  This paper explores adaptive variance reduction methods for stochastic
optimization based on the STORM technique. Existing adaptive extensions of
STORM rely on strong assumptions like bounded gradients and bounded function
values, or suffer an additional $\mathcal{O}(\log T)$ term in the convergence
rate. To address these limitations, we introduce a novel adaptive STORM method
that achieves an optimal convergence rate of $\mathcal{O}(T^{-1/3})$ for
non-convex functions with our newly designed learning rate strategy. Compared
with existing approaches, our method requires weaker assumptions and attains
the optimal convergence rate without the additional $\mathcal{O}(\log T)$ term.
We also extend the proposed technique to stochastic compositional optimization,
obtaining the same optimal rate of $\mathcal{O}(T^{-1/3})$. Furthermore, we
investigate the non-convex finite-sum problem and develop another innovative
adaptive variance reduction method that achieves an optimal convergence rate of
$\mathcal{O}(n^{1/4} T^{-1/2} )$, where $n$ represents the number of component
functions. Numerical experiments across various tasks validate the
effectiveness of our method.


  We consider the problem of estimating the conditional probability
distribution of missing values given the observed ones. We propose an approach,
which combines the flexibility of deep neural networks with the simplicity of
Gaussian mixture models (GMMs). Given an incomplete data point, our neural
network returns the parameters of Gaussian distribution (in the form of Factor
Analyzers model) representing the corresponding conditional density. We
experimentally verify that our model provides better log-likelihood than
conditional GMM trained in a typical way. Moreover, imputation obtained by
replacing missing values using the mean vector of our model looks visually
plausible.


  One main challenge in imbalanced graph classification is to learn expressive
representations of the graphs in under-represented (minority) classes. Existing
generic imbalanced learning methods, such as oversampling and imbalanced
learning loss functions, can be adopted for enabling graph representation
learning models to cope with this challenge. However, these methods often
directly operate on the graph representations, ignoring rich discriminative
information within the graphs and their interactions. To tackle this issue, we
introduce a novel multi-scale oversampling graph neural network (MOSGNN) that
learns expressive minority graph representations based on intra- and
inter-graph semantics resulting from oversampled graphs at multiple scales -
subgraph, graph, and pairwise graphs. It achieves this by jointly optimizing
subgraph-level, graph-level, and pairwise-graph learning tasks to learn the
discriminative information embedded within and between the minority graphs.
Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i)
significantly outperforms five state-of-the-art models, and ii) offers a
generic framework, in which different advanced imbalanced learning loss
functions can be easily plugged in and obtain significantly improved
classification performance.


  The conditions for fully supersymmetric backgrounds of general N=2 locally
supersymmetric theories are derived based on the off-shell superconformal
multiplet calculus. This enables the derivation of a non-renormalization
theorem for a large class of supersymmetric invariants with higher-derivative
couplings. The theorem implies that the invariant and its first order variation
must vanish in a fully supersymmetric background. The conjectured relation of
one particular higher-derivative invariant with a specific five-dimensional
invariant containing the mixed gauge-gravitational Chern-Simons term is
confirmed.


  We solve the disordered Holstein model via the DMRG method to investigate the
combined roles of electron-phonon coupling and disorder on the localization of
a single charge or exciton. The parameter regimes chosen, namely the adiabatic
regime, $\hbar\omega/4t_0 = \omega' < 1$, and the `large' polaron regime,
$\lambda < 1$, are applicable to most conjugated polymers. We show that as a
consequence of the polaron effective mass diverging in the adiabatic limit
(defined as $\omega' \to 0$ subject to fixed $\lambda$) self-localized,
symmetry breaking solutions are predicted by the quantum Holstein model for
infinitesimal disorder -- in complete agreement with the predictions of the
Born-Oppenheimer Holstein model. For other parts of the ($\omega'$, $\lambda$)
parameter space, however, self-localized Born-Oppenheimer solutions are not
expected. If $\omega'$ is not small enough and $\lambda$ is not large enough,
then the polaron is predominately localized by Anderson disorder, albeit more
than for a free particle, because of the enhanced effective mass.
Alternatively, for very small electron-nuclear coupling ($\lambda \ll 1$) the
disorder-induced localization length is always smaller than the classical
polaron size, $2/\lambda$, so that disorder always dominates. We comment on the
implication of our results on the electronic properties of conjugated polymers.


  We report the discovery of up to 35 new supernova remnants (SNRs) from a 42
arcsec resolution 90cm multi-configuration Very Large Array survey of the
Galactic plane covering 4.5 deg< l <22.0 deg and |b| < 1.25 deg. Archival 20cm,
11cm, and 8 micron data have also been used to identify the SNRs and constrain
their properties. The 90cm image is sensitive to SNRs with diameters 2.5 arcmin
to 50 arcmin and down to a surface brightness limit of about 10^{-21} W m^{-2}
Hz^{-1} sr^{-1}. This survey has nearly tripled the number of SNRs known in
this part of the Galaxy, and represents an overall 15% increase in the total
number of Galactic SNRs. These results suggest that further deep low frequency
surveys of the inner Galaxy will solve the discrepancy between the expected
number of Galactic SNRs and the significantly smaller number of currently known
SNRs.


  In this paper, we define distributional analogues of the metaplectic
Eisenstein series for $\text{SL}_2(\mathbb{R})$, which we refer to as
metaplectic Eisenstein distributions. We prove that these distributions have
meromorphic continuation and give an explicit functional equation that they
satisfy. From these results, we deduce the functional equation for the
corresponding classical metaplectic Eisenstein series.


  Recently, there has been a growing interest in monitoring brain activity for
individual recognition system. So far these works are mainly focussing on
single channel data or fragment data collected by some advanced brain
monitoring modalities. In this study we propose new individual recognition
schemes based on spatio-temporal resting state Electroencephalography (EEG)
data. Besides, instead of using features derived from artificially-designed
procedures, modified deep learning architectures which aim to automatically
extract an individual's unique features are developed to conduct
classification. Our designed deep learning frameworks are proved of a small but
consistent advantage of replacing the $softmax$ layer with Random Forest.
Additionally, a voting layer is added at the top of designed neural networks in
order to tackle the classification problem arisen from EEG streams. Lastly,
various experiments are implemented to evaluate the performance of the designed
deep learning architectures; Results indicate that the proposed EEG-based
individual recognition scheme yields a high degree of classification accuracy:
$81.6\%$ for characteristics in high risk (CHR) individuals, $96.7\%$ for
clinically stable first episode patients with schizophrenia (FES) and $99.2\%$
for healthy controls (HC).


  It is argued that continuum realisations of distributions of collisionless
charged particles should accommodate a dynamically evolving number of electric
currents even if the continuum is composed of only one species of particle,
such as electrons. A model is proposed that self-consistently describes the
interaction of such a continuum and its electromagnetic field. It is formulated
using a Lagrangian approach and employs a "folded" flow map to describe the
bulk particle motion. An asymptotic perturbation scheme is developed to analyse
ultra-relativistic multi-component current configurations. The model is fully
relativistic and is formulated over Minkowski spacetime using intrinsic tensor
field theory.


  I argue that the true quantum gravity scale cannot be much larger than the
Planck length, because if it were then the quantum gravity-induced fluctuations
in $\Lam$ would be insufficient to produce the observed cosmic ``dark energy''.
If one accepts this argument, it rules out scenarios of the ``large extra
dimensions'' type. I also point out that the relation between the lower and
higher dimensional gravitational constants in a Kaluza-Klein theory is
precisely what is needed in order that a black hole's entropy admit a
consistent higher dimensional interpretation in terms of an underlying
spatio-temporal discreteness.


  We demonstrate the phenomenon of cumulative inertia in intracellular
transport involving multiple motor proteins in human epithelial cells by
measuring the empirical survival probability of cargoes on the microtubule and
their detachment rates. We found the longer a cargo moves along a microtubule,
the less likely it detaches from it. As a result, the movement of cargoes is
non-Markovian and involves a memory. We observe memory effects on the scale of
up to 2 seconds. We provide a theoretical link between the measured detachment
rate and the super-diffusive Levy walk-like cargo movement.


  We formulate and analyze a general class of stochastic dynamic games with
asymmetric information arising in dynamic systems. In such games, multiple
strategic agents control the system dynamics and have different information
about the system over time. Because of the presence of asymmetric information,
each agent needs to form beliefs about other agents' private information.
Therefore, the specification of the agents' beliefs along with their strategies
is necessary to study the dynamic game. We use Perfect Bayesian equilibrium
(PBE) as our solution concept. A PBE consists of a pair of strategy profile and
belief system. In a PBE, every agent's strategy should be a best response under
the belief system, and the belief system depends on agents' strategy profile
when there is signaling among agents. Therefore, the circular dependence
between strategy profile and belief system makes it difficult to compute PBE.
Using the common information among agents, we introduce a subclass of PBE
called common information based perfect Bayesian equilibria (CIB-PBE), and
provide a sequential decomposition of the dynamic game. Such decomposition
leads to a backward induction algorithm to compute CIB-PBE. We illustrate the
sequential decomposition with an example of a multiple access broadcast game.
We prove the existence of CIB-PBE for a subclass of dynamic games.


  Although catheter ablation (CA) is still the first-line treatment for
persistent atrial fibrillation (AF) patients, its limited long-term success
rate has motivated clinical interest in preoperative prediction on the
procedures outcome to provide optimized patient selection, limit repeated
procedures, hospitalization rates, and treatment costs. To this respect,
dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected
on the ECG have provided promising results. Hence this work explores the
ability of a novel set of frequency and amplitud f-waves features, such as
spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum
area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve
CA outcome prediction. Despite all single indices reported statistically
significant differences between patients who relapsed to AF and those who
maintained sinus rhythm after a follow up of 9 months for 204 6 s-length ECG
intervals extracted from 51 persistent AF patients, they obtained a limited
discriminant ability ranging between 55 and 62%, which was overcome by 15 - 23%
when NFWA, SE and AMSA were combined. Consequently, this combination of
frequency and amplitude features of the fwaves seems to provide new insights
about the atrial substrate remodeling, which could be helpful in improving
preoperative CA outcome prediction.


