  Two recent papers (Ghez et al. 2008, Gillessen et al. 2009) have estimated
the mass of and the distance to the massive black hole in the center of the
Milky Way using stellar orbits. The two astrometric data sets are independent
and yielded consistent results, even though the measured positions do not match
when simply overplotting the two sets. In this letter we show that the two sets
can be brought to excellent agreement with each other when allowing for a small
offset in the definition of the reference frame of the two data sets. The
required offsets in the coordinates and velocities of the origin of the
reference frames are consistent with the uncertainties given in Ghez et al.
(2008). The so combined data set allows for a moderate improvement of the
statistical errors of mass of and distance to Sgr A*, but the overall
accuracies of these numbers are dominated by systematic errors and the
long-term calibration of the reference frame. We obtain R0 = 8.28 +- 0.15(stat)
+- 0.29(sys) kpc and M(MBH) = 4.30 +- 0.20(stat) +- 0.30(sys) x 10^6 Msun as
best estimates from a multi-star fit.


  In this paper we propose a Hamiltonian approach to gapped topological phases
on an open surface with boundary. Our setting is an extension of the Levin-Wen
model to a 2d graph on the open surface, whose boundary is part of the graph.
We systematically construct a series of boundary Hamiltonians such that each of
them, when combined with the usual Levin-Wen bulk Hamiltonian, gives rise to a
gapped energy spectrum which is topologically protected; and the corresponding
wave functions are robust under changes of the underlying graph that maintain
the spatial topology of the system. We derive explicit ground-state
wavefunctions of the system and show that the boundary types are classified by
Morita-equivalent Frobenius algebras. We also construct boundary quasiparticle
creation, measuring and hopping operators. These operators allow us to
characterize the boundary quasiparticles by bimodules of Frobenius algebras.
Our approach also offers a concrete set of tools for computations. We
illustrate our approach by a few examples.


  Increasingly high-stakes decisions are made using neural networks in order to
make predictions. Specifically, meteorologists and hedge funds apply these
techniques to time series data. When it comes to prediction, there are certain
limitations for machine learning models (such as lack of expressiveness,
vulnerability of domain shifts and overconfidence) which can be solved using
uncertainty estimation. There is a set of expectations regarding how
uncertainty should ``behave". For instance, a wider prediction horizon should
lead to more uncertainty or the model's confidence should be proportional to
its accuracy. In this paper, different uncertainty estimation methods are
compared to forecast meteorological time series data and evaluate these
expectations. The results show how each uncertainty estimation method performs
on the forecasting task, which partially evaluates the robustness of predicted
uncertainty.


  Given two intervals $I, J \subset \mathbb{R}$, we ask whether it is possible
to reconstruct a real-valued function $f \in L^2(I)$ from knowing its Hilbert
transform $Hf$ on $J$. When neither interval is fully contained in the other,
this problem has a unique answer (the nullspace is trivial) but is severely
ill-posed. We isolate the difficulty and show that by restricting $f$ to
functions with controlled total variation, reconstruction becomes stable. In
particular, for functions $f \in H^1(I)$, we show that $$ \|Hf\|_{L^2(J)} \geq
c_1 \exp{\left(-c_2 \frac{\|f_x\|_{L^2(I)}}{\|f\|_{L^2(I)}}\right)} \| f
\|_{L^2(I)} ,$$ for some constants $c_1, c_2 > 0$ depending only on $I, J$.
This inequality is sharp, but we conjecture that $\|f_x\|_{L^2(I)}$ can be
replaced by $\|f_x\|_{L^1(I)}$.


  The log-periodic equation for the entropy $S = - (k/a) \sum_{i=1}^{N} p_{i}
\sin(a \ln p_{i})$, based on the forgotten Sharma-Taneja entropy measure, is
studied for the first time with $N$ the total number of system states and
$p_{i}$ the associated probabilities. It is argued that an oscillatory regime
for $S$ could in principle be understood in terms of a linear time-dependent
behavior for the associated probabilities in analogy with a spring system of
frequency $w$ gaining momentum from the surroundings. The physical meaning for
the production of entropy given by the parameter $a$ relates the angle $\omega
t$. We discuss its properties, and make a connection with the non-extensive
Tsallis, R\'{e}nyi, Boltzmann-Gibbs and Shannon entropies as special limiting
cases for systems with constant mass. Our non-trivial form of entropy displays
peculiar concavity and addition properties. Log-periodicity in $S$, concavity
lost, and non-additivity are manifested by increasing the value of the
coefficient $a$, which sets the variations with respect to the behavior of the
monotonic Gibbs entropy function. We have considered systems with linearly
increasing mass and have explained how "oscillating entropy" in such systems
may appear.


