  The ability of likelihood-based probabilistic models to generalize to unseen
data is central to many machine learning applications such as lossless
compression. In this work, we study the generalization of a popular class of
probabilistic model - the Variational Auto-Encoder (VAE). We discuss the two
generalization gaps that affect VAEs and show that overfitting is usually
dominated by amortized inference. Based on this observation, we propose a new
training objective that improves the generalization of amortized inference. We
demonstrate how our method can improve performance in the context of image
modeling and lossless compression.


  Rheological properties, especially 'shear-thinning', of dense colloidal
dispersions are discussed on three different levels. A generalized
phenomonological Maxwell model gives a broad framework connecting glassy
dynamics to the linear and non-linear rheology of dense amorphous particle
solutions. First principles mode coupling theory calculations for the time or
frequency dependent shear modulus give quantitative results for dispersions of
hard colloidal spheres in the linear regime. Schematic models extending mode
coupling theory to the non-linear regime recover the phenomenology of the
generalized Maxwell model, and predict universal features of flow curves,
stress versus shear-rate.


  Standard models of sequential adsorption are implicitly formulated in a {\em
scale invariant} form, by assuming adsorption on an infinite surface, with no
characteristic length scales. In real situations, however, involving complex
surfaces, intrinsic length scales may be relevant. We present an analytic model
of continuous random sequential adsorption, in which the scale invariance
symmetry is explicitly broken. The characteristic length is imposed by a set of
scattered obstacles, previously adsorbed onto the surface. We show, by means of
analytic solutions and numerical simulations, the profound effects of the
symmetry breaking on both the jamming limit and the correlation function of the
adsorbed layer.


  We study the second-order phase transition in the $d$-dimensional Ising model
with long-range interactions decreasing as a power of the distance $1/r^{d+s}$.
For $s$ below some known value $s_*$, the transition is described by a
conformal field theory without a local stress tensor operator, with critical
exponents varying continuously as functions of $s$. At $s=s_*$, the phase
transition crosses over to the short-range universality class. While the
location $s_*$ of this crossover has been known for 40 years, its physics has
not been fully understood, the main difficulty being that the standard
description of the long-range critical point is strongly coupled at the
crossover. In this paper we propose another field-theoretic description which,
on the contrary, is weakly coupled near the crossover. We use this description
to clarify the nature of the crossover and make predictions about the critical
exponents. That the same long-range critical point can be reached from two
different UV descriptions provides a new example of infrared duality.


  Given multiple source word embeddings learnt using diverse algorithms and
lexical resources, meta word embedding learning methods attempt to learn more
accurate and wide-coverage word embeddings.
  Prior work on meta-embedding has repeatedly discovered that simple vector
concatenation of the source embeddings to be a competitive baseline.
  However, it remains unclear as to why and when simple vector concatenation
can produce accurate meta-embeddings.
  We show that weighted concatenation can be seen as a spectrum matching
operation between each source embedding and the meta-embedding, minimising the
pairwise inner-product loss.
  Following this theoretical analysis, we propose two \emph{unsupervised}
methods to learn the optimal concatenation weights for creating meta-embeddings
from a given set of source embeddings.
  Experimental results on multiple benchmark datasets show that the proposed
weighted concatenated meta-embedding methods outperform previously proposed
meta-embedding learning methods.


  This manuscript presents the general approach to the understanding of the
connection between bonding mechanism and electronic structure of graphene on
metals. To demonstrate its validity, two limiting cases of the "weakly" and
"strongly" bonded graphene on Al(111) and Ni(111) are considered, where the
Dirac cone is preserved or fully destroyed, respectively. Furthermore, the
electronic structure, i. e. doping level, hybridization effects, as well as a
gap formation at the Dirac point of the intermediate system, graphene/Cu(111),
is fully understood in the framework of the proposed approach. This work
summarises the long-term debates regarding connection of the bonding strength
and the valence band modification in the graphene/metal systems and paves a way
for the effective control of the electronic states of graphene in the vicinity
of the Fermi level.


  We present emergent dynamics of continuous and discrete thermomechanical
Cucker-Smale(TCS) models equipped with temperature as an extra observable on
general digraph. In previous literature, the emergent behaviors of the TCS
models were mainly studied on a complete graph, or symmetric connected graphs.
Under this symmetric setting, the total momentum is a conserved quantity. This
determines the asymptotic velocity and temperature a priori using the initial
data only. Moreover, this conservation law plays a crucial role in the flocking
analysis based on the elementary $\ell_2$ energy estimates. In this paper, we
consider a more general connection topology which is registered by a general
digraph, and the weights between particles are given to be inversely
proportional to the metric distance between them. Due to this possible symmetry
breaking in communication, the total momentum is not a conserved quantity, and
this lack of conservation law makes the asymptotic velocity and temperature
depend on the whole history of solutions. To circumvent this lack of
conservation laws, we instead employ some tools from matrix theory on the
scrambling matrices and some detailed analysis on the state-transition
matrices. We present two sufficient frameworks for the emergence of
mono-cluster flockings on a digraph for the continuous and discrete models. Our
sufficient frameworks are given in terms of system parameters and initial data.


  GGR News: GGR Program at the APS meeting in Anaheim, CA by David Garfinkle
  We hear that..., by David Garfinkle
  100 years ago, by David Garfinkle
  Research Briefs: Endpoint of the Gregory-Laflamme Instability, by Gary
Horowitz
  Conference Reports: Chandrasekhar Symposium, by David Garfinkle


  Reactive systems (RSs) represent a meta-framework aimed at deriving
behavioral congruences for those computational formalisms whose operational
semantics is provided by reduction rules. RSs proved a flexible specification
device, yet so far most of the efforts dealing with their behavioural semantics
focused on idem pushouts (IPOs) and saturated (also known as dynamic)
bisimulations. In this paper we introduce a novel, intermediate behavioural
equivalence: L-bisimilarity, which is able to recast both its IPO and saturated
counterparts. The equivalence is parametric with respect to a set L of RSs
labels, and it is shown that under mild conditions on L it is indeed a
congruence. Furthermore, L-bisimilarity can also recast the notion of barbed
semantics for RSs, proposed by the same authors in a previous paper. In order
to provide a suitable test-bed, we instantiate our proposal by addressing the
semantics of (asynchronous) CCS and of the calculus of mobile ambients.


  We measure C III] 1907,1909 A emission lines in eleven
gravitationally--lensed star-forming galaxies at z~1.6--3, finding much lower
equivalent widths than previously reported for fainter lensed galaxies (Stark
et al. 2014). While it is not yet clear what causes some galaxies to be strong
C III] emitters, CIII] emission is not a universal property of distant
star-forming galaxies. We also examine C III] emission in 46 star-forming
galaxies in the local universe, using archival spectra from GHRS, FOS, and STIS
on HST, and IUE. Twenty percent of these local galaxies show strong C III]
emission, with equivalent widths <-5 A. Three nearby galaxies show C III]
emission equivalent widths as large as the most extreme emitters yet observed
in the distant universe; all three are Wolf-Rayet galaxies. At all redshifts,
strong C III] emission may pick out low-metallicity galaxies experiencing
intense bursts of star formation. Such local C III] emitters may shed light on
the conditions of star formation in certain extreme high-redshift galaxies.


  We consider the problem of bootstrap percolation on a three dimensional
lattice and we study its finite size scaling behavior. Bootstrap percolation is
an example of Cellular Automata defined on the $d$-dimensional lattice
$\{1,2,...,L\}^d$ in which each site can be empty or occupied by a single
particle; in the starting configuration each site is occupied with probability
$p$, occupied sites remain occupied for ever, while empty sites are occupied by
a particle if at least $\ell$ among their $2d$ nearest neighbor sites are
occupied. When $d$ is fixed, the most interesting case is the one $\ell=d$:
this is a sort of threshold, in the sense that the critical probability $p_c$
for the dynamics on the infinite lattice ${\Bbb Z}^d$ switches from zero to one
when this limit is crossed. Finite size effects in the three-dimensional case
are already known in the cases $\ell\le 2$: in this paper we discuss the case
$\ell=3$ and we show that the finite size scaling function for this problem is
of the form $f(L)={\mathrm{const}}/\ln\ln L$. We prove a conjecture proposed by
A.C.D. van Enter.


  Open set recognition (OSR) is the problem of classifying the known classes,
meanwhile identifying the unknown classes when the collected samples cannot
exhaust all the classes. There are many applications for the OSR problem. For
instance, the frequently emerged new malware classes require a system that can
classify the known classes and identify the unknown malware classes. In this
paper, we propose an add-on extension for loss functions in neural networks to
address the OSR problem. Our loss extension leverages the neural network to
find polar representations for the known classes so that the representations of
the known and the unknown classes become more effectively separable. Our
contributions include: First, we introduce an extension that can be
incorporated into different loss functions to find more discriminative
representations. Second, we show that the proposed extension can significantly
improve the performances of two different types of loss functions on datasets
from two different domains. Third, we show that with the proposed extension,
one loss function outperforms the others in terms of training time and model
accuracy.


  Inspired by some recent experiments and numerical works related to
nanoresonators, we perform classical molecular dynamics simulations to
investigate the thermal expansion and the ability of the device to act as a
strain sensor assisted by thermally-induced vibrations. The proposed model
consists in a chain of atoms interacting anharmonically with both ends clamped
to thermal reservoirs. We analyze the thermal expansion and resonant frequency
shifts as a function of temperature and the applied strain. For the transversal
modes the shift is approximately linear with strain. We also present analytical
results from canonical calculations in the harmonic approximation showing that
thermal expansion is uniform along the device. This prediction also works when
the system operates in a nonlinear oscillation regime at moderate and high
temperatures.


