  We study the lattice dynamics of group IV semiconductors using fully
ab-initio extended Hubbard functional. The onsite and intersite Hubbard
interactions are determined self-consistently with recently developed
pseudohybrid functionals and included in force calculations. We analyze the
Pulay forces by the choice of atomic orbital projectors and the force
contribution of the onsite and intersite Hubbard terms. The phonon dispersions,
Gruneisen parameters, and lattice thermal conductivities of diamond, silicon,
and germanium, which are most-representative covalent-bonding semiconductors,
are calculated and compared with the results using local, semilocal, and hybrid
functionals. The extended Hubbard functional produces increased phonon
velocities and lifetimes, and thus lattice thermal conductivities compared to
local and semilocal functionals, agreeing with experiments very well.
Considering that our computational demand is comparable to simple local
functionals, this work thus suggests a way to perform high-throughput
electronic and structural calculations with a higher accuracy.


  This paper deals with the stability of semi-wavefronts to the following delay
non-local monostable equation: $\dot{v}(t,x) = \Delta v(t,x) - v(t,x) +
\int_{\R^d}K(y)g(v(t-h,x-y))dy, x \in \R^d,\ t >0;$ where $h>0$ and $d\in\Z_+$.
We give two general results for $d\geq1$: on the global stability of
semi-wavefronts in $L^p$-spaces with unbounded weights and the local stability
of planar wavefronts in $L^p$-spaces with bounded weights. We also give a
global stability result for $d=1$ which includes the global stability on
Sobolev spaces. Here $g$ is not assumed to be monotone and the kernel $K$ is
not assumed to be symmetric, therefore non-monotone semi-wavefronts and {\it
backward traveling fronts} appear for which we show their stability. In
particular, the global stability of critical wavefronts is stated.


  We examine the thermodynamic behavior of a static neutral regular
(non-singular) black hole enclosed in a finite isothermal cavity. The cavity
enclosure helps us investigate black hole systems in a canonical or a grand
canonical ensemble. Here we demonstrate the derivation of the reduced action
for the general metric of a regular black hole in a cavity by considering a
canonical ensemble. The new expression of the action contains quantum
corrections at short distances and concludes to the action of a singular black
hole in a cavity at large distances. We apply this formalism to the
noncommutative Schwarzschild black hole, in order to study the phase structure
of the system. We conclude to a possible small/large stable regular black hole
transition inside the cavity that exists neither at the system of a classical
Schwarzschild black hole in a cavity, nor at the asymptotically flat regular
black hole without the cavity. This phase transition seems to be similar with
the liquid/gas transition of a Van der Waals gas.


  This talk introduces perturbative quantum field on a heuristic level. It is
directed at an audience familiar with elements of quantum mechanics, but not
necessarily with high energy physics. It includes a discussion of the
strategies behind experimental tests of fundamental theories, and of the field
theory interpretations of these tests


  When summarizing a collection of views, arguments or opinions on some topic,
it is often desirable not only to extract the most salient points, but also to
quantify their prevalence. Work on multi-document summarization has
traditionally focused on creating textual summaries, which lack this
quantitative aspect. Recent work has proposed to summarize arguments by mapping
them to a small set of expert-generated key points, where the salience of each
key point corresponds to the number of its matching arguments. The current work
advances key point analysis in two important respects: first, we develop a
method for automatic extraction of key points, which enables fully automatic
analysis, and is shown to achieve performance comparable to a human expert.
Second, we demonstrate that the applicability of key point analysis goes well
beyond argumentation data. Using models trained on publicly available
argumentation datasets, we achieve promising results in two additional domains:
municipal surveys and user reviews. An additional contribution is an in-depth
evaluation of argument-to-key point matching models, where we substantially
outperform previous results.


  The strong coupling between two subsystems consisting of quantum emitters and
photonic modes, at which the level splitting of mixed quantum states occurs,
has been a central subject of quantum physics and nanophotonics due to various
important applications. The spectral Rabi-splitting of photon emission or
absorption has been adopted to experimentally characterize the strong coupling
under the equality assumption that it is identical to the level splitting.
Here, we for the first time reveal that the equality assumption is not valid.
It is the invalidity that results in the relativity and diversity of the strong
coupling characterized by the spectral Rabi-splitting to the measured
subsystems, highly correlated with their dissipative decays. The strong
coupling is easier to be observed from the subsystem with larger decay, and can
be classified into pseudo-, dark-, middle-, and super-strong interaction
regimes. We also suggest a prototype of coupled plasmon-exciton system for
possibly future experiment observations on these novel predictions. Our work
brings new fundamental insight to the light-matter interaction in
nanostructures, which will stimulate further researches in this field.


  Recent studies suggest that globular clusters (GCs) may retain a substantial
population of stellar-mass black holes (BHs), in contrast to the long-held
belief of a few to zero BHs. We model the population of BH low-mass X-ray
binaries (BH-LMXBs), an ideal observable proxy for elusive single BHs, produced
from a representative group of Milky Way GCs with variable BH populations. We
simulate the formation of BH-binaries in GCs through exchange interactions
between binary and single stars in the company of tens to hundreds of BHs.
Additionally, we consider the impact of the BH population on the rate of
compact binaries undergoing gravitational wave driven mergers. The
characteristics of the BH-LMXB population and binary properties are sensitive
to the GCs structural parameters as well as its unobservable BH population. We
find that GCs retaining $\sim 1000$ BHs produce a galactic population of $\sim
150$ ejected BH-LMXBs whereas GCs retaining only $\sim20$ BHs produce zero
ejected BH-LMXBs. Moreover, we explore the possibility that some of the
presently known BH-LMXBs might have originated in GCs and identify five
candidate systems.


  We study the localization and decay properties as well as the thermal
conductance of one-dimensional plasmons. Our model contains a Luttinger-liquid
part with spatially random plasmon velocity and interaction parameter as well
as a nonlinearity that is cubic in density. The scaling of the decay rate of
plasmons is obtained in several regimes. At sufficiently high frequencies, it
describes the inelastic life time of localized plasmon excitations that crosses
over to the clean result with lowering frequencies. For higher frequencies, we
analyze implications of many-body-localization effects that lead to a
suppression of the decay rate. We find that the thermal conductance depends in
a non-trivial fashion on the system size $L$. Specifically, it scales as
$L^{-1/2}$ for sufficiently short wires and crosses over to $L^{-2/3}$ scaling
for longer wires.


  We will study monomial ideals $I$ in the exterior algebra as well as in the
polynomial ring whose generic initial ideal is constant for all term orders up
to permutations of variables. First, in the exterior algebra, we determine all
graphs and all flag complexes whose exterior face ideal satisfies the above
condition. Second, in the polynomial ring, it will be shown that the generic
initial ideal $\gin_\sigma(I(G))$ of the edge ideal $I(G)$ of a graph $G$ is
constant for all term orders $\sigma$ up to permutations of variables if and
only if $G$ is a complete bipartite graph.


  In cosmological models with a varying gravitational constant, it is not clear
whether primordial black holes preserve the value of $G$ at their formation
epoch. We investigate this question by using the Tolman-Bondi model to study
the evolution of a background scalar field when a black hole forms from the
collapse of dust in a flat Friedmann universe. Providing the back reaction of
the scalar field on the metric can be neglected, we find that the value of the
scalar field at the event horizon very quickly assumes the background
cosmological value. This suggests that there is very little gravitational
memory.


  Macroscopic realism is the name for a class of modifications to quantum
theory that allow macroscopic objects to be described in a
measurement-independent manner, while largely preserving a fully quantum
mechanical description of the microscopic world. Objective collapse theories
are examples which aim to solve the quantum measurement problem through
modified dynamical laws. Whether such theories describe nature, however, is not
known. Here we describe and implement an experimental protocol capable of
constraining theories of this class, that is more noise tolerant and
conceptually transparent than the original Leggett-Garg test. We implement the
protocol in a superconducting flux qubit, and rule out (by ~84 s.d.) those
theories which would deny coherent superpositions of 170 nA currents over a ~10
ns timescale. Further, we address the 'clumsiness loophole' by determining
classical disturbance with control experiments. Our results constitute strong
evidence for the superposition of states of nontrivial macroscopic
distinctness.


  Oscillation has an important role in bio-dynamical systems such as circadian
rhythms and eukaryotic cell cycle. John Tyson et. al. in Nature Review Mol Cell
Biol 2008 examined a limited number of network topologies consisting of three
nodes and four or fewer edges and identified the network design principles of
biochemical oscillations. Tsai et. al. in Science 2008 studied three different
network motifs, namely a negative feedback loop, coupled negative feedback
loops, and coupled positive and negative feedback loops, and found that the
interconnected positive and negative feedback loops are capable of generating
frequency-tunable oscillations. We enumerate 249 topologically unique network
architectures consisting of three nodes and at least three cyclic inhibitory
edges, and identify network architectural commonalities among three functional
groups: (1) most frequency-tunable yet less robust oscillators, (2) least
frequency-tunable and least robust oscillators, and (3) less frequency-tunable
yet most robust oscillators. We find that Frequency-tunable networks cannot
simultaneously express high robustness, indicating a tradeoff between frequency
tunability and robustness.


  The nearby cluster Abell 1795 is used as a testbed to examine whether hot gas
in cluster galaxies is stripped by the ram pressure of the intracluster medium
(ICM). The expected X-ray emission in and around Abell 1795 galaxies is likely
dominated by the ICM, low-mass X-ray binaries, active galactic nuclei, and hot
gas halos. In order to constrain these components, we use archival Chandra
X-ray Observatory and Sloan Digital Sky Survey (SDSS) observations of Abell
1795 and identify 58 massive (M_star>10^10 M_sun) spectroscopic cluster members
within 5 arcmin of the Chandra optical axis. X-ray images at 0.5-1.5 keV and
4-8 keV were created for each cluster member and then stacked into two
clustercentric radius bins: inner (0.25<R/R_500<1) and outer (1<R/R_500<2.5).
Surface brightness profiles of inner and outer cluster members are fit using
Markov chain Monte Carlo sampling in order to generate model parameters and
measure the 0.5-1.5 keV luminosities of each model component. Leveraging
effective total Chandra exposure times of 3.4 and 1.7 Msec for inner and outer
cluster members, respectively, we report the detection of hot gas halos, in a
statistical sense, around outer cluster members. Outer members have 0.5-1.5 keV
hot halo luminosities (L_X = 8.1(-3.5/+5)x10^39 erg/s) that are six times
larger than the upper limit for inner cluster members (L_X < 1.3x10^39 erg/s).
This result suggests that the ICM is removing hot gas from the halos of Abell
1795 members as they fall into the cluster.