  In melt-blowing processes mico- and nanofibers are produced by the extrusion
of polymeric jets into a directed, turbulent high-speed airflow. Up to now the
physical mechanism for the drastic jet thinning is not fully understood, since
in the existing literature the numerically computed/predicted fiber thickness
differs several orders of magnitude from those experimentally measured. Recent
works suggest that this discrepancy might arise from the neglect of the
turbulent aerodynamic fluctuations in the simulations. In this paper we confirm
this suggestion numerically. Due to the complexity of the process direct
numerical simulations of the multiscale-multiphase problem are not possible.
Hence, we develop a numerical framework for a growing fiber in turbulent air
that makes the simulation of industrial setups feasible. For this purpose we
employ an asymptotic viscoelastic model for the fiber. The turbulent effects
are taken into account by a stochastic aerodynamic force model where the
underlying velocity fluctuations are reconstructed from a $k$-$\epsilon$
turbulence description of the airflow. Our numerical results show the
significance of the turbulence on the jet thinning and give fiber diameters of
realistic order of magnitude.


  Pressure evolution of the superconducting kagome metal CsV$_3$Sb$_5$ is
studied with single-crystal x-ray diffraction and density-functional
band-structure calculations. A highly anisotropic compression observed up to 5
GPa is ascribed to the fast shrinkage of the Cs-Sb distances and suppression of
Cs rattling motion. This prevents Sb displacements required to stabilize the
three-dimensional charge-density-wave (CDW) order and elucidates the
disappearance of the CDW already at 2 GPa despite only minor changes in the
electronic structure of the normal state. At higher pressures, vanadium bands
still change only marginally, whereas antimony bands undergo a major
reconstruction caused by the gradual formation of the interlayer Sb-Sb bonds.
Our results exclude pressure tuning of vanadium kagome bands as the main
mechanism for the non-trivial evolution of superconductivity in real-world
kagome metals. Concurrently, we establish the central role of Sb atoms in the
stabilization of a three-dimensional CDW and Fermi surface reconstruction.


  Audio and vision are two main modalities in video data. Multimodal learning,
especially for audiovisual learning, has drawn considerable attention recently,
which can boost the performance of various computer vision tasks. However, in
video summarization, existing approaches just exploit the visual information
while neglect the audio information. In this paper, we argue that the audio
modality can assist vision modality to better understand the video content and
structure, and further benefit the summarization process. Motivated by this, we
propose to jointly exploit the audio and visual information for the video
summarization task, and develop an AudioVisual Recurrent Network (AVRN) to
achieve this. Specifically, the proposed AVRN can be separated into three
parts: 1) the two-stream LSTM is utilized to encode the audio and visual
feature sequentially by capturing their temporal dependency. 2) the audiovisual
fusion LSTM is employed to fuse the two modalities by exploring the latent
consistency between them. 3) the self-attention video encoder is adopted to
capture the global dependency in the video. Finally, the fused audiovisual
information, and the integrated temporal and global dependencies are jointly
used to predict the video summary. Practically, the experimental results on the
two benchmarks, \emph{i.e.,} SumMe and TVsum, have demonstrated the
effectiveness of each part, and the superiority of AVRN compared to those
approaches just exploiting visual information for video summarization.


  Magnetic reconnection plays a crucial role in powering solar flares,
production of energetic particles, and plasma heating. However, where the
magnetic reconnections occur, how and where the released magnetic energy is
transported, and how it is converted to other forms remain unclear. Here we
report recurring bi-directional plasma outflows located within a large-scale
plasma sheet observed in extreme ultraviolet emission and scattered white light
during the post-impulsive gradual phase of the X8.2 solar flare on 2017
September 10. Each of the bi-directional outflows originates in the plasma
sheet from a discrete site, identified as a magnetic reconnection site. These
reconnection sites reside at very low altitudes ($< 180$ Mm, or 0.26
$R_{\odot}$) above the top of the flare arcade, a distance only $<3\%$ of the
total length of a plasma sheet that extends to at least 10 $R_{\odot}$. Each
arrival of sunward outflows at the looptop region appears to coincide with an
impulsive microwave and X-ray burst dominated by a hot source (10-20 MK) at the
looptop, which is immediately followed by a nonthermal microwave burst located
in the loopleg region. We propose that the reconnection outflows transport the
magnetic energy released at localized magnetic reconnection sites outward in
the form of kinetic energy flux and/or electromagnetic Poynting flux. The
sunward-directed energy flux induces particle acceleration and plasma heating
in the post-flare arcades, observed as the hot and nonthermal flare emissions.