  The recent spike in certified Artificial Intelligence (AI) tools for
healthcare has renewed the debate around adoption of this technology. One
thread of such debate concerns Explainable AI (XAI) and its promise to render
AI devices more transparent and trustworthy. A few voices active in the medical
AI space have expressed concerns on the reliability of Explainable AI
techniques and especially feature attribution methods, questioning their use
and inclusion in guidelines and standards. Despite valid concerns, we argue
that existing criticism on the viability of post-hoc local explainability
methods throws away the baby with the bathwater by generalizing a problem that
is specific to image data. We begin by characterizing the problem as a lack of
semantic match between explanations and human understanding. To understand when
feature importance can be used reliably, we introduce a distinction between
feature importance of low- and high-level features. We argue that for data
types where low-level features come endowed with a clear semantics, such as
tabular data like Electronic Health Records (EHRs), semantic match can be
obtained, and thus feature attribution methods can still be employed in a
meaningful and useful way. Finally, we sketch a procedure to test whether
semantic match has been achieved.


  Instruction generation is a vital and multidisciplinary research area with
broad applications. Existing instruction generation models are limited to
generating instructions in a single style from a particular dataset, and the
style and content of generated instructions cannot be controlled. Moreover,
most existing instruction generation methods also disregard the spatial
modeling of the navigation environment. Leveraging the capabilities of Large
Language Models (LLMs), we propose C-Instructor, which utilizes the
chain-of-thought-style prompt for style-controllable and content-controllable
instruction generation. Firstly, we propose a Chain of Thought with Landmarks
(CoTL) mechanism, which guides the LLM to identify key landmarks and then
generate complete instructions. CoTL renders generated instructions more
accessible to follow and offers greater controllability over the manipulation
of landmark objects. Furthermore, we present a Spatial Topology Modeling Task
to facilitate the understanding of the spatial structure of the environment.
Finally, we introduce a Style-Mixed Training policy, harnessing the prior
knowledge of LLMs to enable style control for instruction generation based on
different prompts within a single model instance. Extensive experiments
demonstrate that instructions generated by C-Instructor outperform those
generated by previous methods in text metrics, navigation guidance evaluation,
and user studies.


  As impacts of introduced species cascade through trophic levels, they can
cause indirect and counter-intuitive effects. To investigate the impact of
invasive species at the network scale, we use a generalized food web model,
capable of propagating changes through networks with a series of ecologically
realistic criteria. Using data from a small British offshore island, we
quantify the impacts of four virtual invasive species (an insectivore, a
herbivore, a carnivore and an omnivore whose diet is based on a rat) and
explore which clusters of species react in similar ways. We find that the
predictions for the impacts of invasive species are ecologically plausible,
even for large networks robust predictions for the impacts of invasive species
can be obtained. Species in the same taxonomic group are similarly impacted by
a virtual invasive species. However, interesting differences within a given
taxonomic group can occur. The results suggest that some native species may be
at risk from a wider range of invasives than previously believed. The
implications of these results for ecologists and land managers are discussed.


  We address the problem of stable and robust control of vehicles with lateral
error dynamics for the application of lane keeping. Lane departure is the
primary reason for half of the fatalities in road accidents, making the
development of stable, adaptive and robust controllers a necessity. Traditional
linear feedback controllers achieve satisfactory tracking performance, however,
they exhibit unstable behavior when uncertainties are induced into the system.
Any disturbance or uncertainty introduced to the steering-angle input can be
catastrophic for the vehicle. Therefore, controllers must be developed to
actively handle such uncertainties. In this work, we introduce a Neural L1
Adaptive controller (Neural-L1) which learns the uncertainties in the lateral
error dynamics of a front-steered Ackermann vehicle and guarantees stability
and robustness. Our contributions are threefold: i) We extend the theoretical
results for guaranteed stability and robustness of conventional L1 Adaptive
controllers to Neural-L1; ii) We implement a Neural-L1 for the lane keeping
application which learns uncertainties in the dynamics accurately; iii)We
evaluate the performance of Neural-L1 on a physics-based simulator, PyBullet,
and conduct extensive real-world experiments with the F1TENTH platform to
demonstrate superior reference trajectory tracking performance of Neural-L1
compared to other state-of-the-art controllers, in the presence of
uncertainties. Our project page, including supplementary material and videos,
can be found at https://mukhe027.github.io/Neural-Adaptive-Control/


  Motivated by recent discovery of yttrium-based high-temperature ternary
superconducting hydrides (e.g., CaYH$_{12}$, LaYH$_{12}$, and ScYH$_{6}$), we
have employed evolutionary algorithm and first-principles calculations to
comprehensively examine the structural stability and superconductivity of the
YMgH$_{x}$ system at high pressure. The hydrogen content $x$ and the pressure
are both important factors in the stability of these candidate structures. We
find that the stability of hydrogen-rich materials frequently necessitates
higher pressure. For instance, the pressures to stabilize $P4/mmm$-YMgH$_{8}$
and $Cmmm$-YMgH$_{12}$ are both more than 250 GPa. Hydrogen-less materials,
such as $I4_{1}/amd$-YMgH$_{2}$ and $P6_{3}/mmc$-YMgH$_{3}$, can be stable at
pressures as low as 100 GPa. In addition, we find a metastable structure for
YMgH$_{6}$ with the same space group as the $P4/mmm$-YMgH$_{8}$. A metastable
sodalite-like face-centered cubic (FCC) structure is also found in YMgH$_{12}$.
These four clathrate structures of $P4/mmm$-YMgH$_{6}$, $P4/mmm$-YMgH$_{8}$,
$Cmmm$-YMgH$_{12}$, and $Fd\bar{3}m$-YMgH$_{12}$ is made up of H14, H18, H24,
and H24 cages, respectively, in which the H-H pair exhibits weak covalent
bonding. According to phonon calculations, $P4/mmm$-YMgH$_{6}$ and
$P4/mmm$-YMgH$_{8}$ require a pressure of 300 GPa to maintain dynamic
stability, however $Cmmm$-YMgH$_{12}$ and $Fd\bar{3}m$-YMgH$_{12}$ can maintain
dynamic stability at pressures of 200 GPa and 250 GPa, respectively.
Electron-phonon coupling calculations indicate that they might be potential
high-temperature superconductors, with superconductivity intimately linked to
the H cage structure. The sodalite structure $Fd\bar{3}m$-YMgH$_{12}$ has a
$T_\mathrm{c}$ value of 190 K and a strong electron-phonon coupling constant of
2.18.


  The morphology and stellar populations of low-luminosity early-type galaxies
in clusters have until now been limited to a few relatively nearby clusters
such as Virgo or Fornax. Scenarii for the formation and evolution of dwarf
galaxies in clusters are therefore not well constrained. We investigate here
the morphology and stellar populations of low-luminosity galaxies in the
relaxed cluster Abell 496 (z=0.0330). Deep multiband imaging obtained with the
CFHT Megacam allowed us to select a sample of faint galaxies (-18.8<M_B<-15.1
mag). We observed 118 galaxies spectroscopically with the ESO VLT
FLAMES/Giraffe spectrograph (R=6300). We present structural analysis and colour
maps for the 48 galaxies belonging to the cluster. We fit the spectra of 46
objects with PEGASE.HR synthetic spectra to estimate the ages, metallicities,
and velocity dispersions. We computed values of $\alpha$/Fe abundance ratios
from the measurements of Lick indices. High-precision estimates of stellar
population properties have been obtained for a large sample of faint galaxies
in a cluster, allowing for the extension of relations between stellar
populations and internal kinematics to the low-velocity dispersion regime. We
have revealed a peculiar population of elliptical galaxies in the core of the
cluster, resembling massive early-type galaxies by their stellar population
properties and velocity dispersions, but having luminosities of about 2 mag
fainter. External mechanisms of gas removal (ram pressure stripping and
gravitational harassment) are more likely to have occurred than internal
mechanisms such as supernova-driven winds. The violent tidal stripping of
intermediate-luminosity, early-type galaxies in the cluster core can explain
the properties of the peculiar elliptical galaxies surrounding the cD galaxy.


  Outlier detection (OD) has received continuous research interests due to its
wide applications. With the development of deep learning, increasingly deep OD
algorithms are proposed. Despite the availability of numerous deep OD models,
existing research has reported that the performance of deep models is extremely
sensitive to the configuration of hyperparameters (HPs). However, the selection
of HPs for deep OD models remains a notoriously difficult task due to the lack
of any labels and long list of HPs. In our study. we shed light on an essential
factor, training time, that can introduce significant variation in the
performance of deep model. Even the performance is stable across other HPs,
training time itself can cause a serious HP sensitivity issue. Motivated by
this finding, we are dedicated to formulating a strategy to terminate model
training at the optimal iteration. Specifically, we propose a novel metric
called loss entropy to internally evaluate the model performance during
training while an automated training stopping algorithm is devised. To our
knowledge, our approach is the first to enable reliable identification of the
optimal training iteration during training without requiring any labels. Our
experiments on tabular, image datasets show that our approach can be applied to
diverse deep models and datasets. It not only enhances the robustness of deep
models to their HPs, but also improves the performance and reduces plenty of
training time compared to naive training.


  We investigate a doubly-bottomed tetraquark state $T_{bb}$ $(bb
\bar{u}\bar{d})$ with quantum number $I(J^P)=0(1^+)$ in $(2+1)$-flavor lattice
QCD. Using the Non-Relativistic QCD (NRQCD) quark action for $b$ quarks, we
have extracted the coupled channel potential between $\bar{B}\bar{B}^*$ and
$\bar{B}^* \bar{B}^*$ in the HAL QCD method at $a \approx 0.09$ {fm} on
$32^3\times 64$ lattices. The potential predicts an existence of a bound
$T_{bb}$ below the $\bar{B}\bar{B}^*$ threshold. At the physical pion mass
$m_\pi\approx140$ {MeV} extrapolated from $m_\pi\approx 410,\, 570,\, 700$
{MeV}, a binding energy with its statistical error is given by $E_{\rm
binding}^{\rm (coupled)} = 83(10)$ MeV from a coupled channel analysis where
effects due to virtual $\bar{B}^* \bar{B}^*$ states are included through the
coupled channel potential, while we obtain $E_{\rm binding}^{\rm (single)} =
155(17)$ MeV only from a potential for a single $\bar{B}\bar{B}^*$ channel.
This difference indicates that the effect from virtual $\bar{B}^* \bar{B}^*$
states is sizable to the binding energy of $T_{bb}$. Adding $\pm 20$ MeV as
empirical systematic error caused by the NRQCD approximation for $b$ quarks,
our estimate of the $T_{bb}$ binding energy becomes $83(10)(20)$ MeV.