  The stretchability of polymeric materials is critical to many applications
such as stretchable electronics and soft robotics, yet the stretchability of
conventional cross-linked linear polymers is limited by the entanglements
between polymer chains. We show using molecular dynamics simulations that
cross-linked ring polymers are significantly more stretchable than cross-linked
linear polymers. Compared to linear polymers, the entanglements between ring
polymers do not act as effective cross-links. As a result, the stretchability
of cross-linked ring polymers is determined by the maximum extension of polymer
strands between cross-links, rather than between entanglements as in
cross-linked linear polymers. The more compact conformation of ring polymers
before deformation also contributes to the increase in stretchability.


  Given a monoidal $\infty$-category $C$ equipped with a monoidal recollement,
we give a simple criterion for an object in $C$ to be dualizable in terms of
the dualizability of each of its factors and a projection formula relating
them. Predicated on this, we then characterize dualizability in any monoidally
stratified $\infty$-category in terms of stratumwise dualizability and a
projection formula for the links.
  Using our criterion, we prove a 1-dimensional bordism hypothesis for
symmetric monoidal recollements. Namely, we provide an algebraic enhancement of
the 1-dimensional framed bordism $\infty$-category that corepresents dualizable
objects in symmetric monoidal recollements.
  We also give a number of examples and applications of our criterion drawn
from algebra and homotopy theory, including equivariant and cyclotomic spectra
and a multiplicative form of the Thom isomorphism.


  Glioma is the most common and aggressive brain tumor. Magnetic resonance
imaging (MRI) plays a vital role to evaluate tumors for the arrangement of
tumor surgery and the treatment of subsequent procedures. However, the manual
segmentation of the MRI image is strenuous, which limits its clinical
application. With the development of deep learning, a large number of automatic
segmentation methods have been developed, but most of them stay in 2D images,
which leads to subpar performance. Moreover, the serious voxel imbalance
between the brain tumor and the background as well as the different sizes and
locations of the brain tumor makes the segmentation of 3D images a challenging
problem. Aiming at segmenting 3D MRI, we propose a model for brain tumor
segmentation with multiple encoders. The structure contains four encoders and
one decoder. The four encoders correspond to the four modalities of the MRI
image, perform one-to-one feature extraction, and then merge the feature maps
of the four modalities into the decoder. This method reduces the difficulty of
feature extraction and greatly improves model performance. We also introduced a
new loss function named "Categorical Dice", and set different weights for
different segmented regions at the same time, which solved the problem of voxel
imbalance. We evaluated our approach using the online BraTS 2020 Challenge
verification. Our proposed method can achieve promising results in the
validation set compared to the state-of-the-art approaches with Dice scores of
0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhanced
tumor, respectively.


  Generalized mutual information (GMI) is used to compute achievable rates for
fading channels with various types of channel state information at the
transmitter (CSIT) and receiver (CSIR). The GMI is based on variations of
auxiliary channel models with additive white Gaussian noise (AWGN) and
circularly-symmetric complex Gaussian inputs. One variation uses reverse
channel models with minimum mean square error (MMSE) estimates that give the
largest rates but are challenging to optimize. A second variation uses forward
channel models with linear MMSE estimates that are easier to optimize. Both
model classes are applied to channels where the receiver is unaware of the CSIT
and for which adaptive codewords achieve capacity. The forward model inputs are
chosen as linear functions of the adaptive codeword's entries to simplify the
analysis. For scalar channels, the maximum GMI is then achieved by a
conventional codebook, where the amplitude and phase of each channel symbol are
modified based on the CSIT. The GMI increases by partitioning the channel
output alphabet and using a different auxiliary model for each partition
subset. The partitioning also helps to determine the capacity scaling at high
and low signal-to-noise ratios. A class of power control policies is described
for partial CSIR, including a MMSE policy for full CSIT. Several examples of
fading channels with AWGN illustrate the theory, focusing on on-off fading and
Rayleigh fading. The capacity results generalize to block fading channels with
in-block feedback, including capacity expressions in terms of mutual and
directed information.


  We devise an abstract, modular scheme to prove continuity of the Lyapunov
exponents for a general class of linear cocycles. The main assumption is the
availability of appropriate large deviation type (LDT) estimates which are
uniform in the data. We provide a modulus of continuity that depends explicitly
on the sharpness of the LDT estimate. Our method uses an inductive procedure
based on the deterministic, general Avalanche Principle in [4]. The main
advantage of this approach, besides the fact that it provides quantitative
estimates, is its versatility, as it applies to quasi-periodic cocycles (one
and multivariable torus translations), to random cocycles (i.i.d. and Markov
systems) and to any other types of base dynamics as long as appropriate LDT
estimates are satisfied. Moreover, compared to other available quantitative
results for quasi-periodic or random cocycles, this method allows for weaker
assumptions.
  This is a draft of a chapter in our forthcoming research monograph [4].


  High definition (HD) map needs to be updated frequently to capture road
changes, which is constrained by limited specialized collection vehicles. To
maintain an up-to-date map, we explore crowdsourcing data from connected
vehicles. Updating the map collaboratively is, however, challenging under
constrained transmission and computation resources in dynamic networks. In this
paper, we propose EdgeMap, a crowdsourcing HD map to minimize the usage of
network resources while maintaining the latency requirements. We design a DATE
algorithm to adaptively offload vehicular data on a small time scale and
reserve network resources on a large time scale, by leveraging the multi-agent
deep reinforcement learning and Gaussian process regression. We evaluate the
performance of EdgeMap with extensive network simulations in a time-driven
end-to-end simulator. The results show that EdgeMap reduces more than 30%
resource usage as compared to state-of-the-art solutions.


  We have analyzed the kinematics of OB2 stars with proper motions and
parallaxes selected by Xu et al. from the Gaia EDR3 catalog. The relative
parallax errors for all the stars in this sample do not exceed 10\%. Based on a
sample of 9750 stars, the group velocity components
$(U,V,W)_\odot=(7.21,7.46,8.52)\pm(0.13,0.20,0.10)$~km/s were obtained and the
parameters of the angular velocity of rotation of the Galaxy:
  $\Omega_0 =29.712\pm0.062$~km/s/kpc,
$\Omega^{'}_0=-4.014\pm0.018$~km/s/kpc$^{2}$ and
  $\Omega^{''}_0=0.674\pm0.009$~km/s/kpc$^{3}$. The circular velocity of
rotation of the solar neighborhood around the center of the Galaxy is
$V_0=240.7\pm3.0$~km/s for the assumed distance of the Sun to the galactic
center $R_0=8.1\pm0.1$~kpc. It is shown that the influence of the systematic
correction to the trigonometric parallaxes of the Gaia EDR3 catalog with the
value $\Delta\pi=-0.040$~mas does not exceed the $\sim1\sigma$ level of the
errors of the sought-for kinematic parameters of the model. Based on the proper
motions of OB stars, the following variances of the residual velocities were
found:
$(\sigma_1,\sigma_2,\sigma_3)=(11.79,9.66,7.21)\pm(0.06,0.05,0.04)$~km/s. It is
shown that the first axis of this ellipsoid slightly deviates from the
direction to the center of the Galaxy $L_1=12.4\pm0.1^\circ$, and the third
axis is oriented almost exactly to the north pole of the Galaxy,
$B_3=87.7\pm0.1^\circ.$


  Charged Higgs phenomenology and detection chance at future linear colliders
through the production process $e^{-}e^{+} \ra H^{+}H^{-} \ra \tau^{+}\nu \tau
\bar{\nu}$ is studied. Both charged Higgs bosons are considered to decay to
$\tau\nu$ and the hadronic decay of $\tau$ leptons is analyzed taking into
account spin and kinematics effects using a proper simulation of the $\tau$
lepton decay. It is shown that within the MSSM framework, with \tanb = 10, a
wide range of charged Higgs masses would have detectable signal beyond the
$5\sigma$ statistical significance at linear colliders with $\sqrt{s} = 500$
and 1000 GeV.


  Relativistic Cowling approximation in which all metric perturbations are
omitted is applied to non-axisymmetric infinitesimal oscillations of uniformly
rotating general relativistic polytropes.
  Frequencies of lower order f-modes which are important in analysis of secular
instability driven by gravitational radiation are investigated and neutral
points of the mode along equilibrium sequences of rotating polytropes are
determined. Since this approximation becomes more accurate as stars are more
relativistic and/or as they rotates more rapidly, we will be able to analyze
how a rotation period of a neutron star may be limited by this instability.
  Possible errors in determining neutral points caused by omitting metric
perturbations are also estimated.


  We address the problem of domain generalization where a decision function is
learned from the data of several related domains, and the goal is to apply it
on an unseen domain successfully. It is assumed that there is plenty of labeled
data available in source domains (also called as training domain), but no
labeled data is available for the unseen domain (also called a target domain or
test domain). We propose a novel neural network architecture, Domain2Vec (D2V)
that learns domain-specific embedding and then uses this embedding to
generalize the learning across related domains. The proposed algorithm, D2V
extends the idea of distribution regression and kernelized domain
generalization to the neural networks setting. We propose a neural network
architecture to learn domain-specific embedding and then use this embedding
along with the data point specific features to label it. We show the
effectiveness of the architecture by accurately estimating domain to domain
similarity. We evaluate our algorithm against standard domain generalization
datasets for image classification and outperform other state of the art
algorithms.


  We prove several new restrictions on the Euler characteristic and signature
of oriented 4-manifolds with (positively or negatively) pinched sectional
curvature. In particular, we show that simply connected 4-manifolds with
$\delta\leq \sec\leq 1$, where $\delta=\frac{1}{1+3\sqrt3}\cong 0.161$, are
homeomorphic to $S^4$ or $\mathbb C P^2$.


