  We consider the problem of domain adaptation in LiDAR-based 3D object
detection. Towards this, we propose a simple yet effective training strategy
called Gradual Batch Alternation that can adapt from a large labeled source
domain to an insufficiently labeled target domain. The idea is to initiate the
training with the batch of samples from the source and target domain data in an
alternate fashion, but then gradually reduce the amount of the source domain
data over time as the training progresses. This way the model slowly shifts
towards the target domain and eventually better adapt to it. The domain
adaptation experiments for 3D object detection on four benchmark autonomous
driving datasets, namely ONCE, PandaSet, Waymo, and nuScenes, demonstrate
significant performance gains over prior arts and strong baselines.


  Efficient skill acquisition, representation, and on-line adaptation to
different scenarios has become of fundamental importance for assistive robotic
applications. In the past decade, dynamical systems (DS) have arisen as a
flexible and robust tool to represent learned skills and to generate motion
trajectories. This work presents a novel approach to incrementally modify the
dynamics of a generic autonomous DS when new demonstrations of a task are
provided. A control input is learned from demonstrations to modify the
trajectory of the system while preserving the stability properties of the
reshaped DS. Learning is performed incrementally through Gaussian process
regression, increasing the robot's knowledge of the skill every time a new
demonstration is provided. The effectiveness of the proposed approach is
demonstrated with experiments on a publicly available dataset of complex
motions.


  An outstanding open issue in our quest for physics beyond Einstein is the
unification of general relativity (GR) and quantum physics. Loop quantum
gravity (LQG) is a leading approach toward this goal. At its heart is the
central lesson of GR: Gravity is a manifestation of spacetime geometry. Thus,
the approach emphasizes the quantum nature of geometry and focuses on its
implications in extreme regimes -- near the big bang and inside black holes --
where Einstein's smooth continuum breaks down. We present a brief overview of
the main ideas underlying LQG and highlight a few recent advances. This report
is addressed to non-experts.


  This research paper presents a novel approach to the prediction of hypoxia in
brain tumors, using multi-parametric Magnetic Resonance Imaging (MRI). Hypoxia,
a condition characterized by low oxygen levels, is a common feature of
malignant brain tumors associated with poor prognosis. Fluoromisonidazole
Positron Emission Tomography (FMISO PET) is a well-established method for
detecting hypoxia in vivo, but it is expensive and not widely available. Our
study proposes the use of MRI, a more accessible and cost-effective imaging
modality, to predict FMISO PET signals. We investigate deep learning models
(DL) trained on the ACRIN 6684 dataset, a resource that contains paired MRI and
FMISO PET images from patients with brain tumors. Our trained models
effectively learn the complex relationships between the MRI features and the
corresponding FMISO PET signals, thereby enabling the prediction of hypoxia
from MRI scans alone. The results show a strong correlation between the
predicted and actual FMISO PET signals, with an overall PSNR score above 29.6
and a SSIM score greater than 0.94, confirming MRI as a promising option for
hypoxia prediction in brain tumors. This approach could significantly improve
the accessibility of hypoxia detection in clinical settings, with the potential
for more timely and targeted treatments.


  We analyze the dynamics of room evacuation for mixed populations that include
both competitive and cooperative individuals through numerical simulations
using the social force model. Cooperative agents represent well-trained
individuals who know how to behave in order to reduce risks within high-density
crowds. We consider that competitive agents can imitate cooperative behavior
when they are in close proximity to cooperators. We study the effects of the
imitation of cooperative behavior on the duration and safety of evacuations,
analyzing evacuation time and other quantities of interest for varying
parameters such as the proportions of mixing, the aspect ratio of the room, and
the parameters characterizing individual behaviors. Our main findings reveal
that the addition of a relatively small number of cooperative agents into a
crowd can reduce evacuation time and the density near the exit door, making the
evacuation faster and safer despite an increase in the total number of agents.
In particular, for long spaces such as corridors, a small number of added
cooperative agents can significantly facilitate the evacuation process. We
compare our results with those of systems without imitation and also study the
general role of cooperation, providing further analysis for homogeneous
populations. Our main conclusions emphasize the potential relevance of training
people how to behave in high-density crowds


  An earlier paper describes a program to prove the Kepler conjecture on sphere
packings. This paper carries out the second step of that program. A sphere
packing leads to a decomposition of $R^3$ into polyhedra. The polyhedra are
divided into two classes. The first class of polyhedra, called quasi-regular
tetrahedra, have density at most that of a regular tetrahedron. The polyhedra
in the remaining class have density at most that of a regular octahedron (about
0.7209).


  Several space missions that will use atomic clocks on board of an
Earth-orbiting satellite are planned for the near future, such as the Atomic
Clock Ensemble in Space (ACES) or the Space Optical Clock on the ISS (I-SOC).
The increasing accuracies of the developed clocks and of the links connecting
them with ground stations impose corresponding accuracy requirements for
theoretical models of electromagnetic signal propagation through the atmosphere
of Earth and for the related time and frequency transfer corrections. We
develop a relativistic model of one- and two-way time and frequency transfer.
In addition to the gravitational effects, it also includes the effects of
atmospheric refractivity and atmospheric flows within the relativistic
framework. The model is based on an analytical solution of the equation of
motion of a light ray in spacetime filled with a medium: the null geodesic
equation of Gordon's optical metric. Explicit formulas for one- and two-way
time and frequency transfer corrections are given using realistic fields of the
gravitational potential, the refractive index, and the wind speed, taking
nonstationarity and deviations from spherical symmetry into account. Numerical
examples are provided that focus on two-way ground-to-satellite transfer, with
satellite parameters similar to those of the ISS. The effect of the atmospheric
refractive index increases as the satellite position moves from zenith to
horizon, and it is shown that the effect ranges from 0 ps to 5 ps for two-way
time transfer and from $10^{-17}$ to $10^{-13}$ for two-way frequency transfer.
The effect of the wind contribution is well below 1 ps for the two-way time
transfer for normal atmospheric conditions, but for the two-way frequency
transfer, the effect can be significant: A contribution of $10^{-17}$ is
possible for a horizontal wind field with a velocity magnitude of about 11 m/s.


  It is arguable whether history is made by great men and women or vice versa,
but undoubtably social connections shape history. Analysing Wikipedia, a global
collective memory place, we aim to understand how social links are recorded
across cultures. Starting with the set of biographies in the English Wikipedia
we focus on the networks of links between these biographical articles on the 15
largest language Wikipedias. We detect the most central characters in these
networks and point out culture-related peculiarities. Furthermore, we reveal
remarkable similarities between distinct groups of language Wikipedias and
highlight the shared knowledge about connections between persons across
cultures.


  We present ZoomTouch - a breakthrough technology for multi-user control of
robot from Zoom in real-time by DNN-based gesture recognition. The users from
digital world can have a video conferencing and manipulate the robot to make
the dexterous manipulations with tangible objects. As the scenario, we proposed
the remote COVID-19 test Laboratory to considerably reduce the time to receive
the data and substitute medical assistant working in protective gear in close
proximity with infected cells. The proposed technology suggests a new type of
reality, where multi-users can jointly interact with remote object, e.g. make a
new building design, joint cooking in robotic kitchen, etc, and discuss/modify
the results at the same time.


  The reaction of Sr2CuO3 with air and liquid water was studied to address the
origin of the reported variable Curie-Weiss impurity contribution to the
magnetic susceptibility X of this compound at low temperatures. Sr2CuO3 was
found to decompose upon exposure to either of these environments. The compound
Sr2Cu(OH)6 was identified as the primary reaction product. A pure sample of
Sr2Cu(OH)6 was then prepared separately. Electron paramagnetic resonance (EPR),
isothermal magnetization versus magnetic field M(H) and X versus temperature T
measurements demonstrate that Sr2Cu(OH)6 contains weakly interacting Cu+2
magnetic moments with spin S = 1/2 and average g-factor 2.133. From a fit of
X(T) by the Curie-Weiss law and of the M(H) isotherms by modified Brillouin
functions, the exchange interaction between adjacent Cu+2 spins was found to be
J/kB = -1.06(4)K, a weakly antiferromagnetic interaction. Our results indicate
that the previously reported, strongly sample-dependent, Curie-Weiss
contribution to X(T) of a polycrystalline Sr2CuO3 sample most likely arises
from exposing the sample to air, resulting in a variable amount of paramagnetic
Sr2Cu(OH)6 on the surface of the sample.


  We have measured the proper motion (PM) and kinematics of the ansae in NGC
7009 using high dispersion echelle spectra and archive narrow band HST images.
Assuming that the ansae are moving at equal and opposite velocities from the
central star we obtain a system radial velocity of -53+-2km/s, the eastern ansa
approaching and the western ansa receding at Vr=5.3+-1km/s with respect to this
value. The PM of the eastern ansa is 28+-8mas/yr, which with our weighted
distance to NGC 7009 of 0.86+-0.34kpc gives Vexp=112+-32km/s. The electron
temperature and density in both ansae were determined to be Te=9000+-400K and
Ne=2300+-400/cc. The dynamic age of the ansae is 925+-260yrs.and the implied PM
of the central star is 1+-0.5mas/yr. This is in qualitative but not
quantitative agreement with previous work.


  Our results for the primordial abundance of a massive Majorana tau neutrino
(hep-ph/9603201) published in PRL76, 2848 (1996) were incorrect due to a
trivial (but annoying) error in the numerical code. In this ERRATUM we present
the corrected results.


  We present abstract acceleration techniques for computing loop invariants for
numerical programs with linear assignments and conditionals. Whereas abstract
interpretation techniques typically over-approximate the set of reachable
states iteratively, abstract acceleration captures the effect of the loop with
a single, non-iterative transfer function applied to the initial states at the
loop head. In contrast to previous acceleration techniques, our approach
applies to any linear loop without restrictions. Its novelty lies in the use of
the Jordan normal form decomposition of the loop body to derive symbolic
expressions for the entries of the matrix modeling the effect of n>=0
iterations of the loop. The entries of such a matrix depend on $n$ through
complex polynomial, exponential and trigonometric functions. Therefore, we
introduces an abstract domain for matrices that captures the linear inequality
relations between these complex expressions. This results in an abstract matrix
for describing the fixpoint semantics of the loop.
  Our approach integrates smoothly into standard abstract interpreters and can
handle programs with nested loops and loops containing conditional branches. We
evaluate it over small but complex loops that are commonly found in control
software, comparing it with other tools for computing linear loop invariants.
The loops in our benchmarks typically exhibit polynomial, exponential and
oscillatory behaviors that present challenges to existing approaches. Our
approach finds non-trivial invariants to prove useful bounds on the values of
variables for such loops, clearly outperforming the existing approaches in
terms of precision while exhibiting good performance.


