  We have conducted a 3-mm molecular-line survey towards 570 high-mass
star-forming clumps, using the Mopra telescope. The sample is selected from the
10,000 clumps identified by the ATLASGAL survey and includes all of the most
important embedded evolutionary stages associated with massive star formation,
classified into five distinct categories (quiescent, protostellar, young
stellar objects, \hii\ regions and photo-dominated regions). The observations
were performed in broadband mode with frequency coverage of 85.2 to 93.4\,GHz
and a velocity resolution of $\sim$0.9\,\kms, detecting emission from 26
different transitions. We find significant evolutionary trends in the detection
rates, integrated line intensities, and abundances of many of the transitions
and also identify a couple of molecules that appear to be invariant to changes
in the dust temperature and evolutionary stage (N$_2$H$^+$\,(1-0) and
HN$^{13}$C\,(1-0)). We use the K-ladders for CH$_3$C$_2$H\,(5-4) and
CH$_3$CH\,(5-4) to calculate the rotation temperatures and find $\sim$1/3 of
the quiescent clumps have rotation temperatures that suggest the presence of an
internal heating source. These sources may constitute a population of very
young protostellar objects that are still dark at 70\,\mum\ and suggest that
the fraction of truly quiescent clumps may only be a few per cent. We also
identify a number of line ratios that show a strong correlation with the
evolutionary stage of the embedded objects and discuss their utility as
diagnostic probes of evolution.


  We consider nonoverlapping domain decomposition methods for the
Rudin--Osher--Fatemi~(ROF) model, which is one of the standard models in
mathematical image processing. The image domain is partitioned into rectangular
subdomains and local problems in subdomains are solved in parallel. Local
problems can adopt existing state-of-the-art solvers for the ROF model. We show
that the nonoverlapping relaxed block Jacobi method for a dual formulation of
the ROF model has the $O(1/n)$ convergence rate of the energy functional, where
$n$ is the number of iterations. Moreover, by exploiting the forward-backward
splitting structure of the method, we propose an accelerated version whose
convergence rate is $O(1/n^2)$. The proposed method converges faster than
existing domain decomposition methods both theoretically and practically, while
the main computational cost of each iteration remains the same. We also provide
the dependence of the convergence rates of the block Jacobi methods on the
image size and the number of subdomains. Numerical results for comparisons with
existing methods are presented.


  Silicon photodetectors operating at near-infrared wavelengths with high-speed
and high sensitivity are becoming critical for emerging applications, such as
Light Detection and Ranging Systems (LIDAR), quantum communications, and
medical imaging. However, such photodetectors present a bandwidth-absorption
trade-off at those wavelengths that have limited their implementation. Photon
trapping structures address this trade-off by enhancing the light-matter
interactions, but maximizing their performance remains a challenge due to a
multitude of factors influencing their design and fabrication. In this paper,
strategies to improve the photon trapping effect while enhancing the speed of
operation are investigated. By optimizing the design of photon trapping
structures and experimentally integrated them in high-speed photodetectors, a
simultaneous broadband absorption efficiency enhancement up to 1000% and a
capacitance reduction of more than 50% has been achieved. Such work also allows
to present empirical equations to correlate the quantum efficiency of
photodetectors with the physical properties of the photon-trapping structures,
material characteristics, and limitations of the fabrication technologies. The
results obtained, open routes towards designing cost-effective CMOS integrated.


  We review recent progress in studying accretion flows on to supermassive
black holes (SMBH). Much of this removes earlier assumptions of symmetry and
regularity, such as aligned and prograde disc rotation. This allows a much
richer variety of effects, often because cancellation of angular momentum
allows rapid infall. Potential applications include lower SMBH spins allowing
faster mass growth and suppressing gravitational-wave reaction recoil in
mergers, gas-assisted SMBH mergers, and near-dynamical accretion in galaxy
centres.


  Immersion and Invariance is a technique for the design of stabilizing and
adaptive controllers and state observers for nonlinear systems. In all these
applications the problem considered is the stabilization of equilibrium points.
Motivated by some modern applications we show that the technique can also be
used to solve the problem of orbital stabilization, where the final objective
is to generate periodic solutions that are attractive. The feasibility of our
result is illustrated with some classical mechanical engineering and
electronics examples.


  Improving the high-temperature performance and low-temperature plasticity of
tantalum (Ta) alloys is a significant scientific challenge. We employed
first-principles calculations to study the interaction between screw
dislocations and solute atoms in the body centered cubic (BCC) structure of Ta,
with a particular focus on solid solution softening and strengthening. We
analyzed the impact of various solute elements on the generalized stacking
fault energy (GSFE), energy barriers within the single-atom column displacement
model, and their interaction with screw dislocations. The results indicate that
Hf and Zr, either individually or in combination, exhibit notable solute
softening effects in BCC Ta, significantly reducing GSFE, energy barriers, and
interaction energies. In contrast, Nb shows relative insensitivity to solute
effects, while Mo, W, and Ir demonstrate solute strengthening effects. The
calculations suggest that the interaction energy between screw dislocations and
solute atoms is a reliable indicator for predicting strengthening and softening
effects. Additionally, we extend these predictions to ternary alloys,
demonstrating that the strengthening and softening phenomena in these materials
can be explained through the electronic work function at the electronic level.


  Context. The lockdown orders established in multiple countries in response to
the Covid-19 pandemics are arguably one of the most widespread and deepest
shock experienced by societies in recent years. Studying their impact trough
the lens of social media offers an unprecedented opportunity to understand the
susceptibility and the resilience of human activity patterns to large-scale
exogenous shocks. Firstly, we investigate the changes that this upheaval has
caused in online activity in terms of time spent online, themes and emotion
shared on the platforms, and rhythms of content consumption. Secondly, we
examine the resilience of certain platform characteristics, such as the daily
rhythms of emotion expression. Data. Two independent datasets about the French
cyberspace: a fine-grained temporal record of almost 100 thousand YouTube
videos and a collection of 8 million Tweets between February 17 and April 14,
2020. Findings. In both datasets we observe a reshaping of the circadian
rhythms with an increase of night activity during the lockdown. The analysis of
the videos and tweets published during lockdown shows a general decrease in
emotional contents and a shift from themes like work and money to themes like
death and safety. However, the daily patterns of emotions remain mostly
unchanged, thereby suggesting that emotional cycles are resilient to exogenous
shocks.


  Hoffstein and Hulse defined the shifted convolution series of two cusp forms
by "shifting" the usual Rankin-Selberg convolution L-series by a parameter h.
We use the theory of harmonic Maass forms to study the behavior in h-aspect of
certain values of these series and prove a polynomial bound as h approaches
infinity. Our method relies on a result of Mertens and Ono, who showed that
these values are Fourier coefficients of mixed mock modular forms.


  By means of time- and temperature-dependent magnetization measurements, we
demonstrate that the timescale of hydrogen diffusion in partially-hydrogenated
LaFe$_{13-x}$Si$_x$H$_y$ is of the order of hours, when the material is held at
temperatures close to its as-prepared Curie temperature, $T_{C0}$. The
diffusion constant is estimated to be $D \approx 10^{-15}$ - $10^{-16}$
m$^2$s$^{-1}$ at room temperature. We examine the evolution of a magnetically
phase-separated state upon annealing for 3 days at a range of temperatures
around $T_{C0}$, and show that the thermodynamic driving force behind hydrogen
diffusion and phase segregation may be attributed to the lower free energy of
hydrogen interstitials in the ferromagnetic state relative to the paramagnetic
state.


  We use ab initio calculations to examine thermodynamic factors that could
promote the formation of recently proposed unique oP10-FeB4 and oP12-FeB2
compounds. We demonstrate that these compact boron-rich phases are stabilized
further under pressure. We also show that chromium tetraboride is more stable
in the new oP10 rather than the reported oI10 structure which opens up the
possibility of realizing an oP10-Fe(x)Cr(1-x)B4 pseudobinary material. In
addition to exhibiting remarkable electronic features, oP10-FeB4 and oP12-FeB2
are expected to be harder than the known Fe-B compounds commonly used for hard
coating applications.


  The moments of central values of families of L-functions have recently
attracted much attention and, with the work of Keating and Snaith, there are
now precise conjectures for their limiting values. We develop a simple method
to establish lower bounds of the conjectured order of magnitude for several
such families of L-functions. As an example we work out the case of the family
of all Dirichlet L-functions to a prime modulus.


  Interpretability in Graph Convolutional Networks (GCNs) has been explored to
some extent in computer vision in general, yet, in the medical domain, it
requires further examination. Moreover, most of the interpretability approaches
for GCNs, especially in the medical domain, focus on interpreting the model in
a post hoc fashion. In this paper, we propose an interpretable graph
learning-based model which 1) interprets the clinical relevance of the input
features towards the task, 2) uses the explanation to improve the model
performance and, 3) learns a population level latent graph that may be used to
interpret the cohort's behavior. In a clinical scenario, such a model can
assist the clinical experts in better decision-making for diagnosis and
treatment planning. The main novelty lies in the interpretable attention module
(IAM), which directly operates on multi-modal features. Our IAM learns the
attention for each feature based on the unique interpretability-specific
losses. We show the application on two publicly available datasets, Tadpole and
UKBB, for three tasks of disease, age, and gender prediction. Our proposed
model shows superior performance with respect to compared methods with an
increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and
2% for the UKBB Age prediction task. Further, we show exhaustive validation and
clinical interpretation of our results.


  We show that the Oldroyd B fluid model is the Eulerian form of a Lagrangian
model with an internal variable that satisfies the second law of thermodynamics
under some conditions on the initial value of the internal variable. We
similarly derive several new nonlinear versions of the Oldroyd B model as well
as Lagrangian formulations of the Zaremba-Jaumann and Oldroyd A fluid models.
We discuss whether or not these other models satisfy the second law.


  In this work we introduce a self-supervised pretraining framework for
transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we
pretrain our architecture on two self-supervised tasks simultaneously to teach
the model a general understanding of the temporal and spatial dynamics of human
auditory cortex during music listening. Our pretraining results are the first
to suggest a synergistic effect of multitask training on fMRI data. Second, we
finetune the pretrained models and train additional fresh models on a
supervised fMRI classification task. We observe significantly improved accuracy
on held-out runs with the finetuned models, which demonstrates the ability of
our pretraining tasks to facilitate transfer learning. This work contributes to
the growing body of literature on transformer architectures for pretraining and
transfer learning with fMRI data, and serves as a proof of concept for our
pretraining tasks and multitask pretraining on fMRI data.