  We apply the concept of free random variables to doubly correlated (Gaussian)
Wishart random matrix models, appearing for example in a multivariate analysis
of financial time series, and displaying both inter-asset cross-covariances and
temporal auto-covariances. We give a comprehensive introduction to the rich
financial reality behind such models. We explain in an elementary way the main
techniques of the free random variables calculus, with a view to promote them
in the quantitative finance community. We apply our findings to tackle several
financially relevant problems, such as of an universe of assets displaying
exponentially decaying temporal covariances, or the exponentially weighted
moving average, both with an arbitrary structure of cross-covariances.


  We consider a multi-armed bandit setting that is inspired by real-world
applications in e-commerce. In our setting, there are a few types of users,
each with a specific response to the different arms. When a user enters the
system, his type is unknown to the decision maker. The decision maker can
either treat each user separately ignoring the previously observed users, or
can attempt to take advantage of knowing that only few types exist and cluster
the users according to their response to the arms. We devise algorithms that
combine the usual exploration-exploitation tradeoff with clustering of users
and demonstrate the value of clustering. In the process of developing
algorithms for the clustered setting, we propose and analyze simple algorithms
for the setup where a decision maker knows that a user belongs to one of few
types, but does not know which one.


The Halting Problem is a version of the Liar's Paradox.


  Considering the upcoming OWFA, we use simulations of the foregrounds and the
$z = 3.35$ HI 21-cm intensity mapping signal to identify the
$(k_{\perp},k_{\parallel})$ modes where the expected 21-cm power spectrum
$P(k_{\perp},k_{\parallel})$ is substantially larger than the predicted
foreground contribution. Only these uncontaminated $k$-modes are used for
measuring $P(k_{\perp},k_{\parallel})$ in the "Foreground Avoidance" technique.
Though the foregrounds are largely localised within a wedge. we find that the
small leakage beyond the wedge surpasses the 21-cm signal across a significant
part of the $(k_{\perp},k_{\parallel})$ plane. The extent of foreground leakage
is extremely sensitive to the frequency window function used to estimate
$P(k_{\perp},k_{\parallel})$. It is possible to reduce the leakage by making
the window function narrower, however this comes at the expense of losing a
larger fraction of the 21-cm signal. It is necessary to balance these competing
effects to identify an optimal window function. Considering a broad class of
cosine window functions, we identify a six term window function as optimal for
21-cm power spectrum estimation with OWFA. Considering only the $k$-modes where
the expected 21-cm power spectrum exceeds the predicted foregrounds by a factor
of $100$ or larger, a $5\,\sigma$ detection of the binned power spectrum is
possible in the $k$ ranges $0.18 \leq k \leq 0.3 \, {\rm Mpc}^{-1}$ and $0.18
\le k \le 0.8 \, {\rm Mpc}^{-1}$ with $1,000-2,000$ hours and $10^4$ hours of
observation respectively.


  A generalized 1-in-3SAT problem is defined and found to be in complexity
class P when restricted to a certain subset of CNF expressions. In particular,
1-in-kSAT with no restrictions on the number of literals per clause can be
decided in polynomial time when restricted to exact READ-3 formulas with equal
number of clauses (m) and variables (n), and no pure literals. Also individual
instances can be checked for easiness with respect to a given SAT problem. By
identifying whole classes of formulas as being solvable efficiently the
approach might be of interest also in the complementary search for hard
instances.


  The R parity odd renormalizable Yukawa interactions of quarks and leptons
with the scalar superpartners have the ability to violate the baryon and lepton
numbers, change the hadron and lepton flavors and make the lightest
supersymmetric particle unstable. The existence of an approximate R parity
symmetry would thus affect in a deep way the conventional framework of the
Minimal Supersymmetric Standard Model where an exact R parity symmetry is
built-in by assumption. The purpose of the present review is to survey in a
systematic way the direct experimental constraints set on the R parity
violating couplings by the low and intermediate energy physics processes. We
consider first the option of bilinear R parity violation and spontaneously
broken R parity symmetry and proceed next to the trilinear R parity violating
interactions. The discussion aims at surveying the indirect coupling constant
bounds derived from fundamental tests of the Standard Model and the variety of
scattering and rare decay processes. We also discuss the constraints imposed by
the renormalization group scale evolution and the cosmological and
astrophysical phenomenology.


  The hot subdwarf B (sdB) stars form an homogeneous group populating an
extension of the horizontal branch (HB) in the ($T_{\rm eff}$--$\log g$)
diagram towards temperatures up to 40,000 K. The recent discovery that many of
them are multimode pulsators has triggered a large observational and
theoretical effort. We discuss the possibility of performing space-based
asteroseismology with {\em FUSE}, and we demonstrate that periodic luminosity
variations are already detectable in archival TTAG data of sdB stars. In
particular, we report on the {\em FUSE} observation of the pulsator PF
1219+534, which shows the presence of periodic variations at 6.9 mHz and 7.8
mHz, consistent with those reported from ground--based observations.


  We will show that Banach spaces are monadic over complete metric spaces via
the unit ball functor. For the forgetful functor, one should take complete
pointed metric spaces.


  Recently, brightness variations occurring on twice the accretion disk
precession period in the old nova and permanent superhump system V603 Aql have
been observed by Bruch & Cook (2018). In an attempt to detect a similar effect
in other cataclysmic variables reported to contain permanent superhumps the
novalike variable UU Aqr was observed during 11 nights in September 2018. While
no traces of superhumps were seen in the data, rendering the quest for
variations related to the disk precession period obsolete, the system exhibits
regular variations with a period of ~4 days.


  Several primal and dual characterizations of regularity properties of
collections of sets in normed linear spaces are discussed. Relationships
between regularity properties of collections of sets and those of set-valued
mappings are provided.


  We investigate Sobolev and Hardy inequalities, specifically weighted
Minerbe's type estimates, in noncompact complete connected Riemannian manifolds
whose geometry is described by an isoperimetric profile. In particular, we
assume that the manifold satisfies the $p$-hyperbolicity property, stated in
terms of a necessary integral Dini condition on the isoperimetric profile. Our
method seems to us to combine sharply the knowledge of the isoperimetric
profile and the optimal Bliss type Hardy inequality depending on the geometry
of the manifold. We recover the well known best Sobolev constant in the
Euclidean case.


  Updating our previous measurements with new data and analysis modifications,
we report preliminary results on the energy-dependent deficit of muon neutrinos
from the Fermilab NuMI beam as observed with the MINOS Far Detector located 735
km away in the Soudan Underground Laboratory. From an exposure of 2.50x10^20
protons on target, we observe 563 charged-current nu_mu interaction candidates
in the Far Detector, where 738+/-30 events are expected in the absence of
neutrino oscillations. We have analyzed these data assuming two-flavor nu_mu to
nu_tau oscillations. From a simultaneous fit to the reconstructed nu_mu energy
spectra obtained during two running periods we obtain the neutrino squared-mass
difference Delta m_{32}^2 = (2.38 +0.20/-0.16) x 10^-3 eV^2/c^4 with errors at
68% confidence level (CL), and mixing angle sin^2(2 theta_23) > 0.84 at 90% CL.
The uncertainties and confidence intervals include both statistical and
systematic errors. All results and plots presented here are preliminary.


  Understanding the forces governing human behavior and social dynamics is a
challenging problem. Individuals' decisions and actions are affected by
interlaced factors, such as physical location, homophily, and social ties. In
this paper, we propose to examine the role that distinct communities, linked to
these factors, play as sources of social influence. The ego network is
typically used in the social influence analysis. Our hypothesis is that
individuals are embedded in communities not only related to their direct social
relationships, but that involve different and complex forces. We analyze
physical, homophily, and social communities to evaluate their relation with
subjects' behavior. We prove that social influence is correlated with these
communities, and each one of them is (differently) significant for individuals.
We define community-based features, which reflect the subject involvement in
these groups, and we use them with a supervised learning algorithm to predict
subject participation in social events. Results indicate that both communities
and ego network are relevant sources of social influence, confirming that the
ego network alone is not sufficient to explain this phenomenon. Moreover, we
classify users according to the degree of social influence they experienced
with respect to their groups, recognizing classes of behavioral phenotypes. To
our knowledge, this is the first work that proves the existence of phenotypes
related to the social influence phenomenon.


  For each $N$ an infinite number of Conformal Field Theories is presented that
has the same fusion rules as SO(N) level 2. These new theories are obtained as
extensions of the chiral algebra of $SO(NM^2)$ level 2, and correspond to new
modular invariant partition functions of these theories.


  We present an extension of the King et al. (2004) model for the flickering of
black hole accretion discs by taking proper account for the thermal properties
of the disc.
  First we develop a one-dimensional, vertically averaged, one-zone model for
an optically thick accretion disc and study the temporal evolution. This limits
the current model to the so-called high/soft state, where the X-Ray spectrum is
dominated by a thermal black-body component.
  Then we couple this disc model to the flickering process as described in King
et al. (2004). Thus we consider the evolution of a poloidal magnetic field
subject to a magnetic dynamo. By comparing to observations of X-Ray binaries in
the high-soft state, we can constrain the strength of the energy density of the
poloidal magnetic field to a few percent of the energy density of the intrinsic
disc magnetic field.