  We present a search for O I in the spectra of nine 4.9 < z_qso < 6.4 QSOs
taken with Keck/HIRES. We detect six systems with N(O I) > 10^13.7 cm^{-2} in
the redshift intervals where O I 1302 falls redward of the Ly-alpha forest.
Four of these lie towards SDSS J1148+5251 (z_qso = 6.42). This imbalance is
unlikely to arise from variations in sensitivity among our data or from a
statistical fluctuation. The excess O I occurs over a redshift interval that
also contains transmission in Ly-alpha and Ly-beta. Therefore, if these O I
systems represent pockets of neutral gas, then they must occur within or near
regions of the IGM that are highly ionized. In contrast, no O I is detected
towards SDSS J1030+0524 (z_qso = 6.30), whose spectrum shows complete
absorption in Ly-alpha and Ly-beta over \Delta z ~ 0.2. Assuming no ionization
corrections, we measure mean abundance ratios <[O/Si]> = -0.04 +/- 0.06,
<[C/O]> = -0.31 +/- 0.09, and <[C/Si]> = -0.34 +/- 0.07 (2 sigma), which are
consistent with enrichment dominated by Type II supernovae. The O/Si ratio
limits the fraction of silicon in these systems contributed by metal-free very
massive stars to < 30%, a result which is insensitive to ionization
corrections. The ionic comoving mass densities along the z_qso > 6.2
sightlines, including only the detected systems, are \Omega(O I) = (7.0 +/-
0.6) * 10^{-8}, \Omega(Si II) = (9.6 +/- 0.9) * 10^{-9}, and \Omega(C II) =
(1.5 +/- 0.2) * 10^{-8}.


  The theory of forbidden 0-1 matrices generalizes Turan-style (bipartite)
subgraph avoidance, Davenport-Schinzel theory, and Zarankiewicz-type problems,
and has been influential in many areas, such as discrete and computational
geometry, the analysis of self-adjusting data structures, and the development
of the graph parameter twin width.
  The foremost open problems in this area is to resolve the Pach-Tardos
conjecture from 2005, which states that if a forbidden pattern
$P\in\{0,1\}^{k\times l}$ is the bipartite incidence matrix of an acyclic graph
(forest), then $\mathrm{Ex}(P,n) = O(n\log^{C_P} n)$, where $C_P$ is a constant
depending only on $P$. This conjecture has been confirmed on many small
patterns, specifically all $P$ with weight at most 5, and all but two with
weight 6.
  The main result of this paper is a clean refutation of the Pach-Tardos
conjecture. Specifically, we prove that $\mathrm{Ex}(S_0,n),\mathrm{Ex}(S_1,n)
\geq n2^{\Omega(\sqrt{\log n})}$, where $S_0,S_1$ are the outstanding weight-6
patterns. We also prove sharp bounds on the entire class of alternating
patterns $(P_t)$, specifically that for every $t\geq 2$,
$\mathrm{Ex}(P_t,n)=\Theta(n(\log n/\log\log n)^t)$. This is the first proof of
an asymptotically sharp bound that is $\omega(n\log n)$.


  Open domain dialog systems face the challenge of being repetitive and
producing generic responses. In this paper, we demonstrate that by conditioning
the response generation on interpretable discrete dialog attributes and
composed attributes, it helps improve the model perplexity and results in
diverse and interesting non-redundant responses. We propose to formulate the
dialog attribute prediction as a reinforcement learning (RL) problem and use
policy gradients methods to optimize utterance generation using long-term
rewards. Unlike existing RL approaches which formulate the token prediction as
a policy, our method reduces the complexity of the policy optimization by
limiting the action space to dialog attributes, thereby making the policy
optimization more practical and sample efficient. We demonstrate this with
experimental and human evaluations.


  We study the effect of supersymmetric contributions to the effective quark
transition $b\to s\gamma\gamma$, including leading order QCD effects. We apply
the discussion to the decay $B_s\to\gamma\gamma$. Even though one-particle
irreducible contributions could play a role, numerical cancelations make the
amplitude for the two-photon emission strongly correlated to the $b\to s\gamma$
amplitude which is sharply constrained by experiment. A quite general statement
follows: as long as non-standard physics effects appear only in the matching of
the Wilson coefficients of the standard effective operator basis, the
deviations from the standard model expectations of the decay rates induced by
$b\to s\gamma\gamma$ are bound to follow closely the corresponding deviations
on $b\to s\gamma$. Effects of new physics are therefore bound to be small.


  Which relativistic field theories give rise to Kepler dynamics in the
two-body problem? We consider a class of Hamiltonians that is the unique
relativistic extension of the Kepler problem preserving its so(4) algebra, and
have orbits related through time reparametrisation to orbits of the original
Kepler problem. For three explicit examples, we give a natural interpretation
in terms of spin-0,-1 and -2 interacting field theories in 5D. These are
organically connected via the classical double copy, which therefore preserves
maximal superintegrability.


  We construct some example of a closed nondegenerate nonflexible polyhedron
$P$ in Euclidean 3-space that is the limit of a sequence of nondegenerate
flexible polyhedra each of which is combinatorially equivalent to $P$. This
implies that the set of flexible nondegenerate polyhedra combinatorially
equivalent to $P$ is not algebraic.


  Given a connected region in two-dimensional space where events of a certain
kind occur according to a certain time-varying density, we consider the problem
of setting up a network of autonomous mobile agents to detect the occurrence of
those events and possibly record them in as effective a manner as possible. We
assume that agents can communicate with one another wirelessly within a fixed
communication radius, and moreover that initially no agent has any information
regarding the event density. We introduce a new distributed algorithm for agent
control based on the notion of an execution mode, which essentially lets each
agent roam the target region either at random or following its local view of a
density-dependent gradient. Agents can switch back and forth between the two
modes, and the precise manner of such changes depends on the setting of various
parameters that can be adjusted as a function of the application at hand. We
provide simulation results on some synthetic applications especially designed
to highlight the algorithm's behavior relative to the possible execution modes.


  Channel pruning is one of the predominant approaches for accelerating deep
neural networks. Most existing pruning methods either train from scratch with a
sparsity inducing term such as group lasso, or prune redundant channels in a
pretrained network and then fine tune the network. Both strategies suffer from
some limitations: the use of group lasso is computationally expensive,
difficult to converge and often suffers from worse behavior due to the
regularization bias. The methods that start with a pretrained network either
prune channels uniformly across the layers or prune channels based on the basic
statistics of the network parameters. These approaches either ignore the fact
that some CNN layers are more redundant than others or fail to adequately
identify the level of redundancy in different layers. In this work, we
investigate a simple-yet-effective method for pruning channels based on a
computationally light-weight yet effective data driven optimization step that
discovers the necessary width per layer. Experiments conducted on ILSVRC-$12$
confirm effectiveness of our approach. With non-uniform pruning across the
layers on ResNet-$50$, we are able to match the FLOP reduction of
state-of-the-art channel pruning results while achieving a $0.98\%$ higher
accuracy. Further, we show that our pruned ResNet-$50$ network outperforms
ResNet-$34$ and ResNet-$18$ networks, and that our pruned ResNet-$101$
outperforms ResNet-$50$.


  The Internet has been instrumental in connecting under-represented and
vulnerable groups of people. Platforms built to foster social interaction and
engagement have enabled historically disenfranchised groups to have a voice.
One such vulnerable group is women. In this paper, we explore the diversity in
online women's ideological spaces using a multi-dimensional approach. We
perform a large-scale, data-driven analysis of over 6M Reddit comments and
submissions from 14 subreddits. We elicit a diverse taxonomy of online women's
ideological spaces, ranging from counterparts to the so-called Manosphere to
Gender-Critical Feminism. We then perform content analysis, finding meaningful
differences across topics and communities. Finally, we shed light on two
platforms, ovarit.com and thepinkpill.co, where two toxic communities of online
women's ideological spaces (Gender-Critical Feminism and Femcels) migrated
after their ban on Reddit.


  We explicitly construct N=4 worldline supersymmetric minimal off-shell
actions for five options of 1/2 partial spontaneous breaking of $N=8, d=1$
Poincar\'e supersymmetry. We demonstrate that the action for the N=4 Goldstone
supermultiplet with four fermions and four auxiliary components is a universal
one. The remaining actions for the Goldstone supermultiplets with physical
bosons are obtained from the universal one by off-shell duality
transformations.


  This paper presents a measurement of the double-differential cross section
for the Drell-Yan $Z/\gamma^{*} \rightarrow \ell^{+} \ell^{-}$ and
photon-induced $\gamma\gamma \rightarrow \ell^{+} \ell^{-}$ processes where
$\ell$ is an electron or muon. The measurement is performed for invariant
masses of the lepton pairs, $m_{\ell\ell}$, between 116 GeV and 1500 GeV, using
a sample of 20.3 fb$^{-1}$ of pp collisions data at centre-of-mass energy of
$\sqrt{s}$ = 8 TeV collected by the ATLAS detector at the LHC in 2012. The data
are presented double differentially in invariant mass and absolute dilepton
rapidity as well as in invariant mass and absolute pseudorapidity separation of
the lepton pair. The single-differential cross section as a function of
$m_{\ell\ell}$ is also reported. The electron and muon channel measurements are
combined and a total experimental precision of better than 1% is achieved at
low $m_{\ell\ell}$. A comparison to next-to-next-to-leading order perturbative
QCD predictions using several recent parton distribution functions and
including next-to-leading order electroweak effects indicates the potential of
the data to constrain parton distribution functions. In particular, a large
impact of the data on the photon PDF is demonstrated.