  We introduce two uncertainty measures, say weighted past varentropy (WPVE)
and weighted paired dynamic varentropy (WPDVE). Several properties have been
studied for these proposed measures. The effect of the monotone transformation
for these measures have been discussed. We have obtained an upper bound of the
WPVE using the weighted past Shannon entropy. A lower bound of the WPVE is also
obtained. The WPVE has been studied for proportional reversed hazard rate
(PRHR) models. Upper and lower bounds of the WPDVE have been derived. We
propose non-parametric kernel estimates of the WPVE and WPDVE. Further, maximum
likelihood estimation has been employed to estimate WPVE and WPDVE for an
exponential population. A numerical simulation is provided to observe the
behaviour of the proposed estimates. Finally, we have analysed a real data set
and obtain the estimated values of WPVE.


  The realistic modeling of STT-MRAM for the simulations of hybrid
CMOS/Spintronics devices in comprehensive simulation environments require a
full description of stochastic switching processes in state of the art
STT-MRAM. Here, we derive an analytical formulation that takes into account the
spin-torque asymmetry of the spin polarization function of magnetic tunnel
junctions studying. We studied its validity range by comparing the analytical
formulas with results achieved numerically within a full micromagnetic
framework. We also find that a reasonable fit of the probability density
function (PDF) of the switching time is given by a Pearson Type IV PDF. The
main results of this work underlines the need of data-driven design of STT-MRAM
that uses a full micromagnetic simulation framework for the statistical
proprieties PDF of switching processes.


  Epidemic is a rapid and wide spread of infectious disease threatening many
lives and economy damages. It is important to fore-tell the epidemic lifetime
so to decide on timely and remedic actions. These measures include closing
borders, schools, suspending community services and commuters. Resuming such
curfews depends on the momentum of the outbreak and its rate of decay. Being
able to accurately forecast the fate of an epidemic is an extremely important
but difficult task. Due to limited knowledge of the novel disease, the high
uncertainty involved and the complex societal-political factors that influence
the widespread of the new virus, any forecast is anything but reliable. Another
factor is the insufficient amount of available data. Data samples are often
scarce when an epidemic just started. With only few training samples on hand,
finding a forecasting model which offers forecast at the best efforts is a big
challenge in machine learning. In the past, three popular methods have been
proposed, they include 1) augmenting the existing little data, 2) using a panel
selection to pick the best forecasting model from several models, and 3)
fine-tuning the parameters of an individual forecastingmodel for the highest
possible accuracy. In this paper, a methodology that embraces these three
virtues of data mining from a small dataset is proposed...


  Accounting for electrons and nuclei simultaneously is a powerful capability
of ab initio molecular dynamics (AIMD). However, AIMD is often unable to
accurately reproduce properties of systems such as water due to inaccuracies in
the underlying electronic density functionals. This shortcoming is often
addressed by added empirical corrections and/or increasing the simulation
temperature. We present here a maximum-entropy approach to directly incorporate
limited experimental data via a minimal bias. Biased AIMD simulations of water
and an excess proton in water are shown to give significantly improved
properties both for observables which were biased to match experimental data
and for unbiased observables. This approach also yields new physical insight
into inaccuracies in the underlying density functional theory as utilized in
the unbiased AIMD.


  We prove the eventological $H$-theorem that complements the Boltzmann
H-theorem from statistical mechanics and serves as a mathematical excuse
(mathematically no less convincing than the Boltzmann H-theorem for the second
law of thermodynamics) for what can be called "the second law of eventology",
which justifies the application of Gibbs and "anti-Gibbs" distributions of sets
of events minimizing relative entropy, as statistical models of the behavior of
a rational subject, striving for an equilibrium eventological choice between
perception and activity in various spheres of her/his co-being.


  Contrary to a recent claim by Anderson ["The Mathematical Theory of Cosmic
Strings", I.O.P. Publishing, Bristol 2003], we show that the Letelier-Gal'tsov
metric does represent a system of crossed straight infinite cosmic strings
moving with arbitrary constant velocities.


  The reach of ab initio many-body theories is rapidly extending over the
nuclear chart. However, dealing fully with three-nucleon, possibly
four-nucleon, interactions makes the solving of the A-body Schr\"odinger
equation particularly cumbersome, if not impossible beyond a certain nuclear
mass. Consequently, ab initio calculations of mid-mass nuclei are typically
performed on the basis of the normal-ordered two-body (NO2B) approximation that
captures dominant effects of three-nucleon forces while effectively working
with two-nucleon operators. A powerful idea currently employed to extend ab
initio calculations to open-shell nuclei consists of expanding the exact
solution of the A-body Schr\"odinger equation while authorizing the approximate
solution to break symmetries of the Hamiltonian. In this context, operators are
normal ordered with respect to a symmetry-breaking reference state such that
proceeding to a naive truncation may lead to symmetry-breaking approximate
operators. The purpose of the present work is to design a normal-ordering
approximation of operators that is consistent with the symmetries of the
Hamiltonian while working in the context of symmetry broken (and potentially
restored) methods. Focusing on many-body formalisms in which U(1) global-gauge
symmetry associated with particle number conservation is broken (and
potentially restored), a particle-number-conserving normal-ordered k-body
(PNOkB) approximation of an arbitrary N-body operator is designed on the basis
of Bogoliubov reference states. A numerical test based on particle-number
projected Hartree-Fock-Bogoliubov calculations permits to check the
particle-number conserving/violating character of a given approximation to a
particle-number conserving operator. Using the presently proposed PNOkB
approximation, ab initio calculations based on symmetry-breaking and restored
formalisms can be safely performed.


  The interacting symplectic fermion model in two spatial dimensions is further
analyzed. As an effective low energy theory, the model is unitary. We show that
a relativistic mass m is dynamically generated and derive a gap equation for
it. By incorporating a finite temperature we study some fundamental properties
of the model, such as the specific heat and spin response, which clearly show
non-Fermi liquid properties. We find that various physical properties are
suppressed at temperatures T< T* where the cross-over scale is T* = m. As a
simplified, toy model of high Tc superconductivity, we thus identify the
pseudogap energy scale with the zero temperature relativistic mass m, and show
that this reproduces some qualitative aspects of the observed phenomenology of
the pseudogap. The effects of the pseudogap and finite temperature on the
d-wave gap equation are analyzed. In this model, the pseudogap is a distinct
phenomenon from superconductivity and in fact competes with it. Our analysis of
Tc suggests that the quantum critical point of our model, where the pseudogap
vanishes, occurs inside the superconducting dome near optimal doping. For an
antiferromagnetic exchange energy of J/k_B ~ 1350K, solutions of the d-wave gap
equation give a maximum Tc of about 110K.


  Can live music events generate complex contagion in music streaming? This
paper finds evidence in the affirmative, but only for the most popular artists.
We generate a novel dataset from Last.fm, a music tracking website, to analyse
the listenership history of 1.3 million users over a two-month time horizon. We
use daily play counts along with event attendance data to run a regression
discontinuity analysis in order to show the causal impact of concert attendance
on music listenership among attendees and their friends network. First, we show
that attending a music artist's live concert increases that artist's
listenership among the attendees of the concert by approximately 1 song per day
per attendee (p-value<0.001). Moreover, we show that this effect is contagious
and can spread to users who did not attend the event. However, the extent of
contagion depends on the type of artist. We only observe contagious increases
in listenership for well-established, popular artists (.06 more daily plays per
friend of an attendee [p<0.001]), while the effect is absent for emerging
stars. We also show that the contagion effect size increases monotonically with
the number of friends who have attended the live event.


  Counterfactual quantum communication is unique in its own way that allows
remote parties to transfer information without sending any message carrier in
the channel. Although no message carrier travels in the channel at the time of
successful information transmission, it is impossible to transmit information
faster than the speed of light, thus without an information carrier. In this
paper, we address an important question What carries the information in
counterfactual quantum communication? and optimize the resource efficiency of
the counterfactual quantum communication in terms of the number of channels
used, time consumed to transmit 1-bit classical information between two remote
parties, and the number of qubits required to accomplish the counterfactual
quantum communication.


  Social interactions are stratified in multiple contexts and are subject to
complex temporal dynamics. The systematic study of these two features of social
systems has started only very recently mainly thanks to the development of
multiplex and time-varying networks. However, these two advancements have
progressed almost in parallel with very little overlap. Thus, the interplay
between multiplexity and the temporal nature of connectivity patterns is poorly
understood. Here, we aim to tackle this limitation by introducing a
time-varying model of multiplex networks. We are interested in characterizing
how these two properties affect contagion processes. To this end, we study SIS
epidemic models unfolding at comparable time-scale respect to the evolution of
the multiplex network. We study both analytically and numerically the epidemic
threshold as a function of the overlap between, and the features of, each
layer. We found that, the overlap between layers significantly reduces the
epidemic threshold especially when the temporal activation patterns of
overlapping nodes are positively correlated. Furthermore, when the average
connectivity across layers is very different, the contagion dynamics are driven
by the features of the more densely connected layer. Here, the epidemic
threshold is equivalent to that of a single layered graph and the impact of the
disease, in the layer driving the contagion, is independent of the overlap.
However, this is not the case in the other layers where the spreading dynamics
are sharply influenced by it. The results presented provide another step
towards the characterization of the properties of real networks and their
effects on contagion phenomena


  Shifting the focus of Type Ia supernova (SN Ia) cosmology to the
near-infrared (NIR) is a promising way to significantly reduce the systematic
errors, as the strategy minimizes our reliance on the empirical
width-luminosity relation and uncertain dust laws. Observations in the NIR are
also crucial for our understanding of the origins and evolution of these
events, further improving their cosmological utility. Any future experiments in
the rest-frame NIR will require knowledge of the SN Ia NIR spectroscopic
diversity, which is currently based on a small sample of observed spectra.
Along with the accompanying paper, Phillips et al. (2018), we introduce the
Carnegie Supernova Project-II (CSP-II), to follow up nearby SNe Ia in both the
optical and the NIR. In particular, this paper focuses on the CSP-II NIR
spectroscopy program, describing the survey strategy, instrumental setups, data
reduction, sample characteristics, and future analyses on the data set. In
collaboration with the Harvard-Smithsonian Center for Astrophysics (CfA)
Supernova Group, we obtained 661 NIR spectra of 157 SNe Ia. Within this sample,
451 NIR spectra of 90 SNe Ia have corresponding CSP-II follow-up light curves.
Such a sample will allow detailed studies of the NIR spectroscopic properties
of SNe Ia, providing a different perspective on the properties of the unburned
material, radioactive and stable nickel produced, progenitor magnetic fields,
and searches for possible signatures of companion stars.