  Using $He@C_{60}$ as an example, we demonstrate that static potential of the
fullerene core essentially alters the cross section of the two-electron
ionization differential in one-electron energy $d\sigma ^{++}(\omega
)/d\epsilon $. We found that at high photon energy prominent oscillations
appear in it due to reflection of the second, slow electron wave on the $%
C_{60}$ shell, which "dies out" at relatively high $\epsilon $ values, of about
2$\div $3 two-electron ionization potentials. The results were presented for
ratios $R_{C_{60}}(\omega ,\epsilon)\equiv d\sigma ^{++}(\omega
,\epsilon)/d\sigma ^{a++}(\omega,\epsilon)$, where $d\sigma
^{a++}(\omega,\epsilon)/d\epsilon$ is the two-electron differential
photoionization cross section. We have calculated the ratio $R_{i,ful}=
\sigma_{i} ^{++}(\omega)/\sigma_{i}^{a++}(\omega)$, that accounts for
reflection of both photoelectrons by the $C_{60}$ shell. We have calculated
also the value of two-electron photoionization cross section $\sigma
^{++}(\omega)$ and found that this value is close to that of an isolated $He$
atom.


  To investigate the universality of the structure of interactions in different
markets, we analyze the cross-correlation matrix C of stock price fluctuations
in the National Stock Exchange (NSE) of India. We find that this emerging
market exhibits strong correlations in the movement of stock prices compared to
developed markets, such as the New York Stock Exchange (NYSE). This is shown to
be due to the dominant influence of a common market mode on the stock prices.
By comparison, interactions between related stocks, e.g., those belonging to
the same business sector, are much weaker. This lack of distinct sector
identity in emerging markets is explicitly shown by reconstructing the network
of mutually interacting stocks. Spectral analysis of C for NSE reveals that,
the few largest eigenvalues deviate from the bulk of the spectrum predicted by
random matrix theory, but they are far fewer in number compared to, e.g., NYSE.
We show this to be due to the relative weakness of intra-sector interactions
between stocks, compared to the market mode, by modeling stock price dynamics
with a two-factor model. Our results suggest that the emergence of an internal
structure comprising multiple groups of strongly coupled components is a
signature of market development.


  Most state-of-the-art semantic segmentation approaches only achieve high
accuracy in good conditions. In practically-common but less-discussed adverse
environmental conditions, their performance can decrease enormously. Existing
studies usually cast the handling of segmentation in adverse conditions as a
separate post-processing step after signal restoration, making the segmentation
performance largely depend on the quality of restoration. In this paper, we
propose a novel deep-learning framework to tackle semantic segmentation and
image restoration in adverse environmental conditions in a holistic manner. The
proposed approach contains two components: Semantically-Guided Adaptation,
which exploits semantic information from degraded images to refine the
segmentation; and Exemplar-Guided Synthesis, which restores images from
semantic label maps given degraded exemplars as the guidance. Our method
cooperatively leverages the complementarity and interdependence of low-level
restoration and high-level segmentation in adverse environmental conditions.
Extensive experiments on various datasets demonstrate that our approach can not
only improve the accuracy of semantic segmentation with degradation cues, but
also boost the perceptual quality and structural similarity of image
restoration with semantic guidance.


  In this paper we present a novel hydrodynamic experiment using liquid $^4$He.
The flow is forced inertially by a canonical oscillating grid using either its
normal (He~I) or superfluid (He~II) phase, generating a statistically
stationary turbulence. We characterise the turbulent properties of the flow
using 2D Lagrangian Particle tracking on hollow glass micro-spheres. As
expected for tracer particles, the Vorono\"{i} tessellation on particle
positions does not show a significant departure from a random Poisson process
neither in He~I nor He~II phase. Particles' positions are tracked with high
temporal resolution, allowing to resolve velocity fluctuations at integral and
inertial scales while properly assessing the noise contribution. Additionally,
we differentiate the particles' positions (by convolution with Gaussian
kernels) in order to access small scale quantities like acceleration. Using
these measured quantities and the formalism of classical Homogeneous Isotropic
Turbulence (HIT) to perform an energy budget across scales we extract the
energy injection rate at the large scale, the energy flux cascading through
inertial scales, down to small scales at which it is dissipated. We found that
in such inertially driven turbulence, regardless of the normal or superfluid
state of the fluid, estimates of energy at the different scales are compatible
with each other and consistent with oscillating grid turbulence results
reported for normal fluids in the literature. The largest discrepancy shows up
at small scales where the signal to noise ratio is harder to control and where
the 2D measurement is contaminated by the 3D nature of the flow. This motivates
to focus future experimental projects towards small scales, low noise and 3D
measurements.


  We study how the loading rate, specimen geometry and microstructural texture
select the dynamics of a crack moving through an heterogeneous elastic material
in the quasi-static approximation. We find a transition, fully controlled by
two dimensionless variables, between dynamics ruled by continuum fracture
mechanics and crackling dynamics. Selection of the latter by the loading,
microstructure and specimen parameters is formulated in terms of scaling laws
on the power spectrum of crack velocity. This analysis defines the experimental
conditions required to observe crackling in fracture. Beyond failure problems,
the results extend to a variety of situations described by models of the same
universality class, e.g. the dynamics in wetting or of domain walls in
amorphous ferromagnets.


  We initiate the study of K-theory Soergel bimodules-a K-theory analog of
classical Soergel bimodules. Classical Soergel bimodules can be seen as a
completed and infinitesimal version of their new K-theoretic analog. We show
that morphisms of K-theory Soergel bimodules can be described geometrically in
terms of equivariant K-theoretic correspondences between Bott-Samelson
varieties. We thereby obtain a natural categorification of K-theory Soergel
bimodules in terms of equivariant coherent sheaves. We introduce a formalism of
stratified equivariant K-motives on varieties with an affine stratification,
which is a K-theoretric analog of the equivariant derived category of
Bernstein-Lunts. We show that Bruhat-stratified torus-equivariant K-motives on
flag varieties can be described in terms of chain complexes of K-theory Soergel
bimodules. Moreover, we propose conjectures regarding an equivariant/monodromic
Koszul duality for flag varieties and the quantum K-theoretic Satake.


  Numerically computing global policies to optimal control problems for complex
dynamical systems is mostly intractable. In consequence, a number of
approximation methods have been developed. However, none of the current methods
can quantify by how much the resulting control underperforms the elusive
globally optimal solution. Here we propose policy decomposition, an
approximation method with explicit suboptimality estimates. Our method
decomposes the optimal control problem into lower-dimensional subproblems,
whose optimal solutions are recombined to build a control policy for the entire
system. Many such combinations exist, and we introduce the value error and its
LQR and DDP estimates to predict the suboptimality of possible combinations and
prioritize the ones that minimize it. Using a cart-pole, a 3-link balancing
biped and N-link planar manipulators as example systems, we find that the
estimates correctly identify the best combinations, yielding control policies
in a fraction of the time it takes to compute the optimal control without a
notable sacrifice in closed-loop performance. While more research will be
needed to find ways of dealing with the combinatorics of policy decomposition,
the results suggest this method could be an effective alternative for
approximating optimal control in intractable systems.


  A key ingredient to searches for physics beyond the Standard Model in
$B^{0}_{s}$ mixing phenomena is the measurement of the
$B^{0}_{s}$-$\bar{B}^{0}_{s}$ oscillation frequency, which is equivalent to the
mass difference $\Delta m_{s}$ of the $B^{0}_{s}$ mass eigenstates. Using the
world's largest $B^{0}_{s}$ meson sample accumulated in a dataset,
corresponding to an integrated luminosity of 1.0 fb$^{-1}$, collected by the
LHCb experiment at the CERN LHC in 2011, a measurement of $\Delta m_{s}$ is
presented. A total of about 34,000 $B^{0}_{s}\rightarrowD^{-}_{s}\pi^{+}$
signal decays are reconstructed, with an average decay time resolution of 44
fs. The oscillation frequency is measured to be \mbox{$\Delta m_{s}$ = 17.768
$\pm$ 0.023 (stat) $\pm$ 0.006 (syst) ps$^{-1}$}, which is the most precise
measurement to date.


  We have searched for star-forming galaxies at z~7 by applying the Lyman-break
technique to newly-released 1.1micron Y-band images from WFC3 on HST. By
comparing these images of the Hubble Ultra Deep Field with the ACS z'-band
(0.85micron), we identify objects with red colours, (z'-Y)_AB>1.3), consistent
with the Ly-alpha forest absorption at z~6.7-8.8. We identify 12 of these
z'-drops down to a limiting magnitude Y_AB<28.5 (equivalent to a star formation
rate of 1.3M_sun/yr at z=7.1), which are undetected in the other ACS filters.
We use the WFC3 J-band image to eliminate contaminant low mass Galactic stars,
which typically have redder colours than z~7 galaxies. One of our z'-drops is a
probably a T-dwarf star. The z~7 z'-drops have much bluer spectral slopes than
Lyman-break galaxies at lower redshift. Our brightest z'-drop is not present in
the NICMOS J-band image of the same field taken 5 years before, and is a
possible transient object. From the 10 remaining z~7 candidates we determine a
lower limit on the star formation rate density of 0.0017M_sun/yr/Mpc^3 for a
Salpeter initial mass function, which rises to 0.0025-0.0034M_sun/yr/Mpc^3
after correction for luminosity bias. The star formation rate density is a
factor of ~10 less than that at z=3-4, and is about half the value at z~6.
While based on a single deep field, our results suggest that this star
formation rate density would produce insufficient Lyman continuum photons to
reionize the Universe unless the escape fraction of these photons is extremely
high (f_esc>0.5), and the clumping factor of the Universe is low. Even then, we
need to invoke a large contribution from galaxies below our detection limit.
The apparent shortfall in ionizing photons might be alleviated if stellar
populations at high redshift are low metallicity or have a top-heavy IMF.


  The study of an extension of the standard model based on the flavor symmetry
A4 is presented. Neutrino Majorana mass terms arise from dimension five
operator and charged lepton masses from renormalizable Yukawa couplings. We
introduce three Higgs doublets that belong to one triplet irreducible
representation of A4. We study the most general A4-invariant scalar potential
and the phenomenological consequences of the model. We find that the reactor
angle could be as large as 0.03, while the atmospheric mixing angle is close to
maximal.


