  Medical articles provide current state of the art treatments and diagnostics
to many medical practitioners and professionals. Existing public databases such
as MEDLINE contain over 27 million articles, making it difficult to extract
relevant content without the use of efficient search engines. Information
retrieval tools are crucial in order to navigate and provide meaningful
recommendations for articles and treatments. Classifying these articles into
broader medical topics can improve the retrieval of related articles. The set
of medical labels considered for the MESINESP task is on the order of several
thousands of labels (DeCS codes), which falls under the extreme multi-label
classification problem. The heterogeneous and highly hierarchical structure of
medical topics makes the task of manually classifying articles extremely
laborious and costly. It is, therefore, crucial to automate the process of
classification. Typical machine learning algorithms become computationally
demanding with such a large number of labels and achieving better recall on
such datasets becomes an unsolved problem.
  This work presents Priberam's participation at the BioASQ task Mesinesp. We
address the large multi-label classification problem through the use of four
different models: a Support Vector Machine (SVM), a customised search engine
(Priberam Search), a BERT based classifier, and a SVM-rank ensemble of all the
previous models. Results demonstrate that all three individual models perform
well and the best performance is achieved by their ensemble, granting Priberam
the 6th place in the present challenge and making it the 2nd best team.


  We consider the problem of Vision-and-Language Navigation (VLN). The majority
of current methods for VLN are trained end-to-end using either unstructured
memory such as LSTM, or using cross-modal attention over the egocentric
observations of the agent. In contrast to other works, our key insight is that
the association between language and vision is stronger when it occurs in
explicit spatial representations. In this work, we propose a cross-modal map
learning model for vision-and-language navigation that first learns to predict
the top-down semantics on an egocentric map for both observed and unobserved
regions, and then predicts a path towards the goal as a set of waypoints. In
both cases, the prediction is informed by the language through cross-modal
attention mechanisms. We experimentally test the basic hypothesis that
language-driven navigation can be solved given a map, and then show competitive
results on the full VLN-CE benchmark.


  The results from hydrodynamical TREESPH simulations of galaxy clusters are
used to investigate the dependence of the final cluster X-ray properties upon
the numerical resolution and the assumed star formation models for the cooled
gas. A comparison between runs with different star formation methods shows that
the results of simulations, based on star formation methods in which gas
conversion into stars is controlled by an efficiency parameter c_{star}, are
sensitive to the simulation numerical resolution. In this respect star
formation methods based instead on a local density threshold, are shown to give
more stable results. Final X-ray luminosities are found to be numerically
stable, with uncertainties of a factor 2.


  In this work, we present experimental data demonstrating the possibility of
using magnonic holographic devices for pattern recognition. The prototype
eight-terminal device consists of a magnetic matrix with micro-antennas placed
on the periphery of the matrix to excite and detect spin waves. The principle
of operation is based on the effect of spin wave interference, which is similar
to the operation of optical holographic devices. Input information is encoded
in the phases of the spin waves generated on the several edges of the magnonic
matrix, while the output corresponds to the amplitude of the inductive voltage
produced by the interfering spin waves on the other side of the matrix. The
level of the output voltage depends on the combination of the input phases as
well as on the internal structure of the magnonic matrix. Experimental data
collected for several magnonic matrixes show the unique output signatures in
which maxima and minima correspond to specific input phase patterns.
Potentially, magnonic holographic devices may provide a higher storage density
compare to the optical counterparts due to a shorter wavelength and
compatibility with conventional electronic devices. The challenges and
shortcoming of the magnonic holographic devices are also discussed.


  We study nonlinear sigma model, especially Skyrme model with twist: twisted
Skyrmion string where twist term, $mkz$, is indicated in vortex solution. To
add gravity, we replace $\eta^{\mu\nu}$ in Lagrangian system with a space-time
metric tensor, $g^{\mu\nu}$, which in view of the time-independence and
cylindrical symmetry of the assumed vortex solution is taken to be a function
of $r$ alone. We use ode45 for numerical calculation, i.e. a tool box in Matlab
to solve coupled Einstein field equations which have ordinary differential
equations (ODE) form.


  We present the Cosmic Lyman-$\alpha$ Transfer code (COLT), a massively
parallel Monte-Carlo radiative transfer code, to simulate Lyman-$\alpha$
(Ly$\alpha$) resonant scattering through neutral hydrogen as a probe of the
first galaxies. We explore the interaction of centrally produced Ly$\alpha$
radiation with the host galactic environment. Ly$\alpha$ photons emitted from
the luminous starburst region escape with characteristic features in the line
profile depending on the density distribution, ionization structure, and bulk
velocity fields. For example, anisotropic ionization exhibits a tall peak close
to line centre with a skewed tail that drops off gradually. Idealized models of
first galaxies explore the effect of mass, anisotropic H II regions, and
radiation pressure driven winds on Ly$\alpha$ observables. We employ mesh
refinement to resolve critical structures. We also post-process an ab initio
cosmological simulation and examine images captured at various escape distances
within the 1 Mpc$^3$ comoving volume. Finally, we discuss the emergent spectra
and surface brightness profiles of these objects in the context of high-$z$
observations. The first galaxies will likely be observed through the red
damping wing of the Ly$\alpha$ line. Observations will be biased toward
galaxies with an intrinsic red peak located far from line centre that reside in
extensive H II super bubbles, which allows Hubble flow to sufficiently redshift
photons away from line centre and facilitate transmission through the
intergalactic medium (IGM). Even with gravitational lensing to boost the
luminosity this preliminary work indicates that Ly$\alpha$ emission from
stellar clusters within haloes of $M_{\rm vir}<10^9~{\rm M}_\odot$ is generally
too faint to be detected by the James Webb Space Telescope (JWST).


  We present a simple mechanism to eliminate cosmological constants both in
supersymmetric and non-supersymmetric theories. This mechanism is based on the
Hodge (Poincare) duality between a 0-form and D-form field strengths in
D-dimensional space-time. The new key ingredient is the introduction of an
extra Chern-Simons term into the D-form field strength H dual to the 0-form
field strength. Our formulation can be also made consistent with supersymmetry.
Typical applications to four-dimensional N=1 supergravity and to
ten-dimensional type IIA supergravity are given. The success of our formulation
for both supersymmetric and non-supersymmetric systems strongly indicates the
validity of our mechanism even after supersymmetry breakings at the classical
level. Our mechanism may well be applicable to quantized systems, at least for
supersymmetric cases with fundamental D-brane actions available.


  Let $H$ be a connected reductive subgroup of a complex connected reductive
group $G$. Fix maximal tori and Borel subgroups of $H$ and $G$. Consider the
pairs $(V,V')$ of irreducible representations of $H$ and $G$ such that $V$ is a
submodule of $V'$. We are interested in the cone $LR(G,H)$ generated by the
pairs of dominant weights of such a pair of representations. Our main result
gives a minimal set of inequalities describing $LR(G,H)$ as a part of the
dominant chamber. In way, we obtain results about the faces of the
Dolgachev-Hu's $G$-ample cone and variations of this cone.


  Using the Spitzer Space Telescope, we have obtained rest frame 9-16mu spectra
of 11 quasars and 9 radio galaxies from the 3CRR catalog at redshifts
1.0<z<1.4. This complete flux-limited 178MHz-selected sample is unbiased with
respect to orientation and therefore suited to study orientation-dependent
effects in the most powerful active galactic nuclei (AGN). The mean radio
galaxy spectrum shows a clear silicate absorption feature (tau_9.7mu = 1.1)
whereas the mean quasar spectrum shows silicates in emission. The mean radio
galaxy spectrum matches a dust-absorbed mean quasar spectrum in both shape and
overall flux level. The data for individual objects conform to these results.
The trend of the silicate depth to increase with decreasing core fraction of
the radio source further supports that for this sample, orientation is the main
driver for the difference between radio galaxies and quasars, as predicted by
AGN unification. However, comparing our high-z sample with lower redshift 3CRR
objects reveals that the absorption of the high-z radio galaxy MIR continuum is
lower than expected from a scaled up version of lower luminosity sources, and
we discuss some effects that may explain these trends.


  After recalling the theoretical and experimental status of QCD in the Regge
limit and the requirement of high energy scattering process of onium-onium type
for testing this limit, we show that the International Linear Collider would be
a major step in this field.


  We give an exposition of White's characterization of empty lattice
tetrahedra. In particular, we describe the second author's proof of White's
theorem that appeared in her doctoral thesis.


  We present a novel reinforcement learning (RL) based task allocation and
decentralized navigation algorithm for mobile robots in warehouse environments.
Our approach is designed for scenarios in which multiple robots are used to
perform various pick up and delivery tasks. We consider the problem of joint
decentralized task allocation and navigation and present a two level approach
to solve it. At the higher level, we solve the task allocation by formulating
it in terms of Markov Decision Processes and choosing the appropriate rewards
to minimize the Total Travel Delay (TTD). At the lower level, we use a
decentralized navigation scheme based on ORCA that enables each robot to
perform these tasks in an independent manner, and avoid collisions with other
robots and dynamic obstacles. We combine these lower and upper levels by
defining rewards for the higher level as the feedback from the lower level
navigation algorithm. We perform extensive evaluation in complex warehouse
layouts with large number of agents and highlight the benefits over
state-of-the-art algorithms based on myopic pickup distance minimization and
regret-based task selection. We observe improvement up to 14% in terms of task
completion time and up-to 40% improvement in terms of computing collision-free
trajectories for the robots.


  Whole brain segmentation on a structural magnetic resonance imaging (MRI) is
essential in non-invasive investigation for neuroanatomy. Historically,
multi-atlas segmentation (MAS) has been regarded as the de facto standard
method for whole brain segmentation. Recently, deep neural network approaches
have been applied to whole brain segmentation by learning random patches or 2D
slices. Yet, few previous efforts have been made on detailed whole brain
segmentation using 3D networks due to the following challenges: (1) fitting
entire whole brain volume into 3D networks is restricted by the current GPU
memory, and (2) the large number of targeting labels (e.g., > 100 labels) with
limited number of training 3D volumes (e.g., < 50 scans). In this paper, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks to cover
overlapped sub-spaces in a standard atlas space. This strategy simplifies the
whole brain learning task to localized sub-tasks, which was enabled by combing
canonical registration and label fusion techniques with deep learning. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by MAS for pre-training. From empirical validation, the
state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68,
while the proposed method achieved 0.78, 0.73, and 0.71 on three validation
cohorts. Moreover, the computational time reduced from > 30 hours using MAS to
~15 minutes using the proposed method. The source code is available online
https://github.com/MASILab/SLANTbrainSeg


  Inequalities for exponential sums are studied. Our results improve an old
result of G. Halasz and a recent result of G. Kos. We prove several other
essentially sharp related results in this paper.