  The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed
significant health challenges worldwide. While respiratory symptoms have been
the primary focus, emerging evidence has highlighted the impact of COVID-19 on
various organ systems, including the gastrointestinal (GI) tract. This study,
based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and
2023, investigates the prevalence and patterns of GI symptoms in individuals
recovering from COVID-19 and leverages machine learning algorithms to identify
predictive factors for these symptoms. The research findings reveal that a
notable percentage of post-COVID-19 patients experience GI symptoms during
their recovery phase. Diarrhea emerged as the most frequently reported symptom,
followed by abdominal pain and nausea. Machine learning analysis uncovered
significant predictive factors for GI symptoms, including age, gender, disease
severity, comorbidities, and the duration of COVID-19 illness. These findings
underscore the importance of monitoring and addressing GI symptoms in
post-COVID-19 care, with machine learning offering valuable tools for early
identification and personalized intervention. This study contributes to the
understanding of the long-term consequences of COVID-19 on GI health and
emphasizes the potential benefits of utilizing machine learning-driven analysis
in predicting and managing these symptoms. Further research is warranted to
delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to
develop targeted interventions for symptom management. Keywords: COVID-19,
gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19
care, long COVID.


  We extend the Vaidya radiating metric to include both a radiation field and a
string fluid. Assuming diffusive transport for the string fluid, we find new
analytic solutions of Einstein's field equations. Our new solutions represent
an extention of Xanthopoulos superposition.


  A simple approximation to shell model is proposed in which the low energy
excitation spectra corresponds to the identical nucleons occupying the same
single particle states where they preferred to form pairs for the ground
states. We call this approximation as nonzero angular momentum pairing shell
model. It not only reduces the dimensionality of the shell model but also
matches the number of low energy levels in experimental spectra for few cases
where exact shell model predicts many more states. The special focus has been
done to consider the realistic interaction derived from free nucleon-nucleon
scattering data to cope with the experimental spectra. The proposed
approximation to shell model has been applied to calculate the energy spectra
of O18 and Ni58 nuclei where only two neutrons occupy the valence states
outside the core. When compared with the experimental data, the results are
found to be encouraging. It is expected that results will be more pronounced if
the even-even nuclei with higher number of valence nucleons are considered.


  Tools for quadrotor trajectory design have enabled single videographers to
create complex aerial video shots that previously required dedicated hardware
and several operators. We build on this prior work by studying film-maker's
working practices which informed a system design that brings expert workflows
closer to end-users. For this purpose, we propose WYFIWYG, a new quadrotor
camera tool which (i) allows to design a video solely via specifying its
frames, (ii) encourages the exploration of the scene prior to filming and (iii)
allows to continuously frame a camera target according to compositional
intentions. Furthermore, we propose extensions to an existing algorithm,
generating more intuitive angular camera motions and producing spatially and
temporally smooth trajectories. Finally, we conduct a user study where we
evaluate how end-users work with current videography tools. We conclude by
summarizing the findings of work as implications for the design of UIs and
algorithms of quadrotor camera tools.


  In this work, we optically trapping microparticles with higher order Frozen
Wave using holographic optical tweezers. Frozen Waves are diffraction resistant
optical beams, obtained by superposing copropagating Bessel beams with the same
frequency and order, obtaining efficient modeling of its shape. Based on this,
we developed a holographic optical tweezers system for the generation of Frozen
Waves and with this, it was possible to create traps in a stable way for the
trapping and guiding of the microparticles in the transverse plane. The
experimental results show that it is possible to obtain excellent stability
condition for optical trapping using higher order Frozen Waves. These results
indicate that the Frozen Waves is promising for optical trapping and guiding of
particles, which may be useful in various application such as biological
research, atomic physics and optical manipulations using structured light with
orbital angular momentum.


  We propose an experimental setup to efficiently measure the dynamic structure
factor of ultracold quantum gases. Our method uses the interaction of the
trapped atomic system with two different cavity modes, which are driven by
external laser fields. By measuring the output fields of the cavity the dynamic
structure factor of the atomic system can be determined. Contrary to previous
approaches the atomic system is not destroyed during the measurement process.


  In this work, we detail a novel application of inverse design and advanced
manufacturing to rapidly develop and experimentally validate modifications to a
shaped charge jet analogue. The shaped charge jet analogue comprises a conical
copper liner, high explosive (HE), and silicone buffer. We apply a genetic
algorithm to determine an optimal buffer design that can be placed between the
liner and the HE that results in the largest possible change in jet velocity.
The use of a genetic algorithm allows for discoveries of unintuitive, complex,
yet optimal buffer designs. Experiments using the optimal design verified the
effectiveness of the buffer and validated the modeling.


  We discuss the prospects for parity non-conservation experiments with highly
charged heavy ions. Energy levels and parity mixing for heavy ions with two to
five electrons are calculated. We investigate two-photon-transitions and the
possibility to observe interference effects between weak-matrix elements and
Stark matrix elements for periodic electric field configurations.


  Magnetic plasma instabilities appear to play an important role in the early
stages of quark-gluon plasma equilibration in the high energy (weak coupling)
limit. Numerical studies of the growth of such instabilities from small seed
fluctuations have found initial exponential growth in their energy, followed by
linear growth once the associated color magnetic fields become so large that
their non-abelian interaction are non-perturbative. In this paper, we use
simulations to determine the nature of this linear energy growth. We find that
the long-wavelength modes associated with the instability have ceased to grow,
but that they cascade energy towards the ultraviolet in the form of plasmon
excitations of ever increasing energy. We find a quasi-steady-state power-law
distribution $f_k \propto k^{-\nu}$ for this cascade, with spectral index $\nu
\simeq 2$.


  The extension of the general relativity theory to higher dimensions, so that
the field equations for the metric remain of second order, is done through the
Lovelock action. This action can also be interpreted as the dimensionally
continued Euler characteristics of lower dimensions. The theory has many
constant coefficients apparently without any physical meaning. However, it is
possible, in a natural way, to reduce to two (the cosmological and Newton's
constant) these several arbitrary coefficients, yielding a restricted Lovelock
gravity. In this process one separates theories in even dimensions from
theories in odd dimensions. These theories have static black hole solutions. In
general relativity, black holes appear as the final state of gravitational
collapse. In this work, gravitational collapse of a regular dust fluid in even
dimensional restricted Lovelock gravity is studied. It is found that black
holes emerge as the final state for these regular initial conditions.


  The cubic $Ia\bar{3}$ (BC8) and tetragonal $P4_32_12$ (ST12) high pressure
modifications of Si and Ge are attractive candidates for applications in
optoelectronic, thermoelectric or plasmonic devices. Si$_x$Ge$_{1-x}$ alloys in
BC8/ST12 modifications could help overcome the indirect and narrow band gaps of
the pure phases and enable tailoring for specific use-cases. Such alloys have
experimentally been found to be stable at ambient conditions after release from
high pressure synthesis, however their fundamental properties are not known. In
this work, we employ {\it ab initio} calculations based on density functional
theory (DFT) to investigate the electronic properties of these compounds as a
function of composition $x$. We obtain the effective band structures of
intermediate alloys by constructing special quasi random structures (SQS) and
unfolding their band structure to the corresponding primitive cell.
Furthermore, we show that the indirect band gap of the ST12 Ge end-member can
be tuned to become direct at $x_\text{Si} \approx 0.16$. Finally, our
investigations also demonstrate that the BC8 modification, on the other hand,
is insensitive to compositional changes and is a narrow direct band gap
semiconductor only in the case of pure Si.


  This paper is on the automated driving architecture and operation of a light
commercial vehicle. Simple longitudinal and lateral dynamic models of the
vehicle and a more detailed CarSim model are developed and used in simulations
and controller design and evaluation. Experimental validation is used to make
sure that the models used represent the actual response of the vehicle as
closely as possible. The vehicle is made drive-by-wire by interfacing with the
existing throttle-by-wire, by adding an active vacuum booster for brake-by-wire
and by adding a steering actuator for steer-by-wire operation. Vehicle
localization is achieved by using a GPS sensor integrated with six axes IMU
with a built-in INS algorithm and a digital compass for heading information.
Front looking radar, lidar and camera are used for environmental sensing.
Communication with the road infrastructure and other vehicles is made possible
by a vehicle to vehicle communication modem. A dedicated computer under real
time Linux is used to collect, process and distribute sensor information. A
dSPACE MicroAutoBox is used for drive-by-wire controls. CACC based longitudinal
control and path tracking of a map of GPS waypoints are used to present the
operation of this automated driving vehicle.


  Splitting of the nuclei of comets into multiple components has been
frequently observed but, to date, no main-belt asteroid has been observed to
break-up. Using the Hubble Space Telescope, we find that main-belt asteroid
P/2013 R3 consists of 10 or more distinct components, the largest up to 200 m
in radius (assumed geometric albedo of 0.05) each of which produces a coma and
comet-like dust tail. A diffuse debris cloud with total mass roughly 2x10^8 kg
further envelopes the entire system. The velocity dispersion among the
components is about V = 0.2 to 0.5 m/s, is comparable to the gravitational
escape speeds of the largest members, while their extrapolated plane-of-sky
motions suggest break-up between February and September 2013. The broadband
optical colors are those of a C-type asteroid. We find no spectral evidence for
gaseous emission, placing model-dependent upper limits to the water production
rate near 1 kg/s. Breakup may be due to a rotationally induced structural
failure of the precursor body.


  We discuss the crossover from the quantum critical, $z\!=\!1$, to the quantum
disordered regime in high-T$_c$ materials in relation to the experimental data
on the nuclear relaxation, bulk susceptibility, and inelastic neutron
scattering. In our scenario, the spin excitations develop a gap
$\Delta\!\sim\!1/\xi$ well above T$_c$, which is supplemented by the
quasiparticle gap below T$_c$. The above experiments yield consistent estimates
for the value of the spin gap, which increases as the correlation length
decreases.


  This paper addresses the problem of object recognition given a set of images
as input (e.g., multiple camera sources and video frames). Convolutional neural
network (CNN)-based frameworks do not exploit these sets effectively,
processing a pattern as observed, not capturing the underlying feature
distribution as it does not consider the variance of images in the set. To
address this issue, we propose the Grassmannian learning mutual subspace method
(G-LMSM), a NN layer embedded on top of CNNs as a classifier, that can process
image sets more effectively and can be trained in an end-to-end manner. The
image set is represented by a low-dimensional input subspace; and this input
subspace is matched with reference subspaces by a similarity of their canonical
angles, an interpretable and easy to compute metric. The key idea of G-LMSM is
that the reference subspaces are learned as points on the Grassmann manifold,
optimized with Riemannian stochastic gradient descent. This learning is stable,
efficient and theoretically well-grounded. We demonstrate the effectiveness of
our proposed method on hand shape recognition, face identification, and facial
emotion recognition.