  Aspect-based sentiment analysis (ABSA) task aim at associating a piece of
text with a set of aspects and meanwhile infer their respective sentimental
polarities. The state-of-the-art approaches are built upon fine-tuning of
various pre-trained language models. They commonly attempt to learn
aspect-specific representation from the corpus. Unfortunately, the aspect is
often expressed implicitly through a set of representatives and thus renders
implicit mapping process unattainable unless sufficient labeled examples are
available. However, high-quality labeled examples may not be readily available
in real-world scenarios. In this paper, we propose to jointly address aspect
categorization and aspect-based sentiment subtasks in a unified framework.
Specifically, we first introduce a simple but effective mechanism to construct
an auxiliary-sentence for the implicit aspect based on the semantic information
in the corpus. Then, we encourage BERT to learn the aspect-specific
representation in response to the automatically constructed auxiliary-sentence
instead of the aspect itself. Finally, we empirically evaluate the performance
of the proposed solution by a comparative study on real benchmark datasets for
both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it
consistently achieves state-of-the-art performance in terms of aspect
categorization and aspect-based sentiment across all datasets and the
improvement margins are considerable. The code of BERT-ASC is available in
GitHub: https://github.com/amurtadha/BERT-ASC.


  As intelligent agents transition from controlled to uncontrolled
environments, they face challenges that sometimes exceed their operational
capabilities. In many scenarios, they rely on assistance from bystanders to
overcome those challenges. Using robots that get stuck in urban settings as an
example, we investigate how agents can prompt bystanders into providing
assistance. We conducted four focus group sessions with 17 participants that
involved bodystorming, where participants assumed the role of robots and
bystander pedestrians in role-playing activities. Generating insights from both
assumed robot and bystander perspectives, we were able to identify potential
non-verbal help-seeking strategies (i.e., addressing bystanders, cueing
intentions, and displaying emotions) and factors shaping the assistive
behaviours of bystanders. Drawing on these findings, we offer design
considerations for help-seeking urban robots and other agents operating in
uncontrolled environments to foster casual collaboration, encompass
expressiveness, align with agent social categories, and curate appropriate
incentives.


  When a particle moves around a Kerr black hole, it radiates gravitational
waves.Some of these waves are absorbed by the black hole. We calculate such
absorption of gravitational waves induced by a particle of mass mu in a
circular orbit on an equatorial plane around a Kerr black hole of mass M. We
assume that the velocity of the particle v is much smaller than the speed of
light c and calculate the energy absorption rate analytically. We adopt an
analytic technique for the Teukolsky equation developed by Mano, Suzuki and
Takasugi. We obtain the energy absorption rate to O((v/c)^8) compared to the
lowest order. We find that the black hole absorption occurs at O((v/c)^5)
beyond the Newtonian-quadrapole luminosity at infinity in the case when the
black hole is rotating, which is O((v/c)^3) lower than the non-rotating case.
Using the energy absorption rate, we investigate its effects on the orbital
evolution of coalescing compact binaries.


  We present an analysis of exclusive charmless semileptonic B-meson decays
based on 377 million BBbar pairs recorded with the BABAR detector at the
Upsilon(4S) resonance. We select four event samples corresponding to the decay
modes B0 --> pi- l+ nu, B+ --> pi0 l+ nu, B0 --> rho- l+ nu, and B+ -> rho0 l+
nu, and find the measured branching fractions to be consistent with isospin
symmetry. Assuming isospin symmetry, we combine the two B --> pi l nu samples,
and similarly the two B --> rho l nu samples, and measure the branching
fractions BF(B0 --> pi- l+ nu) = (1.41 +- 0.05 +- 0.07) x 10^-4 and BF(B0 -->
rho- l+ nu) = (1.75 +- 0.15 +- 0.27) x 10^-4, where the errors are statistical
and systematic. We compare the measured distribution in q^2, the momentum
transfer squared, with predictions for the form factors from QCD calculations
and determine the CKM matrix element |V_ub|. Based on the measured partial
branching fraction for B --> pi l nu in the range q^2 < 12 GeV^2 and the most
recent LCSR calculations we obtain |V_ub| = (3.78 +- 0.13 +0.55-0.40) x 10^-3,
where the errors refer to the experimental and theoretical uncertainties. From
a simultaneous fit to the data over the full q^2 range and the FNAL/MILC
lattice QCD results, we obtain |V_ub| = (2.95 +- 0.31) x 10^-3 from B --> pi l
nu, where the error is the combined experimental and theoretical uncertainty.


  Cost per click is a common metric to judge digital advertising campaign
performance. In this paper we discuss an approach that generates a feature
targeting recommendation to optimise cost per click. We also discuss a
technique to assign bid prices to features without compromising on the number
of features recommended.
  Our approach utilises impression and click stream data sets corresponding to
real time auctions that we have won. The data contains information about device
type, website, RTB Exchange ID. We leverage data across all campaigns that we
have access to while ensuring that recommendations are sensitive to both
individual campaign level features and globally well performing features as
well. We model Bid recommendation around the hypothesis that a click is a
Bernoulli trial and click stream follows Binomial distribution which is then
updated based on live performance ensuring week over week improvement.
  This approach has been live tested over 10 weeks across 5 campaigns. We see
Cost per click gains of 16-60% and click through rate improvement of 42-137%.
At the same time, the campaign delivery was competitive.


  A key tracer of the elusive progenitor systems of Type Ia supernovae (SNe Ia)
is the detection of narrow blueshifted time-varying Na I D absorption lines,
interpreted as evidence of circumstellar material (CSM) surrounding the
progenitor system. The origin of this material is controversial, but the
simplest explanation is that it results from previous mass loss in a system
containing a white dwarf and a non-degenerate companion star. We present new
single-epoch intermediate-resolution spectra of 17 low-redshift SNe Ia taken
with XShooter on the ESO Very Large Telescope. Combining this sample with
events from the literature, we confirm an excess (~20 per cent) of SNe Ia
displaying blueshifted narrow Na I D absorption features compared to
non-blueshifted Na I D features. The host galaxies of SNe Ia displaying
blueshifted absorption profiles are skewed towards later-type galaxies,
compared to SNe Ia that show no Na I D absorption, and SNe Ia displaying
blueshifted narrow Na I D absorption features have broader light curves. The
strength of the Na I D absorption is stronger in SNe Ia displaying blueshifted
Na I D absorption features than those without blueshifted features, and the
strength of the blueshifted Na I D is correlated with the B-V colour of the SN
at maximum light. This strongly suggests the absorbing material is local to the
SN. In the context of the progenitor systems of SNe Ia, we discuss the
significance of these findings and other recent observational evidence on the
nature of SN Ia progenitors. We present a summary that suggests there are at
least two distinct populations of normal, cosmologically useful SNe Ia.


  The equivalence between metric and Palatini formalisms in f(R)-gravity can be
achieved in the general context of theories with divergence free current. This
equivalence is a necessary result of a symmetry which is included in a
particular conservation equation of the current. In fact the conservation
equation, by an appropriate redefinition of the introduced auxiliary field, may
be encoded in a massless scalar field equation.


  We investigate the post-quantum security of the encrypted key exchange(EKE)
protocols based on some basic physical parameters of ion-trap quantum computer,
and show that the EKE protocol with a 40-bit password will be secure against a
quantum adversary with several ion-trap quantum computers. We present a
password encrypted no-key protocol to resist middle-man attack, and prove that
it is also with the post-quantum security. The analysis presented here is
probably of general meaning for the security evaluation of various hybrid
cryptosystems.


  Agile software developers are required to self-organize, occupying various
informal roles as needed in order to successfully deliver software features.
However, previous research has reported conflicting evidence about the way
teams actually undertake this activity. The ability to self-organize is
particularly necessary for software development in globally distributed
environments, where distance has been shown to exacerbate human-centric issues.
Understanding the way successful teams self-organise should inform distributed
team composition strategies and software project governance. We have used
psycholinguistics to study the way IBM Rational Jazz practitioners enacted
various roles, expressed attitudes and shared competencies to successfully
self-organize in their global projects. Among our findings, we uncovered that
practitioners enacted various roles depending on their teams' cohort of
features; and that team leaders were most critical to IBM Jazz teams'
self-organisation. We discuss these findings and highlight their implications
for software project governance.


  With the growth of embedded systems, VLSI design phases complexity and cost
factors across the globe and has become outsourced. Modern computing ICs are
now using system-on-chip for better on-chip processing and communication. In
the era of Internet-of-Things (IoT), security has become one of the most
crucial parts of a System-on-Chip (SoC). Malicious activities generate abnormal
traffic patterns which affect the operation of the system and its performance
which cannot be afforded in a computation hungry world. SoCs have a chance of
functionality failure, leakage of information, even a denial of services (DoS),
Hardware Trojan Horses and many more factors which are categorized as security
threats. In this paper, we aim to compare and describe different types of
malicious security threats and how neural networks can be used to prevent those
attacks. Spiking Neural Networks (SNN), Runtime Neural Architecture (RTNA) are
some of the neural networks which prevent SoCs from attacks. Finally, the
development trends in SoC security are also highlighted.


  The proposition that photon is a topological object (J. Math. Phys. 49,
032303, 2008) is given rigorous foundation based on pure vector field theory
independent of the electromagnetic fields. Holomorphy of 4-dimensional
space-time and the nature of topological obstructions are investigated to
articulate singular vortex model for photon. The utility of the Euclidean
symmetry group is discussed in detail for the proposed photon model.


  In this paper, existence of solutions is established for critical exponential
Kirchhoff systems on the Heisenberg group by using the variational method. The
novelty of our paper is that not only the nonlinear term has critical
exponential growth, but also that Kirchhoff function covers the degenerate
case. Moreover, our result is new even for the Euclidean case.


  The advantage of using a Discrete Variable Representation (DVR) is that the
Hamiltonian of two interacting particles can be constructed in a very simple
form. However the DVR Hamiltonian is approximate and, as a consequence, the
results cannot be considered as variational ones. We will show that the
variational character of the results can be restored by performing a reduced
number of integrals.
  In practice, for a variational description of the lowest n bound states only
n(n+1)/2 integrals are necessary whereas D(D+1)/2 integrals are enough for the
scattering states (D is the dimension of the S matrix). Applications of the
method to the study of dimers of He, Ne and Ar, for both bound and scattering
states, are presented.


  In this paper we show derivations among logarithmic space bounded counting
classes based on closure properties of $\#L$ that leads us to the result that
$NL=C_=L\subseteq PL$.