  The statistical mechanics of phase transitions in dense systems of
polydisperse particles presents distinctive challenges to computer simulation
and analytical theory alike. The core difficulty, namely dealing correctly with
particle size fractionation between coexisting phases, is set out in the
context of a critique of previous simulation work on such systems. Specialized
Monte Carlo simulation techniques and moment free energy method calculations,
capable of treating fractionation exactly, are then described and deployed to
study the fluid-solid transition of an assembly of repulsive spherical
particles described by a top-hat "parent" distribution of particle sizes. The
cloud curve delineating the solid-fluid coexistence region is mapped as a
function of the degree of polydispersity $\delta$, and the properties of the
incipient "shadow" phases are presented. The coexistence region is found to
shift to higher densities as $\delta$ increases, but does not exhibit the sharp
narrowing predicted by many theories and some simulations.


  Elastic autoscaling is the fundamental mechanism that enables the cloud-based
services to continually evolve themselves - through changing the related
software configurations and hardware resource provisions - under time-varying
workloads. However, given the increasingly complex dynamic, uncertainty and
trade-offs related to the runtime QoS and cost/energy of services, cloud
autoscaling system is becoming one of the most complex artifacts constructed by
human and thus its effectiveness is difficult to be preserved. In this article,
we present novel ideas for facilitating cloud autoscaling. Our hypothesis that
cloud ecosystem, represented by a collection of cloud-based services, bears
many similarities with the natural ecosystem. As such, we in- tend to
investigate how ecological view can be adopted to better explain how the
cloud-based services evolve, and to explore what are the key factors that drive
stable and sustainable cloud-based services in the cloud. To achieve this goal,
we aim to transpose ecological principles, theories and models into cloud
autoscaling analogues and spontaneously improve long-term stability and
sustainability of cloud ecosystem.


  We explore how the assumption of ionization equilibrium modulates the modeled
intergalactic medium (IGM) at the end of the hydrogen Epoch of Reionization
using the cosmological radiation hydrodynamic \textsc{Technicolor Dawn}
simulation. In neutral and partially-ionized regions where the metagalactic
ultraviolet background (UVB) is weak, the ionization timescale
$t_\mathrm{ion}\equiv \Gamma^{-1}$ exceeds the Hubble time. Assuming
photoionization equilibrium in such regions artificially boosts the ionization
rate, accelerating reionization. By contrast, the recombination time
$t_\mathrm{rec} < t_\mathrm{ion}$ in photoionized regions, with the result that
assuming photoionization equilibrium artificially increases the neutral
hydrogen fraction. Using snapshots between $8 \geq z \geq 5$, we compare the
predicted Lyman-$\alpha$ forest flux power spectrum with and without the
assumption of ionization equilibrium. Small scales ($k > 0.1$ rad s km$^{-1}$)
exhibit reduced power from $7 \leq z \leq 5.5$ in the ionization equilibrium
case while larger scales are unaffected. This occurs for the same reasons:
ionization equilibrium artificially suppresses the neutral fraction in
self-shielded gas and boosts ionizations in voids, suppressing small-scale
fluctuations in the ionization field. When the volume-averaged neutral fraction
drops below $10^{-4}$, the signature of non-equilibrium ionizations on the
Lyman-$\alpha$ forest (LAF) disappears. Comparing with recent observations
indicates that these non-equilibrium effects are not yet observable in the LAF
flux power spectrum.


  Let A be an augmented algebra over a semi-simple algebra S. We show that the
Ext algebra of S as an A-module, enriched with its natural A-infinity
structure, can be used to reconstruct the completion of A at the augmentation
ideal. We use this technical result to justify a calculation in the physics
literature describing algebras that are derived equivalent to certain
non-compact Calabi-Yau three-folds. Since the calculation produces
superpotentials for these algebras we also include some discussion of
superpotential algebras and their invariants.


  In this paper we introduce a new approach for calculating dynamical
properties within the numerical renormalization group. It is demonstrated that
the method previously used fails for the Anderson impurity in a magnetic field
due to the absence of energy scale separation. The problem is solved by
evaluating the Green function with respect to the reduced density matrix of the
full system, leading to accurate spectra in agreement with the static
magnetization. The new procedure (denoted as DM-NRG) provides a unifying
framework for calculating dynamics at any temperature and represents the
correct extension of Wilson's original thermodynamic calculation.


  Convolutional Neural Networks have dramatically improved in recent years,
surpassing human accuracy on certain problems and performance exceeding that of
traditional computer vision algorithms. While the compute pattern in itself is
relatively simple, significant compute and memory challenges remain as CNNs may
contain millions of floating-point parameters and require billions of
floating-point operations to process a single image. These computational
requirements, combined with storage footprints that exceed typical cache sizes,
pose a significant performance and power challenge for modern compute
architectures. One of the promising opportunities to scale performance and
power efficiency is leveraging reduced precision representations for all
activations and weights as this allows to scale compute capabilities, reduce
weight and feature map buffering requirements as well as energy consumption.
While a small reduction in accuracy is encountered, these Quantized Neural
Networks have been shown to achieve state-of-the-art accuracy on standard
benchmark datasets, such as MNIST, CIFAR-10, SVHN and even ImageNet, and thus
provide highly attractive design trade-offs. Current research has focused
mainly on the implementation of extreme variants with full binarization of
weights and or activations, as well typically smaller input images. Within this
paper, we investigate the scalability of dataflow architectures with respect to
supporting various precisions for both weights and activations, larger image
dimensions, and increasing numbers of feature map channels. Key contributions
are a formalized approach to understanding the scalability of the existing
hardware architecture with cost models and a performance prediction as a
function of the target device size. We provide validating experimental results
for an ImageNet classification on a server-class platform, namely the AWS F1
node.


  This paper reviews the measurements of galaxy correlations at high redshifts,
and discusses how these may be understood in models of hierarchical
gravitational collapse. The clustering of galaxies at redshift one is much
weaker than at present, and this is consistent with the rate of growth of
structure expected in an open universe. If $\Omega=1$, this observation would
imply that bias increases at high redshift, in conflict with observed $M/L$
values for known high-$z$ clusters. At redshift 3, the population of
Lyman-limit galaxies displays clustering which is of similar amplitude to that
seen today. This is most naturally understood if the Lyman-limit population is
a set of rare recently-formed objects. Knowing both the clustering and the
abundance of these objects, it is possible to deduce empirically the
fluctuation spectrum required on scales which cannot be measured today owing to
gravitational nonlinearities. Of existing physical models for the fluctuation
spectrum, the results are most closely matched by a low-density spatially flat
universe. This conclusion is reinforced by an empirical analysis of CMB
anisotropies, in which the present-day fluctuation spectrum is forced to have
the observed form. Open models are strongly disfavoured, leaving $\Lambda$CDM
as the most successful simple model for structure formation.


  Several extensions of the Standard Model of particle physics contain
additional scalars implying a more complex scalar potential compared to that of
the Standard Model. In general these potentials allow for charge and/or color
breaking minima besides the desired one with correctly broken SU(2)_L times
U(1)_Y . Even if one assumes that a metastable local minimum is realized, one
has to ensure that its lifetime exceeds that of our universe. We introduce a
new program called Vevacious which takes a generic expression for a one-loop
effective potential energy function and finds all the tree-level extrema, which
are then used as the starting points for gradient-based minimization of the
one-loop effective potential. The tunneling time from a given input vacuum to
the deepest minimum, if different from the input vacuum, can be calculated. The
parameter points are given as files in the SLHA format (though is not
restricted to supersymmetric models), and new model files can be easily
generated automatically by the Mathematica package SARAH. This code uses
HOM4PS2 to find all the minima of the tree-level potential, PyMinuit to follow
gradients to the minima of the one-loop potential, and CosmoTransitions to
calculate tunneling times.


  The stochastic reaction-diffusion model driven by a multiplicative noise is
examined. We construct the gradient discretisation method (GDM), an abstract
framework combining several numerical method families. The paper provides the
discretisation and proves the convergence of the approximate schemes using a
compactness argument that works under natural assumptions on data. We also
investigate, using a finite volume method, known as the hybrid mixed mimetic
(HMM) approach, the effects of multiplicative noise on the dynamics of the
travelling waves in the excitable media displayed by the model. Particularly,
we consider how sufficiently high noise can cause waves to backfire or fail to
propagate.


  For the first time we have performed a detailed study of the X-ray, optical
and infra-red light curves of the 1975/1976 outburst of the famous black-hole
transient A0620-00 (Nova Mon 1975, V616 Mon). During the various stages of its
outburst the X-rays lag the optical by ~5-20 days. Moreover, the activity
associated with the secondary maximum started even earlier in the infra-red.
This suggests that most of the outburst processes occur in the outer parts of
the disk. We also find various drops in the X-ray intensity (lasting ~1 day or
more). Near one of these a local maximum in the optical light curve occurs.
This maximum appears ~30 days after the secondary maximum, i.e. close to the
viscous time scale of an irradiated disk. We suggest this feature to be an
`echo' of the secondary maximum. At the end of the outburst a similar local
optical maximum occurs, which might be an `echo' of the tertiary maximum. We
find that the drops in the optical and X-rays near the tertiary maximum are
also present in other SXTs. They always occur ~150 days after the start of the
outburst. We find for the first time that the X-ray spectrum of A0620-00 starts
to harden ~100-150 days after the start of its outburst, similar to GS2000+25
and GS1124-68. This suggests we witness the transition from the so-called high
to low state at that time. We show that the optical outburst light curve of
A0620-00 closely resembles that of the cataclysmic variable AL Com. This
strengthens the similarity in the properties of the SXTs and TOADs (or WZ Sge
stars), and shows that the optical outburst light curves of both groups are
governed by the disk properties and not by the compact object. Since
irradiation provides a natural mechanism to prolong the outburst of SXTs, we
suggest this could be of influence as well during TOAD outbursts.