  The past decade has seen a tremendous effort toward unraveling the
relationship between entanglement and emergent spacetime. These investigations
have revealed that entanglement between holographic degrees of freedom is
crucial for the existence of bulk spacetime. We examine this connection from
the other end of the entanglement spectrum and clarify the assertion that
maximally entangled states have no reconstructable spacetime. To do so, we
first define the conditions for bulk reconstructability. Under these terms, we
scrutinize two cases of maximally entangled holographic states. One is the
familiar example of AdS black holes; these are dual to thermal states of the
boundary CFT. Sending the temperature to the cutoff scale makes the state
maximally entangled and the respective black hole consumes the spacetime. We
then examine the de Sitter limit of FRW spacetimes. This limit is maximally
entangled if one formulates the boundary theory on the holographic screen.
Paralleling the AdS black hole, we find the resulting reconstructable region of
spacetime vanishes. Motivated by these results, we prove a theorem showing that
maximally entangled states have no reconstructable spacetime. Evidently, the
emergence of spacetime is endemic to intermediate entanglement. By studying the
manner in which intermediate entanglement is achieved, we uncover important
properties about the boundary theory of FRW spacetimes. With this clarified
understanding, our final discussion elucidates the natural way in which
holographic Hilbert spaces may house states dual to different geometries. This
paper provides a coherent picture clarifying the link between spacetime and
entanglement and develops many promising avenues of further work.


  A propagation method for time-dependent Schr\"odinger equations with an
explicitly time-dependent Hamiltonian is developed where time ordering is
achieved iteratively. The explicit time-dependence of the time-dependent
Schr\"odinger equation is rewritten as an inhomogeneous term. At each step of
the iteration, the resulting inhomogeneous Schr\"odinger equation is solved
with the Chebychev propagation scheme presented in J. Chem. Phys. 130, 124108
(2009). The iteratively time-ordering Chebychev propagator is shown to be
robust, efficient and accurate and compares very favorably to all other
available propagation schemes.


  This modeling work investigates the electrical modulation characteristics of
field-effect gated nanopores. Highly nonlinear current modulations are observed
in nanopores with non-overlapping electric double layers, including those with
pore diameters 100 times the Debye screening length. We attribute this extended
field-effect gating to a descreening effect, i.e. the counter-ions do not fully
relax to screen the gating potential due to the presence of strong ionic
transport.


  The Seebeck coefficient plays a fundamental role in identifying the
efficiency of a thermoelectric device. Its theoretical evaluation for atomistic
models is routinely based on Density Functional Theory calculations combined
with the Landauer-B\"uttiker approach to quantum transport. This combination,
however, suffers from serious drawbacks for devices in the Coulomb blockade
regime. We show how to cure the theory through a simple correction in terms of
the {\em temperature derivative} of the exchange correlation potential. Our
results compare well with both rate equations and experimental findings on
carbon nanotubes.


  Examined in this paper, is the Gray and Wyner achievable lossy rate region
for a tuple of correlated multivariate Gaussian random variables (RVs) $X_1 :
\Omega \rightarrow {\mathbb R}^{p_1}$ and $X_2 : \Omega \rightarrow {\mathbb
R}^{p_2}$ with respect to square-error distortions at the two decoders. It is
shown that among all joint distributions induced by a triple of RVs $(X_1,X_2,
W)$, such that $W : \Omega \rightarrow {\mathbb W} $ is the auxiliary RV taking
continuous, countable, or finite values, the Gray and Wyner achievable rate
region is characterized by jointly Gaussian RVs $(X_1,X_2, W)$ such that $W $
is an $n$-dimensional Gaussian RV. It then follows that the achievable rate
region is parametrized by the three conditional covariances $Q_{X_1,X_2|W},
Q_{X_1|W}, Q_{X_2|W}$ of the jointly Gaussian RVs. Furthermore, if the RV $W$
makes $X_1$ and $X_2$ conditionally independent, then the corresponding subset
of the achievable rate region, is simpler, and parametrized by only the two
conditional covariances $Q_{X_1|W}, Q_{X_2|W}$. The paper also includes the
characterization of the Pangloss plane of the Gray-Wyner rate region along with
the characterizations of the corresponding rate distortion functions, their
test-channel distributions, and structural properties of the realizations which
induce these distributions.


  Quantum networks offer a unifying set of opportunities and challenges across
exciting intellectual and technical frontiers, including for quantum
computation, communication, and metrology. The realization of quantum networks
composed of many nodes and channels requires new scientific capabilities for
the generation and characterization of quantum coherence and entanglement.
Fundamental to this endeavor are quantum interconnects that convert quantum
states from one physical system to those of another in a reversible fashion.
Such quantum connectivity for networks can be achieved by optical interactions
of single photons and atoms, thereby enabling entanglement distribution and
quantum teleportation between nodes.


  Consider an independent site percolation model on $\Z^d$, with parameter $p
\in (0,1)$, where all long range connections in the axes directions are
allowed. In this work we show that given any parameter $p$, there exists and
integer $K(p)$ such that all binary sequences (words) $\xi \in \{0,1\}^{\N}$
can be seen simultaneously, almost surely, even if all connections whose length
is bigger than $K(p)$ are suppressed. We also show some results concerning the
question how $K(p)$ should scale with $p$ when $p$ goes to zero. Related
results are also obtained for the question of whether or not almost all words
are seen.


  Motivated by the initial stages of high-energy heavy-ion collisions, we study
excitations of far-from-equilibrium 2+1 dimensional gauge theories using
classical-statistical lattice simulations. We evolve field perturbations over a
strongly overoccupied background undergoing self-similar evolution. While in
3+1D the excitations are described by hard-thermal loop theory, their structure
in 2+1D is nontrivial and nonperturbative. These nonperturbative interactions
lead to broad excitation peaks in spectral and statistical correlation
functions. Their width is comparable to the frequency of soft excitations,
demonstrating the absence of soft quasiparticles in these theories. Our results
also suggest that excitations at higher momenta are sufficiently long-lived,
such that an effective kinetic theory description for 2+1 dimensional
Glasma-like systems may exist, but its collision kernel must be
nonperturbatively determined.


  We interpret the 750-760 GeV diphoton resonance as one or more of the
spinless components of a singlet superfield arising from the three
27-dimensional representations of $E_6$ in F-theory, which also contain three
copies of colour-triplet charge $\mp 1/3$ vector-like fermions $D_i,\bar{D}_i$
and inert Higgs doublets to which the singlets may couple. For definiteness we
consider (without change) a model that was proposed some time ago which
contains such states, as well as bulk exotics, leading to gauge coupling
unification. The smoking gun prediction of the model is the existence of other
similar spinless resonances, possibly close in mass to 750-760 GeV, decaying
into diphotons, as well as the three families of vector-like fermions
$D_i,\bar{D}_i$.


  An amplitude analysis of $B^-\to J/\psi\Lambda\bar{p}$ decays is performed
using about 4400 signal candidates selected on a data sample of $pp$ collisions
recorded at center-of-mass energies of 7, 8 and 13 TeV with the LHCb detector,
corresponding to an integrated luminosity of 9 fb$^{-1}$. A narrow resonance in
the $J/\psi\Lambda$ system, consistent with a pentaquark candidate with
strangeness, is observed with high significance. The mass and the width of this
new state are measured to be $4338.2\pm 0.7\pm 0.4$MeV and
${7.0\pm1.2\pm1.3}$MeV, where the first uncertainty is statistical and the
second systematic. The spin is determined to be $1/2$ and negative parity is
preferred. Due to the small $Q$-value of the reaction, the most precise single
measurement of the $B^-$ mass to date, $5279.44\pm0.05\pm0.07$MeV, is obtained.


  For a Tychonoff space $X$ and a family $\lambda$ of subsets of $X$, we denote
by $C_{\lambda}(X)$ the $T_1$-space of all real-valued continuous functions on
$X$ with the $\lambda$ -open topology. A topological space is productively
Lindel\"of if its product with every Lindel\"of space is Lindel\"of. A space is
indestructibly productively Lindel\"of if it is productively Lindel\"of in any
extension by countably closed forcing. In this paper, we study indestructibly
productively Lindel\"of and Menger function space $C_{\lambda}(X)$.


  The assurance of real-time properties is prone to context variability.
Providing such assurance at design time would require to check all the possible
context and system variations or to predict which one will be actually used.
Both cases are not viable in practice since there are too many possibilities to
foresee. Moreover, the knowledge required to fully provide the assurance for
self-adaptive systems is only available at runtime and therefore difficult to
predict at early development stages. Despite all the efforts on assurances for
self-adaptive systems at design or runtime, there is still a gap on verifying
and validating real-time constraints accounting for context variability. To
fill this gap, we propose a method to provide assurance of self-adaptive
systems, at design- and runtime, with special focus on real-time constraints.
We combine off-line requirements elicitation and model checking with on-line
data collection and data mining to guarantee the system's goals, both
functional and non-functional, with fine tuning of the adaptation policies
towards the optimization of quality attributes. We experimentally evaluate our
method on a simulated prototype of a Body Sensor Network system (BSN)
implemented in OpenDaVINCI. The results of the validation are promising and
show that our method is effective in providing evidence that support the
provision of assurance.


  The incidence rates of melanoma, the deadliest form of skin cancer, have been
increasing steadily worldwide, presenting a significant challenge to
dermatologists. Early detection of melanoma is crucial for improving patient
survival rates, but identifying suspicious lesions through ugly duckling (UD)
screening, the current method used for skin cancer screening, can be
challenging and often requires expertise in pigmented lesions. To address these
challenges and improve patient outcomes, an artificial intelligence (AI)
decision support tool was developed to assist dermatologists in identifying UD
from wide-field patient images. The tool uses a state-of-the-art object
detection algorithm to identify and extract all skin lesions from patient
images, which are then sorted by suspiciousness using a self-supervised AI
algorithm. A clinical validation study was conducted to evaluate the tool's
performance, which demonstrated an average sensitivity of 93% for the top-10
AI-identified UDs on skin lesions selected by the majority of experts in
pigmented skin lesions. The study also found that dermatologists confidence
increased, and the average majority agreement with the top-10 AI-identified UDs
improved to 100% when assisted by AI. The development of this AI decision
support tool aims to address the shortage of specialists, enable at-risk
patients to receive faster consultations and understand the impact of
AI-assisted screening. The tool's automation can assist dermatologists in
identifying suspicious lesions and provide a more objective assessment,
reducing subjectivity in the screening process. The future steps for this
project include expanding the dataset to include histologically confirmed
melanoma cases and increasing the number of participants for clinical
validation to strengthen the tool's reliability and adapt it for real-world
consultation.