  The use of the defect profile instead of the experimentally observed velocity
profile for the search for similarity parameters has become firmly imbedded in
the turbulent boundary layer literature. However, a search of the literature
reveals that there are no theoretical reasons for this defect profile
preference over the more traditional velocity profile. In the report herein, we
use the flow governing equation approach to develop similarity criteria for the
two profiles. Results show that the derived similarity criteria are identical.
Together with previous work that found that defect profile similarity must be
accompanied by velocity profile similarity, then ones expectations must be that
either profile can be used to search for similarity in experimental datasets.
The choice should therefore be dictated by which one works best for
experimental investigations, which in this case is the velocity profile.


  We report the first IR proper motion measurements of the Herbig-Haro objects
in the Orion Molecular Cloud--One using a four-year time baseline. The [Fe II]
emitting bullets are moving of order 0.08 arcsec per year, or at about 170
\kms. The direction of motion is similar to that inferred from their
morphology. The proper motions of \h2 emitting wakes behind the [Fe II]
bullets, and of newly found \h2 bullets, are also measured. \h2 bullets have
smaller proper motion than [Fe II] bullets, while \h2 wakes with leading [Fe
II] bullets appear to move at similar speeds to their associated bullets. A few
instances of variability in the emission can be attributed to dense, stationary
clumps in the ambient cloud being overrun, setting up a reverse--oriented
bullet. Differential motion between [Fe II] bullets and their trailing \h2
wakes is not observed, suggesting that these are not separating, and also that
they have reached a steady--state configuration over at least 100 years. The
most distant bullets have, on average, larger proper motions, but are not
consistent with free expansion. Nevertheless an impulsive, or short--lived
($\ll$ 1,000 years) duration for their origin seems likely.


  The energy dependence of light (anti)nuclei and (anti)hypertriton production
are investigated in central Au-Au collisions from AGS up to LHC energies at
midrapidity, using the parton and hadron cascade model (PACIAE) together with
the dynamically constrained phase-space coalescence model(DCPC). We find that
the yields, yield ratios of the antiparticles to their corresponding particles,
the coalescence parameters $B_A$ and the strangeness population factor $s_3$ of
light (anti)nuclei and (anti)hypertriton strongly depend on the energy.
Furthermore, we analyze and discuss the strangeness population factor $s_3$ and
the coalescence parameters $B_A$, and find a transition point near by 20 GeV.
These results thus suggest the potential usefulness of the $s_3$ and $B_A$ of
light nuclei production in relativistic heavy-ion collisions as a direct probe
of the transition point associated with the QCD critical phenomena. The results
from PACIAE+DCPC model are well consistent with experimental data.


  This paper investigates the use of different transformations for improving
the randomness of sequences. In particular, convolutional codes are used for
increasing the size of a given sequence and then a random mapping function is
used for further randomization. We have shown how such a method can convert
highly correlated sequences into random ones.


  We prove that the range of Strichartz estimates on a model 2D convex domain
may be further restricted compared to the known counterexamples due to the
first author. Our new family of counterexamples is now built on the parametrix
construction from our earlier work. Interestingly enough, it is sharp in at
least some regions of phase space.


  We report on experiments with cold thermal $^7$Li atoms confined in combined
magnetic and electric potentials. A novel type of three-dimensional trap was
formed by modulating a magnetic guide using electrostatic fields. We observed
atoms trapped in a string of up to six individual such traps, a controlled
transport of an atomic cloud over a distance of 400$\mu$m, and a dynamic
splitting of a single trap into a double well potential. Applications for
quantum information processing are discussed.


  We present several Galileo invariant Lagrangians, which are invariant against
Poincare transformations defined in one higher (spatial) dimension. Thus these
models, which arise in a variety of physical situations, provide a
representation for a dynamical (hidden) Poincare symmetry. The action of this
symmetry transformation on the dynamical variables is nonlinear, and in one
case involves a peculiar field-dependent diffeomorphism. Some of our models are
completely integrable, and we exhibit explicit solutions.


  Holographic Ricci dark energy evolving through its interaction with dark
matter is a natural choice for the running vacuum energy model. We have
analyzed the relative significance of two versions of this model in the light
of SNIa, CMB, BAO and Hubble data sets using the method Bayesian inferences.
The first one, model 1, is the running holographic Ricci dark energy (rhrde)
having a constant additive term in its density form and the second is one,
model 2, having no additive constant, instead the interaction of rhrde with
dark matter is accounted through a phenomenological coupling term. The Bayes
factor of these models in comparison with the standard $\Lambda$CDM have been
obtained by calculating the likelihood of each model for four different data
combinations, SNIa(307)+CMB+BAO, SNIa(307)+CMB+BAO+Hubble data,
SNIa(580)+CMB+BAO and SNIa(580)+CMB+BAO+Hubble data. Suitable flat priors for
the model parameters has been assumed for calculating the likelihood in both
cases. Our analysis shows that, according to the Jeffreys scale, the evidence
for $\Lambda$CDM against both model 1 and model 2 is very strong as the Bayes
factor of both models are much less than one for all the data combinations.


  We derive an $su(1,1)$ coherent state path integral formula for a system of
two one-dimensional anyons in a harmonic potential. By a change of variables we
transform this integral into a coherent states path integral for a harmonic
oscillator with a shifted energy. The shift is the same as the one obtained for
anyons by other methods. We justify the procedure by showing that the change of
variables corresponds to a $su(1,1)$ version of the Holstein-Primakoff
transformation.


  The $\dbar$-dressing scheme based on local nonlinear vector $\dbar$-problem
is developed. It is applicable to multidimensional nonlinear equations for
vector fields, and, after Hamiltonian reduction, to heavenly equation.
Hamiltonian reduction is described explicitely in terms of the $\dbar$-data. An
analogue of Hirota bilinear identity for heavenly equation hierarchy is
introduced, $\tau$-function for the hierarchy is defined. Addition formulae
(generating equations) for the $\tau$-function are found. It is demonstrated
that $\tau$-function for heavenly equation hierarchy is given by the action for
$\dbar$-problem evaluated on the solution of this problem.


  This paper presents an efficient quadratic programming (QP) decoder via the
alternating direction method of multipliers (ADMM) technique, called QP-ADMM,
for binary low-density parity-check (LDPC) codes. Its main contents are as
follows: first, we relax maximum likelihood (ML) decoding problem to a
non-convex quadratic program. Then, we develop an ADMM solving algorithm for
the formulated non-convex QP decoding model. In the proposed QP-ADMM decoder,
complex Euclidean projections onto the check polytope are eliminated and
variables in each updated step can be solved analytically in parallel.
Moreover, it is proved that the proposed ADMM algorithm converges to a
stationary point of the non-convex QP problem under the assumption of sequence
convergence. We also verify that the proposed decoder satisfies the favorable
property of the all-zeros assumption. Furthermore, by exploiting the inside
structures of the QP model, the complexity of the proposed algorithm in each
iteration is shown to be linear in terms of LDPC code length. Simulation
results demonstrate the effectiveness of the proposed QP-ADMM decoder.


  The Eridanus galaxies follow the well-known radio-FIR correlation. Majority
(70%) of these galaxies have their star formation rates below that of the Milky
Way. The galaxies having a significant excess of radio emission are identified
as low luminosity AGNs based on their radio morphologies obtained from the GMRT
observations. There are no powerful AGNs (L{20cm} > 10^{23} W Hz^{-1}) in the
group. The two most far-infrared and radio luminous galaxies in the group have
optical and HI morphologies suggestive of recent tidal interactions. The
Eridanus group also has two far-infrared luminous but radio-deficient galaxies.
It is believed that these galaxies are observed within a few Myr of the onset
of an intense star formation episode after being quiescent for at least a 100
Myr. The upper end of the radio luminosity distribution of the Eridanus
galaxies (L_{20cm} ~ 10^{22} W Hz^{-1}) is consistent with that of the field
galaxies, other groups, and late-type galaxies in nearby clusters.


  We share our experience with the recently released WILDS benchmark, a
collection of ten datasets dedicated to developing models and training
strategies which are robust to domain shifts. Several experiments yield a
couple of critical observations which we believe are of general interest for
any future work on WILDS. Our study focuses on two datasets: iWildCam and FMoW.
We show that (1) Conducting separate cross-validation for each evaluation
metric is crucial for both datasets, (2) A weak correlation between validation
and test performance might make model development difficult for iWildCam, (3)
Minor changes in the training of hyper-parameters improve the baseline by a
relatively large margin (mainly on FMoW), (4) There is a strong correlation
between certain domains and certain target labels (mainly on iWildCam). To the
best of our knowledge, no prior work on these datasets has reported these
observations despite their obvious importance. Our code is public.


  The long-lived stau scenario is an interesting possibility at the LHC. We
study squark mass measurements in this scenario; in particular, we show that
left- and right-handed squark masses are both measurable. In SUSY events,
multiple jets are expected, which become a source of combinatorial backgrounds.
In order to reduce such backgrounds a hemisphere analysis is applied, and we
discuss mass measurements of squarks in decay modes of left-handed squark into
quark + (neutral / charged) Wino and also right-handed squark into quark +
Bino.