  Let $M$ be an $n$-dimensional complete simply connected Riemannian manifold
with sectional curvature bounded above by a nonpositive constant $-\kappa^2$.
Using the cone total curvature $TC(\Gamma)$ of a graph $\Gamma$ which was
introduced by Gulliver and Yamada Math. Z. 2006, we prove that the density at
any point of a soap film-like surface $\Sigma$ spanning a graph $\Gamma \subset
M$ is less than or equal to $\frac{1}{2\pi}\{TC(\Gamma) -
\kappa^{2}\area(p\mbox{$\times\hspace*{-0.178cm}\times$}\Gamma)\}$. From this
density estimate we obtain the regularity theorems for soap film-like surfaces
spanning graphs with small total curvature. In particular, when $n=3$, this
density estimate implies that if \begin{eqnarray*} TC(\Gamma) < 3.649\pi +
\kappa^2 \inf_{p\in M} \area({p\mbox{$\times\hspace*{-0.178cm}\times$}\Gamma}),
\end{eqnarray*} then the only possible singularities of a piecewise smooth
$(\mathbf{M},0,\delta)$-minimizing set $\Sigma$ is the $Y$-singularity cone. In
a manifold with sectional curvature bounded above by $b^2$ and diameter bounded
by $\pi/b$, we obtain similar results for any soap film-like surfaces spanning
a graph with the corresponding bound on cone total curvature.


  The notion of spectral flow has given new insight into the motion of vortices
in superfluids and superconductors. For a BCS superconductor the spectrum of
low energy vortex core states is largely determined by the geometric optics
limit of Andreev reflection. We use this to follow the evolution of the states
when a stationary vortex is immersed in a transport supercurrent. If the core
spectrum were continuous, spectral flow would convert the momentum flowing into
the core via the Magnus effect into unbound quasiparticles --- thus allowing
the vortex to remain stationary without a pinning potential or other sink for
the inflowing momentum. The discrete nature of the states, however, leads to
Bloch oscillations which thwart the spectral flow. The momentum can escape only
via relaxation processes. Taking these into account permits a physically
transparent derivation of the mutual friction coefficients.


  We investigate theoretically acoustic phonon induced decoherence in quantum
dots. We calculate the dephasing of fundamental (interband or intraband)
optical transitions due to real and virtual transitions with higher energy
levels. Up to two acoustic phonon processes (absorption and/or emission) are
taken into account simultaneously in a non-perturbative manner. An analytic
expression of acoustic phonon induced broadening is given as a function of the
electron-phonon matrix elements and is physically interpreted. The theory is
applied to the dephasing of intersublevel transitions in self-assembled quantum
dots.


  Age is an important characteristic of a planetary system, but also one that
is difficult to determine. Assuming that the host star and the planets are
formed at the same time, the challenge is to determine the stellar age.
Asteroseismology provides precise age determination, but in many cases the
required detailed pulsation observations are not available. Here we concentrate
on other techniques, which may have broader applicability but also serious
limitations. Further development of this area requires improvements in our
understanding of the evolution of stars and their age-dependent
characteristics, combined with observations that allow reliable calibration of
the various techniques.


  Recent work has linked the quiescent luminosities and hard/soft spectral
state switches of neutron stars (NSs) to their spinning magnetic fields. It is
shown here that the quiescent luminosities and spectral switches of galactic
black hole candidates (BHCs) could be produced in the same way for spin rates
below 100 Hz and magnetic fields above 10^10 G. It is also shown that the
ultrasoft peaks and large flickering amplitudes of the BHCs would be expected
from the surfaces of massive NSs. None of the few spectral characteristics that
distinguish BHCs from low mass NSs have been explained in terms of event
horizons. Serious consideration of the possibility that they might simply be
massive NSs opens an avenue for proof of event horizons by negation, but
requires the use of a space-time metric that has no event horizon. The Yilmaz
exponential metric used here is shown to have an innermost marginally stable
orbit with radius, binding energy and Keplerian frequency that are within a few
percent of the same quantities for the Schwarzschild metric. A maximum NS mass
of ~10 solar mass is found for the Yilmaz metric. The two metrics essentially
differ only by the presence/absence of a surface for the BHCs, thus enabling
proof or disproof of the existence of an event horizon.


  Instruction tuning benefits from large and diverse datasets, however creating
such datasets involves a high cost of human labeling. While synthetic datasets
generated by large language models (LLMs) have partly solved this issue, they
often contain low-quality data. One effective solution is selectively
annotating unlabelled instructions, especially given the relative ease of
acquiring unlabeled instructions or texts from various sources. However, how to
select unlabelled instructions is not well-explored, especially in the context
of LLMs. Further, traditional data selection methods, relying on input
embedding space density, tend to underestimate instruction sample complexity,
whereas those based on model prediction uncertainty often struggle with
synthetic label quality. Therefore, we introduce SelectLLM, an alternative
framework that leverages the capabilities of LLMs to more effectively select
unlabeled instructions. SelectLLM consists of two key steps: Coreset-based
clustering of unlabelled instructions for diversity and then prompting a LLM to
identify the most beneficial instructions within each cluster. Our experiments
demonstrate that SelectLLM matches or outperforms other state-of-the-art
methods in instruction tuning benchmarks. It exhibits remarkable consistency
across human and synthetic datasets, along with better cross-dataset
generalization, as evidenced by a 10% performance improvement on the Cleaned
Alpaca test set when trained on Dolly data. All code and data are publicly
available (https://github.com/minnesotanlp/select-llm).


  Let $ X_1, \ldots, X_n $ be independent random variables taking values in the
alphabet $ \{0, 1, \ldots, r\} $, and $ S_n = \sum_{i = 1}^n X_i $. The
Shepp--Olkin theorem states that, in the binary case ($ r = 1 $), the Shannon
entropy of $ S_n $ is maximized when all the $ X_i $'s are uniformly
distributed, i.e., Bernoulli(1/2). In an attempt to generalize this theorem to
arbitrary finite alphabets, we obtain a lower bound on the maximum entropy of $
S_n $ and prove that it is tight in several special cases. In addition to these
special cases, an argument is presented supporting the conjecture that the
bound represents the optimal value for all $ n, r $, i.e., that $ H(S_n) $ is
maximized when $ X_1, \ldots, X_{n-1} $ are uniformly distributed over $ \{0,
r\} $, while the probability mass function of $ X_n $ is a mixture (with
explicitly defined non-zero weights) of the uniform distributions over $ \{0,
r\} $ and $ \{1, \ldots, r-1\} $.


  We study the mixing time of a random walk on the torus, alternated with a
Lebesgue measure preserving Bernoulli map. Without the Bernoulli map, the
mixing time of the random walk alone is $O(1/\epsilon^2)$, where $\epsilon$ is
the step size. Our main results show that for a class of Bernoulli maps, when
the random walk is alternated with the Bernoulli map $\varphi$ the mixing time
becomes $O(|\ln \epsilon|)$. We also study the \emph{dissipation time} of this
process, and obtain $O(|\ln \epsilon|)$ upper and lower bounds with explicit
constants.


  The intracluster medium (ICM) contains the vast majority of the baryonic
matter in galaxy clusters and is heated to X-ray radiating temperatures. X-ray
spectroscopy is therefore a key to understand both the morphology and the
dynamics of galaxy clusters. Here we recall crucial evolutionary problems of
galaxy clusters unveiled by 19 years of high-resolution X-ray spectroscopy with
the Reflection Grating Spectrometer (RGS) on board XMM-Newton. Its exquisite
combination of effective area, spectral resolution and excellent performance
over two decades enabled transformational science and important discoveries
such as the lack of strong cooling flows, the constraints on ICM turbulence and
cooling-heating balance. The ability of RGS to resolve individual ICM spectral
lines reveals in great detail the chemical enrichment in clusters by supernovae
and AGB stars. RGS spectra clearly showed that the ICM plasma is overall in
thermal equilibrium which is unexpected given the wealth of energetic phenomena
such as jets from supermassive black holes and mergers.


  The Non-Local Means (NLM) image denoising algorithm pushed the limits of
denoising. But it introduced a new paradigm, according to which one could
capture the similarity of images with the NLM weights. We show that, contrary
to the prevailing opinion, the NLM weights do not allow to get a reliable
measure of the similarity in a noisy image, unless one add a locality
constraint. As an image denoising method, the Non-Local Means prove to be
local. Some works had already pointed out that to get the best denoising
performances with the NLM algorithm, one should run it locally. But no general
conclusion has been yet proposed and the only explanation that was proposed to
justify the experimental results is not sufficient. Our study based on
experimental evidence proves that, on average on natural images, the bias of
the NLM estimator is an increasing function of the radius of the similarity
searching zone. The reason for this phenomenon is that noise disrupts the order
of similarity between patches. Hence the mean squared error between the
original image and the NLM estimation, which is the sum of the bias, the
variance and the covariance of the estimator, has an absolute minimum for a
disk of radius 3 to 4 pixels.


