f$=2), but with
different particle size distributions. Finally, we conclude that the simulated
haze particles should have major implications for the atmospheric thermal
structure and for the properties of condensation clouds.


  The feasibility of high-fidelity single-qubit operations of a hole spin in a
quantum dot molecule by electric g tensor control is demonstrated. Apart from a
constant external magnetic field the proposed scheme allows for an exclusively
electric control of the hole spin. Realistic electric gate bias profiles are
identified for various qubit operations using process-tomography-based optimal
control. They are shown to be remarkably robust against decoherence and
dissipation arising from the interaction of the hole with host-lattice nuclear
spins and phonons, with a fidelity loss of $\approx$ 1 percent for gate
operation times of $\approx 10$ ns. Spin-echo experiments for the hole spin are
modeled to explore dephasing mechanisms and the role of pulse-timing
imperfections on the gate fidelity loss is discussed.


  An ensemble of inelastically colliding grains driven by a vibrating wall in
2D exhibits density clustering. Working in the limit of nearly elastic
collisions and employing granular hydrodynamics, we predict, by a marginal
stability analysis, a spontaneous symmetry breaking of the extended clustering
state (ECS). 2D steady-state solutions found numerically describe localized
clustering state (LCSs). Time-dependent granular hydrodynamic simulations show
that LCSs can develop from natural initial conditions. The predicted
instability should be observable in experiment.


  Confining light in extreme subwavelength scales is a tantalizing task. In
this work, we report a study of individual plasmonic film-coupled nanostar
resonators where plasmonic optical modes are trapped in ultrasmall volumes.
Individual gold nanostars, separated from a flat gold film by a thin dielectric
spacer layer, exhibit a strong light confinement within the sub-10 nm volume of
the nanostar's tips and the film. Through dark field scattering measurements of
many individual nanostars, a statistical observation of the scattered spectra
is obtained and compared with extensive simulation data to reveal the origins
of the resonant peaks. We observe that an individual nanostar on a flat gold
film can result in a resonant spectrum with single, double or multiple peaks.
Further, these resonant peaks are strongly polarized under white light
illumination. Our simulation data revealed that the resonant spectrum of an
individual film-coupled nanostar resonator is related to the symmetry of the
nanostar, as well as the orientation of the nanostar relative to its placement
on the gold substrate. Our results demonstrate a simple method to create an
ultrasmall mode volume plasmonic platform which could be useful for
applications in sensing or enhanced light-matter interactions.


  This letter presents the sparse vector signal detection from one bit
compressed sensing measurements, in contrast to the previous works which deal
with scalar signal detection. In this letter, available results are extended to
the vector case and the GLRT detector and the optimal quantizer design are
obtained. Also, a double-detector scheme is introduced in which a sensor level
threshold detector is integrated into network level GLRT to improve the
performance. The detection criteria of oracle and clairvoyant detectors are
also derived. Simulation results show that with careful design of the threshold
detector, the overall detection performance of double-detector scheme would be
better than the sign-GLRT proposed in [1] and close to oracle and clairvoyant
detectors. Also, the proposed detector is applied to spectrum sensing and the
results are near the well known energy detector which uses the real valued data
while the proposed detector only uses the sign of the data.


  We present a state-of-the-art primordial recombination code, HyRec, including
all the physical effects that have been shown to significantly affect
recombination. The computation of helium recombination includes simple analytic
treatments of hydrogen continuum opacity in the He I 2 1P - 1 1S line, the He
I] 2 3P - 1 1S line, and treats feedback between these lines within the
on-the-spot approximation. Hydrogen recombination is computed using the
effective multilevel atom method, virtually accounting for an infinite number
of excited states. We account for two-photon transitions from 2s and higher
levels as well as frequency diffusion in Lyman-alpha with a full radiative
transfer calculation. We present a new method to evolve the radiation field
simultaneously with the level populations and the free electron fraction. These
computations are sped up by taking advantage of the particular sparseness
pattern of the equations describing the radiative transfer. The computation
time for a full recombination history is ~2 seconds. This makes our code well
suited for inclusion in Monte Carlo Markov chains for cosmological parameter
estimation from upcoming high-precision cosmic microwave background anisotropy
measurements.