  This paper addresses a significant gap in Autonomous Cyber Operations (ACO)
literature: the absence of effective edge-blocking ACO strategies in dynamic,
real-world networks. It specifically targets the cybersecurity vulnerabilities
of organizational Active Directory (AD) systems. Unlike the existing literature
on edge-blocking defenses which considers AD systems as static entities, our
study counters this by recognizing their dynamic nature and developing advanced
edge-blocking defenses through a Stackelberg game model between attacker and
defender. We devise a Reinforcement Learning (RL)-based attack strategy and an
RL-assisted Evolutionary Diversity Optimization-based defense strategy, where
the attacker and defender improve each other strategy via parallel gameplay. To
address the computational challenges of training attacker-defender strategies
on numerous dynamic AD graphs, we propose an RL Training Facilitator that
prunes environments and neural networks to eliminate irrelevant elements,
enabling efficient and scalable training for large graphs. We extensively train
the attacker strategy, as a sophisticated attacker model is essential for a
robust defense. Our empirical results successfully demonstrate that our
proposed approach enhances defender's proficiency in hardening dynamic AD
graphs while ensuring scalability for large-scale AD.


  In this note we define a process algebra TCP (Truly Concurrent Processes)
which corresponds closely with the automata model of concurrency based on
Span(RGraph), the category of spans of reflexive graphs. In TCP, each process
has a fixed set of interfaces. Actions are allowed to occur simultaneously on
all the interfaces of a process. Asynchrony is modelled by the use of silent
actions. Communication is anonymous: communication between two processes P and
Q is described by an operation which connects some of the ports of P to some of
the ports of Q; and a process can only communicate with other processes via its
interfaces. The model is naturally equipped with a compositional semantics in
terms of the operations in Span(RGraph) introduced in [5], and developed in [6,
7, 10].


  The deployment of unmanned aerial vehicle (UAV) for surveillance and
monitoring gives rise to the confidential information leakage challenge in both
civilian and military environments. The security and covert communication
problems for a pair of terrestrial nodes against UAV surveillance are
considered in this paper. To overcome the information leakage and increase the
transmission reliability, a multi-hop relaying strategy is deployed. We aim to
optimize the throughput by carefully designing the parameters of the multi-hop
network, including the coding rates, transmit power, and required number of
hops. In the secure transmission scenario, the expressions of the connection
probability and secrecy outage probability of an end-to-end path are derived
and the closed-form expressions of the optimal transmit power, transmission and
secrecy rates under a fixed number of hops are obtained. In the covert
communication problem, under the constraints of the detection error rate and
aggregate power, the sub-problem of transmit power allocation is a convex
problem and can be solved numerically. Simulation shows the impact of network
settings on the transmission performance. The trade-off between
secrecy/covertness and efficiency of the multi-hop transmission is discussed
which leads to the existence of the optimal number of hops.


  We report radio frequency (rf) electrical readout of graphene mechanical
resonators. The mechanical motion is actuated and detected directly by using a
vector network analyzer, employing a local gate to minimize parasitic
capacitance. A resist-free doubly clamped sample with resonant frequency ~ 34
MHz, quality factor ~ 10000 at 77 K, and signal-to-background ratio of over 20
dB is demonstrated. In addition to being over two orders of magnitude faster
than the electrical rf mixing method, this technique paves the way for use of
graphene in rf devices such as filters and oscillators.


  Reinforcement learning (RL) typically defines a discount factor as part of
the Markov Decision Process. The discount factor values future rewards by an
exponential scheme that leads to theoretical convergence guarantees of the
Bellman equation. However, evidence from psychology, economics and neuroscience
suggests that humans and animals instead have hyperbolic time-preferences. In
this work we revisit the fundamentals of discounting in RL and bridge this
disconnect by implementing an RL agent that acts via hyperbolic discounting. We
demonstrate that a simple approach approximates hyperbolic discount functions
while still using familiar temporal-difference learning techniques in RL.
Additionally, and independent of hyperbolic discounting, we make a surprising
discovery that simultaneously learning value functions over multiple
time-horizons is an effective auxiliary task which often improves over a strong
value-based RL agent, Rainbow.


  Recently, transformers have shown strong ability as visual feature
extractors, surpassing traditional convolution-based models in various
scenarios. However, the success of vision transformers largely owes to their
capacity to accommodate numerous parameters. As a result, new challenges for
adapting large models to downstream tasks arise. On the one hand, classic
fine-tuning tunes all parameters in a huge model for every task and thus easily
falls into overfitting, leading to inferior performance. On the other hand, on
resource-limited devices, fine-tuning stores a full copy of parameters and thus
is usually impracticable for the shortage of storage space. However, few works
have focused on how to efficiently and effectively transfer knowledge in a
vision transformer. Existing methods did not dive into the properties of visual
features, leading to inferior performance. Moreover, some of them bring heavy
inference cost though benefiting storage. To tackle these problems, we propose
consolidator to modify the pre-trained model with the addition of a small set
of tunable parameters to temporarily store the task-specific knowledge while
freezing the backbone model. Motivated by the success of group-wise
convolution, we adopt grouped connections across the features extracted by
fully connected layers to construct tunable parts in a consolidator. To further
enhance the model's capacity to transfer knowledge under a constrained storage
budget and keep inference efficient, we consolidate the parameters in two
stages: 1. between adaptation and storage, and 2. between loading and
inference. On a series of downstream visual tasks, our consolidator can reach
up to 7.56 better accuracy than full fine-tuning with merely 0.35% parameters,
and outperform state-of-the-art parameter-efficient tuning methods by a clear
margin. Code is available at https://github.com/beyondhtx/Consolidator.


  We introduce a Laplacian separation principle for the the eikonal equation on
Markov chains. As application, we prove an isoperimetric concentration
inequality for Markov chains with non-negative Ollivier curvature. That is,
every single point from the concentration profile yields an estimate for every
point of the isoperimetric estimate. Applying to exponential and Gaussian
concentration, we obtain affirmative answers to two open quesions by Erbar and
Fathi. Moreover, we prove that the modified log-Sobolev constant is at least
the minimal Ollivier Ricci curvature, assuming non-negative Ollivier sectional
curvature, i.e., the Ollivier Ricci curvature when replacing the $\ell_1$ by
the $\ell_\infty$ Wasserstein distance. This settles a recent open Problem by
Pedrotti. We give a simple example showing that non-negative Ollivier sectional
curvature is necessary to obtain a modified log-Sobolev inequality via positive
Ollivier Ricci bound. This provides a counterexample to a conjecture by Peres
and Tetali.


  These are the (twice) extended notes of a set of lectures given at the ``12th
Workshop on Hadronic Interactions'' at the IF/UERJ, Rio de Janeiro (31. 5. - 2.
6. 2000). The lectures aim at introducing essential concepts of instanton
physics, with emphasis on the role of instantons in generating tunneling
amplitudes, vacuum structure, and the induced quark interactions associated
with the axial anomaly. A few examples for the impact of instantons on the
physics of hadrons are also mentioned.


  The X-ray burster and dipper 4U1915-05 (also known as XB1916-053) was
observed 19 times for a total exposure of roughly 140 ks between 1996 February
and October with the proportional counter array (PCA) aboard Rossi X-ray Timing
Explorer (RXTE). We present here the detailed timing study of its X-ray
emission. The source was observed in both a high intensity/soft spectral and a
low intensity/hard spectral state. The 2-20 keV luminosities are about
1.4x10^37 and 3.2x10^36 ergs/s in the two regimes respectively (assuming a
source distance of 9.3 kpc). We confirm that 4U1915-05 is an atoll source based
on its spectral behaviour and its aperiodic variability in the frequency range
4x10^{-3}-128 Hz. 4U1915-05 displays red noise below 1 Hz, but is especially
remarkable for its quasi-periodic variability detected for the first time at
several timescales. We detect low frequency quasi-periodic oscillations
(LFQPOs) between 5 and 80 Hz, as well as high frequency QPOs (HFQPOs) between
200 and 1300 Hz. Both LFQPOs and HFQPOs frequencies positively correlate with
the mass accretion rate inferred from the position of the source on the
color-color diagram. In a narrow range of luminosity, we also detect twin
simultaneous HFQPOs separated by 348 +/- 12 Hz.


  We present the first calculation of the Bayesian evidence for different
prototypical single field inflationary scenarios, including representative
classes of small field and large field models. This approach allows us to
compare inflationary models in a well-defined statistical way and to determine
the current "best model of inflation". The calculation is performed numerically
by interfacing the inflationary code FieldInf with MultiNest. We find that
small field models are currently preferred, while large field models having a
self-interacting potential of power p>4 are strongly disfavoured. The class of
small field models as a whole has posterior odds of approximately 3:1 when
compared with the large field class. The methodology and results presented in
this article are an additional step toward the construction of a full numerical
pipeline to constrain the physics of the early Universe with astrophysical
observations. More accurate data (such as the Planck data) and the techniques
introduced here should allow us to identify conclusively the best inflationary
model.


  The group theoretical description of the three-particle problem provides
successful techniques for the solution of different questions. We present here
a review of this approach.


  In this work, using two scalar fields ($\phi$, $\psi$) coupled to 4+1
dimensional gravity, we construct novel topological brane solutions through an
explicit $U(1)$ symmetry breaking term. The potential of this model is
constructed so that two distinct degenerate vacua in the $\phi$ field exist, in
analogy to the $\phi^{4}$ potential. Therefore, brane solutions appear due to
the vacuum structure of the $\phi$ field. However, the topology and vacuum
structure in the $\psi$ direction depends on the symmetry breaking parameter
$\beta^{2}$, which leads to different types of branes. As a result, one can
interpret the present model as a combination of a $\phi^{4}$ brane with an
auxiliary field, which leads to deviations from the $\phi^{4}$ system with the
brane achieving a richer internal structure. Furthermore, we analyse in detail
the behaviour of the superpotentials, the warp factors, the Ricci and
Kretschmann scalars and the Einstein tensor components. In addition to this, we
explore the stability of the brane in terms of the free parameters of the
model. The analysis presented here complements previous work and is
sufficiently novel to be interesting.