  Since graphene was synthesized the interest for building new 2D and 3D
structures based on the carbon allotropes has been growing every day. One of
these 3D structures is know as carbon schwarzites. Schwarzites consist of
carbon nanostructures possessing the shape of Triply-Periodic Minimal Surfaces
(TPMS), which is characterized by a negative Gaussian curvature introduced by
the presence of carbon rings with more than six atoms. Some examples of
schwarzite families include: primitive (P), gyroid (G) and diamond (D).
Previous studies considering different element species of schwarzites have
investigated the mechanical, electrical and thermal properties. In this work,
we investigated the stability of germanium (Ge) schwarzites using density
functional theory with GGA exchange-correlation functional. We chose one
structure of each family (P8bal), (G688) and (D688). It was observed that
regions usually flat in carbon schwarzites acquires buckled configurations as
previously observed on silicene and germanene monolayers. The investigated
structures presented a semiconducting bandgap ranging from $0.13$ to $0.27$ eV.
We also performed calculations of optical properties within the linear regime,
where it was shown that Ge schwarzites structures absorb light from infrared to
ultra-violet frequencies. Therefore, our results open new perspectives of
materials that can be used in optelectronics devices application.


  The hydrogen interstitial and the substitutional Al_Zn, Ga_Zn and In_Zn are
all shallow donors in ZnO and lead to n-type conductivity. Although shallow
donors are expected to repel each other, we show by first principles
calculations that in ZnO these shallow donor impurities attract and form a
complex, leading to a donor level deep in the band gap. This puts a limit on
the n-type conductivity of (Al,Ga,In)-doped ZnO in the presence of hydrogen.


  Given the absence of observations of black hole binaries, it is critical that
the full range of accessible parameter space be explored in anticipation of
future observation with gravitational wave detectors. To this end, we compile
the Hamiltonian equations of motion describing the conservative dynamics of the
most general black hole binaries, as computed by Will and collaborators, and
incorporate an effective treatment of dissipation through gravitational
radiation. We evolve these equations for systems with orbital eccentricity and
precessing spins. We find that, while spin-spin coupling corrections can
destroy constant radius orbits in principle, the effect is so small that orbits
will reliably tend to quasi-spherical orbits as angular momentum and energy are
lost to gravitational radiation. Still, binaries that are initially highly
eccentric may retain eccentricity as they pass into the detectable bandwidth of
ground-based gravitational wave detectors. We also show that a useful set of
natural frequencies for an orbit demonstrating both spin precession and
periastron precession is comprised of (1) the frequency of angular motion in
the orbital plane, (2) the frequency of the plane precession, and (3) the
frequency of radial oscillations. These three natural harmonics shape the
observed waveform.


  Multiplex networks are receiving increasing interests because they allow to
model relationships between networked agents on several layers simultaneously.
In this supplementary material for the paper "Navigability of interconnected
networks under random failures", we extend well-known random walks to
multiplexes and we introduce a new type of walk that can exist only in
multiplexes. We derive exact expressions for vertex occupation time and the
coverage. Finally, we show how the efficiency in exploring the multiplex
critically depends on the underlying topology of layers, the weight of their
inter-connections and the strategy adopted to walk.


  Performance interference can occur when various services are executed over
the same physical infrastructure in a cloud system. This can lead to
performance degradation compared to the execution of services in isolation.
This work proposes a Confirmatory Factor Analysis (CFA)-based model to estimate
performance interference across containers, caused by the use of CPU, memory
and IO across a number of co-hosted applications. The approach provides
resource characterization through human comprehensible indices expressed as
time series, so the interference in the entire execution lifetime of a service
can be analyzed. Our experiments, based on the combination of real services
with different profiles executed in Docker containers, suggest that our model
can accurately predict the overall execution time, for different service
combinations. The approach can be used by a service designer to identify
phases, during the execution life-cycle of a service, that are likely to lead
to a greater degree of interference, and to ensure that only complementary
services are hosted on the same physical machine. Interference-awareness of
this kind will enable more intelligent resource management and scheduling for
cloud systems, and may be used to dynamically modify scheduling decisions.


  In this paper we study the interaction of a Dirac-Pauli particle with an
electromagnetic plane wave, by using a previously given generalization of the
pseudo-classical Lagrangian for a spinning particle with an anomalous magnetic
moment. We derive the explicit expressions for the eigenfunctions and the
Green's functions of the theory. We discuss the validity of the semi-classical
approach by comparing the wavefunctions with the (pseudo)-classical solutions
of the Hamilton-Jacobi equation.


  We apply the orbit method to obtain formula for multiplicities of certain
representations of unipotent groups over the finite field.


  We consider two disordered lattice models on the square lattice: on the
medial lattice the random field Ising model at T=0 and on the direct lattice
the random bond Potts model in the large-q limit at its transition point. The
interface properties of the two models are known to be related by a mapping
which is valid in the continuum approximation. Here we consider finite random
samples with the same form of disorder for both models and calculate the
respective equilibrium states exactly by combinatorial optimization algorithms.
We study the evolution of the interfaces with the strength of disorder and
analyse and compare the interfaces of the two models in finite lattices.


  With new forthcoming intense neutrino beams, for the study of neutrino
oscillations, it is possible to consider other physics experiments that can be
done with these extreme neutrino fluxes available close to the source.


  Nuclear segmentation, classification and quantification within Haematoxylin &
Eosin stained histology images enables the extraction of interpretable
cell-based features that can be used in downstream explainable models in
computational pathology. The Colon Nuclei Identification and Counting (CoNIC)
Challenge is held to help drive forward research and innovation for automatic
nuclei recognition in computational pathology. This report describes our
proposed method submitted to the CoNIC challenge. Our method employs a
multi-task learning framework, which performs a panoptic segmentation task and
a regression task. For the panoptic segmentation task, we use encoder-decoder
type deep neural networks predicting a direction map in addition to a
segmentation map in order to separate neighboring nuclei into different
instances


  We present a rigorous framework that combines single-particle Green's
function theory with density functional theory based on a separation of
electron-electron interactions into short-range and long-range components.
Short-range contributions to the total energy and exchange-correlation
potential are provided by a density functional approximation, while the
long-range contribution is calculated using an explicit many-body Green's
function method. Such a hybrid results in a nonlocal, dynamic, and
orbital-dependent exchange-correlation functional of a single-particle Green's
function. In particular, we present a range-separated hybrid functional called
srSVWN5-lrGF2 which combines the local-density approximation and the
second-order Green's function theory. We illustrate that similarly to density
functional approximations the new functional is weakly basis-set dependent.
Furthermore, it offers an improved description of the short-range dynamical
correlation. The many-body contribution to the functional allows us to mitigate
the many-electron self-interaction error present in most of density functional
approximations and provides a better description of molecular properties.
Additionally, the new functional can be used to scale down the self-energy and,
therefore, introduce an additional sparsity to the self-energy matrix that in
the future can be exploited in calculations for large molecules or periodic
systems.


  We consider the Fokker-Planck equation on metric graphs. Vertex boundary
conditions are imposed in the form of weight continuity and the probability
current conservation. Exact solution of the is obtained for star, tree and loop
graphs. Applications of the model to Brownian motion in networks and other
problems are briefly discussed.


  The reliability of any day-to-day material is critically dictated by its
properties. One factor which governs the behaviour of a material, under a given
condition, is the microstructure. Despite the absence of any phase
transformation, a change in the microstructure would significantly alter the
properties. Therefore, a substantial understanding on the stability of the
microstructure is vital to avert any unexpected catastrophic change in the
material properties. In the present work, one such numerical approach called
phase-field modelling in employed to analyse the stability of two- and
three-dimensional finite structures, which dictate the curvature-driven
evolution of the microstructure. A characteristic feature of this numerical
approach is the introduction of a scalar variable, called the phase field, in
addition to the other thermodynamic variables. While the inclusion of the phase
field obviates the need for the interface tracking, which is a strenuous aspect
of the other conventional techniques, it replaces the sharp interface with a
finite diffuse region. Therefore, before adopting and extending the phase-field
technique, it is shown that the model recovers the governing law, i.e,
Gibbs-Thomson relation, despite the introduction of the diffuse interface.
Subsequently, the numerical treatment is employed to investigate the
volume-diffusion governed curvature-induced transformation.


  Security practices in large organizations are notoriously difficult to
assess. The challenge only increases when organizations turn to third parties
to provide technology and business services, which typically require tight
network integration and sharing of confidential data, potentially increasing
the organization's attack surface. The security maturity of an organization
describes how well it mitigates known risks and responds to new threats. Today,
maturity is typically assessed with audits and questionnaires, which are
difficult to quantify, lack objectivity, and may not reflect current threats.
  This paper demonstrates how external measurement of an organization can be
used to assess the relative quality of security among organizations. Using a
large dataset from BitSight(www.bitsight.com), a cybersecurity ratings company,
containing 3.2 billion measurements spanning nearly 37,000 organizations
collected during calendar year 2015, we show how per-organizational "risk
vectors" can be constructed that may be related to an organization's overall
security posture, or maturity. Using statistical analysis, we then study the
correlation between the risk vectors and botnet infections. For example, we
find that misconfigured TLS services, publicly available unsecured protocols,
and the use of peer-to-peer file sharing correlate with organizations that have
increased rates of botnet infections. We argue that the methodology used to
identify these correlations can easily be applied to other data to provide a
growing picture of organizational security using external measurement.


  The aim of this work is to analyze Kaehler moduli space monodromies of string
compactifications. This is achieved by investigating the monodromy action upon
D-brane probes, which we model in the Landau-Ginzburg phase in terms of matrix
factorizations. The two-dimensional cubic torus and the quintic Calabi-Yau
hypersurface serve as our two prime examples.


  Peer-to-peer file sharing envisions a data-centric dissemination model, where
files consisting of multiple data pieces can be shared from any peer that can
offer the data or from multiple peers simultaneously. This aim, implemented at
the application layer of the network architecture, matches with the objective
of Named Data Networking (NDN), a proposed Internet architecture that features
a data-centric communication model at the network layer. To study the impact of
a data-centric network architecture on peer-to-peer file sharing, we proposed
nTorrent, a peer-to-peer file sharing application on top of NDN. Since the
initial nTorrent proposal in 2017, we have implemented its design in ndnSIM,
the de facto NDN simulator. In this paper, we present the design of our
nTorrent simulation framework, discussing various design decisions and
trade-offs. We also describe our experimentation and validation process to
ensure that our framework possesses the fundamental properties of nTorrent.