  Polarization of opinions has been empirically noted in many online social
network platforms. Traditional models of opinion dynamics, based on statistical
physics principles, do not account for the emergence of polarization and echo
chambers in online network platforms. A recently introduced opinion dynamics
model that incorporates the homophily factor -- the tendency of agents to
connect with those holding similar opinions as their own -- captures
polarization and echo chamber effects. In this work, we provide a non-intrusive
framework for mildly nudging agents in an online community to form random
connections. This is shown to lead to significant depolarization of opinions
and decrease the echo chamber effects. Remarkably, even a mild nudge is seen to
be effective in avoiding polarization, though a large nudge leads to another
undesirable effect, namely, radicalization. Further, we obtain the optimal
nudge factor to avoid the extremes of polarization and radicalization outcomes.


  A massless scalar field is quantized in the background of a spinning string
with cosmic dislocation. By increasing the spin density toward the dislocation
parameter, a region containing closed timelike curves (CTCs) eventually forms
around the defect. Correspondingly, the propagator tends to the ordinary cosmic
string propagator, leading therefore to a mean-square field fluctuation, which
remains well behaved throughout the process, unlike the vacuum expectation
value of the energy-momentum tensor, which diverges due to a subtle mechanism.
These results suggest that back reaction leads to the formation of a "horizon"
that protects from the appearance of CTCs.


  We describe the transverse momentum (or mass) spectra of $\pi^\pm$, $K^\pm$,
$p$, and $\bar{p}$ produced in central gold-gold (Au-Au), central lead-lead
(Pb-Pb), and inelastic proton-proton ($pp$) collisions at different collision
energies range from the AGS to LHC by using a two-component (in most cases)
Erlang distribution in the framework of multi-source thermal model. The fitting
results are consistent with the experimental data and the energy-dependent
chemical potentials of light hadrons ($\pi$, $K$, and $p$) and quarks ($u$,
$d$, and $s$) in central Au-Au, central Pb-Pb, and inelastic $pp$ collisions
from the yield ratios of negative to positive particles obtained from the
normalization constants are then extracted. The study shows that most types of
energy-dependent chemical potentials decrease with increase of collision energy
over a range from the AGS to LHC. The curves of all types of energy-dependent
chemical potentials, obtained from the linear fits of yield ratios vs energy,
have inflection points at the same energy of 3.526 GeV, which is regarded as
the critical energy of phase transition from a hadron liquid-like state to a
quark gas-like state in the collision system and indicates that the hadronic
interactions play an important role in this period. At the RHIC and LHC, all
types of chemical potentials become small and tend to zero at very high energy,
which confirms that the collision system possibly changes completely from the
hadron-dominant liquid-like state to the quark-dominant gas-like state and the
partonic interactions possibly play a dominant role at the LHC.


  In this review article, we develop the perturbative framework for the
calculation of hard scattering processes. We undertake to provide both a
reasonably rigorous development of the formalism of hard scattering of quarks
and gluons as well as an intuitive understanding of the physics behind the
scattering. We emphasize the importance of logarithmic corrections as well as
power counting of the strong coupling constant in order to understand the
behavior of hard scattering processes. We include "rules of thumb" as well as
"official recommendations", and where possible seek to dispel some myths.
Experiences that have been gained at the Fermilab Tevatron are recounted and,
where appropriate, extrapolated to the LHC.


  The trilinear couplings of squarks and sleptons to the Higgs bosons can give
rise to a spectrum of bound states with exotic quantum numbers, for example,
those of a leptoquark.


  Given a row-finite $k$-graph $\Lambda$ with no sources we investigate the
$K$-theory of the higher rank graph $C^*$-algebra, $C^*(\Lambda)$. When $k=2$
we are able to give explicit formulae to calculate the $K$-groups of
$C^*(\Lambda)$. The $K$-groups of $C^*(\Lambda)$ for $k>2$ can be calculated
under certain circumstances and we consider the case $k=3$. We prove that for
arbitrary $k$, the torsion-free rank of $K_0(C^*(\Lambda))$ and
$K_1(C^*\Lambda))$ are equal when $C^*(\Lambda)$ is unital, and for $k=2$ we
determine the position of the class of the unit of $C^*(\Lambda)$ in
$K_0(C^*(\Lambda))$.