  We study the notion of strong integrability for classically integrable
$\lambda$-deformed CFTs and coset CFTs. To achieve this goal we employ the
Poisson brackets of the spatial Lax matrix which we prove that it assumes the
Maillet $r/s$-matrix algebra. As a consequence the system in question are
integrable in the strong sense. Furthermore, we show that the derived Maillet
$r/s$-matrix algebras can be realized in terms of twist functions, at the poles
of which we recover the underlying symmetry algebras.


  We study theoretically the electronic structure, transport and optical
properties for a zigzag single-wall carbon nanotube connected to two normal
conductor leads under the irradiation of an external electromagnetic field at
low temperatures, with particular emphasis on the features of high-frequency
response. Using the standard nonequilibrium Green's function techniques, we
examine the time-averaged density of states, the conductivity, the dielectric
function and the electron energy loss spectra for the system with photon
polarization parallel with the tunneling current direction, respectively.
Through some numerical examples, it is shown that the density of states is
strongly dependent on the incident electron energy, the strength and frequency
of the applied field. For higher electron energies in comparison with
lead-nanotube coupling energy, the system conductance decreases with increasing
the field strength and increases with increasing the field frequency
respectively, and shows some oscillation structures. Moreover, the optical
functions for the system have also a rich structure with the variation of field
frequency. It may demonstrate that this transport dependence on the external
field parameters can be used to give the energy spectra information of carbon
nanotubes and to detect the high-frequency microwave irradiation.


  Where does solar flare energy come from? More specifically, assuming that the
ultimate source of flare energy is mechanical energy in the convection zone,
how is this translated into energy dissipated or stored in the corona? This
question appears to have been given relatively little thought, as attention has
been focussed predominantly on mechanisms for the rapid dissipation of coronal
magnetic energy by way of MHD instabilities and plasma micro instabilities. We
consider three types of flare theory: the steady state "photospheric dynamo"
model in which flare power represents coronal dissipation of currents generated
simultaneously by sub-photospheric flows; the "magnetic energy storage" model
where sub-photospheric flows again induce coronal currents but which in this
case are built up over a longer period before being released suddenly; and
"emerging flux" models, in which new magnetic flux rising to the photosphere
already contains free energy, and does not require subsequent stressing by
photospheric motions. We conclude that photospheric dynamos can power only very
minor flares; that coronal energy storage can in principle meet the
requirements of a major flare, although perhaps not the very largest flares,
but that difficulties in coupling efficiently to the energy source may limit
this mechanism to moderate sized flares; and that emerging magnetic flux tubes,
generated in the solar interior, can carry sufficient free energy to power even
the largest flares ever observed.


  We give a necessary and sufficient condition for a simplicial complex to be
approximately Cohen-Macaulay. Namely it is approximately Cohen-Macaulay if and
only if the ideal associated to its Alexander dual is componentwise linear and
generated in two consecutive degrees. This completes the result of Herzog and
Hibi who proved that a simplicial complex is sequentially Cohen-Macaulay if and
only if the ideal associated to its Alexander dual is componentwise linear.


  Visual pose regression models estimate the camera pose from a query image
with a single forward pass. Current models learn pose encoding from an image
using deep convolutional networks which are trained per scene. The resulting
encoding is typically passed to a multi-layer perceptron in order to regress
the pose. In this work, we propose that scene-specific pose encoders are not
required for pose regression and that encodings trained for visual similarity
can be used instead. In order to test our hypothesis, we take a shallow
architecture of several fully connected layers and train it with pre-computed
encodings from a generic image retrieval model. We find that these encodings
are not only sufficient to regress the camera pose, but that, when provided to
a branching fully connected architecture, a trained model can achieve
competitive results and even surpass current \textit{state-of-the-art} pose
regressors in some cases. Moreover, we show that for outdoor localization, the
proposed architecture is the only pose regressor, to date, consistently
localizing in under 2 meters and 5 degrees.


  The Kerr metric is considered in a synchronous frame of reference obtained by
using proper time and initial conditions for particles that freely move along a
certain set of trajectories as coordinates. Modifying these coordinates in a
certain way (keeping their interpretation as initial values at large
distances), we still have a synchronous frame and the direct analogue of the
Lemaitre metric, the singularities of which are exhausted by the physical Kerr
singularity (the singularity ring).


  Aligning large language models (LLM) with human preference plays a key role
in building modern generative models and can be achieved by reinforcement
learning from human feedback (RLHF). Despite their superior performance,
current RLHF approaches often require a large amount of human-labelled
preference data, which is expensive to collect. In this paper, inspired by the
success of active learning, we address this problem by proposing
query-efficient RLHF methods. We first formalize the alignment problem as a
contextual dueling bandit problem and design an active-query-based proximal
policy optimization (APPO) algorithm with an $\tilde{O}(d^2/\Delta)$ regret
bound and an $\tilde{O}(d^2/\Delta^2)$ query complexity, where $d$ is the
dimension of feature space and $\Delta$ is the sub-optimality gap over all the
contexts. We then propose ADPO, a practical version of our algorithm based on
direct preference optimization (DPO) and apply it to fine-tuning LLMs. Our
experiments show that ADPO, while only making about half of queries for human
preference, matches the performance of the state-of-the-art DPO method.


  Using simple space-time implementation of the random cascade model we
investigate numerically a conjecture made some time ago which was joining the
intermittent behaviour of spectra of emitted particles with the possible
fractal structure of the emitting source. We demonstrate that such details are
seen, as expected, in the Bose-Einstein correlations between identical
particles. \\


  We calculate the first and second moments of L-functions in the family of
quadratic twists of a fixed elliptic curve E over F_q[x], asymptotically in the
limit as the degree of the twists tends to infinity. We also compute moments
involving derivatives of L-functions over quadratic twists, enabling us to
deduce lower bounds on the correlations between the analytic ranks of the
twists of two distinct curves.


  A flavour-tagged time-dependent angular analysis of $B^0_s\to J/\psi\phi$
decays is presented where the $J/\psi$ meson is reconstructed through its decay
to an $e^+e^-$ pair. The analysis uses a sample of $pp$ collision data recorded
with the LHCb experiment at centre-of-mass energies of 7 and 8 TeV,
corresponding to an integrated luminosity of 3 fb$^{-1}$. The $CP$-violating
phase and lifetime parameters of the $B^0_s$ system are measured to be
$\phi_s=0.00\pm0.28\pm0.07$ rad, $\Delta\Gamma_s=0.115\pm0.045\pm0.011$
ps$^{-1}$ and $\Gamma_s=0.608\pm0.018\pm0.012$ ps$^{-1}$ where the first
uncertainty is statistical and the second systematic. This is the first time
that $CP$-violating parameters are measured in the $B^0_s\to J/\psi\phi$ decay
with an $e^+e^-$ pair in the final state. The results are consistent with
previous measurements in other channels and with the Standard Model
predictions.


  Owing to their stability and convergence speed, extragradient methods have
become a staple for solving large-scale saddle-point problems in machine
learning. The basic premise of these algorithms is the use of an extrapolation
step before performing an update; thanks to this exploration step,
extra-gradient methods overcome many of the non-convergence issues that plague
gradient descent/ascent schemes. On the other hand, as we show in this paper,
running vanilla extragradient with stochastic gradients may jeopardize its
convergence, even in simple bilinear models. To overcome this failure, we
investigate a double stepsize extragradient algorithm where the exploration
step evolves at a more aggressive time-scale compared to the update step. We
show that this modification allows the method to converge even with stochastic
gradients, and we derive sharp convergence rates under an error bound
condition.


  Name entity recognition in noisy user-generated texts is a difficult task
usually enhanced by incorporating an external resource of information, such as
gazetteers. However, gazetteers are task-specific, and they are expensive to
build and maintain. This paper adopts and improves the approach of Aguilar et
al. by presenting a novel feature, called Local Distance Neighbor, which
substitutes gazetteers. We tested the new approach on the W-NUT-2017 dataset,
obtaining state-of-the-art results for the Group, Person and Product categories
of Named Entities. Next, we added 851 manually labeled samples to the
W-NUT-2017 dataset to account for named entities in the Tor Darknet related to
weapons and drug selling. Finally, our proposal achieved an entity and surface
F1 scores of 52.96% and 50.57% on this extended dataset, demonstrating its
usefulness for Law Enforcement Agencies to detect named entities in the Tor
hidden services.


  In presence of interactions, a closed, homogeneous (disorder-free) many-body
system is believed to generically heat up to an `infinite temperature' ensemble
when subjected to a periodic drive: in the spirit of the ergodicity hypothesis
underpinning statistical mechanics, this happens as no energy or other
conservation law prevents this. Here we present an interacting Ising chain
driven by a field of time-dependent strength, where such heating onsets only
below a threshold value of the drive amplitude, above which the system exhibits
non-ergodic behaviour. The onset appears at {\it strong, but not fast} driving.
This in particular puts it beyond the scope of high-frequency expansions. The
onset location shifts, but it is robustly present, across wide variations of
the model Hamiltonian such as driving frequency and protocol, as well as the
initial state. The portion of nonergodic states in the Floquet spectrum, while
thermodynamically subdominant, has a finite entropy. We find that the
magnetisation as an {\it emergent} conserved quantity underpinning the
freezing; indeed the freezing effect is readily observed, as initially
magnetised states remain partially frozen {\it up to infinite time}. This
result, which bears a family resemblance to the Kolmogorov-Arnold-Moser theorem
for classical dynamical systems, could be a valuable ingredient for extending
Floquet engineering to the interacting realm.


  We study rare events in networks with both internal and external noise, and
develop a general formalism for analyzing rare events that combines
pair-quenched techniques and large-deviation theory. The probability
distribution, shape, and time scale of rare events are considered in detail for
extinction in the Susceptible-Infected-Susceptible model as an illustration. We
find that when both types of noise are present, there is a crossover region as
the network size is increased, where the probability exponent for large
deviations no longer increases linearly with the network size. We demonstrate
that the form of the crossover depends on whether the endemic state is
localized near the epidemic threshold or not.