  Real landscapes are usually characterized by long-range height-height
correlations, which are quantified by the Hurst exponent $H$. We analyze the
statistical properties of the isoheight lines for correlated landscapes of
$H\in [-1,1]$. We show numerically that, for $H\leq 0$ the statistics of these
lines is compatible with $SLE$ and that established analytic results are
recovered for $H=-1$ and $H=0$. This result suggests that for negative $H$, in
spite of the long-range nature of correlations, the statistics of isolines is
fully encoded in a Brownian motion with a single parameter in the continuum
limit. By contrast, for positive $H$ we find that the one-dimensional time
series encoding the isoheight lines is not Markovian and therefore not
consistent with $SLE$.


  We investigate the $D^*K^*$ and $D^*_s\rho$ interaction in coupled channels
within the hidden gauge formalism. A structure is developed around their
thresholds, short of producing a bound state, which leads to a peak in the
$D_s^+ \pi^-$ mass distribution in the $B^0 \to \bar{D}^0 D_s^+ \pi^-$ decay
compatible with the experimental data. We conclude that the interaction between
the $D^*K^*$ and $D^*_s\rho$ is essential to produce the cusp structure that we
associate to the recently seen $T_{c\bar{s}}(2900)$, and that its experimental
width is mainly due to the decay width of the $\rho$ meson. The peak obtained
together with a smooth background reproduces fairly well the experimental mass
distribution observed in the $B_0 \to \bar{D}^0 D_s^+ \pi^-$ decay.


  Accurate airway extraction from computed tomography (CT) images is a critical
step for planning navigation bronchoscopy and quantitative assessment of
airway-related chronic obstructive pulmonary disease (COPD). The existing
methods are challenging to sufficiently segment the airway, especially the
high-generation airway, with the constraint of the limited label and cannot
meet the clinical use in COPD. We propose a novel two-stage 3D contextual
transformer-based U-Net for airway segmentation using CT images. The method
consists of two stages, performing initial and refined airway segmentation. The
two-stage model shares the same subnetwork with different airway masks as
input. Contextual transformer block is performed both in the encoder and
decoder path of the subnetwork to finish high-quality airway segmentation
effectively. In the first stage, the total airway mask and CT images are
provided to the subnetwork, and the intrapulmonary airway mask and
corresponding CT scans to the subnetwork in the second stage. Then the
predictions of the two-stage method are merged as the final prediction.
Extensive experiments were performed on in-house and multiple public datasets.
Quantitative and qualitative analysis demonstrate that our proposed method
extracted much more branches and lengths of the tree while accomplishing
state-of-the-art airway segmentation performance. The code is available at
https://github.com/zhaozsq/airway_segmentation.


  Computing the marginal likelihood or evidence is one of the core challenges
in Bayesian analysis. While there are many established methods for estimating
this quantity, they predominantly rely on using a large number of posterior
samples obtained from a Markov Chain Monte Carlo (MCMC) algorithm. As the
dimension of the parameter space increases, however, many of these methods
become prohibitively slow and potentially inaccurate. In this paper, we propose
a novel method in which we use the MCMC samples to learn a high probability
partition of the parameter space and then form a deterministic approximation
over each of these partition sets. This two-step procedure, which constitutes
both a probabilistic and a deterministic component, is termed a Hybrid
approximation to the marginal likelihood. We demonstrate its versatility in a
plethora of examples with varying dimension and sample size, and we also
highlight the Hybrid approximation's effectiveness in situations where there is
either a limited number or only approximate MCMC samples available.


  We present a comparative analysis of the self-gravitating solitons arising in
the Einstein-Klein-Gordon, Einstein-Dirac and Einstein-Proca models, for the
particular case of static, spherically symmetric spacetimes. Differently from
the previous study arXiv:1708.05674, the matter fields possess suitable
self-interacting terms in the Lagrangians, which allow for the existence of
$Q$-ball--type solutions for these models in the flat spacetime limit. In spite
of this important difference, our analysis shows that the high degree of
universality observed in arXiv:1708.05674 remains, and various spin-independent
common patterns are observed.


  The analog electronic computers are a type of circuitry used to calculate
specific problems using the physical relationships between the voltages and
currents following classical laws of physics. One specific class of these
circuits are computers based on the interactions between passive circuit
elements. Models presented by G.Kron in 1945 are the example of using such
passive elements to construct a solver for the problem of free quantum
particles confined by rectangular potential. Numerical validation of Kron
second model is conducted for different shapes of particle confining potential.
Model introduced by Kron is generalized by introduction of non-linear resistive
elements what implies deformation of Schr\"odinger equation solution into
Ginzburg-Landau form.


  This paper is being replaced by another of the author's that contains a brief
summary of the problem of positivity of Green's functions, heat kernels, and
principal eigenvalues of higher-order elliptic differential operators.


  We study a data analyst's problem of acquiring data from self-interested
individuals to obtain an accurate estimation of some statistic of a population,
subject to an expected budget constraint. Each data holder incurs a cost, which
is unknown to the data analyst, to acquire and report his data. The cost can be
arbitrarily correlated with the data. The data analyst has an expected budget
that she can use to incentivize individuals to provide their data. The goal is
to design a joint acquisition-estimation mechanism to optimize the performance
of the produced estimator, without any prior information on the underlying
distribution of cost and data. We investigate two types of estimations:
unbiased point estimation and confidence interval estimation.
  Unbiased estimators: We design a truthful, individually rational, online
mechanism to acquire data from individuals and output an unbiased estimator of
the population mean when the data analyst has no prior information on the
cost-data distribution and individuals arrive in a random order. The
performance of this mechanism matches that of the optimal mechanism, which
knows the true cost distribution, within a constant factor. The performance of
an estimator is evaluated by its variance under the worst-case cost-data
correlation.
  Confidence intervals: We characterize an approximately optimal (within a
factor $2$) mechanism for obtaining a confidence interval of the population
mean when the data analyst knows the true cost distribution at the beginning.
This mechanism is efficiently computable. We then design a truthful,
individually rational, online algorithm that is only worse than the
approximately optimal mechanism by a constant factor. The performance of an
estimator is evaluated by its expected length under the worst-case cost-data
correlation.


  We consider the collinear ternary fission which is a sequential ternary decay
with a very short time between the ruptures of two necks connecting the middle
cluster of the ternary nuclear system and outer fragments. In particular, we
consider the case where the Coulomb field of the first massive fragment
separated during the first step of the fission produces a lower pre-scission
barrier in the second step of the residual part of the ternary system. In this
case, we obtain a probability of about $10^{-3}$ for the yield of massive
clusters such as \nuclide[70]{Ni}, \nuclide[80-82]{Ge}, \nuclide[86]{Se}, and
\nuclide[94]{Kr} in the ternary fission of \nuclide[252]{Cf}. These products
appear together with the clusters having mass numbers of $A = 132$--$140$. The
results show that the yield of a heavy cluster such as \nuclide[68-70]{Ni}
would be followed by a product of $A = 138$--$148$ with a large probability as
observed in the experimental data obtained with the FOBOS spectrometer at the
Joint Institute for Nuclear Research. The third product is not observed. The
landscape of the potential energy surface shows that the configuration of the
Ni + Ca + Sn decay channel is lower about 12 MeV than that of the Ca + Ni + Sn
channel. This leads to the fact, that the yield of Ni and Sn is large. The
analysis on the dependence of the velocity of the middle fragment on mass
numbers of the outer products leads to the conclusion that, in the collinear
tripartition channel of \nuclide[252]{Cf}, the middle cluster has a very small
velocity, which does not allow it to be found in experiments.


  Child welfare (CW) agencies use risk assessment tools as a means to achieve
evidence-based, consistent, and unbiased decision-making. These risk
assessments act as data collection mechanisms and have further evolved into
algorithmic systems in recent years. Moreover, several of these algorithms have
reinforced biased theoretical constructs and predictors because of the easy
availability of structured assessment data. In this study, we critically
examine the Washington Assessment of Risk Model (WARM), a prominent risk
assessment tool that has been adopted by over 30 states in the United States
and has been repurposed into more complex algorithms. We compared WARM against
the narrative coding of casenotes written by caseworkers who used WARM. We
found significant discrepancies between the casenotes and WARM data where WARM
scores did not not mirror caseworkers' notes about family risk. We provide the
SIGCHI community with some initial findings from the quantitative
de-construction of a child-welfare algorithm.


  We argue that the data published by the Pierre Auger Collaboration
(arXiv:0711.2256) disfavor at 99% confidence level their hypothesis that most
of the highest-energy cosmic rays are protons from nearby astrophysical
sources, either Active Galactic Nuclei or other objects with a similar spatial
distribution.


  We computationally study the frictional properties of sheared granular media
subjected to harmonic vibration applied at the boundary. Such vibrations are
thought to play an important role in weakening flows, yet the independent
effects of amplitude, frequency, and pressure on the process have remained
unclear. Based on a dimensional analysis and DEM simulations, we show that, in
addition to a previously proposed criterion for peak acceleration that leads to
breaking of contacts, weakening requires the absolute amplitude squared of the
displacement is sufficiently large relative to the confining pressure. The
analysis provides a basis for predicting flows subjected to arbitrary external
vibration and demonstrates that a previously unrecognized second process that
is dependent on dissipation contributes to shear weakening under vibrations.


  We consider a two-user state-dependent multiaccess channel in which the
states of the channel are known non-causally to one of the encoders and only
strictly causally to the other encoder. Both encoders transmit a common message
and, in addition, the encoder that knows the states non-causally transmits an
individual message. We find explicit characterizations of the capacity region
of this communication model in both discrete memoryless and memoryless Gaussian
cases. The analysis also reveals optimal ways of exploiting the knowledge of
the state only strictly causally at the encoder that sends only the common
message when such a knowledge is beneficial. The encoders collaborate to convey
to the decoder a lossy version of the state, in addition to transmitting the
information messages through a generalized Gel'fand-Pinsker binning.
Particularly important in this problem are the questions of 1) optimal ways of
performing the state compression and 2) whether or not the compression indices
should be decoded uniquely. We show that both compression \`a-la noisy network
coding, i.e., with no binning, and compression using Wyner-Ziv binning are
optimal. The scheme that uses Wyner-Ziv binning shares elements with Cover and
El Gamal original compress-and-forward, but differs from it mainly in that
backward decoding is employed instead of forward decoding and the compression
indices are not decoded uniquely. Finally, by exploring the properties of our
outer bound, we show that, although not required in general, the compression
indices can in fact be decoded uniquely essentially without altering the
capacity region, but at the expense of larger alphabets sizes for the auxiliary
random variables.