  Activity recognition is a challenging problem with many practical
applications. In addition to the visual features, recent approaches have
benefited from the use of context, e.g., inter-relationships among the
activities and objects. However, these approaches require data to be labeled,
entirely available beforehand, and not designed to be updated continuously,
which make them unsuitable for surveillance applications. In contrast, we
propose a continuous-learning framework for context-aware activity recognition
from unlabeled video, which has two distinct advantages over existing methods.
First, it employs a novel active-learning technique that not only exploits the
informativeness of the individual activities but also utilizes their contextual
information during query selection; this leads to significant reduction in
expensive manual annotation effort. Second, the learned models can be adapted
online as more data is available. We formulate a conditional random field model
that encodes the context and devise an information-theoretic approach that
utilizes entropy and mutual information of the nodes to compute the set of most
informative queries, which are labeled by a human. These labels are combined
with graphical inference techniques for incremental updates. We provide a
theoretical formulation of the active learning framework with an analytic
solution. Experiments on six challenging datasets demonstrate that our
framework achieves superior performance with significantly less manual
labeling.


  The $m$-spotty byte error control codes provide a good source for detecting
and correcting errors in semiconductor memory systems using high density RAM
chips with wide I/O data (e.g. 8, 16, or 32 bits). $m$-spotty byte error
control codes are very suitable for burst correction. M. \"{O}zen and V. Siap
[7] proved a MacWilliams identity for the $m$-spotty Rosenbloom-Tsfasman
(shortly RT) weight enumerators of binary codes. The main purpose of this paper
is to present the MacWilliams type identities for $m$-spotty RT weight
enumerators of linear codes over finite commutative Frobenius rings.


  In this paper, we aim to compute numerical approximation integral by using an
adaptive Monte Carlo algorithm. We propose a stratified sampling algorithm
based on an iterative method which splits the strata following some quantities
called indicators which indicate where the variance takes relative big values.
The stratification method is based on the optimal allocation strategy in order
to decrease the variance from iteration to another. Numerical experiments show
and confirm the efficiency of our algorithm.


  We review the spin equations for the muon in the 1.45~T muon storage ring at
Brookhaven National Laboratory, which has subsequently been relocated to
Fermilab. Muons are stored in a uniform 1.45~T magnetic field, and vertical
focusing is provided by four sets of electrostatic quadrupoles placed
symmetrically around the storage ring. The storage ring is operated at the
"magic $\gamma = 29.3$" so that the effect of the motional magnetic field
cancels for muons at the magic momentum. We point out the relative sizes of the
various terms in the spin equations, and show that for experiments that use the
magic $\gamma$ and electric quadrupole focusing to store the muon beam, any
proposed effect that multiplies either the motional magnetic field $\vec \beta
\times \vec E$ or the muon pitching motion $\vec \beta \cdot \vec B$ term, will
be smaller by three or more orders of magnitude, relative to the spin
precession from the storage ring magnetic field. We use a recently proposed
General Relativity correction as an example, to demonstrate the smallness of
any such contribution, and point out that the revised preprint from these
authors still contains a conceptual error, that significantly overestimates the
magnitude of their proposed correction. We have prepared this document in the
hope that future authors will find it useful, should they wish to propose
corrections from some additional term added to the Thomas equation, Eq. 13,
below. Our goal is to clarify how the experiment is done, and how the small
corrections due to the presence of the radial electric field and the vertical
pitching motion of themuons (betatron motion) in the storage ring are taken
into account.


  This paper introduces the novel concept of proactive resource allocation
through which the predictability of user behavior is exploited to balance the
wireless traffic over time, and hence, significantly reduce the bandwidth
required to achieve a given blocking/outage probability. We start with a simple
model in which the smart wireless devices are assumed to predict the arrival of
new requests and submit them to the network T time slots in advance. Using
tools from large deviation theory, we quantify the resulting prediction
diversity gain} to establish that the decay rate of the outage event
probabilities increases with the prediction duration T. This model is then
generalized to incorporate the effect of the randomness in the prediction
look-ahead time T. Remarkably, we also show that, in the cognitive networking
scenario, the appropriate use of proactive resource allocation by the primary
users improves the diversity gain of the secondary network at no cost in the
primary network diversity. We also shed lights on multicasting with predictable
demands and show that the proactive multicast networks can achieve a
significantly higher diversity gain that scales super-linearly with T. Finally,
we conclude by a discussion of the new research questions posed under the
umbrella of the proposed proactive (non-causal) wireless networking framework.


  This paper proposes a novel signal processing technique that doubles the
range resolution of FMCW~(Frequency Modulated Continuous Wave) sensing without
increasing the required bandwidth. The proposed design overcomes the resolution
limit imposed by bandwidth by exploiting the phase consistency observed in the
special frequency variation of the beat signal derived from triangle FMCW.
Through this approach, the resolution is doubled while maintaining high
spectrum SNR. A system model for signal processing is presented, and the energy
distribution of the derived beat spectrum is analyzed. The effectiveness of the
proposed technique is validated through model-based simulations.


  We study the thermodynamics of disordered elastic systems, applied to vortex
lattices in the Bragg glass phase. Using the replica variational method we
compute the specific heat of pinned vortons in the classical limit. We find
that the contribution of disorder is positive, linear at low temperature, and
exhibits a maximum. It is found to be important compared to other
contributions, e.g. core electrons, mean field and non linear elasticity that
we evaluate. The contribution of droplets is subdominant at weak disorder in
$d=3$.


  Non-linear absorption phenomena induced by controlled irradiation with a
femtosecond laser beam can be used to tailor materials properties within the
bulk of substrates. One of the most successful applications of this technique
is the ability to fabricate three-dimensional micro-devices integrating
optical, mechanical or fluid handling functions in a single substrate. In this
context, amorphous SiO2 is the most widely studied material. Here we show that
short (50-fs) femtosecond pulses induce the separation of Si and O ions in SiO2
substrates, leading to the formation of micro-crystallites that we identify as
pure crystalline phase of Si. Interestingly, this polymorphic phase
transformation occurs in the absence of laser-induced confined microexplosion
and with moderate numerical aperture. These findings not only unravel a key
mechanism related to the transformation of the material and its subsequent
properties, but also pave the road for the development of three-dimensional
Si-rich structures embedded in a pure silica phase, eventually leading to novel
disruptive approaches for fabricating three- dimensional micro-devices. For
instance, one could imagine a silica-host substrate, in which arbitrary
three-dimensional silicon-based components are direct-write using a femtosecond
laser, rather than through assembly of components coming out of different
substrates or using multiple processing steps.


  A search for Higgs boson pair production $pp \to hh$ is performed with 19.5
fb$^{-1}$ of proton--proton collision data at $\sqrt{s}=$ 8 TeV, which were
recorded by the ATLAS detector at the Large Hadron Collider in 2012. The decay
products of each Higgs boson are reconstructed as a high-momentum $b\bar{b}$
system with either a pair of small-radius jets or a single large-radius jet,
the latter exploiting jet substructure techniques and associated $b$-tagged
track-jets. No evidence for resonant or non-resonant Higgs boson pair
production is observed. The data are interpreted in the context of the
Randall--Sundrum model with a warped extra dimension as well as the
two-Higgs-doublet model. An upper limit on the cross-section for $pp \to
G^{*}_{\mathrm{KK}} \to hh \to b\bar{b} b\bar{b}$ of 3.2 (2.3) fb is set for a
Kaluza--Klein graviton $G^{*}_{\mathrm{KK}}$ mass of 1.0 (1.5) TeV, at the 95\%
confidence level. The search for non-resonant Standard Model $hh$ production
sets an observed 95\% confidence level upper limit on the production
cross-section $\sigma(pp \to hh \to b\bar{b}b\bar{b})$ of 202 fb, compared to a
SM prediction of $\sigma(pp \to hh \to b\bar{b}b\bar{b}) = 3.6 \pm 0.5$ fb.


  Even if laboratory practice is essential for all scientific branches of
knowledge, it is often neglected at High School, due to lack of time and/or
resources. To establish a closer contact between school and experimental
sciences, the University Sapienza of Roma and the Istituto Nazionale di Fisica
Nucleare (INFN) launched the Lab2Go project, with the goal of spreading
laboratory practice among students and teachers in high schools.


  For a manifold $M$ with an integral closed 3-form $\omega$, we construct a
$PU(H)$-bundle and a Lie groupoid over its total space, together with a curving
in the sense of gerbes. If the form is non-degenerate, we furthermore give a
natural Lie 2-algebra quasi-isomorphism from the observables of $(M,\omega)$ to
the weak symmetries of the above geometric structure, generalising the
prequantisation map of Kostant and Souriau.


  In this article we study convexity properties of distance functions in
infinite dimensional Finsler unitary groups, such as the full unitary group,
the unitary Schatten perturbations of the identity and unitary groups of finite
von Neumann algebras. The Finsler structures are defined by translation of
different norms on the tangent space at the identity. We first prove a
convexity result for the metric derived from the operator norm on the full
unitary group. We also prove strong convexity results for the squared metrics
in Hilbert-Schmidt unitary groups and unitary groups of finite von Neumann
algebras, in both cases the tangent spaces are endowed with an inner product
defined with a trace. These results are applied to fixed point properties and
to quantitative metric bounds in certain rigidity problems. Radius bounds for
all convexity and fixed point results are shown to be optimal.


  Conventional approaches to text classification typically assume the existence
of a fixed set of predefined labels to which a given text can be classified.
However, in real-world applications, there exists an infinite label space for
describing a given text. In addition, depending on the aspect (sentiment,
topic, etc.) and domain of the text (finance, legal, etc.), the interpretation
of the label can vary greatly. This makes the task of text classification,
particularly in the zero-shot scenario, extremely challenging. In this paper,
we investigate the task of zero-shot text classification with the aim of
improving the ability of pre-trained language models (PLMs) to generalize to
both seen and unseen data across varying aspects and domains. To solve this we
introduce two new simple yet effective pre-training strategies, Implicit and
Explicit pre-training. These methods inject aspect-level understanding into the
model at train time with the goal of conditioning the model to build task-level
understanding. To evaluate this, we construct and release UTCD, a new benchmark
dataset for evaluating text classification in zero-shot settings. Experimental
results on UTCD show that our approach achieves improved zero-shot
generalization on a suite of challenging datasets across an array of zero-shot
formalizations.