  In the detection of myeloproliferative, the number of cells in each type of
bone marrow cells (BMC) is an important parameter for the evaluation. In this
study, we propose a new counting method, which also consists of three modules
including localization, segmentation and classification. The localization of
BMC is achieved from a color transformation enhanced BMC sample image and
stepwise averaging method (SAM). In the nucleus segmentation, both SAM and
Otsu's method will be applied to obtain a weighted threshold for segmenting the
patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color
weakening transformation, an improved region growing method and the K-Means
algorithm are used. The connected cells with BMC will be separated by the
marker-controlled watershed algorithm. The features will be extracted for the
classification after the segmentation. In this study, the BMC are classified
using the SVM, Random Forest, Artificial Neural Networks, Adaboost and Bayesian
Networks into five classes including one outlier, namely, neutrophilic split
granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes
and the outlier (all other cells not listed). Our experimental results show
that the best average recognition rate is 87.49% for the SVM.


  We construct and classify all possible Magic Squares (MS's) related to
Euclidean or Lorentzian rank-3 simple Jordan algebras, both on normed division
algebras and split composition algebras. Besides the known
Freudenthal-Rozenfeld-Tits MS, the single-split G\"unaydin-Sierra-Townsend MS,
and the double-split Barton-Sudbery MS, we obtain other 7 Euclidean and 10
Lorentzian novel MS's. We elucidate the role and the meaning of the various
non-compact real forms of Lie algebras, entering the MS's as symmetries of
theories of Einstein-Maxwell gravity coupled to non-linear sigma models of
scalar fields, possibly endowed with local supersymmetry, in D = 3, 4 and 5
space-time dimensions. In particular, such symmetries can be recognized as the
U-dualities or the stabilizers of scalar manifolds within space-time with
standard Lorentzian signature or with other, more exotic signatures, also
relevant to suitable compactifications of the so-called M*- and M'- theories.
Symmetries pertaining to some attractor U-orbits of magic supergravities in
Lorentzian space-time also arise in this framework.


  Game designs often center on the game mechanics---rules governing the logical
evolution of the game. We seek to develop an intelligent system that generates
computer games. As first steps towards this goal we present a composable and
cross-domain representation for game mechanics that draws from AI planning
action representations. We use a constraint solver to generate mechanics
subject to design requirements on the form of those mechanics---what they do in
the game. A planner takes a set of generated mechanics and tests whether those
mechanics meet playability requirements---controlling how mechanics function in
a game to affect player behavior. We demonstrate our system by modeling and
generating mechanics in a role-playing game, platformer game, and combined
role-playing-platformer game.


  Quantum Computing aims to streamline machine learning, making it more
effective with fewer trainable parameters. This reduction of parameters can
speed up the learning process and reduce the use of computational resources.
However, in the current phase of quantum computing development, known as the
noisy intermediate-scale quantum era (NISQ), learning is difficult due to a
limited number of qubits and widespread quantum noise. To overcome these
challenges, researchers are focusing on variational quantum circuits (VQCs).
VQCs are hybrid algorithms that merge a quantum circuit, which can be adjusted
through parameters, with traditional classical optimization techniques. These
circuits require only few qubits for effective learning. Recent studies have
presented new ways of applying VQCs to reinforcement learning, showing
promising results that warrant further exploration. This study investigates the
effects of various techniques -- data re-uploading, input scaling, output
scaling -- and introduces exponential learning rate decay in the quantum
proximal policy optimization algorithm's actor-VQC. We assess these methods in
the popular Frozen Lake and Cart Pole environments. Our focus is on their
ability to reduce the number of parameters in the VQC without losing
effectiveness. Our findings indicate that data re-uploading and an exponential
learning rate decay significantly enhance hyperparameter stability and overall
performance. While input scaling does not improve parameter efficiency, output
scaling effectively manages greediness, leading to increased learning speed and
robustness.


  In this paper we investigate the ability of modern machine learning
algorithms in inferring basic offline activities,~e.g., shopping and dining,
from location data. Using anonymized data of thousands of users of a prominent
location-based social network, we empirically demonstrate that not only
state-of-the-art machine learning excels at the task at hand~(F1 score>0.9) but
also tabular models are among the best performers. The findings we report here
not only fill an existing gap in the literature, but also highlight the
potential risks of such capabilities given the ubiquity of location data and
the high accessibility of tabular machine learning models.


  Let M be a smooth 4-manifold which admits a relatively minimal hyperelliptic
genus h Lefschetz fibration over the 2-sphere. If all of the vanishing cycles
for this fibration are nonseparating curves, then we show that M is a 2-fold
cover of a 2-sphere bundle over the 2-sphere, branched over an embedded
surface. If the collection of vanishing cycles for this fibration includes
separating curves, we show that M is the relative minimalization of a Lefshetz
fibration N, with N a 2-fold branched cover of a rational surface, branched
over an embedded surface.


  In robotics, accurate ground-truth position fostered the development of
mapping and localization algorithms through the creation of cornerstone
datasets. In outdoor environments and over long distances, total stations are
the most accurate and precise measurement instruments for this purpose. Most
total station-based systems in the literature are limited to three Degrees Of
Freedoms (DOFs), due to the use of a single-prism tracking approach. In this
paper, we present preliminary work on measuring a full pose of a vehicle,
bringing the referencing system to six DOFs. Three total stations are used to
track in real time three prisms attached to a target platform. We describe the
structure of the referencing system and the protocol for acquiring the ground
truth with this system. We evaluated its precision in a variety of different
outdoor environments, ranging from open-sky to forest trails, and compare this
system with another popular source of reference position, the Real Time
Kinematics (RTK) positioning solution. Results show that our approach is the
most precise, reaching an average positional error of 10 mm and 0.6 deg. This
difference in performance was particularly stark in environments where Global
Navigation Satellite System (GNSS) signals can be weaker due to overreaching
vegetation.


  Experts in Artificial Intelligence (AI) development predict that advances in
the development of intelligent systems and agents will reshape vital areas in
our society. Nevertheless, if such an advance isn't done with prudence, it can
result in negative outcomes for humanity. For this reason, several researchers
in the area are trying to develop a robust, beneficial, and safe concept of
artificial intelligence. Currently, several of the open problems in the field
of AI research arise from the difficulty of avoiding unwanted behaviors of
intelligent agents, and at the same time specifying what we want such systems
to do. It is of utmost importance that artificial intelligent agents have their
values aligned with human values, given the fact that we cannot expect an AI to
develop our moral preferences simply because of its intelligence, as discussed
in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are
addressing the problem of expressing objectives, values, and ends, using
representational cognitive methods. A solution to this problem would be the
dynamic cognitive approach proposed by Dreyfus, whose phenomenological
philosophy defends that the human experience of being-in-the-world cannot be
represented by the symbolic or connectionist cognitive methods. A possible
approach to this problem would be to use theoretical models such as SED
(situated embodied dynamics) to address the values learning problem in AI.


  We study and provide efficient algorithms for multi-objective model checking
problems for Markov Decision Processes (MDPs). Given an MDP, M, and given
multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and
probabilities r\_i \epsilon [0,1], i=1,...,k, we ask whether there exists a
strategy \sigma for the controller such that, for all i, the probability that a
trajectory of M controlled by \sigma satisfies \varphi\_i is at least r\_i. We
provide an algorithm that decides whether there exists such a strategy and if
so produces it, and which runs in time polynomial in the size of the MDP. Such
a strategy may require the use of both randomization and memory. We also
consider more general multi-objective \omega -regular queries, which we
motivate with an application to assume-guarantee compositional reasoning for
probabilistic systems.
  Note that there can be trade-offs between different properties: satisfying
property \varphi\_1 with high probability may necessitate satisfying \varphi\_2
with low probability. Viewing this as a multi-objective optimization problem,
we want information about the "trade-off curve" or Pareto curve for maximizing
the probabilities of different properties. We show that one can compute an
approximate Pareto curve with respect to a set of \omega -regular properties in
time polynomial in the size of the MDP.
  Our quantitative upper bounds use LP methods. We also study qualitative
multi-objective model checking problems, and we show that these can be analysed
by purely graph-theoretic methods, even though the strategies may still require
both randomization and memory.


  We explore the possibilities and limitations of using a coherent second phase
to engineer the thermo-mechanical properties of a martensitic alloy by
modifying the underlying free energy landscape that controls the
transformation. We use molecular dynamics simulations of a model atomistic
system where the properties of a coherent, nanoscale second phase can be varied
systematically. With a base martensitic material that undergoes a
temperature-induced transformation from a cubic austenite to a monoclinic
martensite, the simulations show a significant ability to engineer the
transformation temperatures, from a ~50% reduction to a ~200% increase, with 50
at. % of the cubic second phase. We establish correlations between the
properties of the second phase and the transformation characteristics and
microstructure, via the free energy landscape of the two-phase systems.
Coherency stresses have a strong influence on the martensitic variants observed
and can even cause the non-martensitic second phase to undergo a
transformation. Reducing the stiffness of second phase increases the
transformation strain and modifies the martensitic microstructure, increasing
the volume fraction of the transformed material. This increase in
transformation strain is accompanied by a significant increase in the Af and
thermal hysteresis, while the Ms remains unaltered. Our findings on the
tunability of martensitic transformations can be used for informed searches of
second phases to achieve desired material properties, such as achieving room
temperature, lightweight shape memory alloys.


  This paper studies the multi-cascade influence maximization problem, which
explores strategies for launching one information cascade in a social network
with multiple existing cascades. With natural extensions to the classic models,
we first propose the independent multi-cascade model where the diffusion
process is governed by the so-called activation function. We show that the
proposed model is sufficiently flexible as it generalizes most of the existing
cascade-based models. We then study the multi-cascade influence maximization
problem under the designed model and provide approximation hardness under
common complexity assumptions, namely Exponential Time Hypothesis and $NP
\subseteq DTIME(n^{\poly \log n})$. Given the hardness results, we build a
framework for designing heuristic seed selection algorithms with a testable
data-dependent approximation ratio. The designed algorithm leverages upper and
lower bounds, which reveal the key combinatorial structure behind the
multi-cascade influence maximization problem. The performance of the framework
is theoretically analyzed and practically evaluated through extensive
simulations. The superiority of the proposed solution is supported by
encouraging experimental results, in terms of effectiveness and efficiency.