  Humans can often quickly and efficiently solve complex new learning tasks
given only a small set of examples. In contrast, modern artificially
intelligent systems often require thousands or millions of observations in
order to solve even the most basic tasks. Meta-learning aims to resolve this
issue by leveraging past experiences from similar learning tasks to embed the
appropriate inductive biases into the learning system. Historically methods for
meta-learning components such as optimizers, parameter initializations, and
more have led to significant performance increases. This thesis aims to explore
the concept of meta-learning to improve performance, through the
often-overlooked component of the loss function. The loss function is a vital
component of a learning system, as it represents the primary learning
objective, where success is determined and quantified by the system's ability
to optimize for that objective successfully.


  Handling imbalanced datasets is a challenging problem that if not treated
correctly results in reduced classification performance. Imbalanced datasets
are commonly handled using minority oversampling, whereas the SMOTE algorithm
is a successful oversampling algorithm with numerous extensions. SMOTE
extensions do not have a theoretical guarantee during training to work better
than SMOTE and in many instances their performance is data dependent. In this
paper we propose a novel extension to the SMOTE algorithm with a theoretical
guarantee for improved classification performance. The proposed approach
considers the classification performance of both the majority and minority
classes. In the proposed approach CGMOS (Certainty Guided Minority
OverSampling) new data points are added by considering certainty changes in the
dataset. The paper provides a proof that the proposed algorithm is guaranteed
to work better than SMOTE for training data. Further experimental results on 30
real-world datasets show that CGMOS works better than existing algorithms when
using 6 different classifiers.


  The R CrA region was observed in the 3.5 and 6.2 cm continuum with high
angular resolutions (0.6--1.7 arcseconds) using the Very Large Array. Archival
data sets were also analyzed for comparison, which provided angular resolutions
up to 0.3 arcseconds. A cluster of young stellar objects was detected, and a
rich array of star forming activities was revealed. IRS 7A showed an enhanced
outflow activity recently. The main peak of IRS 7A positionally coincides with
an X-ray source, which suggests that the X-ray emission is directly related to
the central protostar. The Class 0 source SMA 2 is associated with a double
radio source, B 9a and 9b, and seems to be driving two outflows. The B 9
complex is probably a multiple-protostar system. Both B 9a and 9b are
nonthermal radio sources with negative spectral indices. IRS 7B is a compact
radio source surrounded by an extended structure. The compact source
corresponds to the Class 0/I source SMA 1, and it is also closely associated
with an X-ray source, suggesting that magnetic activities start early in the
protostellar stage of evolution. The extended structure of IRS 7B may be a
bipolar outflow. IRS 5 was resolved into two sources with a separation of 0.9
arcseconds. Both IRS 5a and 5b display radio flares and X-ray emission,
suggesting that energetic magnetic processes are active in both members. The
month-scale active phase of IRS 5b implies that the flare activity must involve
large-scale magnetic fields. During the strong flare event of IRS 5b in 1998,
IRS 5a also showed an enhanced level of radio emission. This concurrent
activity suggests that IRS 5 may be an interacting young binary system, but the
interaction mechanism is unknown. Alternatively, what was seen in the radio
images could be a circumbinary halo.


  The quantum theory of a free particle on a portion of two-dimensional
Euclidean space bounded by a circle and subject to non-local boundary
conditions gives rise to bulk and surface states. Starting from this well known
property, a counterpart for gravity is here considered. In particular, if
spatial components of metric perturbations are set to zero at the boundary,
invariance of the full set of boundary conditions under infinitesimal
diffeomorphisms is compatible with non-local boundary conditions on normal
components of metric perturbations if and only if both the gauge-field operator
and the ghost operator are pseudo-differential operators in one-loop quantum
gravity.


  A growing body of applied mathematics literature in recent years has focussed
on the application of fractional calculus to problems of anomalous transport.
In these analyses, the anomalous transport (of charge, tracers, fluid, etc.) is
presumed attributable to long-range correlations of material properties within
an inherently complex, and in some cases self-similar, conducting medium.
Rather than considering an exquisitely discretized (and computationally
intractable) representation of the medium, the complex and spatially correlated
heterogeneity is represented through reformulation of the PDE governing the
relevant transport physics such that its coefficients are, instead, smooth but
paired with fractional-order space derivatives. Here we apply these concepts to
the scalar Helmholtz equation and its use in electromagnetic interrogation of
Earth's interior through the magnetotelluric method. We outline a practical
algorithm for solving the Helmholtz equation using spectral methods coupled
with finite element discretization. Execution of this algorithm for the
magnetotelluric problem reveals several interesting features observable in
field data: long--range correlation of the predicted electromagnetic fields; a
power-law relationship between the squared impedance amplitude and squared
wavenumber whose slope is a function of the fractional exponent within the
governing Helmholtz equation; and, a non-constant apparent resistivity spectrum
whose variability arises solely from the fractional exponent. In geologic
settings characterized by self--similarity (e.g. fracture systems; thick and
richly-textured sedimentary sequences, etc.) we posit that diagnostics are
useful for geologic characterization of features far below the typical
resolution limit of electromagnetic methods in geophysics.


  Considering the dynamics of a polymer with finite extensibility placed in a
chaotic flow with large mean shear, we explain how the statistics of polymer
extension changes with Weissenberg number, ${\it Wi}$, defined as the product
of the polymer relaxation time and the Lyapunov exponent of the flow. Four
regimes, of the ${\it Wi}$ number, are identified. One below the coil-stretched
transition and three above the coil-stretched transition. Specific emphasis is
given to explaining these regimes in terms of the polymer dynamics.


  We study a dynamic optimal transport problem on a network. Despite the cost
for transport along the edges, an additional cost, scaled with a parameter
$\kappa$, has to be paid for interchanging mass between edges and vertices. We
show existence f minimisers using duality and discuss the relationship of the
model to other metrics such as Fisher-Rao and the classical Wasserstein metric.
Finally, we examine the limiting behaviour of the model in terms of the
parameter $\kappa$.


  The dynamical behavior of open quantum systems plays a key role in many
applications of quantum mechanics, examples ranging from fundamental problems,
such as the environment-induced decay of quantum coherence and relaxation in
many-body systems, to applications in condensed matter theory, quantum
transport, quantum chemistry and quantum information. In close analogy to a
classical Markov process, the interaction of an open quantum system with a
noisy environment is often modelled by a dynamical semigroup with a generator
in Lindblad form, which describes a memoryless dynamics leading to an
irreversible loss of characteristic quantum features. However, in many
applications open systems exhibit pronounced memory effects and a revival of
genuine quantum properties such as quantum coherence and correlations. Here,
recent results on the rich non-Markovian quantum dynamics of open systems are
discussed, paying particular attention to the rigorous mathematical definition,
to the physical interpretation and classification, as well as to the
quantification of memory effects. The general theory is illustrated by a series
of examples. The analysis reveals that memory effects of the open system
dynamics reflect characteristic features of the environment which opens a new
perspective for applications, namely to exploit a small open system as a
quantum probe signifying nontrivial features of the environment it is
interacting with. This article further explores the various physical sources of
non-Markovian quantum dynamics, such as structured spectral densities, nonlocal
correlations between environmental degrees of freedom and correlations in the
initial system-environment state, in addition to developing schemes for their
local detection. Recent experiments on the detection, quantification and
control of non-Markovian quantum dynamics are also discussed.


  Physical properties of capsids of plant and animal viruses are important
factors in capsid self-assembly, survival of viruses in the extracellular
environment, and their cell infectivity. Virus shells can have applications as
nanocontainers and delivery vehicles in biotechnology and medicine. Combined
AFM experiments and computational modeling on sub-second timescales of the
indentation nanomechanics of Cowpea Chlorotic Mottle Virus (CCMV) capsid show
that the capsid's physical properties are dynamic and local characteristics of
the structure, which depend on the magnitude and geometry of mechanical input.
Surprisingly, under large deformations the CCMV capsid transitions to the
collapsed state without substantial local structural alterations. The enthalpy
change in this deformation state dH = 11.5 - 12.8 MJ/mol is mostly due to
large-amplitude out-of-plane excitations, which contribute to the capsid
bending, and the entropy change TdS = 5.1 - 5.8 MJ/mol is mostly due to
coherent in-plane rearrangements of protein chains, which result in the capsid
stiffening. Dynamic coupling of these modes defines the extent of elasticity
and reversibility of capsid mechanical deformation. This emerging picture
illuminates how unique physico-chemical properties of protein nanoshells help
define their structure and morphology, and determine their viruses' biological
function.


  Domain wall and electrovac solutions of gauged N=4 D=4 supergravity, with
gauge group SU(2) or SU(2)xSU(2), are interpreted as supersymmetric
Kaluza-Klein vacua of N=1 D=10 supergravity. These vacua are shown to be the
near-horizon geometries of certain intersecting brane solutions.


  Deep neural network (DNN) classifiers are vulnerable to backdoor attacks. An
adversary poisons some of the training data in such attacks by installing a
trigger. The goal is to make the trained DNN output the attacker's desired
class whenever the trigger is activated while performing as usual for clean
data. Various approaches have recently been proposed to detect malicious
backdoored DNNs. However, a robust, end-to-end training approach, like
adversarial training, is yet to be discovered for backdoor poisoned data. In
this paper, we take the first step toward such methods by developing a robust
training framework, COLLIDER, that selects the most prominent samples by
exploiting the underlying geometric structures of the data. Specifically, we
effectively filter out candidate poisoned data at each training epoch by
solving a geometrical coreset selection objective. We first argue how clean
data samples exhibit (1) gradients similar to the clean majority of data and
(2) low local intrinsic dimensionality (LID). Based on these criteria, we
define a novel coreset selection objective to find such samples, which are used
for training a DNN. We show the effectiveness of the proposed method for robust
training of DNNs on various poisoned datasets, reducing the backdoor success
rate significantly.