  Machine learning approaches hold great potential for the automated detection
of lung nodules in chest radiographs, but training the algorithms requires vary
large amounts of manually annotated images, which are difficult to obtain. Weak
labels indicating whether a radiograph is likely to contain pulmonary nodules
are typically easier to obtain at scale by parsing historical free-text
radiological reports associated to the radiographs. Using a repositotory of
over 700,000 chest radiographs, in this study we demonstrate that promising
nodule detection performance can be achieved using weak labels through
convolutional neural networks for radiograph classification. We propose two
network architectures for the classification of images likely to contain
pulmonary nodules using both weak labels and manually-delineated bounding
boxes, when these are available. Annotated nodules are used at training time to
deliver a visual attention mechanism informing the model about its localisation
performance. The first architecture extracts saliency maps from high-level
convolutional layers and compares the estimated position of a nodule against
the ground truth, when this is available. A corresponding localisation error is
then back-propagated along with the softmax classification error. The second
approach consists of a recurrent attention model that learns to observe a short
sequence of smaller image portions through reinforcement learning. When a
nodule annotation is available at training time, the reward function is
modified accordingly so that exploring portions of the radiographs away from a
nodule incurs a larger penalty. Our empirical results demonstrate the potential
advantages of these architectures in comparison to competing methodologies.


  This paper presents a modeling approach to infer scheduling and routing
patterns from digital freight transport activity data for different freight
markets. We provide a complete modeling framework including a new
discrete-continuous decision tree approach for extracting rules from the
freight transport data. We apply these models to collected tour data for the
Netherlands to understand departure time patterns and tour strategies, also
allowing us to evaluate the effectiveness of the proposed algorithm. We find
that spatial and temporal characteristics are important to capture the types of
tours and time-of-day patterns of freight activities. Also, the empirical
evidence indicates that carriers in most of the transport markets are sensitive
to the level of congestion. Many of them adjust the type of tour, departure
time, and the number of stops per tour when facing a congested zone. The
results can be used by practitioners to get more grip on transport markets and
develop freight and traffic management measures.


  The reaction p(e,e'pi+)n was measured at the Mainz Microtron MAMI at an
invariant mass of W=1125 MeV and four-momentum transfers of Q^2=0.117, 0.195
and 0.273 (GeV/c)^2. For each value of Q^2, a Rosenbluth separation of the
transverse and longitudinal cross sections was performed. An effective
Lagrangian model was used to extract the `axial mass' from experimental data.
We find a value of M_A=(1.077+-0.039) GeV which is (0.051+-0.044) GeV larger
than the axial mass known from neutrino scattering experiments. This is
consistent with recent calculations in chiral perturbation theory.


  The paper studies numerical methods that preserve a Lyapunov function of a
dynamical system, i.e. numerical approximations whose energy decreases, just
like in the original differential equation. With this aim, a discrete gradient
method is implemented for numerical integration of a system of ordinary
differential equations. In principle, this procedure yields first order
methods, but the analysis paves the way to the design of higher-order methods.
As a case in point, the proposed method is applied to the Duffing equation
without external forcing, considering that in this case, preserving the
Lyapunov function is more important than accuracy of particular trajectories.
Results are validated by means of numerical experiments, where the discrete
gradient method is compared to standard Runge-Kutta methods. As predicted by
the theory, discrete gradient methods preserve the Lyapunov function, whereas
conventional methods fail to do so, since either periodic solutions appear or
the energy does not decrease. Besides, the discrete gradient method outperforms
conventional schemes when these do preserve the Lyapunov function, in terms of
computational cost, thus the proposed method is promising.


  We review connections between coding-theoretic objects and sparse learning
problems. In particular, we show how seemingly different combinatorial objects
such as error-correcting codes, combinatorial designs, spherical codes,
compressed sensing matrices and group testing designs can be obtained from one
another. The reductions enable one to translate upper and lower bounds on the
parameters attainable by one object to another. We survey some of the
well-known reductions in a unified presentation, and bring some existing gaps
to attention. New reductions are also introduced; in particular, we bring up
the notion of minimum "L-wise distance" of codes and show that this notion
closely captures the combinatorial structure of RIP-2 matrices. Moreover, we
show how this weaker variation of the minimum distance is related to
combinatorial list-decoding properties of codes.


  We examine the mixing time for random walks on graphs. In particular we are
interested on investigating graphs with bottlenecks. Furthermore, the cutoff
phenomenon is examined.


  We use deep HST STIS and NICMOS images of three spectroscopically confirmed
galaxy counterparts of high-redshift damped Ly-alpha (DLA) absorbers (one of
which is a new discovery) to test the hypothesis that high-redshift DLA
galaxies are Lyman-break galaxies. If this hypothesis is correct the emission
properties of DLA galaxies must lie within the range of emission properties
measured for Lyman-break galaxies of similar absolute magnitude. This will be
true regardless of selection biases in the sample of detected DLA galaxies. We
test this prediction using several emission properties: half-light radius,
radial profile (Sersic n parameter), optical-to-near-infrared colour,
morphology, Ly alpha emission equivalent width, and Ly alpha emission velocity
structure. In all cases the measured values for the DLA galaxies lie within the
range measured for the population of Lyman-break galaxies. None of the
measurements is in conflict with the prediction. We conclude that the measured
emission properties of the three DLA galaxies studied here are consistent with
the conjecture that high-redshift DLA galaxies are Lyman-break galaxies. We
show that this result does not conflict with the observation that the few
high-redshift DLA galaxies discovered are mostly fainter than spectroscopically
confirmed L* Lyman-break galaxies.


  The electronic structure of interstitial hydrogen in a compound semiconductor
FeS$_2$ (naturally $n$-type) is inferred from a muon study. An implanted muon
(Mu, a pseudo-hydrogen) forms electronically different defect centers discerned
by the hyperfine parameter ($\omega_{\rm hf}$). A body of evidence indicates
that one muon is situated at the center of an iron-cornered tetrahedron with
nearly isotropic $\omega_{\rm hf}$ (Mu$_{\rm p}$), and that the other exists as
a diamagnetic state (Mu$_{\rm d}$, $\omega_{\rm hf}\simeq 0$). Their response
to thermal agitation indicates that the Mu$_{\rm d}$ center accompanies a
shallow level (donor or acceptor) understood by effective mass model while the
electronic structure of Mu$_{\rm p}$ center is more isolated from host than
Mu$_{\rm d}$ to form a deeper donor level. These observations suggest that
interstitial hydrogen also serves as an electronically active impurity in
FeS$_2$. Based on earlier reports on the hydrogen diffusion in FeS$_2$,
possibility of fast diffusion for Mu$_{\rm p}$ leading to formation of a
complex defect state (Mu$^*_{\rm d}$, $T\le 100$ K) or to motional narrowing
state (Mu$^*_{\rm p}$, $T\ge 150$ K) is also discussed.


  The passive conserved Swift-Hohenberg equation (or phase-field-crystal [PFC]
model) corresponds to a gradient dynamics for a single order parameter field
related to density. It provides a simple microscopic description of the
thermodynamic transition between liquid and crystalline states. In addition to
spatially extended periodic structures, the model describes a large variety of
steady spatially localized structures. In appropriate bifurcation diagrams the
corresponding solution branches exhibit characteristic slanted homoclinic
snaking. In an active PFC model, encoding for instance the active motion of
self-propelled colloidal particles, the gradient dynamics structure is broken
by a coupling between density and an additional polarization field. Then,
resting and traveling localized states are found with transitions characterized
by parity-breaking drift bifurcations. Here, we first briefly review the
snaking behavior of localized states in passive and active PFC models before
discussing the bifurcation behavior of localized states in systems of (i) two
coupled passive PFC equations described by common gradient dynamics, (ii) two
coupled passive PFC where the coupling breaks the gradient dynamics structure,
and (iii) a passive PFC coupled to an active PFC.


  We establish uniform bounds for the solutions $e^{it\Delta}u$ of the
Schr\"{o}dinger equation on arithmetic flat tori, generalising earlier results
by J. Bourgain. We also study the regularity properties of weak-* limits of
sequences of densities of the form $|e^{it\Delta}u_{n}|^{2}$ corresponding to
highly oscillating sequences of initial data $(u_{n})$. We obtain improved
regularity properties of those limits using previous results by N. Anantharaman
and F. Maci\`a on the structure of semiclassical measures for solutions to the
Schr\"{o}dinger equation on the torus.


  The proliferation of imprecise data has motivated both researchers and the
database industry to push statistical techniques into relational database
management systems (RDBMSs). We study algorithms to maintain model-based views
for a popular statistical technique, classification, inside an RDBMS in the
presence of updates to the training examples. We make three technical
contributions: (1) An algorithm that incrementally maintains classification
inside an RDBMS. (2) An analysis of the above algorithm that shows that our
algorithm is optimal among all deterministic algorithms (and asymptotically
within a factor of 2 of a nondeterministic optimal). (3) An index structure
based on the technical ideas that underlie the above algorithm which allows us
to store only a fraction of the entities in memory. We apply our techniques to
text processing, and we demonstrate that our algorithms provide several orders
of magnitude improvement over non-incremental approaches to classification on a
variety of data sets: such as the Cora, UCI Machine Learning Repository data
sets, Citeseer, and DBLife.


  Deep neural networks (DNNs) are proved to be vulnerable against backdoor
attacks. A backdoor is often embedded in the target DNNs through injecting a
backdoor trigger into training examples, which can cause the target DNNs
misclassify an input attached with the backdoor trigger. Existing backdoor
detection methods often require the access to the original poisoned training
data, the parameters of the target DNNs, or the predictive confidence for each
given input, which are impractical in many real-world applications, e.g.,
on-device deployed DNNs. We address the black-box hard-label backdoor detection
problem where the DNN is fully black-box and only its final output label is
accessible. We approach this problem from the optimization perspective and show
that the objective of backdoor detection is bounded by an adversarial
objective. Further theoretical and empirical studies reveal that this
adversarial objective leads to a solution with highly skewed distribution; a
singularity is often observed in the adversarial map of a backdoor-infected
example, which we call the adversarial singularity phenomenon. Based on this
observation, we propose the adversarial extreme value analysis(AEVA) to detect
backdoors in black-box neural networks. AEVA is based on an extreme value
analysis of the adversarial map, computed from the monte-carlo gradient
estimation. Evidenced by extensive experiments across multiple popular tasks
and backdoor attacks, our approach is shown effective in detecting backdoor
attacks under the black-box hard-label scenarios.