  Drones, unmanned aerial vehicles (UAVs), or unmanned aerial systems (UAS) are
expected to be an important component of 5G/beyond 5G (B5G) communications.
This includes their use within cellular architectures (5G UAVs), in which they
can facilitate both wireless broadcast and point-to-point transmissions,
usually using small UAS (sUAS). Allowing UAS to operate within airspace along
with commercial, cargo, and other piloted aircraft will likely require
dedicated and protected aviation spectrum at least in the near term, while
regulatory authorities adapt to their use. The command and control (C2), or
control and non-payload communications (CNPC) link provides safety critical
information for the control of the UAV both in terrestrial-based line of sight
(LOS) conditions and in satellite communication links for so-called beyond LOS
(BLOS) conditions. In this paper, we provide an overview of these CNPC links as
they may be used in 5G and satellite systems by describing basic concepts and
challenges. We review new entrant technologies that might be used for UAV C2 as
well as for payload communication, such as millimeter wave (mmWave) systems,
and also review navigation and surveillance challenges. A brief discussion of
UAV-to-UAV communication and hardware issues are also provided.


  The development of high-quality software or software-intensive systems
requires custom-tailored process models that fit the organizational and project
goals as well as the development contexts. These models are a necessary
prerequisite for creating project plans that are expected to fulfill business
goals. Although project planners require individual process models
custom-tailored to their constraints, software or system developing
organizations also require generic processes (i.e., reference processes) that
capture project-independent knowledge for similar development contexts. The
latter is emphazised by assessment approaches (such as CMMI, SPICE) that
require explicit process descriptions in order to reach a certain capability or
maturity level. Among other concepts such as polymorphism, templates, or
generator-based descriptions, software process patterns are used to describe
generic process knowledge. Several approaches for describing the architecture
of process patterns have already been published (e.g., [7]). However, there is
a lack of descriptions on how to compose process patterns for a specific
decelopment context in order to gain a custom-tailored process model for a
project. This paper focuses on the composition of process patterns in a
goal-oriented way. First, the paper describes which information a process
pattern should contain so that it can be used for systematic composition.
Second, a composition method is sketched. Afterwards, the results of a
proof-of-concept evaluation of the method are described. Finally, the paper is
summarized and open research questions are sketched.


  This paper investigates the sum spectral efficiency maximization problem in
downlink multiuser multiple-input multiple-output (MIMO) systems with
low-resolution quantizers at an access point (AP) and users. In particular, we
consider rate-splitting multiple access (RSMA) to enhance spectral efficiency
by offering opportunities to boost achievable degrees of freedom. Optimizing
RSMA precoders, however, is highly challenging due to the minimum rate
constraint when determining the rate of the common stream. The quantization
errors coupled with the precoders further make the problem more complicated and
difficult to solve. In this paper, we develop a novel RSMA precoding algorithm
incorporating quantization errors for maximizing the sum spectral efficiency.
To this end, we first obtain an approximate spectral efficiency in a smooth
function. Subsequently, we derive the first-order optimality condition in the
form of the nonlinear eigenvalue problem (NEP). We propose a computationally
efficient algorithm to find the principal eigenvector of the NEP as a
sub-optimal solution. Simulation results validate the superior spectral
efficiency of the proposed method. The key benefit of using RSMA over spatial
division multiple access (SDMA) comes from the ability of the common stream to
balance between the channel gain and quantization error in multiuser MIMO
systems with different quantization resolutions.


  We describe a morphological imprint of magnetization found when considering
the relative orientation of the magnetic field direction with respect to the
density structures in simulated turbulent molecular clouds. This imprint was
found using the Histogram of Relative Orientations (HRO): a new technique that
utilizes the gradient to characterize the directionality of density and column
density structures on multiple scales. We present results of the HRO analysis
in three models of molecular clouds in which the initial magnetic field
strength is varied, but an identical initial turbulent velocity field is
introduced, which subsequently decays. The HRO analysis was applied to the
simulated data cubes and mock-observations of the simulations produced by
integrating the data cube along particular lines of sight. In the 3D analysis
we describe the relative orientation of the magnetic field $\mathbf{B}$ with
respect to the density structures, showing that: 1.The magnetic field shows a
preferential orientation parallel to most of the density structures in the
three simulated cubes. 2.The relative orientation changes from parallel to
perpendicular in regions with density over a critical density $n_{T}$ in the
highest magnetization case. 3.The change of relative orientation is largest for
the highest magnetization and decreases in lower magnetization cases. This
change in the relative orientation is also present in the projected maps. In
conjunction with simulations HROs can be used to establish a link between the
observed morphology in polarization maps and the physics included in
simulations of molecular clouds.


  Leveraging sensory information to aid the millimeter-wave (mmWave) and
sub-terahertz (sub-THz) beam selection process is attracting increasing
interest. This sensory data, captured for example by cameras at the
basestations, has the potential of significantly reducing the beam sweeping
overhead and enabling highly-mobile applications. The solutions developed so
far, however, have mainly considered single-candidate scenarios, i.e.,
scenarios with a single candidate user in the visual scene, and were evaluated
using synthetic datasets. To address these limitations, this paper extensively
investigates the sensing-aided beam prediction problem in a real-world
multi-object vehicle-to-infrastructure (V2I) scenario and presents a
comprehensive machine learning-based framework. In particular, this paper
proposes to utilize visual and positional data to predict the optimal beam
indices as an alternative to the conventional beam sweeping approaches. For
this, a novel user (transmitter) identification solution has been developed, a
key step in realizing sensing-aided multi-candidate and multi-user beam
prediction solutions. The proposed solutions are evaluated on the large-scale
real-world DeepSense $6$G dataset. Experimental results in realistic V2I
communication scenarios indicate that the proposed solutions achieve close to
$100\%$ top-5 beam prediction accuracy for the scenarios with single-user and
close to $95\%$ top-5 beam prediction accuracy for multi-candidate scenarios.
Furthermore, the proposed approach can identify the probable transmitting
candidate with more than $93\%$ accuracy across the different scenarios. This
highlights a promising approach for nearly eliminating the beam training
overhead in mmWave/THz communication systems.


  Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and
mortality. Identifying potential DDIs during the drug design process is
critical for patients and society. Although several computational models have
been proposed for DDI prediction, there are still limitations: (1) specialized
design of drug representation for DDI predictions is lacking; (2) predictions
are based on limited labelled data and do not generalize well to unseen drugs
or DDIs; and (3) models are characterized by a large number of parameters, thus
are hard to interpret. In this work, we develop a ChemicAl SubstrucTurE
Representation (CASTER) framework that predicts DDIs given chemical structures
of drugs.CASTER aims to mitigate these limitations via (1) a sequential pattern
mining module rooted in the DDI mechanism to efficiently characterize
functional sub-structures of drugs; (2) an auto-encoding module that leverages
both labelled and unlabelled chemical structure data to improve predictive
accuracy and generalizability; and (3) a dictionary learning module that
explains the prediction via a small set of coefficients which measure the
relevance of each input sub-structures to the DDI outcome. We evaluated CASTER
on two real-world DDI datasets and showed that it performed better than
state-of-the-art baselines and provided interpretable predictions.


  In this paper, we present an alternative perspective on the mean-field LIBOR
market model introduced by Desmettre et al. in arXiv:2109.10779. Our novel
approach embeds the mean-field model in a classical setup, but retains the
crucial feature of controlling the term rate's variances over large time
horizons. This maintains the market model's practicability, since calibrations
and simulations can be carried out efficiently without nested simulations. In
addition, we show that our framework can be directly applied to model term
rates derived from SOFR, ESTR or other nearly risk-free overnight short-term
rates -- a crucial feature since many IBOR rates are gradually being replaced.
These results are complemented by a calibration study and some theoretical
arguments which allow to estimate the probability of unrealistically high rates
in the presented market models.


  I propose a novel approach to balancing equations that is applicable to all
chemical-reaction equations; it is readily accessible to students via
scientific calculators and basic computer spreadsheets that have a
matrix-inversion application. The new approach utilizes the familiar
matrix-inversion operation in an unfamiliar and innovative way; its purpose is
not to identify undetermined coefficients as usual, but, instead, to compute a
matrix null space (or matrix kernel). The null space then provides the
coefficients that balance the equation. Indeed, the null space contains
everything there is to know about balancing any chemical-reaction equation!


  Grover's database search algorithm, although discovered in the context of
quantum computation, can be implemented using any physical system that allows
superposition of states. A physical realization of this algorithm is described
using coupled simple harmonic oscillators, which can be exactly solved in both
classical and quantum domains. Classical wave algorithms are far more stable
against decoherence compared to their quantum counterparts. In addition to
providing convenient demonstration models, they may have a role in practical
situations, such as catalysis.


  The light curves from a variety of celestial objects display aperiodic
variations, often giving rise to red-noise components in their power spectra.
Searching for a narrow power spectrum peak resulting from a periodic modulation
over the frequency range in which ``coloured'' noise components are dominant
has proven a very complex task. We develop here a technique aimed at detecting
periodicities in the presence of ``coloured'' power spectrum components, while
mantaining the highest Fourier frequency resolution. First we introduce a
simple approximation to the statistical properties of the ``coloured'' power
spectra from celestial objects, based on a few examples and the theory of
linear processes. We then estimate the continuum components in the power
spectrum through an ad hoc smoothing technique. By dividing the sample spectrum
by the smoothed one, a white-noise like spectrum is obtained, the approximate
probability distribution of which is derived. A search for coherent pulsations
is then carried out by looking for peaks in the divided spectrum, the chance
probability of which is below a given detection threshold. The technique is
tested and its range of applicability determined through extensive numerical
simulations. We present also an application to the X-ray light curves of
V0332+53, a highly variable accreting X-ray pulsar, and GX13+1, a bright and
variable accreting source in the galactic bulge.