  The problem of detecting and removing redundant constraints is fundamental in
optimization. We focus on the case of linear programs (LPs) in dictionary form,
given by $n$ equality constraints in $n+d$ variables, where the variables are
constrained to be nonnegative. A variable $x_r$ is called redundant, if after
removing $x_r \geq 0$ the LP still has the same feasible region. The time
needed to solve such an LP is denoted by $LP(n,d)$.
  It is easy to see that solving $n+d$ LPs of the above size is sufficient to
detect all redundancies. The currently fastest practical method is the one by
Clarkson: it solves $n+d$ linear programs, but each of them has at most $s$
variables, where $s$ is the number of nonredundant constraints.
  In the first part we show that knowing all of the finitely many dictionaries
of the LP is sufficient for the purpose of redundancy detection. A dictionary
is a matrix that can be thought of as an enriched encoding of a vertex in the
LP. Moreover - and this is the combinatorial aspect - it is enough to know only
the signs of the entries, the actual values do not matter. Concretely we show
that for any variable $x_r$ one can find a dictionary, such that its sign
pattern is either a redundancy or nonredundancy certificate for $x_r$.
  In the second part we show that considering only the sign patterns of the
dictionary, there is an output sensitive algorithm of running time
$\mathcal{O}(d \cdot (n+d) \cdot s^{d-1} \cdot LP(s,d) + d \cdot s^{d} \cdot
LP(n,d))$ to detect all redundancies. In the case where all constraints are in
general position, the running time is $\mathcal{O}(s \cdot LP(n,d) + (n+d)
\cdot LP(s,d))$, which is essentially the running time of the Clarkson method.
Our algorithm extends naturally to a more general setting of arrangements of
oriented topological hyperplane arrangements.


  We verify the inductive blockwise Alperin weight (BAW) and the inductive
Alperin-McKay (AM) conditions introduced by the second author for blocks of
finite quasisimple groups with cyclic defect groups. Furthermore we establish a
criterion that describes conditions under which the inductive AM condition for
blocks with abelian defect groups implies the inductive BAW condition for those
blocks.


  We have measured the sideward flow of neutral strange ($K^0_s$) mesons in 6
AGeV Au + Au collisions. A prominent anti-flow signal is observed for an impact
parameter range (b $\lesssim 7$ fm) which spans central and mid-central events.
Since the $K^0_s$ scattering cross section is relatively small in nuclear
matter, this observation suggests that the in-medium kaon vector potential
plays an important role in high density nuclear matter.


  In the color-flavor-locked (CFL) phase of the QCD superconductor we show that
baryons behave as qualitons (called "superqualitons") with quantum numbers
$B=(1 {\rm mod} 2)/3$, S=1/2 and Y=B. An intriguing possibility implied by this
identification is that light baryonic modes in the form of superqualitons could
be excited below the (color) superconducting gap in the CFL phase, a novel
phenomenon foreign to normal superconductors.


  In a recent paper we studied the entanglement content of zero-density excited
states in complex free quantum field theories, focusing on the symmetry
resolved entanglement entropy (SREE). By zero-density states we mean states
consisting of a fixed, finite number of excitations above the ground state in
an infinite-volume system. The SREE is defined for theories that possess an
internal symmetry and provides a measure of the contribution to the total
entanglement of each symmetry sector. In our work, we showed that the ratio of
Fourier-transforms of the SREEs (i.e. the ratio of charged moments) takes a
very simple and universal form for these states, which depends only on the
number, statistics and symmetry charge of the excitations as well as the
relative size of the entanglement region with respect to the whole system's
size. In this paper we provide numerical evidence for our formulae by computing
functions of the charged moments in two free lattice theories: a 1D Fermi gas
and a complex harmonic chain. We also extend our results in two directions: by
showing that they apply also to excited states of interacting theories (i.e.
magnon states) and by developing a higher dimensional generalisation of the
branch point twist field picture, leading to results in (interacting)
higher-dimensional models.


  Let $Y_K\to X_K$ be a ramified cyclic covering of curves, where $K$ is a
cyclotomic field. In this work we study the $p$-rank of the reduction
$\rm{mod}\, p$ of a model of the jacobian of $Y_K$. In this way, we obtain
counterparts of the Deuring polynomial, defined for elliptic curves, for genus
greater than one. Moreover, we show that curves $Y_K$ give Hecke characters for
cyclotomic fields. To carry out this study we use Jacobi sums and certain
$L$-functions.


  ALP domain walls without strings may be formed in the early Universe. We
point out that such ALP domain walls lead to both isotropic and anisotropic
birefringence of cosmic microwave background (CMB) polarization, which reflects
spatial configuration of the domain walls at the recombination. The
polarization plane of the CMB photon coming from each domain is either not
rotated at all or rotated by a fixed angle. For domain walls following the
scaling solution, the cosmic birefringence of CMB is characterized by $2^{N}$,
i.e. $N$-bit, of information with $N = {\cal O}(10^{3-4})$ being equal to the
number of domains at the last scattering surface, and thus the name, $kilobyte~
cosmic~ birefringence$. The magnitude of the isotropic birefringence is
consistent with the recently reported value, while the anisotropic one is
determined by the structure of domains at the last scattering surface. The
predicted cosmic birefringence is universal over a wide range of the ALP mass
and coupling to photons. The detection of both signals will be a smoking-gun
evidence for the ALP domain walls without strings.


  We present the evolution of dark matter halos in six large cosmological
N-body simulations, called the $\nu^2$GC (New Numerical Galaxy Catalog)
simulations on the basis of the LCDM cosmology consistent with observational
results obtained by the Planck satellite. The largest simulation consists of
$8192^3$ (550 billion) dark matter particles in a box of $1.12 \, h^{-1} \rm
Gpc$ (a mass resolution of $2.20 \times 10^{8} \, h^{-1} M_{\odot}$). Among
simulations utilizing boxes larger than $1 \, h^{-1} \rm Gpc$, our simulation
yields the highest resolution simulation that has ever been achieved. A
$\nu^2$GC simulation with the smallest box consists of eight billions particles
in a box of $70 \, h^{-1} \rm Mpc$ (a mass resolution of $3.44 \times 10^{6} \,
h^{-1} M_{\odot}$). These simulations can follow the evolution of halos over
masses of eight orders of magnitude, from small dwarf galaxies to massive
clusters. Using the unprecedentedly high resolution and powerful statistics of
the $\nu^2$GC simulations, we provide statistical results of the halo mass
function, mass accretion rate, formation redshift, and merger statistics, and
present accurate fitting functions for the Planck cosmology. By combining the
$\nu^2$GC simulations with our new semi-analytic galaxy formation model, we are
able to prepare mock catalogs of galaxies and active galactic nuclei, which
will be made publicly available in the near future.


  We develop a scaling theory for KPZ growth in one dimension by a detailed
study of the polynuclear growth (PNG) model. In particular, we identify three
universal distributions for shape fluctuations and their dependence on the
macroscopic shape. These distribution functions are computed using the
partition function of Gaussian random matrices in a cosine potential.


  Increasingly, phonetic research utilizes data collected from participants who
record themselves on readily available devices. Though such recordings are
convenient, their suitability for acoustic analysis remains an open question,
especially regarding how the individual methods affect acoustic measures over
time. We used Quantile Generalized Additive Mixed Models (QGAMMs) to analyze
measures of F0, intensity, and the first and second formants, comparing files
recorded using a laboratory-standard recording method (Zoom H6 Recorder with an
external microphone), to three remote recording methods, (1) the Awesome Voice
Recorder application on a smartphone (AVR), (2) the Zoom meeting application
with default settings (Zoom-default), and (3) the Zoom meeting application with
the "Turn on Original Sound" setting (Zoom-raw). A linear temporal alignment
issue was observed for the Zoom methods over the course of the long, recording
session files. However, the difference was not significant for utterance-length
files. F0 was reliably measured using all methods. Intensity and formants
presented non-linear differences across methods that could not be corrected for
simply. Overall, the AVR files were most similar to the H6's, and so AVR is
deemed to be a more reliable recording method than either Zoom-default or
Zoom-raw.


  Ferromagnetic metal-organic semiconductor (FM-OSC) hybrid interfaces have
shown to play an important role for spin injection in organic spintronics.
Here, 11,11,12,12-tetracyanonaptho-2,6-quinodimethane (TNAP) is introduced as
an interfacial layer in Co-OSCs heterojunction with an aim to tune the spin
injection. The Co/TNAP interface is investigated by use of X-ray and
ultraviolet photoelectron spectroscopy (XPS/UPS), near edge X-ray absorption
fine structure (NEXAFS) and X-ray magnetic circular dichroism (XMCD). Hybrid
interface states (HIS) are observed at Co/TNAP interface resulting from
chemical interaction between Co and TNAP. The energy level alignment at
Co/TNAP/OSCs interface is also obtained, and a reduction of the hole injection
barrier is demonstrated. XMCD results confirm sizeable spin polarization at the
Co/TNAP hybrid interface.


  Given a semisimple stable autonomous tensor category over a field $K$, to any
group presentation with finite number of generators we associate an element
$Q(P)\in K$ invariant under the Andrews-Curtis moves. We show that in fact,
this is the same invariant as the one produced by the algorithm of Frank Quinn.
The new definition allows us to present a relatively simple proof of the
invariance and to evaluate $Q(P)$ for some presentations. On the basis of some
numerical calculations over different Gelfand-Kazhdan categories, we make a
conjecture which relates the value of $Q(P)$ for two different classes of
presentations.


  The scattering matrix was measured for microwave cavities with two antennas.
It was analyzed in the regime of overlapping resonances. The theoretical
description in terms of a statistical scattering matrix and the rescaled
Breit-Wigner approximation has been applied to this regime. The experimental
results for the auto-correlation function show that the absorption in the
cavity walls yields an exponential decay. This behavior can only be modeled
using a large number of weakly coupled channels. In comparison to the
auto-correlation functions, the cross-correlation functions of the diagonal
S-matrix elements display a more pronounced difference between regular and
chaotic systems.


  Electron-doped high-Tc FeSe reportedly has a strong electron correlation that
is enhanced with doping. It has been noticed that significant electric fields
exist inevitably between FeSe and external donors along with electron transfer.
However, the effects of such fields on electron correlation are yet to be
explored. Here we study potassium- (K-) dosed FeSe layers using
density-functional theory combined with dynamical mean-field theory to
investigate the roles of such electric fields on the strength of the electron
correlation. We find, very interestingly, the electronic potential-energy
difference between the topmost Se and Fe atomic layers, generated by local
electric fields of ionized K atoms, weakens the Se-mediated hopping between Fe
d orbitals. Since it is the dominant hopping channel in FeSe, its reduction
narrows the Fe d bands near the Fermi level, enhancing the electron
correlation. This effect is orbital dependent and occurs in the topmost FeSe
layer only. We also find the K dosing may increase the Se height, enhancing the
electron correlation further. These results shed new light on the comprehensive
study of high-Tc FeSe and other low-dimensional systems.