  Standard sequential generation methods assume a pre-specified generation
order, such as text generation methods which generate words from left to right.
In this work, we propose a framework for training models of text generation
that operate in non-monotonic orders; the model directly learns good orders,
without any additional annotation. Our framework operates by generating a word
at an arbitrary position, and then recursively generating words to its left and
then words to its right, yielding a binary tree. Learning is framed as
imitation learning, including a coaching method which moves from imitating an
oracle to reinforcing the policy's own preferences. Experimental results
demonstrate that using the proposed method, it is possible to learn policies
which generate text without pre-specifying a generation order, while achieving
competitive performance with conventional left-to-right generation.


  The dynamical characterization of the Millisecond Pulsar (MSP) parameters is
a key issue in understanding these systems. We present an analytical analysis
of the orbital parameters of binary MSPs with long periods (Porb > 2 d) and
circular (e < 0.1) orbits, produced by an asymmetric kick model imparted during
the Accretion Induced Collapse (AIC) of white dwarfs process. It turns out that
the distribution of orbits peaks up to P_orb;f < 90 d with strong
circularization. Considering the different assumptions about the distribution
of companions He stars 3M< Mcom < 5M, the binary will affect the setups of the
balance condition of minimum energy. Our analytical approach is just the first
approach to the more complete models required for describing all binary
parameters after an asymmetric kick. Therefore, we have also run some numerical
simulations in order to compare their results with the analytical studies. We
aim to initiate the first exploration of the full complexity of the problem,
when combining a variable kick time and a variable kick vector direction.
Indeed, the numerical simulations show patterns resembling the complex behavior
found in chaotic scattering problems. Although we deal with a deterministic
problem and bounded orbits, the regular characteristic orbits are found in more
realistic phases during the AIC process. In addition, the overall process can
show complex behaviors strongly associated with the internal kick mechanisms.
This would lead us to identify the nature of regular orbits and their orbital
morphology.


  The rare-earth ferroelectromagnet YMnO3 consists of weakly coupled triangular
layers of S=2 spins. Below T_N ~ 70 K muon-spin relaxation data show two
oscillatory relaxing signals due to magnetic order, with no relaxing signal
resolvable (which would be expected for different coexisting spin
distributions). The transition temperature T_N increases with applied
hydrostatic pressure, even though the ordered moment decreases. These results
suggest that pressure increases both the exchange coupling between the layers
and the frustration within the layers.


  Parser combinators are a well-known mechanism used for the compositional
construction of parsers, and have shown to be particularly useful in writing
parsers for rich grammars with data-dependencies and global state. Verifying
applications written using them, however, has proven to be challenging in large
part because of the inherently effectful nature of the parsers being composed
and the difficulty in reasoning about the arbitrarily rich data-dependent
semantic actions that can be associated with parsing actions. In this paper, we
address these challenges by defining a parser combinator framework called
Morpheus equipped with abstractions for defining composable effects tailored
for parsing and semantic actions and a rich specification language used to
define safety properties over the constituent parsers comprising a program.
Even though its abstractions yield many of the same expressivity benefits as
other parser combinator systems, Morpheus is carefully engineered to yield a
substantially more tractable automated verification pathway. We demonstrate its
utility in verifying a number of realistic, challenging parsing applications,
including several cases that involve non-trivial data-dependent relations.


  Recent text-to-image (T2I) diffusion models have achieved remarkable progress
in generating high-quality images given text-prompts as input. However, these
models fail to convey appropriate spatial composition specified by a layout
instruction. In this work, we probe into zero-shot grounded T2I generation with
diffusion models, that is, generating images corresponding to the input layout
information without training auxiliary modules or finetuning diffusion models.
We propose a Region and Boundary (R&B) aware cross-attention guidance approach
that gradually modulates the attention maps of diffusion model during
generative process, and assists the model to synthesize images (1) with high
fidelity, (2) highly compatible with textual input, and (3) interpreting layout
instructions accurately. Specifically, we leverage the discrete sampling to
bridge the gap between consecutive attention maps and discrete layout
constraints, and design a region-aware loss to refine the generative layout
during diffusion process. We further propose a boundary-aware loss to
strengthen object discriminability within the corresponding regions.
Experimental results show that our method outperforms existing state-of-the-art
zero-shot grounded T2I generation methods by a large margin both qualitatively
and quantitatively on several benchmarks.


  In this work we combine two distinct machine learning methodologies,
sequential Monte Carlo and Bayesian experimental design, and apply them to the
problem of inferring the dynamical parameters of a quantum system. We design
the algorithm with practicality in mind by including parameters that control
trade-offs between the requirements on computational and experimental
resources. The algorithm can be implemented online (during experimental data
collection), avoiding the need for storage and post-processing. Most
importantly, our algorithm is capable of learning Hamiltonian parameters even
when the parameters change from experiment-to-experiment, and also when
additional noise processes are present and unknown. The algorithm also
numerically estimates the Cramer-Rao lower bound, certifying its own
performance.


  By working with coefficients in $\mathbb{Z}$ or $\mathbb{R}$, one can define
two different notions of stability for a sandpile on a graph. We call a
sandpile immutable when these notions agree. Our main results give
linear-algebraic characterizations for large classes of immutable sandpiles.


  Determining the timeline for the formation of massive YSOs requires the
identification and characterisation of all the phases that a massive forming
YSO undergoes. It is of particular interest to verify the observability of the
phase in which the object is rapidly accreting while not yet igniting the
fusion of hydrogen that marks the arrival on the ZAMS. One of the candidate
prototypical objects for this phase is Mol160/IRAS23385+6053, which previous
studies suggest it could be in a pre-Hot Core stage. We further investigate
this issue by means of Spitzer imaging and spectroscopy in the 5-70 micron
range. The dense core of Mol160/IRAS23385+6053, which up to now had only been
detected at submm and mm wavelenghts has been revealed for the first time at 24
and 70 micron by Spitzer. The complete 24 micron -3.4 mm continuum cannot be
fitted with a standard model of a Zero-Age Main-Sequence (ZAMS) star embedded
in an envelope. A simple greybody fit yields a mass of 220 solar masses. The
luminosity is slightly in excess of 3000 solar luminosities, which is a factor
of 5 less than previous estimates when only IRAS fluxes were available between
20 and 100 micron. The source is under-luminous by the same factor with respect
to UCHII regions or Hot-Cores of similar circumstellar mass, and simple models
show that this is compatible with an earlier evolutionary stage. Spectroscopy
between 5 and 40 microns revelas typical PDR/PIR conditions, where the required
UV illumination may be provided by other sources revealed at 24 microns in the
same region, and which can be plausibly modeled as moderately embedded
intermediate-mass ZAMS stars. Our results strengthen the suggestion that the
central core in Mol160/IRAS23385+6053 is a massive YSO actively accreting from
its circumstellar envelope and which did not yet begin hydrogen fusion.


  The metric of an FRW universe filled with pressureless dust is shown to
agree, close to any spacetime point, with a curved Newtonian-type metric where
Einstein's equations simplify to those of Newtonian gravity. The agreement is
shown to quadratic order in the local coordinates, so the curvatures agree.
This result is established by expressing both metrics in Riemann normal form.
This approach gives a local Newtonian understanding of cosmology that avoids
the paradoxes of global Newtonian cosmology.


  "Cut-out sets" are fractals that can be obtained by removing a sequence of
disjoint regions from an initial region of d-dimensional euclidean space.
Conversely, a description of some fractals in terms of their void complementary
set is possible. The essential property of a sequence of fractal voids is that
their sizes decrease as a power law, that is, they follow Zipf's law. We prove
the relation between the box dimension of the fractal set (in d <= 3) and the
exponent of the Zipf law for convex voids; namely, if the Zipf law exponent e
is such that 1 < e < d/(d-1) and, in addition, we forbid the appearance of
degenerate void shapes, we prove that the corresponding cut-out set has box
dimension d/e (d-1 < d/e < d). We explore the application of this result to the
large scale distribution of matter in cosmology, in connection with ``cosmic
foam'' models.


  Researchers are using deep learning models to explore the emergence of
language in various language games, where agents interact and develop an
emergent language to solve tasks. We focus on the factors that determine the
expressivity of emergent languages, which reflects the amount of information
about input spaces those languages are capable of encoding. We measure the
expressivity of emergent languages based on the generalisation performance
across different games, and demonstrate that the expressivity of emergent
languages is a trade-off between the complexity and unpredictability of the
context those languages emerged from. Another contribution of this work is the
discovery of message type collapse, i.e. the number of unique messages is lower
than that of inputs. We also show that using the contrastive loss proposed by
Chen et al. (2020) can alleviate this problem.


  High-order harmonic generation (HHG) from atoms is a coherent light source
that opens up attosecond physics, but it is the application of HHG to solids
that brings much of excitement for the last decade. Here we report a completely
new kind of harmonics in a quasi-one-dimensional and hexagonal barium titanium
sulfide: Under circularly polarized laser excitation, harmonics are generated
only at first, fifth, seventh and eleventh orders. These magic harmonics appear
only with circularly polarized light, not with linearly polarized light.
Neither cubic nor tetragonal cells have magic harmonics even with circularly
polarized light. Through a careful group-theory analysis, we find that two
subgroups of symmetry operations unique to the hexagonal symmetry cancel out
third and ninth harmonics. This feature presents a rare opportunity to develop
HHG into a crystal-structure characterization tool for phase transitions
between hexagonal and nonhexagonal structures.