  We analytically study two-color QCD with an even number of flavors at high
baryon density. This theory is free from the fermion sign problem. Chiral
symmetry is broken spontaneously by the diquark condensate. Based on the
symmetry breaking pattern we construct the low-energy effective Lagrangian for
the Nambu-Goldstone bosons. We identify a new epsilon-regime at high baryon
density in which the quark mass dependence of the partition function can be
determined exactly. We also derive Leutwyler-Smilga-type spectral sum rules for
the complex eigenvalues of the Dirac operator in terms of the fermion gap. Our
results can in principle be tested in lattice QCD simulations.


  We experimentally identify the magnetostatic modes active for Brillouin light
scattering in the optical whispering gallery modes of a yttrium iron garnet
sphere. Each mode is identified by magnetic field dispersion of
ferromagnetic-resonance spectroscopy and coupling strength to the known field
distribution of the microwave drive antenna. Our optical measurements confirm
recent predictions that higher-order magnetostatic modes can also generate
optical scattering, according to the selection rules derived from the axial
symmetry. From this we summarize the selection rules for Brillouin light
scattering. We give experimental evidence that the optomagnonic coupling to
non-uniform magnons can be higher than that of the uniform Kittel mode.


  In this paper, we will show a new characterization of operator monotone
functions by a matrix reverse Cauchy inequality.


  The mutual interference between similar radar systems can result in reduced
radar sensitivity and increased false alarm rates. To address the synchronous
and asynchronous interference mitigation problems in similar radar systems, we
first propose herein two slow-time coding schemes to modulate the pulses within
a coherent processing interval (CPI) for a single-input-single-output (SISO)
scenario. Specifically, the first coding scheme relies on Doppler shifting and
the second one is devised based on an optimization approach. We further extend
our discussion to the more general case of multiple-input-multiple-output
(MIMO) radars and propose an efficient algorithm to design waveforms to
mitigate mutual interference in such systems. The proposed coding schemes are
computationally efficient in practice and the incorporation of the coding
schemes requires only a slight modification of the existing systems. Our
numerical examples indicate that the proposed coding schemes can reduce the
interference power level in a desired area of the cross-ambiguity function
significantly.


  We explore the character of a novel set of ``field-linked'' states that were
predicted in [A. V. Avdeenkov and J. L. Bohn, Phys. Rev. Lett. 90, 043006
(2003)]. These states exist at ultralow temperatures in the presence of an
electrostatic field, and their properties are strongly dependent on the field's
strength. We clarify the nature of these quasi-bound states by constructing
their wave functions and determining their approximate quantum numbers. As the
properties of field-linked states are strongly defined by anisotropic dipolar
and Stark interactions, we construct adiabatic surfaces as functions of both
the intermolecular distance and the angle that the intermolecular axis makes
with the electric field. Within an adiabatic approximation we solve the 2-D
Schrodinger equation to find bound states, whose energies correlate well with
resonance features found in fully-converged multichannel scattering
calculations.


  In this paper we present a research on identification of audio recording
devices from background noise, thus providing a method for forensics. The audio
signal is the sum of speech signal and noise signal. Usually, people pay more
attention to speech signal, because it carries the information to deliver. So a
great amount of researches have been dedicated to getting higher
Signal-Noise-Ratio (SNR). There are many speech enhancement algorithms to
improve the quality of the speech, which can be seen as reducing the noise.
However, noises can be regarded as the intrinsic fingerprint traces of an audio
recording device. These digital traces can be characterized and identified by
new machine learning techniques. Therefore, in our research, we use the noise
as the intrinsic features. As for the identification, multiple classifiers of
deep learning methods are used and compared. The identification result shows
that the method of getting feature vector from the noise of each device and
identifying them with deep learning techniques is viable, and well-preformed.


  Inelastic neutron scattering has been used to determine the microscopic
Hamiltonian describing two high-spin variants of the high-anisotropy Mn$_6$
nanomagnet. The energy spectrum of both systems is characterized by the
presence of several excited total-spin multiplets partially overlapping the
S=12 ground multiplet. This implies that the relaxation processes of these
molecules are different from those occurring in prototype giant-spin
nanomagnets. In particular, we show that both the height of the energy barrier
and resonant tunnelling processes are greatly influenced by low-lying excited
total-spin multiplets.


  Naturally occurring networks exhibit quantitative features revealing
underlying growth mechanisms. Numerous network mechanisms have recently been
proposed to reproduce specific properties such as degree distributions or
clustering coefficients. We present a method for inferring the mechanism most
accurately capturing a given network topology, exploiting discriminative tools
from machine learning. The Drosophila melanogaster protein network is
confidently and robustly (to noise and training data subsampling) classified as
a duplication-mutation-complementation network over preferential attachment,
small-world, and other duplication-mutation mechanisms. Systematic
classification, rather than statistical study of specific properties, provides
a discriminative approach to understand the design of complex networks.


  In this note we show that unsatisfiable systems of linear equations with a
constant number of variables per equation over prime finite fields have
polynomial-size constant-degree semi-algebraic proofs of unsatisfiability.
These are proofs that manipulate polynomial inequalities over the reals with
variables ranging in $\{0,1\}$. This upper bound is to be put in contrast with
the known fact that, for certain explicit systems of linear equations over the
two-element field, such refutations require linear degree and exponential size
if they are restricted to so-called static semi-algebraic proofs, and even
tree-like semi-algebraic and sums-of-squares proofs. Our upper bound is a more
or less direct translation of an argument due to Grigoriev, Hirsch and
Pasechnik (Moscow Mathematical Journal, 2002) who did it for a family of linear
systems of interest in propositional proof complexity. We point out that their
method is more general and can be thought of as simulating Gaussian
elimination.


  We present the first study of the quark mass dependence of the heavy quark
momentum and spatial diffusion coefficients using lattice QCD with light
dynamical quarks corresponding to a pion mass of 320 MeV. We find that, for the
temperature range 195 MeV $<T<$ 293 MeV, the spatial diffusion coefficients of
the charm and bottom quarks are smaller than those obtained in phenomenological
models that describe the $p_T$ spectra and elliptic flow of open heavy flavor
hadrons.


  This paper presents watermarking algorithms using d-sequences so that the
peak signal to noise ratio (PSNR) is maximized and the distortion introduced in
the image due to the embedding is minimized. By exploiting the cross
correlation property of decimal sequences, the concept of embedding more than
one watermark in the same cover image is investigated.


  Multi-electrode arrays serve to record electrical signals of many neurons in
the brain simultaneously. For most of the past century, electrodes that
penetrate brain tissue have had exactly one shape: a straight needle. Certainly
this was a good starting choice at the time, but there is no reason to think
that a straight line would be the optimal shape in all Neuroscience
applications. Here I argue that, in fact, a wide variety of curved shapes is
equally practical: all possible helices. I discuss the manufacture and
manipulation of such devices, and illustrate a few use cases where they will
likely outperform conventional needles. With some collective action from the
research community, curved arrays could be manufactured and distributed at low
cost.


  Laser driving in systems with competing or coupled electronic orders can lead
to the enhancement of orders, or even to the appearance of hidden phases
without an equilibrium analogue. Here we consider a model for A$_3$C$_{60}$
which exhibits a unique interplay between conventional and odd-frequency (or
composite) orders. In particular, we show that photo-doping of the
antiferromagnetic Mott insulating phase, as realized in Cs$_3$C$_{60}$, results
in a paramagnetic gapped state with broken orbital symmetry. This hidden phase,
which does not exist under equilibrium conditions, can be interpreted as an
odd-frequency orbital-ordered state, and is conceptually related to the
equilibrium Jahn-Teller metal in more weakly correlated compounds. Our study
demonstrates the appearance of pure odd-frequency order via the nonthermal
melting of magnetic order, and provides an interesting example of
nonequilibrium control of electronic orders in a multi-orbital system.


  This is the first of a series of papers devoted to certain pairs of commuting
nilpotent elements in a semisimple Lie algebra that enjoy quite remarkable
properties and which are expected to play a major role in Representation
theory. The properties of these pairs and their role is similar to those of the
principal nilpotents. To any principal nilpotent pair we associate a
two-parameter analogue of the Kostant partition function, and propose the
corresponding two-parameter analogue of the weight multiplicity formula.
  In a different direction, each principal nilpotent pair gives rise to a
harmonic polynomial on the Cartesian square of the Cartan subalgebra, that
transforms under an irreducible representation of the Weyl group. In the
special case of sl_n, the conjugacy classes of principal nilpotent pairs and
the irreducible representations of the Symmetric group, S_n, are both
parametrised (in a compatible way) by Young diagrams. In general, our theory
provides a natural generalization to arbitrary Weyl groups of the classical
construction of simple S_n-modules in terms of Young's symmetrisers.


  Unsupervised learning of 3D-aware generative adversarial networks (GANs)
using only collections of single-view 2D photographs has very recently made
much progress. These 3D GANs, however, have not been demonstrated for human
bodies and the generated radiance fields of existing frameworks are not
directly editable, limiting their applicability in downstream tasks. We propose
a solution to these challenges by developing a 3D GAN framework that learns to
generate radiance fields of human bodies or faces in a canonical pose and warp
them using an explicit deformation field into a desired body pose or facial
expression. Using our framework, we demonstrate the first high-quality radiance
field generation results for human bodies. Moreover, we show that our
deformation-aware training procedure significantly improves the quality of
generated bodies or faces when editing their poses or facial expressions
compared to a 3D GAN that is not trained with explicit deformations.