  This paper contains a detailed derivation of the photoinduced current density
at third order in the coupling between a semiconductor and a multifrequency
photon field, starting from its standard textbook expression as a third order
time integral of a triple commutator. Due to a major intrinsic problem linked
to this triple commutator, such a derivation has been made possible quite
recently only, thanks to the tools developed in the composite-boson many-body
theory we have just constructed. The photoinduced current density is shown to
ultimately read in a compact form, in terms of the Pauli and Coulomb
scatterings for exciton-exciton interactions introduced in this theory.
Representation in Shiva diagrams is also given to better grasp the physics of
the various contributions.


  We survey recent results on Calderon's inverse problem with partial data,
focusing on three and higher dimensions.


  We formulate an asymmetric (or non-commutative) distance between tasks based
on Fisher Information Matrices, called Fisher task distance. This distance
represents the complexity of transferring the knowledge from one task to
another. We provide a proof of consistency for our distance through theorems
and experiments on various classification tasks from MNIST, CIFAR-10,
CIFAR-100, ImageNet, and Taskonomy datasets. Next, we construct an online
neural architecture search framework using the Fisher task distance, in which
we have access to the past learned tasks. By using the Fisher task distance, we
can identify the closest learned tasks to the target task, and utilize the
knowledge learned from these related tasks for the target task. Here, we show
how the proposed distance between a target task and a set of learned tasks can
be used to reduce the neural architecture search space for the target task. The
complexity reduction in search space for task-specific architectures is
achieved by building on the optimized architectures for similar tasks instead
of doing a full search and without using this side information. Experimental
results for tasks in MNIST, CIFAR-10, CIFAR-100, ImageNet datasets demonstrate
the efficacy of the proposed approach and its improvements, in terms of the
performance and the number of parameters, over other gradient-based search
methods, such as ENAS, DARTS, PC-DARTS.


  We comment on the recent papers by Costa et al and Emparan, which show how
one might generate supergravity solutions describing certain dielectric branes
in ten dimensions. The ``basic'' such solutions describe either N fundamental
strings or N D4-branes expanding into a D6-brane, with topology M^2 x S^5 or
M^5 x S^2 respectively. Treating these solutions in a unified way, we note that
they allow for precisely two values of the radius of the relevant sphere, and
that the solution with the smaller value of the radius has the lower energy.
Moreover, the possible radii in both cases agree up to numerical factors with
the corresponding solutions of the D6-brane worldvolume theory. We thus argue
that these supergravity solutions are the correct gravitational description of
the dielectric branes of Emparan and Myers.


  Genomic (DNA) sequences encode an enormous amount of information for gene
regulation and protein synthesis. Similar to natural language models,
researchers have proposed foundation models in genomics to learn generalizable
features from unlabeled genome data that can then be fine-tuned for downstream
tasks such as identifying regulatory elements. Due to the quadratic scaling of
attention, previous Transformer-based genomic models have used 512 to 4k tokens
as context (<0.001% of the human genome), significantly limiting the modeling
of long-range interactions in DNA. In addition, these methods rely on
tokenizers or fixed k-mers to aggregate meaningful DNA units, losing single
nucleotide resolution where subtle genetic variations can completely alter
protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a
large language model based on implicit convolutions was shown to match
attention in quality while allowing longer context lengths and lower time
complexity. Leveraging Hyena's new long-range capabilities, we present
HyenaDNA, a genomic foundation model pretrained on the human reference genome
with context lengths of up to 1 million tokens at the single nucleotide-level -
an up to 500x increase over previous dense attention-based models. HyenaDNA
scales sub-quadratically in sequence length (training up to 160x faster than
Transformer), uses single nucleotide tokens, and has full global context at
each layer. We explore what longer context enables - including the first use of
in-context learning in genomics. On fine-tuned benchmarks from the Nucleotide
Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 18 datasets
using a model with orders of magnitude less parameters and pretraining data. On
the GenomicBenchmarks, HyenaDNA surpasses SotA on 7 of 8 datasets on average by
+10 accuracy points. Code at https://github.com/HazyResearch/hyena-dna.


  Sw 1644+57/GRB 110328A is a remarkable cosmological X-ray outburst detected
by the {\it Swift} satellite. Its early-time ($t\lesssim 0.1$ days since the
trigger) X-ray emission resembles some gamma-ray bursts (GRBs), e.g., GRB
090417B. But the late-time flaring X-ray plateau lasting $> 40$ days renders it
unique. We examine the possibilities that the outburst is a super-long GRB
powered either by the fallback accretion onto a nascent black hole or by a
millisecond pulsar, and find out that these two scenarios can address some but
not all of the main observational features. We then focus on the model of tidal
disruption of a (giant) star by a massive black hole. The mass of the
tidal-disrupted star is estimated to be $\gtrsim$ a few solar masses. A
simple/straightforward argument for a magnetic origin of the relativistic
outflow is presented.


  We observe the failure process of a fiber bundle model with a variable stress
release range, $\gamma$, higher the value of $\gamma$ lower the stress release
range. By tuning $\gamma$ from low to high, it is possible to go from the
mean-field (MF) limit of the model to local load sharing (LLS) where local
stress concentration plays a crucial role. In the MF limit, the avalanche size
$s$ and energy $E$ emitted during the avalanche are highly correlated producing
the same distribution for both $P(s)$ and $Q(E)$: a scale-free distribution
with a universal exponent -5/2. With increasing $\gamma$, the model enters the
LLS limit. In this limit, due to the presence of local stress concentration
such correlation $C(\gamma)$ between $s$ and $E$ decreases where the nature of
the decreases depends highly on the dimension of the bundle. In 1d, the
$C(\gamma)$ stars from a high value for low $\gamma$ and decreases towards zero
when $\gamma$ is increased. As a result, $Q(E)$ and $P(s)$ are similar at low
$\gamma$, an exponential one, and then $Q(E)$ becomes power-law for high-stress
release range though $P(s)$ remains exponential. On the other hand, in 2d, the
$C(\gamma)$ decreases slightly with $\gamma$ but remains at a high value. Due
to such a high correlation, the distribution of both $s$ and $E$ is exponential
in the LLS limit independent of how large $\gamma$ is.


  In this work, we investigate the spectra in an Aharonov-Bohm quantum-ring
interferometer forming a Josephson junction between two topological
superconductors (TSC) nanowires. The TSCs host Majorana bound states at their
edges, and both the magnetic flux and the superconducting phase difference
between the TSCs are used as control parameters. We use a tight-binding
approach to model the quantum ring coupled to both TSCs, described by the
Kitaev effective Hamiltonian. We solve the problem by means of exact numerical
diagonalization of the Bogoliubov-de Gennes (BdG) Hamiltonian and obtain the
spectra for two sizes of the quantum ring as a function of the magnetic flux
and the phase difference between the TSCs. Depending on the size of the quantum
ring and the coupling, the spectra display several patterns. Those are denoted
as line, point and undulated nodes, together with flat bands, which are
topologically protected. The first three patterns can be possibly detected by
means of persistent and Josephson currents. Hence, our results could be useful
to understand the spectra and their relation with the behavior of the current
signals.


  Recent studies show strong generative performance in domain translation
especially by using transfer learning techniques on the unconditional
generator. However, the control between different domain features using a
single model is still challenging. Existing methods often require additional
models, which is computationally demanding and leads to unsatisfactory visual
quality. In addition, they have restricted control steps, which prevents a
smooth transition. In this paper, we propose a new approach for high-quality
domain translation with better controllability. The key idea is to preserve
source features within a disentangled subspace of a target feature space. This
allows our method to smoothly control the degree to which it preserves source
features while generating images from an entirely new domain using only a
single model. Our extensive experiments show that the proposed method can
produce more consistent and realistic images than previous works and maintain
precise controllability over different levels of transformation. The code is
available at https://github.com/LeeDongYeun/FixNoise.


  We give synthetic proofs of many new results in triangle geometry, focusing
especially on fixed points of certain affine maps which are defined in terms of
the cevian triangle $DEF$ of a point $P$ with respect to a given triangle
$ABC$, as well as the cevian triangle of the isotomic conjugate $P'$ of $P$
with respect to $ABC$. We prove a formula for the cyclocevian map in terms of
the isotomic and isogonal maps using an entirely synthetic argument, and show
that the complement $Q$ of the isotomic conjugate $P'$ has many interesting
properties. If $T_P$ is the affine map taking $ABC$ to $DEF$, we show
synthetically that $Q$ is the unique ordinary fixed point of $T_P$ when $P$ is
any point not lying on the sides of triangle $ABC$, its anti-complementary
triangle, or the Steiner circumellipse of $ABC$. We also show that $T_P(Q')=P$
if $Q'$ is the complement of $P$, and that the affine map $T_P T_{P'}$ is
either a homothety or a translation which always has the $P$-ceva conjugate of
$Q$ as a fixed point. Finally, we show that $P$ lies on the Steiner
circumellipse if and only if $T_PT_{P'}=K^{-1}$, where $K$ is the complement
map for $ABC$. This paper forms the foundation for several more papers to
follow, in which the conic on the 5 points $A,B,C,P,Q$ is studied and its
center is characterized as a fixed point of the map $\lambda=T_{P'} T_P^{-1}$.


  We compute the integral cohomology ring of configuration spaces of two points
on a given real projective space. Apart from an integral class, the resulting
ring is a quotient of the known integral cohomology of the dihedral group of
order 8 (in the case of unordered configurations, thus has only 2- and
4-torsion) or of the elementary abelian 2-group of rank 2 (in the case of
ordered configurations, thus has only 2-torsion). As an application, we
complete the computation of the symmetric topological complexity of real
projective spaces of dimensions of the form 2^i+j for non-negative i and j with
j<3.


  In this report, we study the algebraic geometry aspect of Hofstadter type
models through the algebraic Bethe equation. In the diagonalization problem of
certain Hofstadter type Hamiltonians, the Bethe equation is constructed by
using the Baxter vectors on a high genus spectral curve. When the spectral
variables lie on rational curves, we obtain the complete and explicit solutions
of the polynomial Bethe equation; the relation with the Bethe ansatz of
polynomial roots is discussed. Certain algebraic geometry properties of Bethe
equation on the high genus algebraic curves are discussed in cooperation with
the consideration of the physical model.