  We present a comprehensive introduction to spacetime algebra that emphasizes
its practicality and power as a tool for the study of electromagnetism. We
carefully develop this natural (Clifford) algebra of the Minkowski spacetime
geometry, with a particular focus on its intrinsic (and often overlooked)
complex structure. Notably, the scalar imaginary that appears throughout the
electromagnetic theory properly corresponds to the unit 4-volume of spacetime
itself, and thus has physical meaning. The electric and magnetic fields are
combined into a single complex and frame-independent bivector field, which
generalizes the Riemann-Silberstein complex vector that has recently resurfaced
in studies of the single photon wavefunction. The complex structure of
spacetime also underpins the emergence of electromagnetic waves, circular
polarizations, the normal variables for canonical quantization, the distinction
between electric and magnetic charge, complex spinor representations of Lorentz
transformations, and the dual (electric-magnetic field exchange) symmetry that
produces helicity conservation in vacuum fields. This latter symmetry manifests
as an arbitrary global phase of the complex field, motivating the use of a
complex vector potential, along with an associated transverse and
gauge-invariant bivector potential, as well as complex (bivector and scalar)
Hertz potentials. Our detailed treatment aims to encourage the use of spacetime
algebra as a readily available and mature extension to existing vector calculus
and tensor methods that can greatly simplify the analysis of fundamentally
relativistic objects like the electromagnetic field.


  We consider large classes of chiral extensions of the Standard Model,
including new quark generations that do not involve additional neutrinos as
well as lepton generations without quarks. An analysis of renormalization flows
of Yukawa and quartic scalar couplings reveals that additional quarks are not
compatible with a scenario of grand unification without violating the strong
bounds from direct and Higgs searches at colliders. Constraints from direct
searches, electroweak precision observables, and Higgs physics, together with
the assumption that additional new physics beyond the extended chiral field
content should enter significantly above the TeV scale, allows us to make
predictions for searches at the LHC.


  Topological data analysis is an emerging field that applies the study of
topological invariants to data. Perhaps the simplest of these invariants is the
number of connected components or clusters. In this work, we explore a
topological framework for cluster analysis and show how it can be used as a
basis for explainability in unsupervised data analysis. Our main object of
study will be hierarchical data structures referred to as Topological
Hierarchical Decompositions (THDs). We give a number of examples of how
traditional clustering algorithms can be topologized, and provide preliminary
results on the THDs associated with Reeb graphs and the mapper algorithm. In
particular, we give a generalized construction of the mapper functor as a
pixelization of a cosheaf in order to generalize multiscale mapper.


  We present new ALMA Band 7 observations of the edge-on debris disc around the
M1V star GSC 07396-00759. At ~20 Myr old and in the beta Pictoris Moving Group
along with AU Mic, GSC 07396-00759 joins it in the handful of low mass M-dwarf
discs to be resolved in the sub-mm. With previous VLT/SPHERE scattered light
observations we present a multi-wavelength view of the dust distribution within
the system under the effects of stellar wind forces. We find the mm dust grains
to be well described by a Gaussian torus at 70 au with a FWHM of 48 au and we
do not detect the presence of CO in the system. Our ALMA model radius is
significantly smaller than the radius derived from polarimetric scattered light
observations, implying complex behaviour in the scattering phase function. The
brightness asymmetry in the disc observed in scattered light is not recovered
in the ALMA observations, implying that the physical mechanism only affects
smaller grain sizes. High resolution follow-up observations of the system would
allow investigation into its unique dust features as well as provide a true
coeval comparison for its smaller sibling AU Mic, singularly well observed
amongst M-dwarfs systems.


  We construct a new motivic integration morphism, the so-call bounded
integral, that interpolates both the integration morphisms with and without
volume forms of Hrushovski and Kazhdan. This is done within the framework of
model theory of algebraically closed valued fields of equicharacteristic zero.
As an application, we recover and extend some results of Hrushovski and Loeser
about the motivic Milnor fiber.


  We consider the relativistic generalization of the quantum $A_{N-1}$
Calogero-Sutherland models due to Ruijsenaars, comprising the rational,
hyperbolic, trigonometric and elliptic cases. For each of these cases, we find
an exact common eigenfunction for a generalization of Ruijsenaars analytic
difference operators that gives, as special cases, many different kernel
functions; in particular, we find kernel functions for Chalykh-
Feigin-Veselov-Sergeev-type deformations of such difference operators which
generalize known kernel functions for the Ruijsenaars models. We also discuss
possible applications of our results.


  An enhanced Fourier law (EFL) that accounts for quasi-ballistic phonon
transport effects in a formulation entirely in terms of physical observables,
is derived from the Boltzmann transport equation, assuming a gray population of
quasi-ballistic phonon modes. This equation is generalized to an arbitrary
phonon population. Other phonon transport models are analyzed in the context of
the generalized EFL and connections are made between the generalized EFL and
other models, revealing the essential unity of seemingly disparate models
reported in the literature.


  The many-body wave-function of an interacting one-dimensional electron system
is probed, focusing on the low-density, strong interaction regime. The
properties of the wave-function are determined using tunneling between two
long, clean, parallel quantum wires in a GaAs/AlGaAs heterostructure, allowing
for gate-controlled electron density. As electron density is lowered to a
critical value the many-body state abruptly changes from an extended state with
a well-defined momentum to a localized state with a wide range of momentum
components. The signature of the localized states appears as discrete tunneling
features at resonant gate-voltages, corresponding to the depletion of single
electrons and showing Coulomb-blockade behavior. Typically 5-10 such features
appear, where the one-electron state has a single-lobed momentum distribution,
and the few-electron states have double-lobed distributions with peaks at $\pm
k_F$. A theoretical model suggests that for a small number of particles (N<6),
the observed state is a mixture of ground and thermally excited spin states.


  Evaluation of symmetry-forbidden or weakly-allowed vibronic spectra requires
treating the transition dipole moment beyond the Condon approximation. To
include vibronic spectral features not captured in the global harmonic models,
we have recently implemented an on-the-fly ab initio extended thawed Gaussian
approximation, where the propagated wavepacket is a Gaussian multiplied by a
linear polynomial. To include more anharmonic effects, here we represent the
initial wavepacket by a superposition of three independent Gaussian wavepackets
- one for the Condon term and two displaced Gaussians for the Herzberg-Teller
part. Application of this ab initio "three thawed Gaussians approximation" to
vibrationally resolved electronic spectra of the phenyl radical and benzene
shows a clear improvement over the global harmonic and Condon approximations.
The orientational averaging of spectra, the relation between the gradient of
the transition dipole moment and nonadiabatic coupling vectors, and the details
of the extended and three thawed Gaussians approximation are discussed.


  Research in 3D semantic segmentation has been increasing performance metrics,
like the IoU, by scaling model complexity and computational resources, leaving
behind researchers and practitioners that (1) cannot access the necessary
resources and (2) do need transparency on the model decision mechanisms. In
this paper, we propose SCENE-Net, a low-resource white-box model for 3D point
cloud semantic segmentation. SCENE-Net identifies signature shapes on the point
cloud via group equivariant non-expansive operators (GENEOs), providing
intrinsic geometric interpretability. Our training time on a laptop is 85~min,
and our inference time is 20~ms. SCENE-Net has 11 trainable geometrical
parameters and requires fewer data than black-box models. SCENE--Net offers
robustness to noisy labeling and data imbalance and has comparable IoU to
state-of-the-art methods. With this paper, we release a 40~000 Km labeled
dataset of rural terrain point clouds and our code implementation.


  We investigate the heavier electroweakino sectors in several versions of the
MSSM, which has not been explored so far in the light of the LHC data, and
obtain new bounds using the ATLAS Run I constraints in the $3l +
{E\!\!\!\!/_T}$ channel. We also venture beyond the trilepton events and
predict several novel multilepton + ${E\!\!\!\!/_T}$ signatures of these
electroweakinos which may show up before the next shutdown of the LHC.


  We present the gas-phase oxygen abundance (O/H) for a sample of 131
star-forming galaxies at intermediate redshifts ($0.2<z<1.0$). The sample
selection, the spectroscopic observations (mainly with VLT/FORS) and associated
data reduction, the photometric properties, the emission-line measurements, and
the spectral classification are fully described in a companion paper (Paper I).
We use two methods to estimate the O/H abundance ratio: the {} "standard"
$R\_{\mathrm{23}}$ method which is based on empirical calibrations, and the
CL01 method which is based on grids of photo-ionization models and on the
fitting of emission lines. For most galaxies, we have been able to solve the
problem of the metallicity degeneracy between the high- and low-metallicity
branches of the O/H vs. $R\_{\mathrm{23}}$ relationship using various secondary
indicators. The luminosity -- metallicity ($L-Z$) relation has been derived in
the $B$- and $R$-bands, with metallicities derived with the two methods
($R\_{\mathrm{23}}$ and CL01). In the analysis, we first consider our sample
alone and then a larger one which includes other samples of
intermediate-redshift galaxies drawn from the literature. The derived $L-Z$
relations at intermediate redshifts are very similar (same slope) to the $L-Z$
relation obtained for the local universe. Our sample alone only shows a small
evolution of the $L-Z$ relation with redshift up to $z\sim1.0$. We only find
statistical variations consistent with the uncertainty in the derived
parameters. Including other samples of intermediate-redshift galaxies, we find
however that galaxies at $z\sim1$ appear to be metal-deficient by a factor of
$\sim3$ compared with galaxies in the local universe. For a given luminosity,
they contain on average about one third of the metals locked in local galaxies.


  We focus on the challenging problem of efficient mouse 3D pose estimation
based on static images, and especially single depth images. We introduce an
approach to discriminatively train the split nodes of trees in random forest to
improve their performance on estimation of 3D joint positions of mouse. Our
algorithm is capable of working with different types of rodents and with
different types of depth cameras and imaging setups. In particular, it is
demonstrated in this paper that when a top-mounted depth camera is combined
with a bottom-mounted color camera, the final system is capable of delivering
full-body pose estimation including four limbs and the paws. Empirical
examinations on synthesized and real-world depth images confirm the
applicability of our approach on mouse pose estimation, as well as the closely
related task of part-based labeling of mouse.