  Our common understanding of the physical world deeply relies on the notion
that events are ordered with respect to some time parameter, with past events
serving as causes for future ones. Nonetheless, it was recently found that it
is possible to formulate quantum mechanics without any reference to a global
time or causal structure. The resulting framework includes new kinds of quantum
resources that allow performing tasks - in particular, the violation of causal
inequalities - which are impossible for events ordered according to a global
causal order. However, no physical implementation of such resources is known.
Here we show that a recently demonstrated resource for quantum computation -
the quantum switch - is a genuine example of "indefinite causal order". We do
this by introducing a new tool - the causal witness - which can detect the
causal nonseparability of any quantum resource that is incompatible with a
definite causal order. We show however that the quantum switch does not violate
any causal nequality.


  A metamaterial with a negative effective index of refraction is made from a
three-dimensional hexagonal lattice photonic crystal with a metallic basis
embedded in foam. It has been simulated with Ansoft HFSSTM in a frequency range
from 7.0 to 12.0 GHz. Simulated results tested experimentally and negative
refraction verified in some frequencies. Experimental results are in excellent
agreement with simulations.


  Typestates are state machines used in object-oriented programming to specify
and verify correct order of method calls on an object. To avoid inconsistent
object states, typestates enforce linear typing, which eliminates - or at best
limits - aliasing. However, aliasing is an important feature in programming,
and the state-of-the-art on typestates is too restrictive if we want typestates
to be adopted in real-world software systems.
  In this paper, we present a type system for an object-oriented language with
typestate annotations, which allows for unrestricted aliasing, and as opposed
to previous approaches it does not require linearity constraints. The typestate
analysis is global and tracks objects throughout the entire program graph,
which ensures that well-typed programs conform and complete the declared
protocols. We implement our framework in the Scala programming language and
illustrate our approach using a running example that shows the interplay
between typestates and aliases.


  Language Models (LMs) have demonstrated exceptional performance across
various Natural Language Processing (NLP) tasks. Despite these advancements,
LMs can inherit and amplify societal biases related to sensitive attributes
such as gender and race, limiting their adoption in real-world applications.
Therefore, fairness has been extensively explored in LMs, leading to the
proposal of various fairness notions. However, the lack of clear agreement on
which fairness definition to apply in specific contexts (\textit{e.g.,}
medium-sized LMs versus large-sized LMs) and the complexity of understanding
the distinctions between these definitions can create confusion and impede
further progress. To this end, this paper proposes a systematic survey that
clarifies the definitions of fairness as they apply to LMs. Specifically, we
begin with a brief introduction to LMs and fairness in LMs, followed by a
comprehensive, up-to-date overview of existing fairness notions in LMs and the
introduction of a novel taxonomy that categorizes these concepts based on their
foundational principles and operational distinctions. We further illustrate
each definition through experiments, showcasing their practical implications
and outcomes. Finally, we discuss current research challenges and open
questions, aiming to foster innovative ideas and advance the field. The
implementation and additional resources are publicly available at
https://github.com/LavinWong/Fairness-in-Large-Language-Models/tree/main/definitions.


  In this paper, we define some polynomial invariants for virtual knots and
links. In the first part we use Manturov's parity axioms to obtain a new
polynomial invariant of virtual knots. This invariant can be regarded as a
generalization of the odd writhe polynomial defined by the first author. The
relation between this new polynomial invariant and the affine index polynomial
is discussed. In the second part we introduce a polynomial invariant for long
flat virtual knots. In the third part we define a polynomial invariant for
2-component virtual links. This polynomial invariant can be regarded as a
generalization of the linking number.