  The scaling behavior of model C describing the dynamical behaviour of the
$n$-component nonconserved order parameter coupled statically to a scalar
conserved density is considered in $d$-dimensional space. Conditions for the
realization of different types of scaling regimes in the $(n,d)$ plane are
studied within the field-theoretical renormalization group approach. Borders
separating these regions are calculated on the base of high-order RG functions
using $\epsilon$-expansions as well as by fixed dimension $d$ approach with
resummation.


  In this paper, we give a quantum circuit that calculates symmetrized
functions. Our algorithm applies the original Grover's algorithm or a variant
thereof such as AFGA (adaptive fixed point Grover's algorithm). Our algorithm
uses AFGA in conjunction with two new techniques we call "targeting two
hypotheses" and "blind targeting". Suppose AFGA drives the starting state
$|s\rangle$ to the target state $|t\rangle$. When targeting two hypotheses,
$|t\rangle$ is a superposition $a_0|0\rangle + a_1|1\rangle$ of two orthonormal
states or hypotheses $|0\rangle$ and $|1\rangle$. When targeting blindly, the
value of $\langle t| s\rangle$ is not known a priori.


  The drug overdose crisis in the United States continues to intensify.
Fatalities have increased five-fold since 1999 reaching a record high of
108,000 deaths in 2021. The epidemic has unfolded through distinct waves of
different drug types, uniquely impacting various age, gender, race and ethnic
groups in specific geographical areas. One major challenge in designing
effective interventions is the forecasting of age-specific overdose patterns at
the local level so that prevention and preparedness can be effectively
delivered. We develop a forecasting method that assimilates observational data
obtained from the CDC WONDER database with an age-structured model of addiction
and overdose mortality. We apply our method nationwide and to three select
areas: Los Angeles County, Cook County and the five boroughs of New York City,
providing forecasts of drug-overdose mortality and estimates of relevant
epidemiological quantities, such as mortality and age-specific addiction rates.


  We analyze the existing solar neutrino experiment data and show the allowed
regions. The result from SNO's salt phase itself restricts quite a lot the
allowed region's area. Reactor neutrinos play an important role in determining
oscillation parameters. KamLAND gives decisive conclusion on the solution to
the solar neutrino puzzle, in particular, the spectral distortion in the 766.3
Ty KamLAND data gives another new improvement in the constraint of solar
MSW-LMA solutions. We confirm that at 99.73% C.L. the high-LMA solution is
excluded.


  The electron scattering from periodic line defects on the surface of
topological insulators with hexagonal warping effect is investigated
theoretically by means of a transfer matrix method. The influence of surface
line defects, acting as structural ripples on propagation of electrons are
studied in two perpendicular directions due to the asymmetry of warped energy
contour under momentum exchange. The transmission profiles and the details of
resonant peaks which vary with the number of defects and the strength of their
potentials are strongly dependent on the direction in which the line defects
extend. At low energies, the quantum interference between the incident and
reflected propagating electrons has the dominant contribution in transmission
resonances, while at high energies the multiple scattering processes on the
constant-energy contour also appear because of the strong warping effect. By
increasing the spatial separation between the line defects, the minimum value
of electrical conductance remains significantly high at low incident energies,
while the minimum value may approach zero at high energies as the number of
defects is increased. Our findings suggest that the potential ripples on the
surface of topological insulators can be utilized to control the local
electronic properties of these materials.


  A common forecasting setting in real world applications considers a set of
possibly heterogeneous time series of the same domain. Due to different
properties of each time series such as length, obtaining forecasts for each
individual time series in a straight-forward way is challenging. This paper
proposes a general framework utilizing a similarity measure in Dynamic Time
Warping to find similar time series to build neighborhoods in a k-Nearest
Neighbor fashion, and improve forecasts of possibly simple models by averaging.
Several ways of performing the averaging are suggested, and theoretical
arguments underline the usefulness of averaging for forecasting. Additionally,
diagnostics tools are proposed allowing a deep understanding of the procedure.


  Motivated by nonconvex, inconsistent feasibility problems in imaging, the
relaxed alternating averaged reflections algorithm, or relaxed Douglas-Rachford
algorithm (DR$\lambda$), was first proposed over a decade ago. Convergence
results for this algorithm are limited either to convex feasibility or
consistent nonconvex feasibility with strong assumptions on the regularity of
the underlying sets. Using an analytical framework depending only on metric
subregularity and pointwise almost averagedness, we analyze the convergence
behavior of DR$\lambda$ for feasibility problems that are both nonconvex and
inconsistent. We introduce a new type of regularity of sets, called
super-regular at a distance, to establish sufficient conditions for local
linear convergence of the corresponding sequence. These results subsume and
extend existing results for this algorithm.


  We have developed a new dynamical model of the torus region in active
galactic nucleus (AGN), using a three-dimensional radiation hydrodynamics
algorithm. These new simulations have the specific aim to explore the role of
radiatively-driven outflows, which is hotly debated in current literature as a
possible explanation for the observed infrared emission from the polar regions
of AGN. In this first paper, we only consider radiative effects induced by the
primary radiation from the AGN. The simulations generate a disk & outflow
structure that qualitatively agrees with observations, although the outflow is
radial rather than polar, likely due to the lack of radiation pressure from hot
dust. We find cut-offs between the wind and disk at gas temperatures of 1000 K
and dust temperatures of 100 K, producing kinematic signatures that can be used
for interpretation of high resolution infrared observations. We also produce
line emission maps to aid in the interpretation of recent ALMA observations and
future JWST observations. We investigate a number of simulation parameters, and
find that the anisotropy of the radiation field is equally important to the
Eddington factor, despite the anisotropy often being assumed to have a single
sometimes arbitrary form in many previous works. We also find that supernovae
can have a small but significant impact, but only at extremely high star
formation rates.


  Quantum correlations in $\psi(3770) \to D^0\bar{D}^0$ provide unique access
to information about strong phase differences. Precision determination of the
CKM phase $\gamma/\phi_3$ via $B \to D K$ decays depends upon constraints on
charm mixing amplitudes, measurements of doubly-Cabibbo suppressed amplitudes
and relative phases, and studies of correlated charmed meson decays tagged by
flavor or CP eigenstates. CP-tagged $D^0 \to K^-\pi^+\pi^-\pi^+$ decays and
CP-tagged $D^0 \to K^0_S\pi^+\pi^-$ Dalitz plots are only available at CLEO-c.
Using the 818 pb$^{-1}$ CLEO-c data sample produced by the Cornell Electron
Storage Ring (CESR) at $\sqrt{s} = 3.77$ GeV, we perform analyses of these
decays. We describe the techniques used to measure the $D$-decay parameters,
and the CLEO-c impact on measurements of $\gamma/\phi_3$.


  It will be shown to be consistent that there are at least two non-isomorphic
selective ultrafilters, but no stable ordered-union ultrafilters. This answers
a question of Blass from his 1987 paper which introduced the concept of a
stable ordered-union ultrafilter.


  Varying the amounts of silicon and carbon, different composites have been
prepared by ball milling of Si, Ni$_{3.4}$Sn$_4$, Al and C. Silicon and carbon
contents are varied from 10 to 30 wt.% Si, and 0 to 20 wt.% C. The
microstructural and electrochemical properties of the composites have been
investigated by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and
electrochemical galvanostatic cycling up to 1000 cycles. Impact of silicon and
carbon contents on the phase occurrence, electrochemical capacity and
cycle-life are compared and discussed. For C-content comprised between 9 and 13
wt.% and Si-content >= 20 wt.%, Si nanoparticles are embedded in a
Ni$_{3.4}$Sn$_4$-Al-C matrix which is chemically homogeneous at the micrometric
scale. For other carbon contents and low Si-amount (10 wt.%), no homogeneous
matrix is formed around Si nanoparticles. When homogenous matrix is formed,
both Ni$_3$Sn$_4$ and Si participate to the reversible lithiation mechanism,
whereas no reaction between Ni$_3$Sn$_4$ and Li is observed for no homogenous
matrix. Moreover, best cycle-life performances are obtained when Si
nanoparticles are embedded in a homogenous matrix and Si-content is moderate
(<= 20 wt.%). Composites with carbon in the 9-13 wt.% range and 20 wt.% silicon
lead to the best balance between capacity and life duration upon cycling. This
work experimentally demonstrates that embedding Si in an intermetallic/carbon
matrix allows to efficiently accommodate Si volume changes on cycling to ensure
long cycle-life.


  A PT-symmetric Bose-Einstein condensate can be theoretically described using
a complex optical potential, however, the experimental realization of such an
optical potential describing the coherent in- and outcoupling of particles is a
nontrivial task. We propose an experiment for a quantum mechanical realization
of a PT-symmetric system, where the PT-symmetric currents of a two-well system
are implemented by coupling two additional wells to the system, which act as
particle reservoirs. In terms of a simple four-mode model we derive conditions
under which the two middle wells of the Hermitian four-well system behave
exactly as the two wells of the PT-symmetric system. We apply these conditions
to calculate stationary solutions and oscillatory dynamics. By means of frozen
Gaussian wave packets we relate the Gross-Pitaevskii equation to the four-mode
model and give parameters required for the external potential, which provides
approximate conditions for a realistic experimental setup.


  Scanning tunneling microscopy (STM) is perhaps the most promising way to
detect the superconducting gap size and structure in the canonical
unconventional superconductor Sr$_2$RuO$_4$ directly. However, in many cases,
researchers have reported being unable to detect the gap at all in simple STM
conductance measurements. Recently, an investigation of this issue on various
local topographic structures on a Sr-terminated surface found that
superconducting spectra appeared only in the region of small nanoscale canyons,
corresponding to the removal of one RuO surface layer. Here, we analyze the
electronic structure of various possible surface structures using first
principles methods, and argue that bulk conditions favorable for
superconductivity can be achieved when removal of the RuO layer suppresses the
RuO$_4$ octahedral rotation locally. We further propose alternative
terminations to the most frequently reported Sr termination where
superconductivity surfaces should be observed.