  This paper provides a GIT construction of the Moduli Space of Stable Maps as
a GIT quotient of the Graph Space by SL(2,C). As a corollary, we get a
birational map from the 0-pointed Moduli Space to a projective variety.


  In the process of developing a new digital music interface, the author faced
three questions that have attracted little to no attention in the literature.
By tracking body joints, a performer can use body parts to directly control a
digital music instrument. An immediate question that follows asks which limb(s)
is more effective for the instrument. The next question asks that movement
should be measured relative to a particular reference point. And the last
question asks about the mathematical form of the mapping function from the
movement feature to the sound parameters. This paper attempts to discuss why
finding an answer to these questions is worthwhile and to provide possible
solutions that require further investigation.


  Medical image registration and segmentation are critical tasks for several
clinical procedures. Manual realisation of those tasks is time-consuming and
the quality is highly dependent on the level of expertise of the physician. To
mitigate that laborious task, automatic tools have been developed where the
majority of solutions are supervised techniques. However, in medical domain,
the strong assumption of having a well-representative ground truth is far from
being realistic. To overcome this challenge, unsupervised techniques have been
investigated. However, they are still limited in performance and they fail to
produce plausible results. In this work, we propose a novel unified
unsupervised framework for image registration and segmentation that we called
PC-SwinMorph. The core of our framework is two patch-based strategies, where we
demonstrate that patch representation is key for performance gain. We first
introduce a patch-based contrastive strategy that enforces locality conditions
and richer feature representation. Secondly, we utilise a 3D
window/shifted-window multi-head self-attention module as a patch stitching
strategy to eliminate artifacts from the patch splitting. We demonstrate,
through a set of numerical and visual results, that our technique outperforms
current state-of-the-art unsupervised techniques.


  We have examined theoretically the electronic band structure and Fermi
surface of tetragonal low-temperature superconductor Bi2Pd. Our main results
are that (i) the Pd 4d and Bi 6p states determine the main peculiarities of the
multiple-sheets FS topology, thus for this material the complicated
superconducting gap structure with different energy gaps on different FS sheets
should be assumed; (ii) the effect of the spin-orbit coupling is of minor
importance for the distributions of the near-Fermi electronic states; and (iii)
this phase adopts 3D-like type owing to the directional bonds between the
adjacent atomic sheets.


  This paper addressses the kinetics and dynamics of a family of domain wall
solutions along classical antiferromagnetic Heisenberg spin chains at low
energies. The equation of motion is derived and found to have long range
position- and velocity-dependent two-body forces. A 'quiescent' regime is
identified where the forces between walls are all repelling. Outside this
regime some of the interactions are attractive, giving rise to wall collisions
whereupon the colliding walls annihilate. The momentum of the system is found
to be conserved in the quiescent regime and to suffer discontinuous jumps upon
annihilation. The dynamics are illustrated by an exact solution for a double
wall system and a numerical solution for a many-wall system.
  On circular chains the equations support stable periodic stripes that can
rotate as a rigid body. It is found that the stripes are more stable the faster
they rotate. The periodic structure can be destabilised by perturbing the
walls' angular velocity in which case there is a transition to another periodic
structure, possibly via a cascade of annihilation events.