  In this paper, we provide a system level perspective on the design of control
electronics for large scale quantum systems. Quantum computing systems with
high-fidelity control and readout, coherent coupling, calibrated gates, and
reconfigurable circuits with low error rates are expected to have superior
quantum volumes. Cryogenic CMOS plays a crucial role in the realization of
scalable quantum computers, by minimizing the feature size, lowering the cost,
power consumption, and implementing low latency error correction. Our approach
toward achieving scalable feed-back based control systems includes the design
of memory based arbitrary waveform generators (AWG's), wide band radio
frequency analog to digital converters, integrated amplifier chain, and state
discriminators that can be synchronized with gate sequences. Digitally assisted
designs, when implemented in an advanced CMOS node such as 7 nm can reap the
benefits of low power due to scaling. A qubit readout chain demands several
amplification stages before the digitizer. We propose the co-integration of our
in-house developed InP HEMT LNAs with CMOS LNA stages to achieve the required
gain at the digitizer input with minimal area. Our approach using high
impedance matching between the HEMT LNA and the cryogenic CMOS receiver can
relax the design constraints of an inverter-based CMOS LNA, paving the way
toward a fully integrated qubit readout chain. The qubit state discriminator
consists of a digital signal processor that computes the qubit state from the
digitizer output and a pre-determined threshold. The proposed system realizes
feedback-based optimal control for error mitigation and reduction of the
required data rate through the serial interface to room temperature
electronics.


  The DIRAC experiment at CERN has achieved a sizeable production of
$\pi^+\pi^-$ atoms and has significantly improved the precision on its lifetime
determination. From a sample of 21227 atomic pairs, a 4% measurement of the
S-wave $\pi\pi$ scattering length difference $|a_0-a_2| =
(.0.2533^{+0.0080}_{-0.0078}|_\mathrm{stat}.{}^{+0.0078}_{-0.0073}|_\mathrm{syst})M_{\pi^+}^{-1}$
has been attained, providing an important test of Chiral Perturbation Theory.


  When the Stable Unit Treatment Value Assumption (SUTVA) is violated and there
is interference among units, there is not a uniquely defined Average Treatment
Effect (ATE), and alternative estimands may be of interest, among them average
unit-level differences in outcomes under different homogeneous treatment
policies. We term this target the Homogeneous Assignment Average Treatment
Effect (HAATE). We consider approaches to experimental design with multiple
treatment conditions under partial interference and, given the estimand of
interest, we show that difference-in-means estimators may perform better than
correctly specified regression models in finite samples on root mean squared
error (RMSE). With errors correlated at the cluster level, we demonstrate that
two-stage randomization procedures with intra-cluster correlation of treatment
strictly between zero and one may dominate one-stage randomization designs on
the same metric. Simulations demonstrate performance of this approach; an
application to online experiments at Facebook is discussed.


  The identification of predictive biomarkers from a large scale of covariates
for subgroup analysis has attracted fundamental attention in medical research.
In this article, we propose a generalized penalized regression method with a
novel penalty function, for enforcing the hierarchy structure between the
prognostic and predictive effects, such that a nonzero predictive effect must
induce its ancestor prognostic effects being nonzero in the model. Our method
is able to select useful predictive biomarkers by yielding a sparse,
interpretable, and predictable model for subgroup analysis, and can deal with
different types of response variable such as continuous, categorical, and
time-to-event data. We show that our method is asymptotically consistent under
some regularized conditions. To minimize the generalized penalized regression
model, we propose a novel integrative optimization algorithm by integrating the
majorization-minimization and the alternating direction method of multipliers,
which is named after \texttt{smog}. The enriched simulation study and real case
study demonstrate that our method is very powerful for discovering the true
predictive biomarkers and identifying subgroups of patients.


  In a Bayesian analysis, the likelihood that specific candidate parameters
govern the evolution of a quantum system are conditioned on the outcome of
measurements which, in turn, cause measurement backaction on the state of the
system [M. Tsang, Phys. Rev. Lett. 108, 170502 (2012)]. Specializing to the
distinction of two candidate hypotheses, we study the achievements of
continuous monitoring of the radiation emitted by a quantum system followed by
an optimal projective measurement on its conditioned final state. Our study of
the radiative decay of a driven two-level system shows an intricate interplay
between the maximum information available from photon counting and homodyne
detection and the final projective measurement on the emitter. We compare the
results with theory predicting a lower bound for the probability to assign a
wrong hypothesis by any combined measurement on the system and its radiative
environment.


  Nowadays, it is thought that there are only two approaches to political
economy: public finance and public choice; however, this research aims to
introduce a new insight by investigating scholastic sources. We study the
relevant classic books from the thirteenth to the seventeenth centuries and
reevaluate the scholastic literature by doctrines of public finance and public
choice. The findings confirm that the government is the institution for
realizing the common good according to scholastic attitude. Therefore,
scholastic thinkers saw a common mission for the government based on their
essentialist attitude toward human happiness. Social conflicts and lack of
social consent are the product of diversification in ends and desires; hence,
if the end of humans were unified, there would be no conflict of interest.
Accordingly, if the government acts according to its assigned mission, the lack
of public consent is not significant. Based on the scholastic point of view
this study introduces the third approach to political economy, which can be,
consider an analytical synthesis among classical doctrines.


  The massive growth of sports videos has resulted in a need for automatic
generation of sports highlights that are comparable in quality to the
hand-edited highlights produced by broadcasters such as ESPN. Unlike previous
works that mostly use audio-visual cues derived from the video, we propose an
approach that additionally leverages contextual cues derived from the
environment that the game is being played in. The contextual cues provide
information about the excitement levels in the game, which can be ranked and
selected to automatically produce high-quality basketball highlights. We
introduce a new dataset of 25 NCAA games along with their play-by-play stats
and the ground-truth excitement data for each basket. We explore the
informativeness of five different cues derived from the video and from the
environment through user studies. Our experiments show that for our study
participants, the highlights produced by our system are comparable to the ones
produced by ESPN for the same games.


  The study of neutrino interactions has recently experienced a renaissance,
motivated by the fact that neutrino oscillation experiments depend critically
on an accurate models of neutrino interactions. These models have to predict
not only the signal and background populations that oscillation experiments see
at near and far detectors, but they must also predict how the neutrino's energy
which enters a nucleus gets transferred to energies of the particles that leave
the nucleus after the neutrino interacts. Over the past year there have been a
number of new results on many different neutrino (and antineutrino) interaction
channels using several different target nuclei. These results are often not in
agreement with predictions extraolated from charged lepton scattering
measurements, or even from predictions anchored to neutrino measurements on
deuterium. These new measurements are starting to give the community the
handles needed to improve the theoretical description of neutrino interactions,
which ultimately pave the way for precision oscillation measurements. This
report briefly summarizes recent results and points out where those results
differ from the predictions based on current models.


  We study the family of K3 surfaces of Picard rank sixteen associated with the
double cover of the projective plane branched along the union of six lines, and
the family of its Van Geemen-Sarti partners, i.e., K3 surfaces with special
Nikulin involutions, such that quotienting by the involution and blowing up
recovers the former. We prove that the family of Van Geemen-Sarti partners is a
four-parameter family of K3 surfaces with $H \oplus E_7(-1) \oplus E_7(-1)$
lattice polarization. We describe explicit Weierstrass models on both families
using even modular forms on the bounded symmetric domain of type $IV$. We also
show that our construction provides a geometric interpretation, called
geometric two-isogeny, for the F-theory/heterotic string duality in eight
dimensions. As a result, we obtain novel F-theory models, dual to non-geometric
heterotic string compactifications in eight dimensions with two non-vanishing
Wilson line parameters.


  We report on a series of three-dimensional magnetohydrodynamic simulations of
active galactic nucleus (AGN) jet propagation in realistic models of magnetized
galaxy clusters. We are primarily interested in the details of energy transfer
between jets and the intracluster medium (ICM) to help clarify what role such
flows could have in the reheating of cluster cores. Our simulated jets feature
a range of intermittency behaviors, including intermittent jets that
periodically switch on and off and one model jet that shuts down completely,
naturally creating a relic plume. The ICM into which these jets propagate
incorporates tangled magnetic field geometries and density substructure
designed to mimic some likely features of real galaxy clusters. We find that
our jets are characteristically at least 60% efficient at transferring thermal
energy to the ICM. Irreversible heat energy is not uniformly distributed,
however, instead residing preferentially in regions very near the jet/cocoon
boundaries. While intermittency affects the details of how, when, and where
this energy is deposited, all of our models generically fail to heat the
cluster cores uniformly. Both the detailed density structure and nominally weak
magnetic fields in the ICM play interesting roles in perturbing the flows,
particularly when the jets are non-steady. Still, this perturbation is never
sufficient to isotropize the jet energy deposition, suggesting that some other
ingredient is required for AGN jets to successfully reheat cluster cores.


  The MeerKAT (64 x 13.5m dish radio interferometer) is South Africa's
precursor instrument for the Square Kilometre Array (SKA), exploring dish
design, instrumentation, and the characteristics of a Karoo desert site and is
projected to be on sky in 2016. One of two top-priority, Key Projects is a
single deep field, integrating for 5000 hours total with the aim to detect
neutral atomic hydrogen through its 21 cm line emission out to redshift unity
and beyond.
  This first truly deep HI survey will help constrain fueling models for galaxy
assembly and evolution. It will measure the evolution of the cosmic neutral gas
density and its distribution over galaxies over cosmic time, explore evolution
of the gas in galaxies, measure the Tully-Fisher relation, measure OH maser
counts, and address many more topics.
  Here we present the observing strategy and envisaged science case for this
unique deep field, which encompasses the Chandra Deep Field-South (and the
footprints of GOODS, GEMS and several other surveys) to produce a singular
legacy multi-wavelength data-set.


  This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers.


  Computational experiments are used to show that grain boundary mobility is
independent of driving force in a two-dimensional, square-lattice Ising model
with Metropolis kinetics. This is established over the entire Monte Carlo
temperature range. A calibration methodology is then introduced which endows
the Monte Carlo algorithm with time and length scales and allows the Monte
Carlo parameters to be expressed directly in terms of experimentally measurable
parameters. A comparison of results obtained for a variety of driving forces
and temperature conditions indicates that such Calibrated Monte Carlo models
are able to capture the grain boundary kinetics predicted by sharp-interface
theory.