  Recent breakthroughs in artificial intelligence (AI) algorithms have
highlighted the need for novel computing hardware in order to truly unlock the
potential for AI. Physics-based hardware, such as thermodynamic computing, has
the potential to provide a fast, low-power means to accelerate AI primitives,
especially generative AI and probabilistic AI. In this work, we present the
first continuous-variable thermodynamic computer, which we call the stochastic
processing unit (SPU). Our SPU is composed of RLC circuits, as unit cells, on a
printed circuit board, with 8 unit cells that are all-to-all coupled via
switched capacitances. It can be used for either sampling or linear algebra
primitives, and we demonstrate Gaussian sampling and matrix inversion on our
hardware. The latter represents the first thermodynamic linear algebra
experiment. We also illustrate the applicability of the SPU to uncertainty
quantification for neural network classification. We envision that this
hardware, when scaled up in size, will have significant impact on accelerating
various probabilistic AI applications.


  Recently it has been shown that antibrackets may be expressed in terms of
Poisson brackets and vice versa for commuting functions in the original
bracket. Here we also introduce generalized brackets involving higher
antibrackets or higher Poisson brackets where the latter are of a new type. We
give generating functions for these brackets for functions in arbitrary
involutions in the original bracket. We also give master equations for
generalized Maurer-Cartan equations. The presentation is completely symmetric
with respect to Poisson brackets and antibrackets.


  The reason of significantly higher transmissibility of SARS Covid (2019
CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be
attributed to mutations reported in structural proteins, and the role played by
non-structural proteins (nsps) and accessory proteins (ORFs) for viral
replication, assembly, and shedding. Envelope protein E is one of the four
structural proteins of minimum length. Recent studies have confirmed critical
role played by the envelope protein in the viral life cycle including assembly
of virion exported from infected cell for its transmission. However, the
determinants of the highly complex viral - host interactions of envelope
protein, particularly with host Golgi complex, have not been adequately
characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids
differ in four amino acid locations. The additional amino acid Gly (G) at
location 70 makes CoV length 76. The amino acid pair EG at location 69-70 of
CoV in place of amino acid R in location 69 of CoV-2, has been identified as a
major determining factor in the current investigation. This paper concentrates
on the design of computational model to compare the structure/function of wild
and mutants of CoV-2 with wild and mutants of CoV in the functionally important
region of the protein chain pair. We hypothesize that differences of CAML model
parameter of CoV-2 and CoV characterize the deviation in structure and function
of envelope proteins in respect of interaction of virus with host Golgi
complex; and this difference gets reflected in the difference of their
transmissibility. The hypothesis has been validated from single point
mutational study on- (i) human HBB beta-globin hemoglobin protein associated
with sickle cell anemia, (ii) mutants of envelope protein of Covid-2 infected
patients reported in recent publications.


  We prove that the minimum number of Hamilton cycles in a hamiltonian
threshold graph of order $n$ is $2^{\lfloor (n-3)/2\rfloor}$ and this minimum
number is attained uniquely by the graph with degree sequence
$n-1,n-1,n-2,\ldots,\lceil n/2\rceil,\lceil n/2\rceil,\ldots,3,2$ of $n-2$
distinct degrees. This graph is also the unique graph of minimum size among all
hamiltonian threshold graphs of order $n.$


  It is shown that electron injection into a laser-driven plasma bubble can be
manipulated by applying an external magnetic field in the presence of a plasma
density gradient. The down-ramp of the density-tailored plasma locally reduces
the plasma wave phase velocity, which triggers injection. The longitudinal
magnetic field dynamically induces an expanding hole in the electron density
distribution at the rear of the wake bubble, which reduces the peak electron
velocity in its vicinity. Electron injection is suppressed when the electron
velocity drops below the phase velocity, which depends on the size of the
density hole. This enables the start and end of electron injection to be
independently controlled, which allows generation of sub-femtosecond electron
bunches with peak currents of a few kilo-Ampere, for an applied magnetic field
of $\sim 10$ Tesla.


  I consider some of the issues we face in trying to understand dark energy.
Huge fluctuations in the unknown dark energy equation of state can be hidden in
distance data, so I argue that model-independent tests which signal if the
cosmological constant is wrong are valuable. These can be constructed to remove
degeneracies with the cosmological parameters. Gravitational effects can play
an important role. Even small inhomogeneity clouds our ability to say something
definite about dark energy. I discuss how the averaging problem confuses our
potential understanding of dark energy by considering the backreaction from
density perturbations to second-order in the concordance model: this effect
leads to at least a 10% increase in the dynamical value of the deceleration
parameter, and could be significantly higher. Large Hubble-scale inhomogeneity
has not been investigated in detail, and could conceivably be the cause of
apparent cosmic acceleration. I discuss void models which defy the Copernican
principle in our Hubble patch, and describe how we can potentially rule out
these models.


  SNO+ is a large liquid scintillator-based experiment located 2km underground
at SNOLAB, Sudbury, Canada. It reuses the Sudbury Neutrino Observatory
detector, consisting of a 12m diameter acrylic vessel which will be filled with
about 780 tonnes of ultra-pure liquid scintillator. Designed as a multipurpose
neutrino experiment, the primary goal of SNO+ is a search for the neutrinoless
double-beta decay (0$\nu\beta\beta$) of 130Te. In Phase I, the detector will be
loaded with 0.3% natural tellurium, corresponding to nearly 800 kg of 130Te,
with an expected effective Majorana neutrino mass sensitivity in the region of
55-133 meV, just above the inverted mass hierarchy. Recently, the possibility
of deploying up to ten times more natural tellurium has been investigated,
which would enable SNO+ to achieve sensitivity deep into the parameter space
for the inverted neutrino mass hierarchy in the future. Additionally, SNO+ aims
to measure reactor antineutrino oscillations, low-energy solar neutrinos, and
geoneutrinos, to be sensitive to supernova neutrinos, and to search for exotic
physics. A first phase with the detector filled with water will begin soon,
with the scintillator phase expected to start after a few months of water data
taking. The 0$\nu\beta\beta$ Phase I is foreseen for 2017.


  Visuals captured by high-flying aerial drones are increasingly used to assess
biodiversity and animal population dynamics around the globe. Yet, challenging
acquisition scenarios and tiny animal depictions in airborne imagery, despite
ultra-high resolution cameras, have so far been limiting factors for applying
computer vision detectors successfully with high confidence. In this paper, we
address the problem for the first time by combining deep object detectors with
super-resolution techniques and altitude data. In particular, we show that the
integration of a holistic attention network based super-resolution approach and
a custom-built altitude data exploitation network into standard recognition
pipelines can considerably increase the detection efficacy in real-world
settings. We evaluate the system on two public, large aerial-capture animal
datasets, SAVMAP and AED. We find that the proposed approach can consistently
improve over ablated baselines and the state-of-the-art performance for both
datasets. In addition, we provide a systematic analysis of the relationship
between animal resolution and detection performance. We conclude that
super-resolution and altitude knowledge exploitation techniques can
significantly increase benchmarks across settings and, thus, should be used
routinely when detecting minutely resolved animals in aerial imagery.


  The results of a multi-lepton search conducted by the CMS collaboration with
5 fb$^{-1}$ of data collected from 7 TeV $pp$ collisions are used to place the
first bound on the rare flavor-changing decay of the top quark to a Higgs boson
and charm quark. Combining results from a number of exclusive three- and
four-lepton search channels yields an estimated upper limit of ${\rm Br}(t \to
ch) < 2.7%$ for a Higgs boson mass of 125 GeV. The sensitivity of future
dedicated searches for $t \to c h$ could be improved by adding exclusive same
sign di-lepton channels, as well as by sub-dividing channels based on $b$-quark
tagging and partial kinematic top quark and Higgs boson tagging. This bound may
be interpreted more widely within a range of new physics processes that yield
final states with a $W$-boson in association with a Higgs boson. For such
processes with kinematics that are similar to top--anti-top production and
decay, the estimated limit on cross section times branching ratio corresponds
to roughly $\sigma \cdot {\rm Br}(pp \to WhX) < 9$ pb.


  We investigate the problem of optimally reversing the action of an arbitrary
quantum channel C which acts independently on each component of an ensemble of
n identically prepared d-dimensional quantum systems. In the limit of large
ensembles, we construct the optimal reversing channel R* which has to be
applied at the output ensemble state, to retrieve a smaller ensemble of m
systems prepared in the input state, with the highest possible rate m/n. The
solution is found by mapping the problem into the optimal reversal of Gaussian
channels on quantum-classical continuous variable systems, which is here solved
as well. Our general results can be readily applied to improve the
implementation of robust long-distance quantum communication. As an example, we
investigate the optimal reversal rate of phase flip channels acting on a
multi-qubit register.


  Previous methods on knowledge base question generation (KBQG) primarily focus
on enhancing the quality of a single generated question. Recognizing the
remarkable paraphrasing ability of humans, we contend that diverse texts should
convey the same semantics through varied expressions. The above insights make
diversifying question generation an intriguing task, where the first challenge
is evaluation metrics for diversity. Current metrics inadequately assess the
above diversity since they calculate the ratio of unique n-grams in the
generated question itself, which leans more towards measuring duplication
rather than true diversity. Accordingly, we devise a new diversity evaluation
metric, which measures the diversity among top-k generated questions for each
instance while ensuring their relevance to the ground truth. Clearly, the
second challenge is how to enhance diversifying question generation. To address
this challenge, we introduce a dual model framework interwoven by two selection
strategies to generate diverse questions leveraging external natural questions.
The main idea of our dual framework is to extract more diverse expressions and
integrate them into the generation model to enhance diversifying question
generation. Extensive experiments on widely used benchmarks for KBQG
demonstrate that our proposed approach generates highly diverse questions and
improves the performance of question answering tasks.