  Neural processes (NPs) learn stochastic processes and predict the
distribution of target output adaptively conditioned on a context set of
observed input-output pairs. Furthermore, Attentive Neural Process (ANP)
improved the prediction accuracy of NPs by incorporating attention mechanism
among contexts and targets. In a number of real-world applications such as
robotics, finance, speech, and biology, it is critical to learn the temporal
order and recurrent structure from sequential data. However, the capability of
NPs capturing these properties is limited due to its permutation invariance
instinct. In this paper, we proposed the Recurrent Attentive Neural Process
(RANP), or alternatively, Attentive Neural Process-RecurrentNeural
Network(ANP-RNN), in which the ANP is incorporated into a recurrent neural
network. The proposed model encapsulates both the inductive biases of recurrent
neural networks and also the strength of NPs for modelling uncertainty. We
demonstrate that RANP can effectively model sequential data and outperforms NPs
and LSTMs remarkably in a 1D regression toy example as well as
autonomous-driving applications.


  Brain tissue deformation resulting from head impacts is primarily caused by
rotation and can lead to traumatic brain injury. To quantify brain injury risk
based on measurements of kinematics on the head, finite element (FE) models and
various brain injury criteria based on different factors of these kinematics
have been developed, but the contribution of different kinematic factors has
not been comprehensively analyzed across different types of head impacts in a
data-driven manner. To better design brain injury criteria, the predictive
power of rotational kinematics factors, which are different in 1) the
derivative order (angular velocity, angular acceleration, angular jerk), 2) the
direction and 3) the power (e.g., square-rooted, squared, cubic) of the angular
velocity, were analyzed based on different datasets including laboratory
impacts, American football, mixed martial arts (MMA), NHTSA automobile
crashworthiness tests and NASCAR crash events. Ordinary least squares
regressions were built from kinematics factors to the 95\% maximum principal
strain (MPS95), and we compared zero-order correlation coefficients, structure
coefficients, commonality analysis, and dominance analysis. The angular
acceleration, the magnitude, and the first power factors showed the highest
predictive power for the majority of impacts including laboratory impacts,
American football impacts, with few exceptions (angular velocity for MMA and
NASCAR impacts). The predictive power of rotational kinematics in three
directions (x: posterior-to-anterior, y: left-to-right, z:
superior-to-inferior) of kinematics varied with different sports and types of
head impacts.


  The field-effect technique, popular thanks to its application in common
field-effect transistors, is here applied to metallic thin films by using as a
dielectric a novel polymer electrolyte solution. The maximum injected surface
charge, determined by a suitable modification of a classic method of
electrochemistry called double-step chronocoulometry, reached some units in
10^15 charges/cm^2. At room temperature, relative variations of resistance up
to 8%, 1.9% and 1.6% were observed in the case of gold, silver and copper,
respectively and, if the films are thick enough (> 25 nm), results can be
nicely explained within a free-electron model with parallel resistive channels.
The huge charge injections achieved make this particular field-effect technique
very promising for a vast variety of materials such as unconventional
superconductors, graphene and 2D-like materials.


  The development and the use of quantum technologies are hindered by a
fundamental challenge: Quantum materials exhibit macroscopic quantum properties
at extremely low temperatures due to the loss of quantum coherence at elevated
temperatures. Here, based on our recent discovery of room temperature
superfluorescence in perovskites, we present the Quantum Analog of Vibration
Isolation, 'QAVI', model and explain how it protects the quantum phase against
dephasing at high temperatures. We then postulate the requirements for
observation of macroscopic quantum phenomena at practical temperatures and
propose a unified model for all macroscopic quantum phase transitions. We
further present the general features of the temperature and density phase
diagram of macroscopic quantum phase transitions that are mediated by the QAVI
process and identify the similarities observed in the phase diagram of high Tc
superconductors. Understanding this fundamental quantum coherence protection
mechanism is imperative to accelerate the discovery of high temperature
macroscopic quantum phenomena, and offers significant potential for developing
quantum technologies functioning under practical conditions.


  User privacy concerns are widely regarded as a key obstacle to the success of
modern smart cyber-physical systems. In this paper, we analyse, through an
example, some of the requirements that future data collection architectures of
these systems should implement to provide effective privacy protection for
users. Then, we give an example of how these requirements can be implemented in
a smart home scenario. Our example architecture allows the user to balance the
privacy risks with the potential benefits and take a practical decision
determining the extent of the sharing. Based on this example architecture, we
identify a number of challenges that must be addressed by future data
processing systems in order to achieve effective privacy management for smart
cyber-physical systems.


  We show that various disconnected components of the moduli space of
superstring vacua with 16 supercharges admit a rationale in terms of BPS
un-orientifolds, i.e. type I toroidal compactifications with constant
non-vanishing but quantized vacuum expectation values of the NS-NS
antisymmetric tensor. These include various heterotic vacua with reduced rank,
known as CHL strings, and their dual type II (2,2) superstrings below D=6. Type
I vacua without open strings allow for an interpretation of several
disconnected components with N_V=10-D. An adiabatic argument relates these
unconventional type I superstrings to type II (4,0) superstrings without
D-branes. The latter are connected by U-duality below D=6 to type II (2,2)
superstrings. We also comment on the relation between some of these vacua and
compactifications of the putative M-theory on unorientable manifolds as well as
F-theory vacua.


  Intense laser-plasma interactions are an essential tool for the laboratory
study of ion acceleration at a collisionless shock. With two-dimensional
particle-in-cell calculations of a multicomponent plasma we observe two
electrostatic collisionless shocks at two distinct longitudinal positions when
driven with a linearly-polarized laser at normalized laser vector potential a0
that exceeds 10. Moreover, these shocks, associated with protons and carbon
ions, show a power-law dependence on a0 and accelerate ions to different
velocities in an expanding upstream with higher flux than in a single-component
hydrogen or carbon plasma. This results from an electrostatic ion two-stream
instability caused by differences in the charge-to-mass ratio of different
ions. Particle acceleration in collisionless shocks in multicomponent plasma
are ubiquitous in space and astrophysics, and these calculations identify the
possibility for studying these complex processes in the laboratory.


  This is second of the two invited lectures presented (by D. V. Ahluwalia) at
the ``XVII International School of Theoretical Physics: Standard Model and
Beyond' 93.'' The text is essentially based on a recent publication by the
present authors [Mod. Phys. Lett. A (in press)]. Here, after briefly reviewing
the $(j,0)\oplus(0,j)$ Dirac-like construct in the front form, we present a
detailed construction of the $(j,0)\oplus(0,j)$ Majorana-like fields.


  We define Ratner-Marklof-Strombergsson measures. These are probability
measures supported on cut-and-project sets in R^d (d > 1) which are invariant
and ergodic for the action of the groups ASL_d(R) or SL_d(R). We classify the
measures that can arise in terms of algebraic groups and homogeneous dynamics.
Using the classification, we prove analogues of results of Siegel, Weil and
Rogers about a Siegel summation formula and identities and bounds involving
higher moments. We deduce results about asymptotics, with error estimates, of
point-counting and patch-counting for typical cut-and-project sets.


  Decay properties of hidden-strangeness partners of X(3872) are studied in
tetra-quark pictures. As the results, it is seen that their decay properties
are strongly model dependent, and therefore, experimental studies of them will
select a realistic model.


  Deep probabilistic forecasting is gaining attention in numerous applications
ranging from weather prognosis, through electricity consumption estimation, to
autonomous vehicle trajectory prediction. However, existing approaches focus on
improvements on the most common scenarios without addressing the performance on
rare and difficult cases. In this work, we identify a long tail behavior in the
performance of state-of-the-art deep learning methods on probabilistic
forecasting. We present two moment-based tailedness measurement concepts to
improve performance on the difficult tail examples: Pareto Loss and Kurtosis
Loss. Kurtosis loss is a symmetric measurement as the fourth moment about the
mean of the loss distribution. Pareto loss is asymmetric measuring right
tailedness, modeling the loss using a generalized Pareto distribution (GPD). We
demonstrate the performance of our approach on several real-world datasets
including time series and spatiotemporal trajectories, achieving significant
improvements on the tail examples.


  Web intelligence can be considered as a subset of Artificial Intelligence. It
uses existing data in web to produce new data, knowledge and wisdom to support
decision making and new predictions for web users. Artificial Intelligence is
ever changing and evolving field of computer science and it is extensively used
in wide array of web based business applications. Although it is used
substantially in web based systems in developed countries, it is not examined
whether it is being substantially used in Sri Lanka. Every Sri Lankan citizen
depends on Public Service more or less throughout his/ her life time and at
least more than 3 times: at birth, marriage and death. So providing most of
these services to its citizen, Sri Lankan Government uses more or less of its
country web portal. This paper presents a model to evaluate web intelligence
capability based on weight to key functionalities with respect to web
intelligence. The government websites were checked by the proposed criteria to
show the potential of using web intelligent technology to provide website based
services. The result indicates that the use of web intelligence techniques
openly and publicly to provide web based services through government web portal
to its citizens is not satisfactory. It also indicates that lack of using the
technologies pertaining to web intelligence in the public service web hinders
the most of the advantages that citizen and government can gain from such
technological involvement.


  Social media platforms are extensively used for sharing personal emotions,
daily activities, and various life events, keeping people updated with the
latest happenings. From the moment a user creates an account, they continually
expand their network of friends or followers, freely interacting with others by
posting, commenting, and sharing content. Over time, user behavior evolves
based on demographic attributes and the networks they establish. In this
research, we propose a predictive method to understand how a user evolves on
social media throughout their life and to forecast the next stage of their
evolution. We fine-tune a GPT-like decoder-only model (we named it E-GPT:
Evolution-GPT) to predict the future stages of a user's evolution in online
social media. We evaluate the performance of these models and demonstrate how
user attributes influence changes within their network by predicting future
connections and shifts in user activities on social media, which also addresses
other social media challenges such as recommendation systems.