  In this paper we extend our techniques, developed in a previous paper (Du,
etc, JHEP 05(2016)086) for direct evaluation of arbitrary $n$-point tree-level
MHV amplitudes in 4d Yang-Mills and gravity theory using the Cachazo-He-Yuan
(CHY) formalism, to the 4d Einstein-Yang-Mills (EYM) theory. Any single-trace
color-ordered $n$-point tree-level MHV amplitude in EYM theory, obtained by a
direct evaluation of the CHY formula, is of an elegant factorized form of a
Parke-Taylor factor and a Hodges determinant, much simpler and more compact
than the existing formulas in the literature. We prove that our new expression
is equivalent to the conjectured Selivanov-Bern-De Freitas-Wong (SBDW) formula,
with the help of a new theorem showing that the SBDW generating function has a
graph theory interpretation. Together with Ref. (Du, etc, JHEP 05(2016)086), we
provide strong analytic evidence for hidden simplicity in quantum field theory.


  Knowledge distillation (KD) is a promising yet challenging model compression
technique that transfers rich learning representations from a well-performing
but cumbersome teacher model to a compact student model. Previous methods for
image super-resolution (SR) mostly compare the feature maps directly or after
standardizing the dimensions with basic algebraic operations (e.g. average,
dot-product). However, the intrinsic semantic differences among feature maps
are overlooked, which are caused by the disparate expressive capacity between
the networks. This work presents MiPKD, a multi-granularity mixture of prior KD
framework, to facilitate efficient SR model through the feature mixture in a
unified latent space and stochastic network block mixture. Extensive
experiments demonstrate the effectiveness of the proposed MiPKD method.


  We perform a statistical analysis of the binary black hole problem in the
post-Newtonian approximation by systematically sampling and evolving the
parameter space of initial configurations for quasi-circular inspirals. Through
a principal component analysis of spin and orbital angular momentum variables
we systematically look for uncorrelated quantities and find three of them which
are highly conserved in a statistical sense, both as functions of time and with
respect to variations in initial spin orientations. We also look for and find
the variables that account for the largest variations in the problem. We
present binary black hole simulations of the full Einstein equations analyzing
to what extent these results might carry over to the full theory in the
inspiral and merger regimes. Among other applications these results should be
useful both in semi-analytical and numerical building of templates of
gravitational waves for gravitational wave detectors.


  A canonical formalism of the rank-three tensor model with the notion of local
time is proposed. The consistency of the local time evolution is guaranteed by
imposing that local Hamiltonians and the so(N) kinematical symmetry of the
tensor model should form a first class constraint algebra. By imposing some
physically reasonable assumptions, it is shown that there exist only two such
local Hamiltonians with a slight difference in index contraction. The first
class constraint algebra is shown to approach the DeWitt constraint algebra of
the general relativity in a certain locality limit. Quantization of the system
is briefly discussed.


  Can collective quantum effects make a difference in a meaningful
thermodynamic operation? Focusing on energy storage and batteries, we
demonstrate that quantum mechanics can lead to an enhancement in the amount of
work deposited per unit time, i.e., the charging power, when $N$ batteries are
charged collectively. We first derive analytic upper bounds for the collective
\emph{quantum advantage} in charging power for two choices of constraints on
the charging Hamiltonian. We then highlight the importance of entanglement by
proving that the quantum advantage vanishes when the collective state of the
batteries is restricted to be in the separable ball. Finally, we provide an
upper bound to the achievable quantum advantage when the interaction order is
restricted, i.e., at most $k$ batteries are interacting. Our result is a
fundamental limit on the advantage offered by quantum technologies over their
classical counterparts as far as energy deposition is concerned.


  The difficulty in determining the mass of a sample increases as its size
diminishes. At the nanoscale, there are no direct methods for resolving the
mass of single molecules or nanoparticles and so more sophisticated approaches
based on electromechanical phenomena are required. More importantly, one
demands that such nanoelectromechanical techniques could provide not only
information about the mass of the target molecules but also about their
geometrical properties. In this sense, we report a theoretical study that
illustrates in detail how graphene membranes can operate as
nanoelectromechanical mass-sensor devices. Wide graphene sheets were exposed to
different types and amounts of molecules and molecular dynamic simulations were
employed to treat these doping processes statistically. We demonstrate that the
mass variation effect and information about the graphene-molecule interactions
can be inferred through dynamical response functions. Our results confirm the
potential use of graphene as mass detector devices with remarkable precision in
estimating variations in mass at molecular scale and other physical properties
of the dopants.