  It is suggested that an understanding of blackbody radiation within classical
physics requires the presence of classical electromagnetic zero-point
radiation, the restriction to relativistic (Coulomb) scattering systems, and
the use of discrete charge. The contrasting scaling properties of
nonrelativistic classical mechanics and classical electrodynamics are noted,
and it is emphasized that the solutions of classical electrodynamics found in
nature involve constants which connect together the scales of length, time, and
energy. Indeed, there are analogies between the electrostatic forces for groups
of particles of discrete charge and the van der Waals forces in equilibrium
thermal radiation. The differing Lorentz- or Galilean-transformation properties
of the zero-point radiation spectrum and the Rayleigh-Jeans spectrum are noted
in connection with their scaling properties. Also, the thermal effects of
acceleration within classical electromagnetism are related to the existence of
thermal equilibrium within a gravitational field. The unique scaling and
phase-space properties of a discrete charge in the Coulomb potential suggest
the possibility of an equilibrium between the zero-point radiation spectrum and
matter which is universal (independent of the particle mass), and an
equilibrium between a universal thermal radiation spectrum and matter where the
matter phase space depends only upon the ratio mc^2/kT. The observations and
qualitative suggestions made here run counter to the ideas of currently
accepted quantum physics.


  We present a search in IceCube data for neutrino emission from Galactic TeV
gamma-ray sources detected by the HAWC gamma-ray observatory. HAWC serves as
the excellent instrument to complement IceCube with its energy range extending
to very high energies. Assuming that the highest energy photons originate from
the decay of pions, rather than from accelerated leptons, the very high energy
gamma-rays observed by HAWC are expected to be correlated with neutrinos. Using
eight years of IceCube data, we report on two analyses that investigate a
possible neutrino--gamma ray correlation. The first is a stacked analysis of
identified HAWC point sources and the second is a template method which
accounts for the full morphology of HAWC sources, including their measured
extension.


  The tip of the red giant branch has been used to measure distances to 500
nearby galaxies with the Hubble Space Telescope (HST) which are available in
the Color-Magnitude Diagrams and Tip of the Red Giant Branch (CMDs/TRGB)
catalog on the Extragalactic Distance Database (EDD). Our established methods
are employed to perform an independent reduction of the targets presented by
the Carnegie-Chicago Hubble Program (CCHP) in the series of papers culminating
in Freedman (2021). Our distinct methodology involves modeling the observed
luminosity function of red giant branch and asymptotic giant branch stars,
which differs from the edge-detection algorithms employed by the CCHP. We find
excellent agreement between distances for 11 hosts with new imaging, all at D <
20 Mpc. However, we are unable to measure the TRGB for 4 of the 5 hosts that
use archival data designed to measure distances with Cepheids, all at D > 23
Mpc. With two new HST observations taken in the halo of the megamaser host NGC
4258, the first with the same ACS F606W and F814W filters and the
post-servicing electronics used for SN Ia hosts, we then calibrate our TRGB
distance scale to the geometric megamaser distance. Using our TRGB distances,
we find a value of the Hubble Constant of $H_{0}$ = 71.5 $\pm$ 1.8 km/s/Mpc
when using either the Pantheon or Carnegie Supernova Project (CSP) samples of
supernovae. In the future, the James Webb Space Telescope will extend
measurements of the TRGB to additional hosts of SN Ia and surface brightness
fluctuation measurements for separate paths to $H_{0}$.


  We prove that a graph whose chromatic number is 2 is a homotopy test graph.
We also prove that there is a graph $K$ with two involutions $\gamma_1$ and
$\gamma_2$ such that $(K,\gamma_1)$ is a Stiefel-Whitney test graph, but
$(K,\gamma_2)$ is not. These are answers to some of the problems suggested by
Kozlov.


  Slot Filling (SF) aims to extract the values of certain types of attributes
(or slots, such as person:cities\_of\_residence) for a given entity from a
large collection of source documents. In this paper we propose an effective DNN
architecture for SF with the following new strategies: (1). Take a regularized
dependency graph instead of a raw sentence as input to DNN, to compress the
wide contexts between query and candidate filler; (2). Incorporate two
attention mechanisms: local attention learned from query and candidate filler,
and global attention learned from external knowledge bases, to guide the model
to better select indicative contexts to determine slot type. Experiments show
that this framework outperforms state-of-the-art on both relation extraction
(16\% absolute F-score gain) and slot filling validation for each individual
system (up to 8.5\% absolute F-score gain).


  The radio, optical, X-ray and gamma-ray nebulae that surround many pulsars
are thought to arise from synchrotron and inverse Compton emission. The energy
powering this emission, as well as the magnetic fields and relativistic
particles, are supplied by a "wind" driven by the central object. The inner
parts of the wind can be described using the equations of MHD, but these break
down in the outer parts, when the density of charge carriers drops below a
critical value. This paper reviews the wave properties of the inner part
(striped wind), and uses a relativistic two-fluid model (cold electrons and
positrons) to re-examine the nonlinear electromagnetic modes that propagate in
the outer parts. It is shown that in a radial wind, two solutions exist for
circularly polarised electromagnetic modes. At large distances one of them
turns into a freely expanding flow containing a vacuum wave, whereas the other
decelerates, corresponding to a confined flow.


  Noise spectroscopy elucidates the fundamental noise sources in spin systems,
thereby serving as an essential tool toward developing spin qubits with long
coherence times for quantum information processing, communication, and sensing.
But existing techniques for noise spectroscopy that rely on microwave fields
become infeasible when the microwave power is too weak to generate Rabi
rotations of the spin. Here, we demonstrate an alternative all-optical approach
to performing noise spectroscopy. Our approach utilizes coherent Raman
rotations of the spin state with controlled timing and phase to implement
Carr-Purcell-Meiboom-Gill pulse sequences. Analyzing the spin dynamics under
these sequences enables us to extract the noise spectrum of a dense ensemble of
nuclear spins interacting with a single spin in a quantum dot, which has thus
far only been modeled theoretically. By providing spectral bandwidths of over
100 MHz, our approach enables the studies of spin dynamics and decoherence for
a broad range of solid-state spin qubits.


  We study the eigenvalue spectrum of different lattice Dirac operators
(staggered, fixed point, overlap) and discuss their dependence on the
topological sectors. Although the model is 2D (the Schwinger model with
massless fermions) our observations indicate possible problems in 4D
applications. In particular misidentification of the smallest eigenvalues due
to non-identification of the topological sector may hinder successful
comparison with Random Matrix Theory (RMT).


  Increasing the number of closely-packed air bubbles immersed in water changes
the frequency of the Minnaert resonance. The collective interactions between
bubbles in a small ensemble are primarily in the same phase, causing them to
radiate a spherically-symmetric field that peaks at a frequency lower than the
Minnaert resonance for a single bubble. In contrast, large periodic arrays
include bubbles that are further apart than half the wavelength, so that
collective resonances have bubbles oscillating in opposite phases, ultimately
creating a fundamental resonance at a frequency higher than the single-bubble
Minnaert resonance. This work investigates the transition in resonance behavior
using a modal analysis of a mass-spring system and a boundary element method.
We significantly reduce the computational complexity of the full-wave solver to
a linear dependence on the number of bubbles in a rectangular array. The
simulated acoustic fields confirm the initial downshift in resonance frequency
and the strong influence of collective resonances when the array has hundreds
of bubbles covering more than half the wavelength. These results are essential
in understanding the low-frequency resonance characteristics of bubble
ensembles, which have important applications in diverse fields such as
underwater acoustics, quantum physics, and metamaterial design.


  In this paper, a set of tools is introduced that simplifies the synthesis and
rapid-prototyping of single-loop rational kinematic chains. It allows the user
to perform rational motion interpolation of up to four given poses and yields
the design parameters of a linkage that can execute this motion. The package
also provides a visualization of the output and performs a self-collision
analysis with the possibility to adapt the design parameters. The results can
be imported into CAD-systems for fast 3D printing.


  In this article we summarise and discuss the infrared, radio, and X-ray
emission from the supermassive black hole in the Galactic Centre, SgrA*. We
include new results from near-infrared polarimetric imaging observations
obtained on May 31st, 2006. In that night, a strong flare in Ks band (2.08
microns) reaching top fluxes of ~16 mJy could be observed. This flare was
highly polarised (up to ~40%) and showed clear sub-structure on a time scale of
15 minutes, including a swing in the polarisation angle of about 70 degrees.
For the first time we were able to observe both polarised flux and short-time
variability, with high significance in the same flare event. This result adds
decisive information to the puzzle of the SgrA* activity. The observed
polarisation angle during the flare peak is the same as observed in two events
in 2004 and 2005. Our observations strongly support the dynamical emission
model of a decaying plasma hotspot orbiting SgrA* on a relativistic orbit. The
observed polarisation parameters and their variability with time might allow to
constrain the orientation of accretion disc and spin axis with respect to the
Galaxy.


  An argument is provided that confronts the strong equivalence principle (SEP)
of general relativity with the superposition principle (SP) of quantum
mechanics. The result has implications on the possibility of synthesizing
Majorana bound states in 1D systems or the possible violation of the strong
equivalence principle by quantum effects. The argument is formulated by
inducing a very special fine tuning of coupling constants in the Kitaev
Hamiltonian representing 1D topological superconductors.


  New theoretical results from large-scale relativistic close coupling
calculations reveal the precise effect of resonances in collisional excitation
of x-ray lines of Ne-like Fe XVII. Employing the Breit-Pauli R-matrix method
and a 89-level eigenfunction expansion including up to n = 4 levels shows
significant resonance enhancement of the collision strengths of forbidden and
intercombination transitions. The present results differ from all previous
calculations, heretofore without detailed resonance structures, and should help
resolve longstanding discrepancies. In particular, the present line ratios of
three benchmark diagnostic lines 3C, 3D, and 3E at 15.014, 15.265, and 15.456 A
respectively, are in excellent agreement with two independent measurements on
Electron-Beam-Ion-Traps [Laming et al, Astrophys. J, 545, L161 (2000) and Brown
et al, Astrophys. J, 502, 1015 (1998)]. The strong energy dependence due to
resonances in these and other cross sections is demonstrated for the first
time. It is of general importance and strongly manifests itself in x-ray plasma
diagnostics.