  We study the behavior of test ideals and F-singularities in families. In
particular, we obtain generic (and non-generic) restriction theorems for test
ideals and non-F-pure ideals. Additionally, we study the global behavior of
certain canonical linear systems (induced by Frobenius) associated to adjoint
line bundles, in families. As a consequence, we obtain some positivity results
for pushforwards of some adjoint line bundles and for certain subsheaves of
these.


  Determining the state of a qubit on a timescale much shorter than its
relaxation time is an essential requirement for quantum information processing.
With the aid of a new type of non-degenerate parametric amplifier, we
demonstrate the continuous detection of quantum jumps of a transmon qubit with
90% fidelity in state discrimination. Entirely fabricated with standard
two-step optical lithography techniques, this type of parametric amplifier
consists of a dispersion engineered Josephson junction (JJ) array. By using
long arrays, containing $10^3$ JJs, we can obtain amplification at multiple
eigenmodes with frequencies below $10~\mathrm{GHz}$, which is the typical range
for qubit readout. Moreover, by introducing a moderate flux tunability of each
mode, employing superconducting quantum interference device (SQUID) junctions,
a single amplifier device could potentially cover the entire frequency band
between 1 and $10~\mathrm{GHz}$.


  Biomedical data is growing exponentially, and managing it is increasingly
challenging. While Findable, Accessible, Interoperable and Reusable (FAIR) data
principles provide guidance, their adoption has proven difficult, especially in
larger enterprises like pharmaceutical companies. In this manuscript, we
describe how we leverage an Ontology-Based Data Management (OBDM) strategy for
digital transformation in Novo Nordisk Research & Early Development. Here, we
include both our technical blueprint and our approach for organizational change
management. We further discuss how such an OBDM ecosystem plays a pivotal role
in the organizations digital aspirations for data federation and discovery
fuelled by artificial intelligence. Our aim for this paper is to share the
lessons learned in order to foster dialogue with parties navigating similar
waters while collectively advancing the efforts in the fields of data
management, semantics and data driven drug discovery.


  In my Montreal lecture notes of 1988, it was suggested that the theory of
linear quantum groups can be presented in the framework of the category of {\it
quadratic algebras} (imagined as algebras of functions on "quantum linear
spaces"), and quadratic algebras of their inner (co)homomorphisms.
  Soon it was understood (E. Getzler and J. Jones, V. Ginzburg, M. Kapranov, M.
Kontsevich, M. Markl, B. Vallette et al.) that the class of {\it quadratic
operads} can be introduced and the main theorems about quadratic algebras can
be generalised to the level of such operads, if their components are {\it
linear spaces} (or objects of more general monoidal categories.)
  When quantum cohomology entered the scene, it turned out that the basic tree
level (genus zero) (co)operad of quantum cohomology not only is {\it quadratic}
one, but its {\it components are themselves quadratic algebras.}
  In this short note, I am studying the interaction of quadratic algebras
structure with operadic structure in the context of enriched category formalism
due to G. M. Kelly et al.


  The Rabi frequency (coupling strength) of an electric-dipole transition is an
important experimental parameter in laser-cooling and other atomic physics
experiments. Though the relationship between Rabi frequency and atomic
wavefunctions and/or atomic lifetimes is discussed in many references, there is
a need for a concise, self-contained, accessible introduction to such
calculations suitable for use by the typical student of laser cooling
(experimental or theoretical). In this paper, I outline calculations of the
Rabi frequencies for atoms with sub-structure due to orbital, spin and nuclear
angular momentum. I emphasize the physical meaning of the calculations.


  This paper presents a time-causal analogue of the Gabor filter, as well as a
both time-causal and time-recursive analogue of the Gabor transform, where the
proposed time-causal representations obey both temporal scale covariance and a
cascade property with a simplifying kernel over temporal scales. The motivation
behind these constructions is to enable theoretically well-founded
time-frequency analysis over multiple temporal scales for real-time situations,
or for physical or biological modelling situations, when the future cannot be
accessed, and the non-causal access to future in Gabor filtering is therefore
not viable for a time-frequency analysis of the system.
  We develop the theory for these representations, obtained by replacing the
Gaussian kernel in Gabor filtering with a time-causal kernel, referred to as
the time-causal limit kernel, which guarantees simplification properties from
finer to coarser levels of scales in a time-causal situation, similar as the
Gaussian kernel can be shown to guarantee over a non-causal temporal domain. In
these ways, the proposed time-frequency representations guarantee well-founded
treatment over multiple scales, in situations when the characteristic scales in
the signals, or physical or biological phenomena, to be analyzed may vary
substantially, and additionally all steps in the time-frequency analysis have
to be fully time-causal.


  We carry out \textit{ab initio} study of ground state phase diagram of
spin-1/2 cold fermionic atoms within two-fold degenerate $p$-band of an
anisotropic optical lattice. Using the Gutzwiller variational approach, we show
that a robust ferromagnetic phase exists for a vast range of band fillings and
interacting strengths. The ground state crosses over from spin density wave
state to spin-1 Neel state at half filling. Additional harmonic trap will
induce spatial separation of varies phases. We also discuss several relevant
observable consequences and detection methods. Experimental test of the results
reported here may shed some light on the long-standing issue of itinerant
ferromagnetism.


  The breakdown point in its different variants is one of the central notions
to quantify the global robustness of a procedure. We propose a simple
supplementary variant which is useful in situations where we have no obvious or
only partial equivariance: Extending the Donoho and Huber(1983) Finite Sample
Breakdown Point, we propose the Expected Finite Sample Breakdown Point to
produce less configuration-dependent values while still preserving the finite
sample aspect of the former definition. We apply this notion for joint
estimation of scale and shape (with only scale-equivariance available),
exemplified for generalized Pareto, generalized extreme value, Weibull, and
Gamma distributions. In these settings, we are interested in highly-robust,
easy-to-compute initial estimators; to this end we study Pickands-type and
Location-Dispersion-type estimators and compute their respective breakdown
points.


  We present results of a study of the charmless vector-vector decays B^0 ->
K^{*0} rho^0 and B^+ ->K^{*0} rho^+. The results are based on a 140 fb^{-1}
data sample collected by the Belle detector at the KEKB asymmetric e^+e^-
collider. We obtain the branching fraction B(B^+ -> K^{*0} rho^+)=(6.6 +-
2.2(stat.) +- 0.8 (syst.)) x 10^{-6}, and set upper limits on the branching
fractions B(B^0 -> K^{*0} rho^0)<2.6 x 10^{-6} and B(B^0 -> f_0(980)
K^{*0})<5.2 x 10^{-6}. We also perform a helicity analysis of the rho and K^*
vector mesons in the decay B^+ -> K^{*0} rho^+, and obtain the longitudinal
polarization fraction R_0(B^+ -> K^{*0} rho^+)=0.50 +-
0.19(stat.)+0.05-0.07}(syst.).


  By going beyond the Born-Oppenheimer approximation and treating the
non-adiabatic coupling terms (NACTs) as equivalent to a frictional force in a
molecular system, the classical equations of motion are solved for a test case
of H3+. Using an ab initio potential energy surface for the ground electronic
state and its NACTs with the first excited state of H3+, it is shown that (D+,
H2) collisions are slowed enough to result in trapping and formation of a
stable DH2+.


  Electron mobility, energy spectra and intrinsic carrier concentrations in the
n-type Hg0.32Cd0.68Te / Hg1-xCdxTe / Hg0.32Cd0.68Te quantum well (QW) in
semi-metallic state are numerically modeled. Energy spectra and wave functions
were calculated in the framework of the 8-band k-p Hamiltonian. In our model,
electron scattering on longitudinal optical phonons, charged impurities, and
holes has been taken into account, and the mobility has been calculated by an
iterative solution of the Boltzmann transport equation. Our results show that
the increase of the electron concentration in the well enhances the screening
of the 2D electron gas, decreases the hole concentration, and can ultimately
lead to a high electron mobility at liquid nitrogen temperatures. The increase
of the electron concentration in the QW could be achieved in situ by
delta-doping of barriers or by applying the top-gate potential. Our modeling
has shown that for low molar composition x the concentration of holes in the
well is high in a wide range of electron concentrations; in this case, the
purity of samples does not significantly influence the electron mobility. These
results are important in the context of establishing optimal parameters for the
fabrication of high-mobility Hg1-xCdxTe quantum wells able to operate at liquid
nitrogen temperature and thus suitable for applications in terahertz detectors.


  Experimental conclusions from air shower observations on cosmic-ray photons
above 10^19 eV are based on the comparison to detailed shower simulations. For
the calculations, the photonuclear cross-section needs to be extrapolated over
several orders of magnitude in energy. The uncertainty from the cross-section
extrapolation translates into an uncertainty of the predicted shower features
for primary photons and, thus, into uncertainties for a possible data
interpretation. After briefly reviewing the current status of ultra-high energy
photon studies, the impact of the uncertainty of the photonuclear cross-section
for shower calculations is investigated. Estimates for the uncertainties in the
main shower observables are provided. Photon discrimination is shown to be
possible even for rapidly rising cross-sections. When photon-initiated showers
are identified, it is argued that the sensitivity of photon shower observables
to the photonuclear cross-section can in turn be exploited to constrain the
cross-section at energies not accessible at colliders.


  We study the conductance threshold of clean nearly straight quantum wires in
the magnetic field. As a quantitative example we solve exactly the scattering
problem for two-electrons in a wire with planar geometry and a weak bulge. From
the scattering matrix we determine conductance via the Landauer-Buettiker
formalism. The conductance anomalies found near 0.25(2e^2/h) and 0.75(2e^2/h)
are related to a singlet resonance and a triplet resonance, respectively, and
survive to temperatures of a few degrees. With increasing in-plane magnetic
field the conductance exhibits a plateau at e^2/h, consistent with recent
experiments.