  Deep learning methods employ multiple processing layers to learn hierarchical
representations of data and have produced state-of-the-art results in many
domains. Recently, a variety of model designs and methods have blossomed in the
context of natural language processing (NLP). In this paper, we review
significant deep learning related models and methods that have been employed
for numerous NLP tasks and provide a walk-through of their evolution. We also
summarize, compare and contrast the various models and put forward a detailed
understanding of the past, present and future of deep learning in NLP.


  We provide an exact analysis of a class of randomized algorithms for solving
overdetermined least-squares problems. We consider first-order methods, where
the gradients are pre-conditioned by an approximation of the Hessian, based on
a subspace embedding of the data matrix. This class of algorithms encompasses
several randomized methods among the fastest solvers for least-squares
problems. We focus on two classical embeddings, namely, Gaussian projections
and subsampled randomized Hadamard transforms (SRHT). Our key technical
innovation is the derivation of the limiting spectral density of SRHT
embeddings. Leveraging this novel result, we derive the family of normalized
orthogonal polynomials of the SRHT density and we find the optimal
pre-conditioned first-order method along with its rate of convergence. Our
analysis of Gaussian embeddings proceeds similarly, and leverages classical
random matrix theory results. In particular, we show that for a given sketch
size, SRHT embeddings exhibits a faster rate of convergence than Gaussian
embeddings. Then, we propose a new algorithm by optimizing the computational
complexity over the choice of the sketching dimension. To our knowledge, our
resulting algorithm yields the best known complexity for solving least-squares
problems with no condition number dependence.


  Let $\omega(\mathcal{F})=\sum_{\{A,B\}\subset\mathcal{F}}|A\cap B|$ and
$\omega(\mathcal{A},\mathcal{B})=\sum_{(A,B)\in \mathcal{A}\times
\mathcal{B}}|A\cap B|$. A family $\mathcal{F}$ is intersecting if $F_1\cap
F_2\neq \emptyset$ for any $F_1,F_2\in\mathcal{F}$ and two family $\mathcal{A}$
and $\mathcal{B}$ are crossing-intersecting if $A\cap B\neq \emptyset$ for any
$(A,B)\in \mathcal{A}\times\mathcal{B}$. For an intersecting family
$\mathcal{F}$, Erd\H{o}s, Ko and Rado determined the upper bound of
$|\mathcal{F}|$, consequently yielding an upper bound of
$\binom{|\mathcal{F}|}{2}=\sum_{\{A,B\}\subset\mathcal{F}}1$. If we replace $1$
with $|A\cap B|$ in the summation $\sum_{\{A,B\}\subset\mathcal{F}}1$, then
this summation transforms into $\omega(\mathcal{F})$. In this paper, for an
intersecting family $\mathcal{F}$, we determine the upper bound of
$\omega(\mathcal{F})$, which is a generalization of Erd\H{o}s-Ko-Rado Theorem.
Further, for crossing-intersecting families $\mathcal{A}$ and $\mathcal{B}$, we
determine the upper bound of $\omega(\mathcal{A},\mathcal{B})$.


  Adding fundamental matter of mass m_Q to N=4 Yang Mills theory, we study
quarkonium, and "generalized quarkonium" containing light adjoint particles. At
large 't Hooft coupling the states of spin<=1 are anomalously light (Kruczenski
et al., hep-th/0304032). We examine their form factors, and show these hadrons
are unlike any known in QCD. By a traditional yardstick they appear infinite in
size (as with strings in flat space) but we show that this is a failure of the
yardstick. All of the hadrons are actually of finite size ~ \sqrt{g^2N}/m_Q,
regardless of their radial excitation level and of how many valence adjoint
particles they contain. Certain form factors for spin-1 quarkonia vanish in the
large-g^2N limit; thus these hadrons resemble neither the observed J/Psi
quarkonium states nor rho mesons.


  The production process of quarkonia in proton-proton (pp) collision is a very
good probe of the parton structure of the proton. Recent experimental data of
the production of J/psi+vector boson or quarkonium pairs at the LHC and
Tevatron suggest the relevance of double parton scatterings (DPS). We discuss
here the single parton scattering (SPS) contribution to the J/psi +Z, J/psi +W,
and J/psi +J/psi productions in hadron collisions. By revisiting the
computations of the SPS contributions to the J/psi+Z and J/psi +W productions,
we demonstrate that the ATLAS data in fact show evidence for DPS.


  Given two elliptic operators L and M in nondivergence form, with coefficients
A_L(x), A_M(x) and drift terms b_L(x), b_M(x), respectively, satisfying a
Carleson measure disagreement condition in a Lipschitz domain Omega in R^{n+1},
then their harmonic measures are mutually absolutely continuous. As an
application of this, a new approximation argument and known results we obtain
necessary and sufficient conditions for a single operator L (in divergence or
nondivergence form) to have regular harmonic measure with respect to Lebesgue
measure. The results are sharp in all cases.


  The static equilibrium deformation of a heavy spring due to its own weight is
calculated for two cases. First for a spring hanging in a constant
gravitational field, then for a spring which is at rest in a rotating system
where it is stretched by the centrifugal force. Two different models are
considered. First a discrete model assuming a finite number of point masses
connected by springs of negligible weight. Then the continuum limit of this
model. In the second case the differential equation for the deformation is
obtained by demanding that the potential energy is minimized. In this way a
simple application of the variational calculus is obtained.


  The quantum version of a fundamental entropic data-processing inequality is
presented. It establishes a lower bound for the entropy that can be generated
in the output channels of a scattering process, which involves a collection of
independent input bosonic modes (e.g., the modes of the electromagnetic field).
The impact of this inequality in quantum information theory is potentially
large and some relevant implications are considered in this work.


  The goal of this article is two-fold: First, to prove a (two-variable) main
conjecture for a CM field $F$ without assuming the $p$-ordinary hypothesis of
Katz, making use of what we call the Rubin-Stark $\mathcal{L}$-restricted
Kolyvagin systems which is constructed out of the conjectural Rubin-Stark Euler
system of rank $g$. (We are also able to obtain weaker unconditional results in
this direction.) Second objective is to prove the Park-Shahabi plus/minus main
conjecture for a CM elliptic curve $E$ defined over a general totally real
field again using (a twist of the) Rubin-Stark Kolyvagin system. This latter
result has consequences towards the Birch and Swinnerton-Dyer conjecture for
$E$.


  We introduce Contrastive Intrinsic Control (CIC), an algorithm for
unsupervised skill discovery that maximizes the mutual information between
state-transitions and latent skill vectors. CIC utilizes contrastive learning
between state-transitions and skills to learn behavior embeddings and maximizes
the entropy of these embeddings as an intrinsic reward to encourage behavioral
diversity. We evaluate our algorithm on the Unsupervised Reinforcement Learning
Benchmark, which consists of a long reward-free pre-training phase followed by
a short adaptation phase to downstream tasks with extrinsic rewards. CIC
substantially improves over prior methods in terms of adaptation efficiency,
outperforming prior unsupervised skill discovery methods by 1.79x and the next
leading overall exploration algorithm by 1.18x.


  We present MAPPDFpol1.0, a new determination of the helicity-dependent parton
distribution functions (PDFs) of the proton from a set of longitudinally
polarised inclusive and semi-inclusive deep-inelastic scattering data. The
determination includes, for the first time, next-to-next-to-leading order QCD
corrections to both processes, and is carried out in a framework that combines
a neural-network parametrisation of PDFs with a Monte Carlo representation of
their uncertainties. We discuss the quality of the determination, in particular
its dependence on higher-order corrections, on the choice of data set, and on
theoretical constraints.


  Data cleaning is often an important step to ensure that predictive models,
such as regression and classification, are not affected by systematic errors
such as inconsistent, out-of-date, or outlier data. Identifying dirty data is
often a manual and iterative process, and can be challenging on large datasets.
However, many data cleaning workflows can introduce subtle biases into the
training processes due to violation of independence assumptions. We propose
ActiveClean, a progressive cleaning approach where the model is updated
incrementally instead of re-training and can guarantee accuracy on partially
cleaned data. ActiveClean supports a popular class of models called convex loss
models (e.g., linear regression and SVMs). ActiveClean also leverages the
structure of a user's model to prioritize cleaning those records likely to
affect the results. We evaluate ActiveClean on five real-world datasets UCI
Adult, UCI EEG, MNIST, Dollars For Docs, and WorldBank with both real and
synthetic errors. Our results suggest that our proposed optimizations can
improve model accuracy by up-to 2.5x for the same amount of data cleaned.
Furthermore for a fixed cleaning budget and on all real dirty datasets,
ActiveClean returns more accurate models than uniform sampling and Active
Learning.


  Using Infrared Array Camera (IRAC) images at 3.6, 4.5, 5.8, and 8 microns
from the GLIMPSE Legacy science program on the Spitzer Space Telescope, we
searched for infrared counterparts to the 95 known supernova remnants that are
located within galactic longitudes 65>|l|>10 degrees and latitudes |b|<1
degree. Eighteen infrared counterparts were detected. Many other supernova
remnants could have significant infrared emission but are in portions of the
Milky Way too confused to allow separation from bright HII regions and
pervasive mid-infrared emission from atomic and molecular clouds along the line
of sight. Infrared emission from supernova remnants originates from synchrotron
emission, shock-heated dust, atomic fine-structure lines, and molecular lines.
The detected remnants are G11.2-0.3, Kes 69, G22.7-0.2, 3C 391, W 44, 3C 396,
3C 397, W 49B, G54.4-0.3, Kes 17, Kes 20A, RCW 103, G344.7-0.1, G346.6-0.2, CTB
37A, G348.5-0.0, and G349.7+0.2. The infrared colors suggest emission from
molecular lines (9 remnants), fine-structure lines (3), and PAH (4), or a
combination; some remnants feature multiple colors in different regions. None
of the remnants are dominated by synchrotron radiation at mid-infrared
wavelengths. The IRAC-detected sample emphasizes remnants interacting with
relatively dense gas, for which most of the shock cooling occurs through
molecular or ionic lines in the mid-infrared.