  Aims: The present study was conducted to determine the optical extinction
curve for Cerro Paranal under typical clear-sky observing conditions, with an
accuracy of 0.01 mag/airmass. Methods: The extinction curve of Paranal was
obtained through low-resolution spectroscopy of 8 spectrophotometric standard
stars observed with FORS1 mounted at the 8.2 m Very Large Telescope, covering a
spectral range 3300-8000 A. A total of 600 spectra were collected on more than
40 nights distributed over six months, from October 2008 to March 2009. The
average extinction curve was derived using a global fit algorithm, which
allowed us to simultaneously combine all the available data. The main
atmospheric parameters were retrieved using the LBLRTM radiative transfer code,
which was also utilised to study the impact of variability of the main
molecular bands of O2, O3, and H2O, and to estimate their column densities.
Results: In general, the extinction curve of Paranal appears to conform to
those derived for other astronomical sites in the Atacama desert, like La Silla
and Cerro Tololo. However, a systematic deficit with respect to the extinction
curve derived for Cerro Tololo before the El Chichon eruption is detected below
4000 A. We attribute this downturn to a non standard aerosol composition,
probably revealing the presence of volcanic pollutants above the Atacama
desert. An analysis of all spectroscopic extinction curves obtained since 1974
shows that the aerosol composition has been evolving during the last 35 years.
The persistence of traces of non meteorologic haze suggests the effect of
volcanic eruptions, like those of El Chichon and Pinatubo, lasts several
decades. The usage of the standard CTIO and La Silla extinction curves
implemented in IRAF and MIDAS produce systematic over/under-estimates of the
absolute flux.


  We introduce a novel validation framework to measure the true robustness of
learning models for real-world applications by creating source-inclusive and
source-exclusive partitions in a dataset via clustering. We develop a
robustness metric derived from source-aware lower and upper bounds of model
accuracy even when data source labels are not readily available. We clearly
demonstrate that even on a well-explored dataset like MNIST, challenging
training scenarios can be constructed under the proposed assessment framework
for two separate yet equally important applications: i) more rigorous learning
model comparison and ii) dataset adequacy evaluation. In addition, our findings
not only promise a more complete identification of trade-offs between model
complexity, accuracy and robustness but can also help researchers optimize
their efforts in data collection by identifying the less robust and more
challenging class labels.


  We establish bounds on the decay of time-dependent multipoint correlation
functionals of one-dimensional quasi-free fermions in terms of the decay
properties of their two-point function. At a technical level, this is done with
the help of bounds on certain bordered determinants and pfaffians. These
bounds, which we prove, go beyond the well-known Hadamard estimates. Our main
application of these results is a proof of strong (exponential) dynamical
localization of spin-correlation functions in disordered $XY$-spin chains.


  We present a coherently-combined femtosecond fiber CPA system based on a
rod-type, Ytterbium-doped, multicore fiber with 4x4 cores. A high average power
of up to 500 W (after combination and compression) could be achieved at 10 MHz
repetition rate with an excellent beam quality. Additionally, <500 fs pulses
with up to 600 uJ of pulse energy were also realized with this setup. This
architecture is intrinsically power scalable by increasing the number of cores
in the fiber.


  We apply the constructions of "The p-adic Shintani cocycle" to the Shintani
cocycle of Charollois-Dasgupta-Greenberg, obtaining cocycles on arithmetic
subgroups of GL_n(Q$ valued in maps from "deformation vectors" R^n-Q^n to
p-adic measures.


  The Southern Ocean (in the region 60-180$^\circ$E) south of the Indian Ocean,
Australia, and the West Pacific is noted for the frequent occurrence and
severity of its storms. These storms give rise to high-amplitude secondary
microseisms from sources, including the deep ocean regions, and primary
microseisms where the swells impinge on submarine topographic features. A
better understanding of the varying microseism wavefield enables improvements
to seismic imaging and development of proxy observables to complement sparse in
situ wave observations and hindcast models of the global ocean wave climate. We
analyze 12-26 years of seismic data from 11 seismic stations either on the East
Antarctic coast or sited in the Indian Ocean, Australia, and New Zealand. The
power spectral density of the seismic wavefield is calculated to explore how
the time-changing microseism intensity varies with (i) sea ice coverage
surrounding Antarctica and (ii) the Southern Annular Mode (SAM) climate index.
Variations in sea ice extent are found to be the dominant control on the
microseism intensity at Antarctic stations, which exhibit a seasonal pattern
phase-shifted by 4-5 months compared to stations in other continents. Peaks in
extremal intensity at East Antarctic stations occur in March-April, with the
highest peaks for secondary microseisms occurring during negative SAM events.
This relationship between microseism intensity and the SAM index is opposite to
that observed on the Antarctic Peninsula. This work informs the complexity of
microseism amplitudes in the Southern Hemisphere and assists ongoing
interdisciplinary investigations of interannual variability and long-term
trends.


  The Total Internal Reflection Ultrasonic Sensor (TIRUS) is a device
especially aimed at detecting subsurface flaws in a tested specimen based on
the frustrated total internal reflection of bulk ultrasonic waves in the sensor
body [1]. In the fabricated experimental sample of the TIRUS a transducer of
the sensor emitted a slow shear wave with the velocity ~600 m/s (SSW) into the
[110] direction of the TeO2 crystal (Fig. 1) [2]. The sensor design was such
that the displacement vector of the SSW lay in the planes of reflecting
surfaces of the crystal, that is, it was a shear horizontal (SH) wave with
respect to these planes. It was assumed that with such a design no other waves
should exist in the crystal except those polarized as the SSW [3]. The total
internal reflection of a probing ultrasonic wave incident at the interface
between the sensor and a tested object is frustrated if there is a subsurface
flaw in the evanescent field under the interface (Fig. 1, a). As a result, the
reflected wave is affected, and the sensor output changes.


  We study spectra and pseudospectra of certain bounded linear operators on
$\ell^2({\mathbb Z})$. The operators are generally non-normal, and their matrix
representation has a characteristic off-diagonal decay. Based on a result of
Chandler-Wilde, Chonchaiya and Lindner for tridiagonal infinite matrices, we
demonstrate an efficient algorithm for the computation of upper and lower
bounds on the pseudospectrum of operators that are merely norm limits of band
matrices -- the so-called band-dominated operators. After approximation by a
band matrix and fixing a parameter $n\in{\mathbb N}$, one looks at $n$
consecutive columns $\{k+1,...,k+n\}$, $k\in{\mathbb Z}$, of the corresponding
matrix and computes the smallest singular value of that section via QR
factorization. We here propose a QR factorization by a sequence of Givens
rotations in such a way that a large part of the computation can be reused for
the factorization of the next submatrix -- when $k$ is replaced by $k+1$. The
computational cost for the next factorization(s) is ${\mathcal O}(nd)$ as
opposed to a naive implementation with ${\mathcal O}(nd^2)$, where $d$ is the
bandwidth. So our algorithm pays off for large bands, which is attractive when
approximating band-dominated operators with a full (i.e. not banded) matrix.


  Quantum many-body states that frequently appear in physics often obey an
entropy scaling law, meaning that an entanglement entropy of a subsystem can be
expressed as a sum of terms that scale linearly with its volume and area, plus
a correction term that is independent of its size. We conjecture that these
states have an efficient dual description in terms of a set of marginal density
matrices on bounded regions, obeying the same entropy scaling law locally. We
prove a restricted version of this conjecture for translationally invariant
systems in two spatial dimensions. Specifically, we prove that a
translationally invariant marginal obeying three non-linear constraints -- all
of which follow from the entropy scaling law straightforwardly -- must be
consistent with some global state on an infinite lattice. Moreover, we derive a
closed-form expression for the maximum entropy density compatible with those
marginals, deriving a variational upper bound on the thermodynamic free energy.
Our construction's main assumptions are satisfied exactly by solvable models of
topological order and approximately by finite-temperature Gibbs states of
certain quantum spin Hamiltonians.


  Quantum impurity solvers have a broad range of applications in theoretical
studies of strongly correlated electron systems. Especially, they play a key
role in dynamical mean-field theory calculations of correlated lattice models
and realistic materials. Therefore, the development and implementation of
efficient quantum impurity solvers is an important task. In this paper, we
present an open source interacting quantum impurity solver toolkit (dubbed
$i$QIST). This package contains several highly optimized quantum impurity
solvers which are based on the hybridization expansion continuous-time quantum
Monte Carlo algorithm, as well as some essential pre- and post-processing
tools. We first introduce the basic principle of continuous-time quantum Monte
Carlo algorithm and then discuss the implementation details and optimization
strategies. The software framework, major features, and installation procedure
for $i$QIST are also explained. Finally, several simple tutorials are presented
in order to demonstrate the usage and power of $i$QIST.


  In [Li ${\it et \, al.}$, Phys. Rev. A ${\bf 98}$, 020502(R) (2018)] it was
claimed that the model-potential computations of the Lamb shift on the
$^2P_{1/2}-{}^2P_{3/2}$ fine structure in fluorinelike uranium lead to a
discrepancy between theory and experiment. Later, it was reported by [Volotka
${\it et \, al.}$, Phys. Rev. A ${\bf 100}$, 010502(R) (2019)] that {\it ab
initio} QED calculation, including the first-order one-electron QED
contributions and the related effects of two-electron screening, yields the
result which restores the agreement between theory and experiment and strongly
disagrees with the model-potential Lamb shift values. In the present paper, the
model Lamb shift operator [Shabaev ${\it et \, al.}$, Phys. Rev. A ${\bf 88}$,
012513 (2013)] is used to evaluate the QED effects on the
$^2P_{1/2}-{}^2P_{3/2}$ fine structure in F-like ions. The calculations are
performed by incorporating this operator into the Dirac-Coulomb-Breit equation
employing different methods. It is demonstrated that the methods, based on
including the Lamb shift operator either into the Dirac-Fock equations or into
the calculations by perturbation theory, lead to the theoretical results which
are in good agreement with each other and with experiment. The restriction of
these results to the first order in the QED effects leads to a value which
agrees with the aforementioned ${\it ab\, initio}$ QED result.


  The invention of scanning probe microscopy has revolutionized the way
electronic phenomena are visualized. While present-day probes can access a
variety of electronic properties at a single location in space, a scanning
microscope that can directly probe the quantum mechanical existence of an
electron at multiple locations would provide direct access to key quantum
properties of electronic systems, so far unreachable. Here, we demonstrate a
conceptually new type of scanning probe microscope - the Quantum Twisting
Microscope (QTM) - capable of performing local interference experiments at its
tip. The QTM is based on a unique van-der-Waals tip, allowing the creation of
pristine 2D junctions, which provide a multitude of coherently-interfering
paths for an electron to tunnel into a sample. With the addition of a
continuously scanned twist angle between the tip and sample, this microscope
probes electrons in momentum space similar to the way a scanning tunneling
microscope probes electrons in real space. Through a series of experiments, we
demonstrate room temperature quantum coherence at the tip, study the twist
angle evolution of twisted bilayer graphene, directly image the energy bands of
monolayer and twisted bilayer graphene, and finally, apply large local
pressures while visualizing the evolution of the flat energy bands of the
latter. The QTM opens the way for novel classes of experiments on quantum
materials.