  There is a growing interest in characterizing circular data found in
biological systems. Such data are wide ranging and varied, from signal phase in
neural recordings to nucleotide sequences in round genomes. Traditional
clustering algorithms are often inadequate due to their limited ability to
distinguish differences in the periodic component. Current clustering schemes
that work in a polar coordinate system have limitations, such as being only
angle-focused or lacking generality. To overcome these limitations, we propose
a new analysis framework that utilizes projections onto a cylindrical
coordinate system to better represent objects in a polar coordinate system.
Using the mathematical properties of circular data, we show our approach always
finds the correct clustering result within the reconstructed dataset, given
sufficient periodic repetitions of the data. Our approach is generally
applicable and adaptable and can be incorporated into most state-of-the-art
clustering algorithms. We demonstrate on synthetic and real data that our
method generates more appropriate and consistent clustering results compared to
standard methods. In summary, our proposed analysis framework overcomes the
limitations of existing polar coordinate-based clustering methods and provides
a more accurate and efficient way to cluster circular data.


  We study Coulomb blockade oscillations of thermoelectric coefficients of a
single electron transistor based on a quantum dot strongly coupled to one of
the leads by a quantum point contact. At temperatures below the charging energy
E_C the transport of electrons is dominated by strong inelastic cotunneling. In
this regime we find analytic expressions for the thermopower as a function of
temperature T and the reflection amplitude $r$ in the contact. In the case when
the electron spins are polarized by a strong external magnetic field, the
thermopower shows sinusoidal oscillations as a function of the gate voltage
with the amplitude of the order of $e^{-1}|r|\frac{T}{E_C}$. We obtain
qualitatively different results in the absence of the magnetic field. At
temperatures between $E_C$ and $E_C|r|^2$ the thermopower oscillations are
sinusoidal with the amplitude of order $e^{-1}|r|^2 \ln \frac{E_C}{T}$. On the
other hand, at $T\ll E_C|r|^2$ we find non-sinusoidal oscillations of the
thermopower with the amplitude $\sim e^{-1} |r| \sqrt{T/E_C} \ln(E_C/T)$.


  We give asymptotic estimates of the variance of the number of integer points
in translated thin annuli in any dimension.


  This friendly introduction to tropical geometry is meant to be accessible to
first year students in mathematics. The topics discussed here are basic
tropical algebra, tropical plane curves, some tropical intersections, and
Viro's patchworking. Each definition is explained with concrete examples and
illustrations. To a great exten, this text is an updated of a translation from
a french text by the first author. There is also a newly added section
highlighting new developments and perspectives on tropical geometry. In
addition, the final section provides an extensive list of references on the
subject.


  The dynamics of power-grid networks is becoming an increasingly active area
of research within the physics and network science communities. The results
from such studies are typically insightful and illustrative, but are often
based on simplifying assumptions that can be either difficult to assess or not
fully justified for realistic applications. Here we perform a comprehensive
comparative analysis of three leading models recently used to study
synchronization dynamics in power-grid networks -- a fundamental problem of
practical significance given that frequency synchronization of all power
generators in the same interconnection is a necessary condition for a power
grid to operate. We show that each of these models can be derived from first
principles within a common framework based on the classical model of a
generator, thereby clarifying all assumptions involved. This framework allows
us to view power grids as complex networks of coupled second-order phase
oscillators with both forcing and damping terms. Using simple illustrative
examples, test systems, and real power-grid datasets, we study the inherent
frequencies of the oscillators as well as their coupling structure, comparing
across the different models. We demonstrate, in particular, that if the network
structure is not homogeneous, generators with identical parameters need to be
modeled as non-identical oscillators in general. We also discuss an approach to
estimate the required (dynamical) parameters that are unavailable in typical
power-grid datasets, their use for computing the constants of each of the three
models, and an open-source MATLAB toolbox that we provide for these
computations.


  Neuromorphic computing has the potential to further the success of
software-based artificial neural networks (ANNs) by designing hardware from a
different perspective. Current research in neuromorphic hardware targets
dramatic improvements to ANN performance by increasing energy efficiency, speed
of operation, and even seeks to extend the utility of ANNs by natively adding
functionality such as spiking operation. One promising neuromorphic hardware
platform is based on superconductive electronics, which has the potential to
incorporate all of these advantages at the device level in addition to offering
the potential of near lossless communications both within the neuromorphic
circuits as well as between disparate superconductive chips. Here we explore
one of the fundamental brain-inspired architecture components, the fan-in and
fan-out as realized in superconductive circuits based on Josephson junctions.
From our calculations and WRSPICE simulations we find that the fan-out should
be limited only by junction count and circuit size limitations, and we
demonstrate results in simulation at a level of 1-to-10,000, similar to that of
the human brain. We find that fan-in has more limitations, but a fan-in level
on the order of a few 100-to-1 should be achievable based on current
technology. We discuss our findings and the critical parameters that set the
limits on fan-in and fan-out in the context of superconductive neuromorphic
circuits.


  In Jin et al. (2014), the limiting spectral distribution (LSD) of a
symmetrized auto-cross covariance matrix is derived using matrix manipulation,
with finite $(2+\delta)$-th moment assumption. Here we give an alternative
method using a result in Bai and Silverstein (2010), in which a weaker
condition of finite 2nd moment is assumed.


  Emerging systems such as smart grids or intelligent transportation systems
often require end-user applications to continuously send information to
external data aggregators performing monitoring or control tasks. This can
result in an undesirable loss of privacy for the users in exchange of the
benefits provided by the application. Motivated by this trend, this paper
introduces privacy concerns in a system theoretic context, and addresses the
problem of releasing filtered signals that respect the privacy of the user data
streams. Our approach relies on a formal notion of privacy from the database
literature, called differential privacy, which provides strong privacy
guarantees against adversaries with arbitrary side information. Methods are
developed to approximate a given filter by a differentially private version, so
that the distortion introduced by the privacy mechanism is minimized. Two
specific scenarios are considered. First, the notion of differential privacy is
extended to dynamic systems with many participants contributing independent
input signals. Kalman filtering is also discussed in this context, when a
released output signal must preserve differential privacy for the measured
signals or state trajectories of the individual participants. Second,
differentially private mechanisms are described to approximate stable filters
when participants contribute to a single event stream, extending previous work
on differential privacy under continual observation.


  This text aims to give a pedagogical introduction into the main concepts of
the theory of structure formation in the universe. The text is suited for
graduate students of astronomy with a moderate background in general
relativity. A special focus is laid on deriving the results formally from first
principles. In the first chapter we introduce the homogeneous and isotropic
universe defining the framework for the theory of structure formation, which is
discussed in the three following chapters. In the second chapter we describe
the theory in the Newtonian framework and in the third chapter for the general
relativistic case. The final chapter discusses the generation of perturbations
in the very early universe for the simplest models of inflation.


  We study the effect of A site disorder on magnetic field induced melting of
charge order (CO) in half doped manganites using a Monte-Carlo technique.
Strong A-site disorder destroys CO even without an applied field. At moderate
disorder, the zero field CO state survives but has several intriguing features
in its field response. Our spatially resolved results track the broadening of
the field melting transition due to disorder and explain the unusual dependence
of the melting scales on bandwidth and disorder. In combination with our
companion paper on field melting of charge order in clean systems we provide an
unified understanding of CO melting across all half doped manganites.


  Hybrid stars like Nu Eridani and 12 Lacertae show two different types of
pulsations: (i) low-order acoustic and gravity modes of the Beta Cephei type
with periods of about 3-6 hours, and (ii) high-order gravity modes of the SPB
type with periods of about 1.5-3 days. Theoretical computations using both OPAL
and OP opacity data well reproduce short period low-order pulsations of the
Beta Cep type and show a tendency to instability of high-order gravity modes,
especially for stellar models built with the OP opacities. However, instability
at observed long periods has not been achieved. We test effects of artificial
opacity modifications in the deep envelope on the instability of some hybrid
star models. For Nu Eri models, an opacity increase both in the Z opacity bump
region at temperature of about 200 000 K and in the region of the deeper
opacity bump at temperature of about 2-2.5 million degrees (this bump is also
mainly due to excited ions of the iron-group elements) may result in
instability of the high-order gravity modes with the observed periods. The
shortest observed period of 3 hours can also be excited in modified models.
However, the required opacity increase seems to be quite large (up to 2 times
in some stellar layers) which may be incompatible with atomic physics.


  The reliability and precision of dynamic database are vital for the optimal
operating and global control of integrated energy systems. One of the effective
ways to obtain the accurate states is state estimations. A novel robust dynamic
state estimation methodology for integrated natural gas and electric power
systems is proposed based on Kalman filter. To take full advantage of
measurement redundancies and predictions for enhancing the estimating accuracy,
the dynamic state estimation model coupling gas and power systems by gas
turbine units is established. The exponential smoothing technique and gas
physical model are integrated in Kalman filter. Additionally, the time-varying
scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The
proposed method is applied to an integrated gas and power systems formed by
GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating
results show that the method can obtain the accurate dynamic states under three
different measurement error conditions, and the filtering performance are
better than separate estimation methods. Additionally, the proposed method is
robust when the measurements experience bad data.


  In this paper we return to a model with domain wall fermions in a waveguide.
This model contains a Yukawa coupling $y$ which is needed for gauge invariance.
A previous paper left the analysis for large values of this coupling
incomplete. We fill the gap by developing a systematic expansion suitable for
large $y$, and using this, we gain an analytic understanding of the phase
diagram and fermion spectrum. We find that in a sense all the species doublers
come back for large $y$. The conclusion that no lattice chiral gauge theory can
be obtained from this approach therefore remains valid.


  This self-contained discussion relates the long-run average holding cost per
unit time to the long-run average response time per customer in a $G/G/1$ queue
with no assumption made on the order of service. The only restriction
established is that the system be ergodic. This is achieved using standard
queuing theory. The practical relevance of such a result is discussed in the
context of simulation output analysis as well as through an application to
formulating a Markov Decision Process that minimises long-run average response
time per customer.