  In this work, we propose a multigrid preconditioner for Jacobian-free
Newton-Krylov (JFNK) methods. Our multigrid method does not require knowledge
of the Jacobian at any level of the multigrid hierarchy. As it is common in
standard multigrid methods, the proposed method also relies on three building
blocks: transfer operators, smoothers, and a coarse level solver. In addition
to the restriction and prolongation operator, we also use a projection operator
to transfer the current Newton iterate to a coarser level. The three-level
Chebyshev semi-iterative method is employed as a smoother, as it has good
smoothing properties and does not require the representation of the Jacobian
matrix. We replace the direct solver on the coarsest level with a matrix-free
Krylov subspace method, thus giving rise to a truly Jacobian-free multigrid
preconditioner. We will discuss all building blocks of our multigrid
preconditioner in detail and demonstrate the robustness and the efficiency of
the proposed method using several numerical examples.


  In M-theory vacua with vanishing 4-form F, one can invoke the ordinary
Riemannian holonomy H \subset SO(1,10) to account for unbroken supersymmetries
n=1, 2, 3, 4, 6, 8, 16, 32. However, the generalized holonomy conjecture, valid
for non-zero F, can account for more exotic fractions of supersymmetry, in
particular 16<n<32. The conjectured holonomies are given by H \subset G where G
are the generalized structure groups G=SO(d-1,1) x G(spacelike), G=ISO(d-1) x
G(null) and G=SO(d) x G(timelike) with 1<=d<11. For example,
G(spacelike)=SO(16), G(null)=[SU(8) x U(1)] \ltimes R^{56} and
G(timelike)=SO*(16) when d=3. Although extending spacetime symmetries, there is
no conflict with the Coleman-Mandula theorem. The holonomy conjecture rules out
certain vacua which are otherwise permitted by the supersymmetry algebra.


  We examine the quantum corrections to the static energy for Higgs winding
configurations in order to ascertain whether such corrections may stabilize
solitons in the standard model. We evaluate the effective action for winding
configurations in Weinberg-Salam theory without U(1)-gauge fields or fermions.
For a configuration whose size, $a \ll m^{-1}$ where $m = \max{m_W,m_H}$, m_W
is the W-mass, and m_H is the Higgs mass, the static energy goes like
$g^{-2}m_W^2a [1+b_0g^2\ln(1/ma)]^{c_0/b_0}$ in the semiclassical limit. Here g
is the SU(2)-gauge coupling constant and b_0, c_0 are positive numbers
determined by renormalization-group techniques. We discuss the limitations of
this result for extremely small configurations and conclude that quantum
fluctuations do not stabilize winding configurations where we have confidence
in SU(2)-Higgs as a renormalizable field theory.


  Vehicle-to-everything (V2X) services are attracting a lot of attention in the
research and industry communities due to their applicability in the landscape
of connected and autonomous vehicles. Such applications have stringent
performance requirements in terms of complex data processing and low latency
communications which are utilized to ensure road safety and improve road
conditions. To address these challenges, the placement of V2X applications
through leveraging of edge computing paradigm, that distributes the computing
capabilities to access points in proximity to the vehicles, presents itself as
a viable solution. However, the realistic implementation of the edge enabled
V2X applications is hindered by the limited computational power provided at the
edge and the nature of V2X applications that are composed of multiple
independent V2X basic services. To address these challenges, this work targets
the efficient placement of V2X basic services in a highway scenario subject to
the delay constraints of V2X applications using them and the limited
computational resources at the edge. To that end, this work formulates a binary
integer linear programming model that minimizes the delay of V2X applications
while satisfying the resource requirements of V2X basic services. To
demonstrate the soundness of the approach, simulations with varying vehicle
densities were conducted, and the results reported show that it can satisfy the
delay requirements of V2X applications.


  Hierarchical organization is an important, prevalent characteristic of
complex systems; in order to understand their organization, the study of the
underlying (generally complex) networks that describe the interactions between
their constituents plays a central role. Numerous previous works have shown
that many real-world networks in social, biologic and technical systems present
hierarchical organization, often in the form of a hierarchy of community
structures. Many artificial benchmark graphs have been proposed in order to
test different community detection methods, but no benchmark has been developed
to throughly test the detection of hierarchical community structures. In this
study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi
(LFR) ensemble of benchmark graphs, adopting the rule of constructing
hierarchical networks proposed by Ravasz and Barab\'asi. We employ this
benchmark to test three of the most popular community detection algorithms, and
quantify their accuracy using the traditional Mutual Information and the
recently introduced Hierarchical Mutual Information. The results indicate that
the Ravasz-Barab\'asi-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark
generates a complex hierarchical structure constituting a challenging benchmark
for the considered community detection methods.


  Some time ago, Fisher and de Gennes pointed out that long-ranged correlations
in a fluid close to its critical point Tc cause distinct forces between
immersed colloidal particles which can even lead to flocculation [C. R. Acad.
Sc. Paris B 287, 207 (1978)]. Here we calculate such forces between pairs of
spherical particles as function of both relevant thermodynamic variables, i.e.,
the reduced temperature t = (T-Tc)/Tc and the field h conjugate to the order
parameter. This provides the basis for specific predictions concerning the
phase behavior of a suspension of colloidal particles in a near-critical
solvent.


  We determine the pressure of a cold and dense electron gas to a nearly
complete next-to-next-to-next-to-leading order (N$^3$LO) in the fine-structure
constant $\alpha_e$, utilizing a new result for the two-loop photon self-energy
from a companion paper. Our result contains all infrared-sensitive
contributions to the pressure at this order, including the coefficient of the
$O(\alpha_e^3 \ln \alpha_e^{ })$ term, and leaves only a single coefficient
associated with the contributions of unresummed hard momenta undetermined.
Moreover, we explicitly demonstrate the complete cancellation of infrared
divergences according to the effective field theory paradigm by determining
part of the hard contributions at this order. Our calculation provides the
first improvement to a 45-year-old milestone result and demonstrates the
feasibility of the corresponding N$^3$LO calculation for cold and dense quark
matter.


  While the interaction of ultra-intense ultra-short laser pulses with near-
and overcritical plasmas cannot be directly observed, experimentally accessible
quantities (observables) often only indirectly give information about the
underlying plasma dynamics. Furthermore, the information provided by
observables is incomplete, making the inverse problem highly ambiguous.
Therefore, in order to infer plasma dynamics as well as experimental parameter,
the full distribution over parameters given an observation needs to considered,
requiring that models are flexible and account for the information lost in the
forward process. Invertible Neural Networks (INNs) have been designed to
efficiently model both the forward and inverse process, providing the full
conditional posterior given a specific measurement. In this work, we benchmark
INNs and standard statistical methods on synthetic electron spectra. First, we
provide experimental results with respect to the acceptance rate, where our
results show increases in acceptance rates up to a factor of 10. Additionally,
we show that this increased acceptance rate also results in an increased
speed-up for INNs to the same extent. Lastly, we propose a composite algorithm
that utilizes INNs and promises low runtimes while preserving high accuracy.


  YVO3 exhibits a well separated sequence of orbital and spin order transitions
at 200 K and 116 K, followed by a combined spin-orbital reorientation at 77 K.
It is shown that the spin order can be destroyed by a sufficiently strong
optical pulse within less than 4 ps. In contrast, the orbital reordering
transition from C-type to G-type orbital order is slower than 100 ps and goes
via an intermediate nonthermal phase. We propose that the dynamics of phase
transitions is subjected to symmetry relations between the associated phases.


  We study the list decodability of different ensembles of codes over the real
alphabet under the assumption of an omniscient adversary. It is a well-known
result that when the source and the adversary have power constraints $ P $ and
$ N $ respectively, the list decoding capacity is equal to $
\frac{1}{2}\log\frac{P}{N} $. Random spherical codes achieve constant list
sizes, and the goal of the present paper is to obtain a better understanding of
the smallest achievable list size as a function of the gap to capacity. We show
a reduction from arbitrary codes to spherical codes, and derive a lower bound
on the list size of typical random spherical codes. We also give an upper bound
on the list size achievable using nested Construction-A lattices and infinite
Construction-A lattices. We then define and study a class of infinite
constellations that generalize Construction-A lattices and prove upper and
lower bounds for the same. Other goodness properties such as packing goodness
and AWGN goodness of infinite constellations are proved along the way. Finally,
we consider random lattices sampled from the Haar distribution and show that if
a certain number-theoretic conjecture is true, then the list size grows as a
polynomial function of the gap-to-capacity.


  We study the dependence on various parameters of the exceptional set in
Vojta's conjecture. In particular, by making use of certain elliptic surfaces,
we answer in the negative the often-raised question of whether Vojta's
conjecture holds when extended to all algebraic points (that is, if the
conjecture holds without fixing a bound on the degree of the algebraic points).


  We discuss the statistical distribution of galaxy shapes and viewing angles
under the assumption of triaxiality by deprojecting observed Surface Brightness
(SB) profiles of 56 Brightest Cluster Galaxies coming from a recently published
large deep-photometry sample. For the first time, we address this issue by
directly measuring axis ratio profiles without limiting ourselves to a
statistical analysis of average ellipticities. We show that these objects are
strongly triaxial, with triaxiality parameters 0.39 $ \leq T \leq $ 0.72, have
on average axis ratios $< p(r) > = $ 0.84 and $< q(r) > =$ 0.68, and are more
spherical in the central regions but flatten out at large radii. Measured
shapes in the outskirts agree well with the shapes found for simulated massive
galaxies and their dark matter halos from both the IllustrisTNG and the
Magneticum simulations, possibly probing the nature of dark matter. In
contrast, both simulations fail to reproduce the observed inner regions of
BCGs, producing too flattened objects.


  Satisfaction measurement, which emerges in every sector today, is a very
important factor for many companies. In this study, it is aimed to reach the
highest accuracy rate with various machine learning algorithms by using the
data on Yemek Sepeti and variations of this data. The accuracy values of each
algorithm were calculated together with the various natural language processing
methods used. While calculating these accuracy values, the parameters of the
algorithms used were tried to be optimized. The models trained in this study on
labeled data can be used on unlabeled data and can give companies an idea in
measuring customer satisfaction. It was observed that 3 different natural
language processing methods applied resulted in approximately 5% accuracy
increase in most of the developed models.