  We recently introduced the graph invariant twin-width, and showed that
first-order model checking can be solved in time $f(d,k)n$ for $n$-vertex
graphs given with a witness that the twin-width is at most $d$, called
$d$-contraction sequence or $d$-sequence, and formulas of size $k$ [Bonnet et
al., FOCS '20]. The inevitable price to pay for such a general result is that
$f$ is a tower of exponentials of height roughly $k$. In this paper, we show
that algorithms based on twin-width need not be impractical. We present
$2^{O(k)}n$-time algorithms for $k$-Independent Set, $r$-Scattered Set,
$k$-Clique, and $k$-Dominating Set when an $O(1)$-sequence is provided. We
further show how to solve weighted $k$-Independent Set, Subgraph Isomorphism,
and Induced Subgraph Isomorphism, in time $2^{O(k \log k)}n$. These algorithms
are based on a dynamic programming scheme following the sequence of
contractions forward. We then show a second algorithmic use of the contraction
sequence, by starting at its end and rewinding it. As an example, we establish
that bounded twin-width classes are $\chi$-bounded. This significantly extends
the $\chi$-boundedness of bounded rank-width classes, and does so with a very
concise proof. The third algorithmic use of twin-width builds on the second
one. Playing the contraction sequence backward, we show that bounded twin-width
graphs can be edge-partitioned into a linear number of bicliques, such that
both sides of the bicliques are on consecutive vertices, in a fixed vertex
ordering. Given that biclique edge-partition, we show how to solve the
unweighted Single-Source Shortest Paths and hence All-Pairs Shortest Paths in
sublinear time $O(n \log n)$ and time $O(n^2 \log n)$, respectively. Finally we
show that Min Dominating Set and related problems have constant integrality
gaps on bounded twin-width classes, thereby getting constant approximations on
these classes.


  The aim of this paper is to perform stability analysis of anisotropic
dissipative cylindrical collapsing model in $f(R,T,R_{\mu\nu} T^{\mu\nu})$
gravity. In this context, the modified version of hydrodynamical equation is
explored by means of dynamical equations and radial perturbation scheme. We
examined the role of adiabatic index, dissipation as well as the particular
cosmological model on the onset of dynamical instability of the evolving
cylindrical system that was initially in hydrostatic equilibrium with Newtonian
and post Newtonian approximations. It is pointed out that extra curvature terms
of $f(R,T,R_{\mu\nu}T^{\mu\mu\nu})$ gravity tends to increase the stability,
while that heat radiations push the system to enter into unstable window.
Further, our results reveal the significance of adiabatic index in the
stability analysis of cylindrical celestial model.


  We investigate spectral properties of the atomic fluorescence and the output
field of the cavity-mode of a single-atom dressed-state laser in a photonic
crystal. We pay a particular attention to the behavior of the spectra in the
presence of the frequency dependent reservoir and search for signatures of the
thresholdless lasing. Although the thresholdless behavior has been predicted by
analyzing the photon statistics of the cavity field, we find that the threshold
behavior still exists in the spectrum of the cavity field. We find that the
structure of cavity field spectrum depends strongly on the strange of the
pumping rate. For low pumping rates, the spectrum is not monochromatic, it is
composed of a set of discrete lines reveling the discrete (quantum) structure
of the combined dressed-atom plus the cavity field system. We find that for a
certain value of the pumping rate, the multi-peak structure converts into a
single very narrow line centered at the cavity field frequency. A physical
explanation of the behavior of the spectra is provided in terms of dressed
states of the system.


  Computing accurate yet efficient approximations to the solutions of the
electronic Schr\"odinger equation has been a paramount challenge of
computational chemistry for decades. Quantum Monte Carlo methods are a
promising avenue of development as their core algorithm exhibits a number of
favorable properties: it is highly parallel, and scales favorably with the
considered system size, with an accuracy that is limited only by the choice of
the wave function ansatz. The recently introduced machine-learned
parametrizations of quantum Monte Carlo ansatzes rely on the efficiency of
neural networks as universal function approximators to achieve state of the art
accuracy on a variety of molecular systems. With interest in the field growing
rapidly, there is a clear need for easy to use, modular, and extendable
software libraries facilitating the development and adoption of this new class
of methods. In this contribution, the DeepQMC program package is introduced, in
an attempt to provide a common framework for future investigations by unifying
many of the currently available deep-learning quantum Monte Carlo
architectures. Furthermore, the manuscript provides a brief introduction to the
methodology of variational quantum Monte Carlo in real space, highlights some
technical challenges of optimizing neural network wave functions, and presents
example black-box applications of the program package. We thereby intend to
make this novel field accessible to a broader class of practitioners both from
the quantum chemistry as well as the machine learning communities.


  Second-order methods, which utilize gradients as well as Hessians to optimize
a given function, are of major importance in mathematical optimization. In this
work, we prove tight bounds on the oracle complexity of such methods for smooth
convex functions, or equivalently, the worst-case number of iterations required
to optimize such functions to a given accuracy. In particular, these bounds
indicate when such methods can or cannot improve on gradient-based methods,
whose oracle complexity is much better understood. We also provide
generalizations of our results to higher-order methods.


  The interplay between 1D traveling pulses with oscillatory tails (TPO) and
heterogeneities of bump type is studied for a generalized three-component
FitzHugh-Nagumo equation. First, we present that stationary pulses with
oscillatory tails (SPO) form a ``snaky'' structure in homogeneous spaces, after
which TPO branches form a ``figure-eight-like stack of isolas'' located
adjacent to the snaky structure of SPO. Herein, we adopted input resources as a
bifurcation parameter. A drift bifurcation from SPO to TPO was observed by
introducing another parameter at which these two solution sheets merged. In
contrast to the monotone tail case, a nonlocal interaction appeared in the
heterogeneous problem between the TPO and heterogeneity, which created
infinitely many saddle solutions and finitely many stable stationary solutions
distributed across the entire line. TPO displayed a variety of dynamics
including pinning and depinning processes along with penetration (PEN) and
rebound (REB). The stable/unstable manifolds of these saddles interacted with
the TPO in a complex manner, which created a subtle dependence on the initial
condition, and a difficulty to predict the behavior after collision. A
systematic global exploration of solution branches induced by heterogeneities
enabled us to find all the asymptotic states after collision to predict the
solution results without solving the PDEs. The reduction method of
finite-dimensional ODE system allowed us to clarify the detailed mechanism of
the transitions from PEN to pinning and pinning to REB based on a dynamical
system perspective. Consequently, the basin boundary between two distinct
outputs against the heterogeneities yielded an infinitely many successive
reconnection of heteroclinic orbits among those saddles, because the strength
of heterogeneity increased and caused the aforementioned subtle dependence of
initial condition.


  CO adsorption on Cu(111) and Cu(001) surfaces has been studied within
ab-initio density functional theory (DFT). The structural, vibrational and
thermodynamic properties of the adsorbate-substrate complex have been
calculated. Calculations within the generalized gradient approximation (GGA)
predict adsorption in the threefold hollow on Cu(111) and in the bridge-site on
Cu(001), instead of on-top as found experimentally. It is demonstrated that the
correct site preference is achieved if the underestimation of the HOMO-LUMO gap
of CO characteristic for DFT is correct by applying a molecular DFT+U approach.
The DFT+U approach also produces good agreement with the experimentally
measured adsorption energies, while introducing only small changes in the
calculated geometrical and vibrational properties further improving agreement
with experiment which is fair already at the GGA level.


  In this paper we introduce a property of ergodic flows, called Property B. We
prove that any ergodic hyperfinite equiva- lence relation of type III_o whose
associated flow satisfies this property is not of product type. A consequence
of this result is that any properly ergodic ow with Property B is not
approximately transitive. We use Property B to construct a non-AT flow which -
up to conjugacy - is a flow built under a function with the dyadic odometer as
base automorphism.


  We consider the conditions under which a rotating magnetic object can produce
a magnetically powered outflow in an initially unmagnetized medium stratified
under gravity. 3D MHD simulations are presented in which the footpoints of
localized, arcade-shaped magnetic fields are put into rotation. It is shown how
the effectiveness in producing a collimated magnetically powered outflow
depends on the rotation rate, the strength and the geometry of the field. The
flows produced by uniformly rotating, non-axisymmetric fields are found to
consist mainly of buoyant plumes heated by dissipation of rotational energy.
Collimated magnetically powered flows are formed if the field and the rotating
surface are arranged such that a toroidal magnetic field is produced. This
requires a differential rotation of the arcades' footpoints. Such jets are
well-collimated; we follow their propagation through the stratified atmosphere
over 100 times the source size. The magnetic field is tightly wound and its
propagation is dominated by the development of non-axisymmetric instabilities.
We observe a Poynting flux conversion efficiency of over 75% in the longest
simulations. Applications to the collapsar model and protostellar jets are
discussed.


  If the inflationary phase lasted longer than the minimal period, the length
scales observed today originate from modes that were smaller than the Planck
length during inflation. It was recently argued that this "trans-Planckian
problem" can never arise in a consistent string theory framework, which places
a stringent constraint on the energy scale of inflation, $V^{1/4}\lesssim 10^9$
GeV. In this paper, we show that this requirement corresponds to a very small
Hubble scale during inflation, $H_{\rm inf}\lesssim 1$ GeV, and therefore has
serious consequences on scenarios where the dark matter density was generated
by amplification of quantum fluctuations during inflation. We also present a
class of inflationary models which both satisfy the above limit for the scale
of inflation and are in perfect agreement with observational data.


  We give a tensorial description of the Turaev cobracket on any genus 0
compact surface through the standard group-like expansion, where the Bernoulli
numbers appear.


  Geometrization says `` any closed oriented three-manifold which is prime (not
a connected sum) carries one of the eight Thurston geometries OR it has
incompressible torus walls whose complementary components each carry one of
four particular Thurston geometries" (see Introduction and Figure 1). These
geometric components have finite volume for the hyperbolic geometries (the H
labeled vertices). They also have finite volume for each of the two geometries
appearing as Seifert fibrations (the S labeled vertices). The remaining pieces
(the I labeled vertices) have Euclidean geometries of linear volume growth.
Then these vertex geometries are combined topologically to recover the original
manifold. This, by cutting off the toroidal ends and then gluing the torus
boundaries by affine mappings (indicated by the labeled edges in Figure 1). The
point of this work is to make the affine gluing respect an interpretation of
the metric geometry in terms of a new notion of `` regional Lie generated
geometry". The vertex regions use four geometries in Lie form combined in the
overlap edge regions via affine geometry. The Theorem solves, using
Geometrization, a 45 year old question/approach to the Poincar\'{e} Conjecture.
This was described in a '76 Princeton Math dept. preprint and finally
documented in the 1983 reference by Thurston and the second author.