  White dwarfs are the end state of the evolution of more than 97% of all
stars, and therefore carry information on the structure and evolution of the
Galaxy through their luminosity function and initial-to-final mass relation.
Examining the new spectra of all white or blue stars in the Sloan Digital Sky
Survey Data Release 16, we report the spectral classification of 2410 stars,
down to our identification cut-off of signal-to-noise ratio equal to three. We
newly identify 1404 DAs, 189 DZs, 103 DCs, 12 DBs, and 9 CVs. The remaining
objects are a mix of carbon or L stars (dC/L), narrow-lined hydrogen-dominated
stars (sdA), dwarf F stars and P Cyg objects. As white dwarf stars were not
targeted by SDSS DR16, the number of new discoveries is much smaller than in
previous releases. We also report atmospheric parameters and masses for a
subset consisting of 555 new DAs, 10 new DBs, and 85 DZs for spectra with
signal-to-noise ratio larger than 10.


  Massive stars (those larger than 8 solar masses at formation) have radiative
envelopes that cannot sustain a dynamo, the mechanism that produces magnetic
fields in lower-mass stars. Despite this, approximately 7\% of massive stars
have observed magnetic fields, the origin of which is debated. We used
multi-epoch interferometric and spectroscopic observations to characterize HD
148937, a binary system of two massive stars. We found that only one star is
magnetic and that it appears younger than its companion. The system properties
and a surrounding bipolar nebula can be reproduced with a model in which two
stars merged (in a previous triple system) to produce the magnetic massive
star. Our results provide observational evidence that magnetic fields form in
at least some massive stars through stellar mergers.


  The effective shock wave singularity at the outgoing leg of the inner horizon
of a linearly perturbed fast spinning black hole is studied numerically for
either scalar field, or vacuum, gravitational perturbations. We demonstrate the
occurrence of the Marolf-Ori singularity, including changes of order unity in
the scalar field $\phi$ for the scalar field model, and in the Weyl scalars
$\psi_0$ and $\psi_4$ (rescaled appropriately by the horizon function $\Delta$)
and the Kretschmann curvature scalar $K$ for the vacuum, gravitational
perturbations model for both null and timelike geodesic observers. We quantify
the shock sharpening effect and show that in all cases its rate agrees with
expectations.


  In this talk, we present the recent results of charm physics from the BESIII
experiment. It covers the studies of the pure leptonic and semi-leptonic decays
of charmed hadrons, from which the |Vcs| are precisely determined and the LFU
is tested.


  The bottom-up construction of synthetic cells is one of the most intriguing
and interesting research arenas in synthetic biology. Synthetic cells are built
by encapsulating biomolecules inside lipid vesicles (liposomes), allowing the
synthesis of one or more functional proteins. Thanks to the in situ synthesized
proteins, synthetic cells become able to perform several biomolecular
functions, which can be exploited for a large variety of applications. This
paves the way to several advanced uses of synthetic cells in basic science and
biotechnology, thanks to their versatility, modularity, biocompatibility, and
programmability. In the previous WIVACE (2012) we presented the
state-of-the-art of semi-synthetic minimal cell (SSMC) technology and
introduced, for the first time, the idea of chemical communication between
synthetic cells and natural cells. The development of a proper synthetic
communication protocol should be seen as a tool for the nascent field of
bio/chemical-based Information and Communication Technologies (bio-chem-ICTs)
and ultimately aimed at building soft-wet-micro-robots. In this contribution
(WIVACE, 2013) we present a blueprint for realizing this project, and show some
preliminary experimental results. We firstly discuss how our research goal
(based on the natural capabilities of biological systems to manipulate chemical
signals) finds a proper place in the current scientific and technological
contexts. Then, we shortly comment on the experimental approaches from the
viewpoints of (i) synthetic cell construction, and (ii) bioengineering of
microorganisms, providing up-to-date results from our laboratory. Finally, we
shortly discuss how autopoiesis can be used as a theoretical framework for
defining synthetic minimal life, minimal cognition, and as bridge between
synthetic biology and artificial intelligence.


  The temperatures of the heavy ions ($T_i$) in the solar corona provide
critical information about the heating mechanism of the million-degree corona.
However, the measurement of $T_i$ is usually challenging due to the nonthermal
motion, instrumental limitations, and the optically thin nature of the coronal
emissions. We present the measurement of $T_i$ and its dependency on the ion
charge-to-mass ratio ($Z/A$) at the polar coronal hole boundary, only assuming
that heavy ions have the same nonthermal velocity. To improve the $Z/A$
coverage and study the influence of the instrumental broadening, we used a
coordinated observation from the extreme-ultraviolet Imaging Spectrometer (EIS)
on board the Hinode satellite and the Solar Ultraviolet Measurements of Emitted
Radiation (SUMER) on board the Solar and Heliospheric Observatory (SOHO). We
found that the $T_i$ of ions with $Z/A$ less than 0.20 or greater than 0.33 are
much higher than the local electron temperature. We ran the Alfv\'en Wave Solar
Model-realtime to investigate the formation of optically thin emissions along
the line of sight (LOS). The simulation suggested that plasma bulk motions
along the LOS broaden the widths of hot emission lines in the coronal hole
(e.g., Fe XII, Fe XIII). We discussed other factors that might affect the $T_i$
measurement, including the non-Gaussian wings in some bright SUMER lines, which
can be fitted by a double-Gaussian or a $\kappa$ distribution. Our study
confirms the preferential heating of heavy ions in coronal holes and provides
new constraints on coronal heating models.


  Infrastructure networks are vulnerable to both cyber and physical attacks.
Building a secure and resilient networked system is essential for providing
reliable and dependable services. To this end, we establish a two-player
three-stage game framework to capture the dynamics in the infrastructure
protection and recovery phases. Specifically, the goal of the infrastructure
network designer is to keep the network connected before and after the attack,
while the adversary aims to disconnect the network by compromising a set of
links. With costs for creating and removing links, the two players aim to
maximize their utilities while minimizing the costs. In this paper, we use the
concept of subgame perfect equilibrium (SPE) to characterize the optimal
strategies of the network defender and attacker. We derive the SPE explicitly
in terms of system parameters. Finally, we use a case study of UAV-enabled
communication networks for disaster recovery to corroborate the obtained
analytical results.


  We consider a dynamic pricing problem for repeated contextual second-price
auctions with multiple strategic buyers who aim to maximize their long-term
time discounted utility. The seller has limited information on buyers' overall
demand curves which depends on a non-parametric market-noise distribution, and
buyers may potentially submit corrupted bids (relative to true valuations) to
manipulate the seller's pricing policy for more favorable reserve prices in the
future. We focus on designing the seller's learning policy to set contextual
reserve prices where the seller's goal is to minimize regret compared to the
revenue of a benchmark clairvoyant policy that has full information of buyers'
demand. We propose a policy with a phased-structure that incorporates
randomized "isolation" periods, during which a buyer is randomly chosen to
solely participate in the auction. We show that this design allows the seller
to control the number of periods in which buyers significantly corrupt their
bids. We then prove that our policy enjoys a $T$-period regret of
$\widetilde{\mathcal{O}}(\sqrt{T})$ facing strategic buyers. Finally, we
conduct numerical simulations to compare our proposed algorithm to standard
pricing policies. Our numerical results show that our algorithm outperforms
these policies under various buyer bidding behavior.


  We propose a scheme to create and transfer thermal squeezed states and
random-phase coherent states in a system of two interacting levitated
nanoparticles. In this coupled levitated system, we create a thermal squeezed
state of motion in one of the nanoparticles by parametrically driving it and
then transferring the state to the other nanoparticle with high fidelity. The
transfer mechanism is based on inducing a non-reciprocal type of coupling in
the system by suitably modulating the phases of the trapping lasers and the
inter-particle distance between the levitated nanoparticles. This
non-reciprocal coupling creates a unidirectional channel where information
flows from one nanoparticle to the other nanoparticle but not vice versa,
thereby allowing for transfer of mechanical states between the nanoparticles
with high fidelity. We also affirm this transfer mechanism by creating and
efficiently transferring a random-phase coherent state in the coupled levitated
system. Further, we make use of the feedback nonlinearity and parametric
driving to create simultaneous bistability in the coupled levitated system. Our
results may have potential applications in quantum information processing,
quantum metrology, and in exploring many-body physics under a controlled
environment.


  We describe a work of a multi-cathode counter of the developed design in a
single electron counting mode with a cathode made of aluminum alloy. The
results of the calibration of the counter are presented. The coefficient of gas
amplification was found from the calibration spectra. The electric fields and
operation of this detector in two configurations are described and the original
idea to find the effect from electrons emitted from the surface of a cathode by
difference of the rates measured in two volume configurations is expounded.
Furthermore, the advantage of using a multi-cathode counter for measurement of
the intensity of single electron emission from a metal is explained.


  This paper is concerned with the existence and uniqueness of the strong
solution to the incompressible Navier-Stokes equations with Navier-slip
boundary conditions in a two-dimensional strip domain where the slip
coefficients may not have defined sign. In the meantime, we also establish a
number of Gagliardo-Nirenberg inequalities in the corresponding Sobolev spaces
which will be applicable to other similar situations.


  We demonstrate theoretically that quantum dots in bilayers of graphene can be
realized. A position-dependent doping breaks the equivalence between the upper
and lower layer and lifts the degeneracy of the positive and negative momentum
states of the dot. Numerical results show the simultaneous presence of electron
and hole confined states for certain doping profiles and a remarkable angular
momentum dependence of the quantum dot spectrum which is in sharp contrast with
that for conventional semiconductor quantum dots. We predict that the optical
spectrum will consist of a series of non-equidistant peaks.


  Logarithmic conformal field theories are based on vertex algebras with
non-semisimple representation categories. While examples of such theories have
been known for more than 25 years, some crucial aspects of local logarithmic
CFTs have been understood only recently, with the help of a description of
conformal blocks by modular functors. We present some of these results, both
about bulk fields and about boundary fields and boundary states. We also
describe some recent progress towards a derived modular functor.
  This is a summary of work with Terry Gannon, Simon Lentner, Svea Mierach,
Gregor Schaumann and Yorck Sommerh\"auser.