  This report prepared by the Montreal AI Ethics Institute provides
recommendations in response to the National Security Commission on Artificial
Intelligence (NSCAI) Key Considerations for Responsible Development and
Fielding of Artificial Intelligence document. The report centres on the idea
that Responsible AI should be made the Norm rather than an Exception. It does
so by utilizing the guiding principles of: (1) alleviating friction in existing
workflows, (2) empowering stakeholders to get buy-in, and (3) conducting an
effective translation of abstract standards into actionable engineering
practices. After providing some overarching comments on the document from the
NSCAI, the report dives into the primary contribution of an actionable
framework to help operationalize the ideas presented in the document from the
NSCAI. The framework consists of: (1) a learning, knowledge, and information
exchange (LKIE), (2) the Three Ways of Responsible AI, (3) an
empirically-driven risk-prioritization matrix, and (4) achieving the right
level of complexity. All components reinforce each other to move from
principles to practice in service of making Responsible AI the norm rather than
the exception.


  Structured access is an emerging paradigm for the safe deployment of
artificial intelligence (AI). Instead of openly disseminating AI systems,
developers facilitate controlled, arm's length interactions with their AI
systems. The aim is to prevent dangerous AI capabilities from being widely
accessible, whilst preserving access to AI capabilities that can be used
safely. The developer must both restrict how the AI system can be used, and
prevent the user from circumventing these restrictions through modification or
reverse engineering of the AI system. Structured access is most effective when
implemented through cloud-based AI services, rather than disseminating AI
software that runs locally on users' hardware. Cloud-based interfaces provide
the AI developer greater scope for controlling how the AI system is used, and
for protecting against unauthorized modifications to the system's design. This
chapter expands the discussion of "publication norms" in the AI community,
which to date has focused on the question of how the informational content of
AI research projects should be disseminated (e.g., code and models). Although
this is an important question, there are limits to what can be achieved through
the control of information flows. Structured access views AI software not only
as information that can be shared but also as a tool with which users can have
arm's length interactions. There are early examples of structured access being
practiced by AI developers, but there is much room for further development,
both in the functionality of cloud-based interfaces and in the wider
institutional framework.


  Let $\Sigma = \mathbb B^n/\Gamma$ be a complex hyperbolic space with discrete
subgroup $\Gamma$ of the automorphism group of the unit ball $\mathbb B^n$ and
$\Omega $ be a quotient of $\mathbb B^n \times\mathbb B^n$ under the diagonal
action of $\Gamma$ which is a holomorphic $\mathbb B^n$-fiber bundle over
$\Sigma$. The goal of this article is to investigate the relation between
symmetric differentials of $\Sigma$ and the weighted $L^2$ holomorphic
functions of $\Omega$. If there exists a holomorphic function on $\Omega$ and
it vanishes up to $k$-th order on the maximal compact complex variety in
$\Omega$, then there exists a symmetric differential of degree $k+1$ on
$\Sigma$. Using this property, we show that $\Sigma$ always has a symmetric
differential of degree $N$ for any $N \geq n+2$. Moreover if $\Sigma$ is
compact, for each symmetric differential over $\Sigma$ we construct a weighted
$L^2$ holomorphic function on $\Omega$. We also show that any bounded
holomorphic function on $\Omega$ is constant when $H^0 (\Sigma, S^{m}
T_{\Sigma}^* )=0$ for every $0 < m \leq n+1$.


  This paper introduces key machine learning operations that allow the
realization of robust, joint 6D pose estimation of multiple instances of
objects either densely packed or in unstructured piles from RGB-D data. The
first objective is to learn semantic and instance-boundary detectors without
manual labeling. An adversarial training framework in conjunction with
physics-based simulation is used to achieve detectors that behave similarly in
synthetic and real data. Given the stochastic output of such detectors,
candidates for object poses are sampled. The second objective is to
automatically learn a single score for each pose candidate that represents its
quality in terms of explaining the entire scene via a gradient boosted tree.
The proposed method uses features derived from surface and boundary alignment
between the observed scene and the object model placed at hypothesized poses.
Scene-level, multi-instance pose estimation is then achieved by an integer
linear programming process that selects hypotheses that maximize the sum of the
learned individual scores, while respecting constraints, such as avoiding
collisions. To evaluate this method, a dataset of densely packed objects with
challenging setups for state-of-the-art approaches is collected. Experiments on
this dataset and a public one show that the method significantly outperforms
alternatives in terms of 6D pose accuracy while trained only with synthetic
datasets.


  This paper considers non-cooperative and fully-distributed power-allocation
for secondary-users (SUs) in spectrum-sharing environments when
normalized-interference to each secondary-user is uncertain. We model each
uncertain parameter by the sum of its nominal (estimated) value and a bounded
additive error in a convex set, and show that the allocated power always
converges to its equilibrium, called robust Nash equilibrium (RNE). In the case
of a bounded and symmetric uncertainty set, we show that the power allocation
problem for each SU is simplified, and can be solved in a distributed manner.
We derive the conditions for RNE's uniqueness and for convergence of the
distributed algorithm; and show that the total throughput (social utility) is
less than that at NE when RNE is unique. We also show that for multiple RNEs,
the the social utility may be higher at a RNE as compared to that at the
corresponding NE, and demonstrate that this is caused by SUs' orthogonal
utilization of bandwidth for increasing the social utility. Simulations confirm
our analysis.


  Understanding and quantifying cause and effect is an important problem in
many domains. The generally-agreed solution to this problem is to perform a
randomised controlled trial. However, even when randomised controlled trials
can be performed, they usually have relatively short duration's due to cost
considerations. This makes learning long-term causal effects a very challenging
task in practice, since the long-term outcome is only observed after a long
delay. In this paper, we study the identification and estimation of long-term
treatment effects when both experimental and observational data are available.
Previous work provided an estimation strategy to determine long-term causal
effects from such data regimes. However, this strategy only works if one
assumes there are no unobserved confounders in the observational data. In this
paper, we specifically address the challenging case where unmeasured
confounders are present in the observational data. Our long-term causal effect
estimator is obtained by combining regression residuals with short-term
experimental outcomes in a specific manner to create an instrumental variable,
which is then used to quantify the long-term causal effect through instrumental
variable regression. We prove this estimator is unbiased, and analytically
study its variance. In the context of the front-door causal structure, this
provides a new causal estimator, which may be of independent interest. Finally,
we empirically test our approach on synthetic-data, as well as real-data from
the International Stroke Trial.


  It has been well established that Functional Connectomes (FCs), as estimated
from functional MRI (fMRI) data, have an individual fingerprint that can be
used to identify an individual from a population (subject-identification).
Although identification rate is high when using resting-state FCs, other tasks
show moderate to low values. Furthermore, identification rate is
task-dependent, and is low when distinct cognitive states, as captured by
different fMRI tasks, are compared. Here we propose an embedding framework,
GEFF (Graph Embedding for Functional Fingerprinting), based on group-level
decomposition of FCs into eigenvectors. GEFF creates an eigenspace
representation of a group of subjects using one or more task FCs (Learning
Stage). In the Identification Stage, we compare new instances of FCs from the
Learning subjects within this eigenspace (validation dataset). The validation
dataset contains FCs either from the same tasks as the Learning dataset or from
the remaining tasks that were not included in Learning. Assessment of
validation FCs within the eigenspace results in significantly increased
subject-identification rates for all fMRI tasks tested and potentially
task-independent fingerprinting process. It is noteworthy that combining
resting-state with one fMRI task for GEFF Learning Stage covers most of the
cognitive space for subject identification. In addition to
subject-identification, GEFF was also used for identification of cognitive
states, i.e. to identify the task associated to a given FC, regardless of the
subject being already in the Learning dataset or not (subject-independent
task-identification). In addition, we also show that eigenvectors from the
Learning Stage can be characterized as task-dominant, subject dominant or
neither, providing a deeper insight into the extent of variance in functional
connectivity across individuals and cognitive states.


  Energy system models underpin decisions by energy system planners and
operators. Energy system modelling faces a transformation: accounting for
changing meteorological conditions imposed by climate change. To enable that
transformation, a community of practice in energy-climate modelling has started
to form that aims to better integrate energy system models with weather and
climate models. Here, we evaluate the disconnects between the energy system and
climate modelling communities, then lay out a research agenda to bridge those
disconnects. In the near-term, we propose interdisciplinary activities for
expediting uptake of future climate data in energy system modelling. In the
long-term, we propose a transdisciplinary approach to enable development of (1)
energy-system-tailored climate datasets for historical and future
meteorological conditions and (2) energy system models that can effectively
leverage those datasets. This agenda increases the odds of meeting ambitious
climate mitigation goals by systematically capturing and mitigating climate
risk in energy sector decision making.


  Event cameras are a new type of sensors that are different from traditional
cameras. Each pixel is triggered asynchronously by event. The trigger event is
the change of the brightness irradiated on the pixel. If the increment or
decrement of brightness is higher than a certain threshold, an event is output.
Compared with traditional cameras, event cameras have the advantages of high
dynamic range and no motion blur. Accumulating events to frames and using
traditional SLAM algorithm is a direct and efficient way for event-based SLAM.
Different event accumulator settings, such as slice method of event stream,
processing method for no motion, using polarity or not, decay function and
event contribution, can cause quite different accumulating results. We
conducted the research on how to accumulate event frames to achieve a better
event-based SLAM performance. For experiment verification, accumulated event
frames are fed to the traditional SLAM system to construct an event-based SLAM
system. Our strategy of setting event accumulator has been evaluated on the
public dataset. The experiment results show that our method can achieve better
performance in most sequences compared with the state-of-the-art event frame
based SLAM algorithm. In addition, the proposed approach has been tested on a
quadrotor UAV to show the potential of applications in real scenario. Code and
results are open sourced to benefit the research community of event cameras.