  Minkowski Tensors are tensorial generalizations of the scalar Minkowski
Functionals. Due to their tensorial nature they contain additional
morphological information of structures, in particular about shape and
alignment, in comparison to the scalar Minkowski functionals. They have
recently been used [29] to study the statistical isotropy of temperature and E
mode data from the Planck satellite. The calculation in [29] relied on
stereographic projection of the fields to extract the shape and alignment
information. In this work, we calculate Minkowski Tensors directly on the
sphere and compute the net alignment in the data, based on a recent work that
extends the definition of Minkowski Tensors to random fields on curved spaces.
This method circumvents numerical errors that can be introduced by the
stereographic projection. We compare the resulting net alignment parameter
values obtained from the frequency coadded CMB temperature data cleaned by the
SMICA pipeline, to those obtained from simulations that include instrumental
beam effects and residual foreground and noise. We find very good agreement
between the two within $\approx$ 1$\sigma$ . We further compare the alignments
obtained from the beam-convolved CMB maps at individual Planck frequencies to
those in the corresponding simulations. We find no significant difference
between observed data and simulations across all Planck frequencies, except for
the 30 GHz channel. For the 30 GHz channel we find $\approx$ 2$\sigma$
difference between the data and the simulations. This mild disagreement most
likely originates from inaccurate estimation of the instrumental beam at 30
GHz.


  In this paper we propose an existence and uniqueness theory for the solutions
of a system of non-linear hyperbolic conservation laws, structured in age and
maturity variables, representing a tissue environment. In particular we are
interested in the investigation of the role of stem cells in its homeostasis.
The main result presented in this paper is the consistence of the stability
results arising from the analisys of the model we designed with the
experimental observations on which several branches of medicine are currently
attempting to trade on their research activity.


  The present review is devoted to a Chiral Cosmological Model as the
self-gravitating nonlinear sigma model with the potential of (self)interactions
employed in cosmology. The chiral cosmological model has successive
applications in descriptions of the inflationary epoch of the Universe
evolution; the present accelerated expansion of the Universe also can be
described by the chiral fields multiplet as the dark energy in wide sense.
  To be more illustrative we are often addressed to the two-component chiral
cosmological model. Namely, the two-component chiral cosmological model
describing the phantom field with interaction to a canonical scalar field is
analyzed in details. New generalized model of quintom character is proposed and
exact solutions are founded out.
  In the review we represented the perturbation theory for chiral cosmological
model with the aim to describe the structure formation using the progress
achieved in the inflation theory. It was shown that cosmological perturbations
from chiral fields can be decomposed for inflaton and the dark sector
perturbations. The two-component model is investigated in details, the general
solution for shortwave approximation is obtained and analyzed for power law
Universe expansion.
  New issue for understanding the features of Universe evolution is proposed by
consideration of the dark sector fields on the inflaton background. The results
are illustrated for the solutions in the long-wave approximation.


  A polynomial threshold function (PTF) $f:\mathbb{R}^n \rightarrow \mathbb{R}$
is a function of the form $f(x) = \mathsf{sign}(p(x))$ where $p$ is a
polynomial of degree at most $d$. PTFs are a classical and well-studied
complexity class with applications across complexity theory, learning theory,
approximation theory, quantum complexity and more. We address the question of
designing pseudorandom generators (PRG) for polynomial threshold functions
(PTFs) in the gaussian space: design a PRG that takes a seed of few bits of
randomness and outputs a $n$-dimensional vector whose distribution is
indistinguishable from a standard multivariate gaussian by a degree $d$ PTF.
  Our main result is a PRG that takes a seed of $d^{O(1)}\log ( n /
\varepsilon)\log(1/\varepsilon)/\varepsilon^2$ random bits with output that
cannot be distinguished from $n$-dimensional gaussian distribution with
advantage better than $\varepsilon$ by degree $d$ PTFs. The best previous
generator due to O'Donnell, Servedio, and Tan (STOC'20) had a quasi-polynomial
dependence (i.e., seedlength of $d^{O(\log d)}$) in the degree $d$. Along the
way we prove a few nearly-tight structural properties of restrictions of PTFs
that may be of independent interest.


  In bin-picking scenarios, multiple instances of an object of interest are
stacked in a pile randomly, and hence, the instances are inherently subjected
to the challenges: severe occlusion, clutter, and similar-looking distractors.
Most existing methods are, however, for single isolated object instances, while
some recent methods tackle crowd scenarios as post-refinement which accounts
multiple object relations. In this paper, we address recovering 6D poses of
multiple instances in bin-picking scenarios in depth modality by multi-task
learning in deep neural networks. Our architecture jointly learns multiple
sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects;
and joint registration of multiple objects. For training data generation, depth
images of physically plausible object pose configurations are generated by a 3D
object model in a physics simulation, which yields diverse occlusion patterns
to learn. We adopt a state-of-the-art object detector, and 2D offsets are
further estimated via a network to refine misaligned 2D detections. The depth
and 3D pose estimator is designed to generate multiple hypotheses per
detection. This allows the joint registration network to learn occlusion
patterns and remove physically implausible pose hypotheses. We apply our
architecture on both synthetic (our own and Sileane dataset) and real (a public
Bin-Picking dataset) data, showing that it significantly outperforms
state-of-the-art methods by 15-31% in average precision.


  We give a succinct proof of a duality theorem obtained by R\'ev\'esz in 1991
which concerns extremal quantities related to trigonomertic polynomials. The
key tool of our new proof is an intersection formula on dual cones in real
Banach spaces. We show another application of this intersection formula which
is related to the integral estimates of non-negative positive definite
functions.


  It is known that the dual of the general adversary bound can be used to build
quantum query algorithms with optimal complexity. Despite this result, not many
quantum algorithms have been designed this way. This paper shows another
example of such algorithm.
  We use the learning graph technique from arXiv:1105.4024 to give a quantum
algorithm for $k$-distinctness problem that runs in $o(n^{3/4})$ queries, for a
fixed $k$, given some prior knowledge on the structure of the input. The best
known quantum algorithm for the unconditional problem uses $O(n^{k/(k+1)})$
queries.


  It is widely appreciated that surface tension can dominate the behavior of
liquids at small scales. Solids also have surface stresses of a similar
magnitude, but they are usually overlooked. However, recent work has shown that
these can play an central role in the mechanics of soft solids such as gels.
Here, we review this emerging field. We outline the theory of surface stresses,
from both mechanical and thermodynamic perspectives, emphasizing the
relationship between surface stress and surface energy. We describe a wide
range of phenomena at interfaces and contact lines where surface stresses play
an important role. We highlight how surface stresses causes dramatic departures
from classic theories for wetting (Young-Dupr\'{e}), adhesion
(Johnson-Kendall-Roberts), and composites (Eshelby). A common thread is the
importance of the ratio of surface stress to an elastic modulus, which defines
a length scale below which surface stresses can dominate.


  We will describe the relationship between the indecomposable characters of
the finitary symmetric group and its ergodic invariant random subgroups; and we
will interpret each Thoma character as an asymptotic limit of a naturally
associated sequence of characters induced from linear characters of Young
subgroups of finite symmetric groups.


  Based on recent work [L. Machura, M. Kostur, P. Talkner, J. Luczka, and P.
Hanggi, Phys. Rev. Lett. 98, 040601 (2007)], we extend the study of inertial
Brownian motors to the case of an asymmetric potential. It is found that some
transport phenomena appear in the presence of an asymmetric potential. Within
tailored parameter regimes, there exists two optimal values of the load at
which the mean velocity takes its maximum, which means that a load can
facilitate the transport in the two parameter regimes. In addition, the
phenomenon of multiple current reversals can be observed when the load is
increased.


  We report on the site-selected growth of bright single InAsP quantum dots
embedded within InP photonic nanowire waveguides emitting at telecom
wavelengths. We demonstrate a dramatic dependence of the emission rate on both
the emission wavelength and the nanowire diameter. With an appropriately
designed waveguide, tailored to the emission wavelength of the dot, an increase
in count rate by nearly two orders of magnitude (0.4kcps to 35kcps) is obtained
for quantum dots emitting in the telecom O-band. Using
emission-wavelength-optimised waveguides, we demonstrate bright, narrow
linewidth emission from single InAsP quantum dots with an unprecedented tuning
range from 880nm to 1550nm. These results pave the way towards efficient single
photon sources at telecom wavelengths using deterministically grown InAsP/InP
nanowire quantum dots.


  This paper considers a wireless link with randomly arriving data that is
queued and served over a time-varying channel. It is known that any algorithm
that comes within $\epsilon$ of the minimum average power required for queue
stability must incur average queue size at least $\Omega(\log(1/\epsilon))$.
However, the optimal convergence time is unknown, and prior algorithms give
convergence time bounds of $O(1/\epsilon^2)$. This paper develops a scheduling
algorithm that, for any $\epsilon>0$, achieves the optimal
$O(\log(1/\epsilon))$ average queue size tradeoff with an improved convergence
time of $O(\log(1/\epsilon)/\epsilon)$. This is shown to be within a
logarithmic factor of the best possible convergence time. The method uses the
simple drift-plus-penalty technique with an improved convergence time analysis.


  We apply novel, recently developed plasma ray-tracing techniques to model the
propagation of radio photons produced by axion dark matter in neutron star
magnetospheres and combine this with both archival and new data for the
galactic centre magnetar PSR J1745-2900. The emission direction to the observer
and the magnetic orientation are not constrained for this object leading to
parametric uncertainty. Our analysis reveals that ray-tracing greatly reduces
the signal sensitivity to this uncertainty, contrary to previous calculations
where there was no emission at all in some directions. Based on a
Goldreich-Julian model for the magnetosphere and a Navarro-Frank-White model
for axion density in the galactic centre, we obtain the most robust limits on
the axion-photon coupling, to date. These are comparable to those from the CAST
solar axion experiment in the mass range $\sim 4.2-60\,\mu{\rm eV}$. If the
dark matter density is larger, as might predicted by a "spike" model, the
limits could be much stronger. The dark matter density in the region of the
galactic centre is now the biggest uncertainty in these calculations.