  A point-to-point discrete-time scheduling problem of transmitting $B$
information bits within $T$ hard delay deadline slots is considered assuming
that the underlying energy-bit cost function is a convex monomial. The
scheduling objective is to minimize the expected energy expenditure while
satisfying the deadline constraint based on information about the unserved
bits, channel state/statistics, and the remaining time slots to the deadline.
At each time slot, the scheduling decision is made without knowledge of future
channel state, and thus there is a tension between serving many bits when the
current channel is good versus leaving too many bits for the deadline. Under
the assumption that no other packet is scheduled concurrently and no outage is
allowed, we derive the optimal scheduling policy. Furthermore, we also
investigate the dual problem of maximizing the number of transmitted bits over
$T$ time slots when subject to an energy constraint.


  The morphometry of a kidney tumor revealed by contrast-enhanced Computed
Tomography (CT) imaging is an important factor in clinical decision making
surrounding the lesion's diagnosis and treatment. Quantitative study of the
relationship between kidney tumor morphology and clinical outcomes is difficult
due to data scarcity and the laborious nature of manually quantifying imaging
predictors. Automatic semantic segmentation of kidneys and kidney tumors is a
promising tool towards automatically quantifying a wide array of morphometric
features, but no sizeable annotated dataset is currently available to train
models for this task. We present the KiTS19 challenge dataset: A collection of
multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes
for 300 patients who underwent nephrectomy for kidney tumors at our center
between 2010 and 2018. 210 (70%) of these patients were selected at random as
the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge
and have been released publicly. With the presence of clinical context and
surgical outcomes, this data can serve not only for benchmarking semantic
segmentation models, but also for developing and studying biomarkers which make
use of the imaging and semantic segmentation masks.


  We obtain a conservative Hamiltonian describing the interactions of two
charged bodies in Yang-Mills through $\mathcal{O}(\alpha^2)$ and to all orders
in velocity. Our calculation extends a recently-introduced framework based on
scattering amplitudes and effective field theory (EFT) to consider
color-charged objects. These results are checked against the direct integration
of the observables in the Kosower-Maybee-O'Connell (KMOC) formalism. At the
order we consider we find that the linear and color impulses in a scattering
event can be concisely described in terms of the eikonal phase, thus extending
the domain of applicability of a formula originally proposed in the context of
spinning particles.


  The random matrix ensembles are applied to the quantum chaotic systems. The
quantum systems are studied using the finite dimensional real, complex and
quaternion Hilbert spaces of the eigenfunctions. The linear operators
describing the systems act on these Hilbert spaces and they are treated as
random matrices in generic bases of the eigenfunctions. The random
eigenproblems are presented and solved. Examples of random operators are
presented with connection to physical problems.


  While the observation of nucleon decay would be a smoking gun of Grand
Unified Theories (GUTs) in general, the ratios between the decay rates of the
various channels carry rich information about the specific GUT model
realization. To investigate this fingerprint of GUT models in the context of
supersymmetric (SUSY) GUTs, we present the software tool \texttt{SusyTCProton},
which is an extension of the module \texttt{SusyTC} to be used with the
\texttt{REAP} package. It allows to calculate nucleon decay rates from the
relevant dimension five GUT operators specified at the GUT scale, including the
full loop-dressing at the SUSY scale. As an application, we investigate the
fingerprints of two example GUT toy models with different flavor structures,
performing an MCMC analysis to include the experimental uncertainties for the
charged fermion masses and CKM mixing parameters. While both toy models provide
equally good fits to the low energy data, we show how they could be
distinguished via their predictions of ratios for nucleon decay rates. Together
with \texttt{SusyTCProton} we also make the additional module
\texttt{ProtonDecay} public. It can be used independently from \texttt{REAP}
and allows to calculate nucleon decay rates from given $D=5$ and $D=6$ operator
coefficients (accepting the required SUSY input for the $D=5$ case in SLHA
format). The $D=6$ functionality can also be used to calculate nucleon decay in
non-SUSY GUTs.


  Federated learning (FL) enables distributed optimization of machine learning
models while protecting privacy by independently training local models on each
client and then aggregating parameters on a central server, thereby producing
an effective global model. Although a variety of FL algorithms have been
proposed, their training efficiency remains low when the data are not
independently and identically distributed (non-i.i.d.) across different
clients. We observe that the slow convergence rates of the existing methods are
(at least partially) caused by the catastrophic forgetting issue during the
local training stage on each individual client, which leads to a large increase
in the loss function concerning the previous training data at the other
clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated
knowledge forgetting in the local training stage by regularizing locally
trained parameters with the loss on generated pseudo data, which encode the
knowledge of previous training data learned by the global model. Our
comprehensive experiments demonstrate that FedReg not only significantly
improves the convergence rate of FL, especially when the neural network
architecture is deep and the clients' data are extremely non-i.i.d., but is
also able to protect privacy better in classification problems and more robust
against gradient inversion attacks. The code is available at:
https://github.com/Zoesgithub/FedReg.


  Quantum optical sensors are ubiquitous in various fields of research, from
biological or medical sensors to large-scale experiments searching for dark
matter or gravitational waves. Gravitational-wave detectors have been very
successful in implementing cavities and quantum squeezed light for enhancing
sensitivity to signals from black hole or neutron star mergers. However, the
sensitivity to weak forces is limited by available energy and optical
decoherence in the system. Here, we derive the fundamental sensitivity limit of
cavity and squeezed-light enhanced interferometers with optical loss.This limit
is attained by the optimal use of an additional internal squeeze operation,
which allows to mitigate readout loss. We demonstrate the application of
internal squeezing to various scenarios and confirm that it indeed allows to
reach the best sensitivity in cavity and squeezed-light enhanced linear force
sensors. Our work establishes the groundwork for the future development of
optimal sensors in real-world scenarios where, up until now, the application of
squeezed light was curtailed by various sources of decoherence.


  Adversarial examples pose a threat to deep neural network models in a variety
of scenarios, from settings where the adversary has complete knowledge of the
model in a "white box" setting and to the opposite in a "black box" setting. In
this paper, we explore the use of output randomization as a defense against
attacks in both the black box and white box models and propose two defenses. In
the first defense, we propose output randomization at test time to thwart
finite difference attacks in black box settings. Since this type of attack
relies on repeated queries to the model to estimate gradients, we investigate
the use of randomization to thwart such adversaries from successfully creating
adversarial examples. We empirically show that this defense can limit the
success rate of a black box adversary using the Zeroth Order Optimization
attack to 0%. Secondly, we propose output randomization training as a defense
against white box adversaries. Unlike prior approaches that use randomization,
our defense does not require its use at test time, eliminating the Backward
Pass Differentiable Approximation attack, which was shown to be effective
against other randomization defenses. Additionally, this defense has low
overhead and is easily implemented, allowing it to be used together with other
defenses across various model architectures. We evaluate output randomization
training against the Projected Gradient Descent attacker and show that the
defense can reduce the PGD attack's success rate down to 12% when using
cross-entropy loss.


  This paper presents a novel solution to the age long problem of password
security at input level. In our solution, each of the various characters from
which a password could be composed is encoded with a random single digit
integer and presented to the user via an input interface form. A legitimate
user entering his password only needs to carefully study the sequence of code
that describe his password, and then enter these code in place of his actual
password characters. This approach does not require the input code to be hidden
from anyone or converted to placeholder characters for security reasons. Our
solution engine regenerates new code for each character each time the carriage
return key is struck, producing a hardened password that is convincingly more
secure than conventional password entry system against both online and offline
attackers. Using empirical data and a prototype implementation of our scheme,
we give evidence that our approach is viable in practice, in terms of ease of
use, improved security, and performance.


  In this article we perform an extensive study of the spaces of automorphic
forms for GL(2) of weight two and level N, for N an ideal in the ring of
integers of the quartic CM field generated by the twelfth roots of unity. This
study is conducted through the computation of the group cohomology of the
congruence subgroup of upper triangular matrices modulo N, and the
corresponding Hecke action which this space admits. Combining this Hecke data
with the Faltings-Serre method for proving equivalence of Galois
representations, we are able to provide the first known examples of modular
elliptic curves over this field.


  Exploring novel materials with enhanced optical nonlinearities at low power
levels with ultrafast response and small footprints is of great interests for
information processing, communication, sensing and quantum systems. Recent
progress on nonlinear metamaterials and metasurfaces suggests promising
solutions to overcome the limitations of nonlinear materials in nature. Here we
present a design concept for highly enhanced saturable absorption effect based
on subwavelength-thick (<1/5$\lambda$0) hybrid graphene-plasmonic metasurface
structures in infrared wavelengths. Our theoretical and experimental results
demonstrated that, by exciting nonequilibrium carriers inside nanoscale
hotspots, one could not only enhance the saturable absorption in graphene, but
also reduce the saturation fluence by over 3 orders of magnitude (from ~ 1
mJ/cm2 to ~100 nJ/cm2). Our pump-probe measurement results suggested an
ultrashort saturable absorption recovery time (<60 fs), which is ultimately
determined by the relaxation dynamics of photoexcited carriers in graphene. We
also observed pulse narrowing effects in our devices based on the
autocorrelation measurement results. Such design concepts can be tailored via
structure engineering to operate in broader wavelength ranges up to mid- and
far- infrared spectral regions. These ultrafast low-saturation fluence
saturable absorber designs can enable low-threshold, compact, self-starting
mode-locked lasers, laser pulse shaping, and high-speed optical information
processing.


  We describe a transducer for low-temperature atomic force microscopy based on
electromechanical coupling due to a strain-dependent kinetic inductance of a
superconducting nanowire. The force sensor is a bending triangular plate
(cantilever) whose deflection is measured via a shift in the resonant frequency
of a high-Q superconducting microwave resonator at 4.5 GHz. We present design
simulations including mechanical finite-element modeling of surface strain and
electromagnetic simulations of meandering nanowires with large kinetic
inductance. We discuss a lumped-element model of the force sensor and describe
the role of an additional shunt inductance for tuning the coupling to the
transmission line used to measure the microwave resonance. A detailed
description of our fabrication is presented, including information about the
process parameters used for each layer. We also discuss the fabrication of
sharp tips on the cantilever using focused electron beam-induced deposition of
platinum. Finally, we present measurements that characterize the spread of
mechanical resonant frequency, the temperature dependence of the microwave
resonance, and the sensor's operation as an electromechanical transducer of
force.