  The $\Sopfr(n)$ function is defined as the sum of prime factors of $n$ each
of which is taken with its multiplicity. This function is studied numerically.
The analogy between $\Sopfr(n)$ and the primes distribution function is drawn
and some conjectures for prime numbers formulated in terms of the $\Sopfr(n)$
function are suggested.


  With increasingly more sophisticated phishing campaigns in recent years,
phishing emails lure people using more legitimate-looking personal contexts. To
tackle this problem, instead of traditional heuristics-based algorithms, more
adaptive detection systems such as natural language processing (NLP)-powered
approaches are essential to understanding phishing text representations.
Nevertheless, concerns surrounding the collection of phishing data that might
cover confidential information hinder the effectiveness of model learning. We
propose a decentralized phishing email detection framework called Federated
Phish Bowl (FedPB) which facilitates collaborative phishing detection with
privacy. In particular, we devise a knowledge-sharing mechanism with federated
learning (FL). Using long short-term memory (LSTM) for phishing detection, the
framework adapts by sharing a global word embedding matrix across the clients,
with each client running its local model with Non-IID data. We collected the
most recent phishing samples to study the effectiveness of the proposed method
using different client numbers and data distributions. The results show that
FedPB can attain a competitive performance with a centralized phishing
detector, with generality to various cases of FL retaining a prediction
accuracy of 83%.


  We focus on solving constrained convex optimization problems using mini-batch
stochastic gradient descent. Dynamic sample size rules are presented which
ensure a descent direction with high probability. Empirical results from two
applications show superior convergence compared to fixed sample
implementations.


  In recent studies, line search methods have been demonstrated to
significantly enhance the performance of conventional stochastic gradient
descent techniques across various datasets and architectures, while making an
otherwise critical choice of learning rate schedule superfluous. In this paper,
we identify problems of current state-of-the-art of line search methods,
propose enhancements, and rigorously assess their effectiveness. Furthermore,
we evaluate these methods on orders of magnitude larger datasets and more
complex data domains than previously done. More specifically, we enhance the
Armijo line search method by speeding up its computation and incorporating a
momentum term into the Armijo criterion, making it better suited for stochastic
mini-batching. Our optimization approach outperforms both the previous Armijo
implementation and a tuned learning rate schedule for the Adam and SGD
optimizers. Our evaluation covers a diverse range of architectures, such as
Transformers, CNNs, and MLPs, as well as data domains, including NLP and image
data.
  Our work is publicly available as a Python package, which provides a simple
Pytorch optimizer.


  We investigate the correlations between the observed fraction of Lyman alpha
emission from star-forming galaxies and the large scale structure by
post-processing snapshots of a large, high resolution hydrodynamical simulation
with a Lyman alpha radiative transfer code at redshifts z=4, 3, 2. We find
correlations of the observed fraction with density, density gradient along the
line of sight, velocity and velocity gradient along the line of sight, all
within the same order of magnitude (tens of percent). Additionally, a
correlation with the angular momentum of the dark matter halo is detected. In
contrast to a previous study, we find no significant deformation of the 2-point
correlation function due to selection effects from radiative transfer in the
IGM within the limited statistics of the simulation volume.


  Inspired by recent work in the theory of central projections onto
hypersurfaces, we characterize self-linked perfect ideals of grade 2 as those
with a Hilbert--Burch matrix that has a maximal symmetric subblock. We also
prove that every Gorenstein perfect algebra of grade 1 can be presented, as a
module, by a symmetric matrix. Both results are derived from the same
elementary lemma about symmetrizing a matrix that has, modulo a nonzerodivisor,
a symmetric syzygy matrix. In addition, we establish a correspondence, roughly
speaking, between Gorenstein perfect algebras of grade 1 that are birational
onto their image, on the one hand, and self-linked perfect ideals of grade 2
that have one of the self-linking elements contained in the second symbolic
power, on the other hand. Finally, we provide another characterization of these
ideals in terms of their symbolic Rees algebras, and we prove a criterion for
these algebras to be normal.