  Recent years have seen a huge development in spatial modelling and prediction
methodology, driven by the increased availability of remote-sensing data and
the reduced cost of distributed-processing technology. It is well known that
modelling and prediction using infinite-dimensional process models is not
possible with large data sets, and that both approximate models and, often,
approximate-inference methods, are needed. The problem of fitting simple global
spatial models to large data sets has been solved through the likes of
multi-resolution approximations and nearest-neighbour techniques. Here we
tackle the next challenge, that of fitting complex, nonstationary, multi-scale
models to large data sets. We propose doing this through the use of
superpositions of spatial processes with increasing spatial scale and
increasing degrees of nonstationarity. Computation is facilitated through the
use of Gaussian Markov random fields and parallel Markov chain Monte Carlo
based on graph colouring. The resulting model allows for both distributed
computing and distributed data. Importantly, it provides opportunities for
genuine model and data scaleability and yet is still able to borrow strength
across large spatial scales. We illustrate a two-scale version on a data set of
sea-surface temperature containing on the order of one million observations,
and compare our approach to state-of-the-art spatial modelling and prediction
methods.


  Identifying companies with similar profiles is a core task in finance with a
wide range of applications in portfolio construction, asset pricing and risk
attribution. When a rigorous definition of similarity is lacking, financial
analysts usually resort to 'traditional' industry classifications such as
Global Industry Classification System (GICS) which assign a unique category to
each company at different levels of granularity. Due to their discrete nature,
though, GICS classifications do not allow for ranking companies in terms of
similarity. In this paper, we explore the ability of pre-trained and finetuned
large language models (LLMs) to learn company embeddings based on the business
descriptions reported in SEC filings. We show that we can reproduce GICS
classifications using the embeddings as features. We also benchmark these
embeddings on various machine learning and financial metrics and conclude that
the companies that are similar according to the embeddings are also similar in
terms of financial performance metrics including return correlation.


  In this thesis we investigate certain types of monomial ideals of polynomial
rings over fields. We are interested in minimal free resolutions of these
ideals (or equivalently the quotients of the polynomial ring by the ideals)
considered as modules over the polynomial ring. There is no simple method of
finding such resolutions but in the case of Stanley-Reisner ideals Hochster's
formula and its variants provide a way to compute the Betti numbers of these
resolutions.
  Even with these formulae it is not in general possible to find especially
explicit or useful descriptions of the Betti numbers. However we restrict our
attention to those ideals which are generated by square free monomials of
degree 2. The purpose of this is to associate these ideals with graphs. This
provides a link between algebraic objects, the monomial ideals, and
combinatorial objects, the graphs. This correspondence enables us do define new
numerical invariants of graphs: the Betti numbers and projective dimension of
the corresponding graph ideals.
  We find explicit descriptions of the Betti numbers and projective dimensions
of cycles and forests. In the case of forests we find a method of describing
the Betti numbers in terms of the Betti numbers of subforests. This also leads
to a description of the projective dimension of a forests in terms of the
projective dimensions of its subforests.
  It turns out that the projective dimension of forests can be defined in
purely combinatorial terms and hence it gives a new combinatorial numerical
invariant of forests.


  The GMRT Radio Halo Survey, carried out at 610 MHz to investigate the
statistical properties of cluster radio halos in a complete cluster sample
selected in the redshift interval z=0.2-0.4, has significantly improved our
understanding of the origin of cluster radio halos and relics. Here we briefly
summarize the most relevant results of our investigation. A low frequency
follow-up is in progress with the GMRT at 325 MHz and 240 MHz on the diffuse
sources and candidated found at 610 MHz. We briefly report some preliminary
results on these low frequency observations. Cluster radio halos with different
radio spectral properties have been unexpectedly found.


  This paper studies the variation diminishing property of $k$-positive linear
time-invariant (LTI) systems, which map inputs with $k-1$ sign changes to
outputs with at most the same variation. We characterize this property for the
Toeplitz and Hankel operators of finite-dimensional systems. Our main result is
that these operators have a dominant approximation in the form of series or
parallel interconnections of $k$ first order positive systems. This is shown by
expressing the $k$-positivity of a LTI system as the external positivity (that
is, $1$-positivity) of $k$ compound LTI systems. Our characterization
generalizes well known properties of externally positive systems ($k=1$) and
totally positive systems ($k=\infty$; also known as relaxation systems).


  In this work we present a practical algorithm for calibrating a magnetometer
for the presence of magnetic disturbances and for magnetometer sensor errors.
To allow for combining the magnetometer measurements with inertial measurements
for orientation estimation, the algorithm also corrects for misalignment
between the magnetometer and the inertial sensor axes. The calibration
algorithm is formulated as the solution to a maximum likelihood problem and the
computations are performed offline. The algorithm is shown to give good results
using data from two different commercially available sensor units. Using the
calibrated magnetometer measurements in combination with the inertial sensors
to determine the sensor's orientation is shown to lead to significantly
improved heading estimates.


  The rich and diverse dynamics of particle-based systems ultimately originates
from the coupling of their degrees of freedom via internal interactions. To
arrive at a tractable approximation of such many-body problems, coarse-graining
is often an essential step. Power functional theory provides a unique and
microscopically sharp formulation of this concept. The approach is based on an
exact one-body variational principle to describe the dynamics of both
overdamped and inertial classical and quantum many-body systems. In
equilibrium, density functional theory is recovered, and hence spatially
inhomogeneous systems are described correctly. The dynamical theory operates on
the level of time-dependent one-body correlation functions. Two- and
higher-body correlation functions are accessible via the dynamical test
particle limit and the nonequilibrium Ornstein-Zernike route. We describe the
structure of this functional approach to many-body dynamics, including much
background as well as applications to a broad range of dynamical situations,
such as the van Hove function in liquids, flow in nonequilibrium steady states,
motility-induced phase separation of active Brownian particles, lane formation
in binary colloidal mixtures, and both steady and transient shear phenomena.


  This paper addresses the problem of human rendering in the video with
temporal appearance constancy. Reconstructing dynamic body shapes with
volumetric neural rendering methods, such as NeRF, requires finding the
correspondence of the points in the canonical and observation space, which
demands understanding human body shape and motion. Some methods use rigid
transformation, such as SE(3), which cannot precisely model each frame's unique
motion and muscle movements. Others generate the transformation for each frame
with a trainable network, such as neural blend weight field or translation
vector field, which does not consider the appearance constancy of general body
shape. In this paper, we propose CAT-NeRF for self-awareness of appearance
constancy with Tx$^2$Former, a novel way to combine two Transformer layers, to
separate appearance constancy and uniqueness. Appearance constancy models the
general shape across the video, and uniqueness models the unique patterns for
each frame. We further introduce a novel Covariance Loss to limit the
correlation between each pair of appearance uniquenesses to ensure the
frame-unique pattern is maximally captured in appearance uniqueness. We assess
our method on H36M and ZJU-MoCap and show state-of-the-art performance.


  Eta Carinae (Eta Car) is one of the most massive binary stars in the Milky
Way. It became the second-brightest star in the sky during its mid-19th century
"Great Eruption," but then faded from view (with only naked-eye estimates of
brightness). Its eruption is unique among known astronomical transients in that
it exceeded the Eddington luminosity limit for 10 years. Because it is only 2.3
kpc away, spatially resolved studies of the nebula have constrained the ejected
mass and velocity, indicating that in its 19th century eruption, Eta Car
ejected more than 10 M_solar in an event that had 10% of the energy of a
typical core-collapse supernova without destroying the star. Here we report the
discovery of light echoes of Eta Carinae which appear to be from the 1838-1858
Great Eruption. Spectra of these light echoes show only absorption lines, which
are blueshifted by -210 km/s, in good agreement with predicted expansion
speeds. The light-echo spectra correlate best with those of G2-G5 supergiant
spectra, which have effective temperatures of ~5000 K. In contrast to the class
of extragalactic outbursts assumed to be analogs of Eta Car's Great Eruption,
the effective temperature of its outburst is significantly cooler than allowed
by standard opaque wind models. This indicates that other physical mechanisms
like an energetic blast wave may have triggered and influenced the eruption.


  We study the spin ladder model with interactions between spins on neighboring
rungs. The model Hamiltonian with the exact singlet ground state degenerated
with ferromagnetic state is obtained. The singlet ground state wave function
has a special recurrent form and depends on two parameters. Spin correlations
in the singlet ground state show double-spiral structure with period of spirals
equals to the system size. For special values of parameters they have
exponential decay. The spectrum of the model is gapless and there are
asymptotically degenerated excited states for special values of parameters in
the thermodynamic limit.


  In the present manuscript, we study analytic properties of zeta functions
defined by partial Euler products.


  Incomplete multi-view clustering becomes an important research problem, since
multi-view data with missing values are ubiquitous in real-world applications.
Although great efforts have been made for incomplete multi-view clustering,
there are still some challenges: 1) most existing methods didn't make full use
of multi-view information to deal with missing values; 2) most methods just
employ the consistent information within multi-view data but ignore the
complementary information; 3) For the existing incomplete multi-view clustering
methods, incomplete multi-view representation learning and clustering are
treated as independent processes, which leads to performance gap. In this work,
we proposed a novel Incomplete Contrastive Multi-View Clustering method with
high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency
relation transfer plus graph convolutional network to tackle missing values
problem. Secondly, instance-level attention fusion and high-confidence guiding
are proposed to exploit the complementary information while instance-level
contrastive learning for latent representation is designed to employ the
consistent information. Thirdly, an end-to-end framework is proposed to
integrate multi-view missing values handling, multi-view representation
learning and clustering assignment for joint optimization. Experiments compared
with state-of-the-art approaches demonstrated the effectiveness and superiority
of our method. Our code is publicly available at
https://github.com/liunian-Jay/ICMVC.


  In this paper, we study the problem of formal verification for Answer Set
Programming (ASP), namely, obtaining a formal proof showing that the answer
sets of a given (non-ground) logic program P correctly correspond to the
solutions to the problem encoded by P, regardless of the problem instance. To
this aim, we use a formal specification language based on ASP modules, so that
each module can be proved to capture some informal aspect of the problem in an
isolated way. This specification language relies on a novel definition of
(possibly nested, first order) program modules that may incorporate local
hidden atoms at different levels. Then, verifying the logic program P amounts
to prove some kind of equivalence between P and its modular specification.
Under consideration for acceptance in TPLP.