  A ray-theoretic phase space description of linear waves in a two-fluid
(charges and neutrals) magnetized plasma is used to calculate analytic decay
rates and mode transmission and conversion coefficients between fast and slow
waves in two dimensions due to finite ion-neutral collision frequencies at
arbitrary ionization fraction. This is relevant to partially ionized
astrophysical plasmas, in particular solar and stellar atmospheres. The most
important parameter governing collisional effects is the ratio of the wave
frequency to the neutral-charges collision frequency,
$\epsilon=\omega/\nu_{nc}$, with secondary dependence on ionization fraction
and wave attack angle. Comparison is made to the one-fluid magnetohydrodynamic
(MHD) case, and it is found that acoustic-to-acoustic and magnetic-to-magnetic
transmission through the Alfv\'en-acoustic equipartition layer is decreased by
a term of $\mathcal{O}(\epsilon^2)$ relative to one-fluid (infinite collision
frequency), and correspondingly acoustic-to-magnetic and magnetic-to-acoustic
conversion is increased. The neutral acoustic mode is shown to dissipate
rapidly as $\nu_{nc}\to\infty$. Away from the mode conversion region,
dissipative decay along the remaining magneto-acoustic rays scales as
$\mathcal{O}(\epsilon)$ and is found to be much more effective on magnetically
dominated rays compared to acoustically dominated rays. This produces a steep
jump in dissipation in mode conversion regions, where the rays change
character, and can produce localized heating there and beyond. Applications to
the solar chromosphere are discussed.


  We report on measurements of the angular dependence of the irreversibility
temperature $T_{irr}(\theta) $ in $YBa_2Cu_3O_{7-\delta }$ thin films, defined
by the onset of a third harmonic signal and measured by a miniature Hall probe.
From the functional form of $T_{irr}(\theta)$ we conclude that the origin of
the irreversibility line in unirradiated films is a dynamic crossover from an
unpinned to a pinned vortex liquid. In irradiated films the irreversibility
temperature is determined by the trapping angle.


  Structural parameters of model star clusters are measured in radial profiles
built from number-density, mass-density and surface-brightness distributions,
assuming as well different photometric conditions. Star clusters of different
ages, structure and mass functions are modelled by assuming that the radial
distribution of stars follows a pre-defined analytical form. Near-infrared
surface brightness and mass-density profiles result from mass-luminosity
relations taken from a set of isochrones. Core, tidal and half-light, half-mass
and half-star count radii, together with the concentration parameter, are
measured in the three types of profiles, which are built under different
photometric depths. While surface-brightness profiles are almost insensitive to
photometric depth, radii measured in number-density and mass-density profiles
change significantly with it. Compared to radii derived with deep photometry,
shallow profiles result in lower values. This effect increases for younger
ages. Radial profiles of clusters with a spatially-uniform mass function
produce radii that do not depend on depth. With deep photometry, number-density
profiles yield radii systematically larger than those derived from
surface-brightness ones. In general, low-noise surface-brightness profiles
result in uniform structural parameters that are essentially independent of
photometric depth. For less-populous star clusters, those projected against
dense fields and/or distant ones, which result in noisy surface-brightness
profiles, this work provides a quantitative way to estimate the intrinsic radii
by means of number-density profiles built with depth-limited photometry.


  For n\geq 2, there is a free field realization of the affine vertex
superalgebra A associated to psl(n|n) at critical level inside the
bc\beta\gamma system W of rank n^2. We show that the commutant C=Com(A,W) is
purely bosonic and is freely generated by n+1 fields. We identify the Zhu
algebra of C with the ring of invariant differential operators on the space of
n\times n matrices under SL_n \times SL_n, and we classify the irreducible,
admissible C-modules with finite dimensional graded pieces. For n\leq 4, C is
isomorphic to the W_n^{(2)}-algebra at critical level, and we conjecture that
this holds for all n.


  Even though the Internet and social media have increased the amount of news
and information people can consume, most users are only exposed to content that
reinforces their positions and isolates them from other ideological
communities. This environment has real consequences with great impact on our
lives like severe political polarization, easy spread of fake news, political
extremism, hate groups and the lack of enriching debates, among others.
Therefore, encouraging conversations between different groups of users and
breaking the closed community is of importance for healthy societies. In this
paper, we characterize and study users who break their community on Twitter
using natural language processing techniques and graph machine learning
algorithms. In particular, we collected 9 million Twitter messages from 1.5
million users and constructed the retweet networks. We identified their
communities and topics of discussion associated to them. With this data, we
present a machine learning framework for social media users classification
which detects "community breakers", i.e. users that swing from their closed
community to another one. A feature importance analysis in three Twitter
polarized political datasets showed that these users have low values of
PageRank, suggesting that changes are driven because their messages have no
response in their communities. This methodology also allowed us to identify
their specific topics of interest, providing a fully characterization of this
kind of users.


  Conventional attention-based Neural Machine Translation (NMT) conducts
dynamic alignment in generating the target sentence. By repeatedly reading the
representation of source sentence, which keeps fixed after generated by the
encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced
state-of-the-art NMT. In this paper, we propose a new attention mechanism,
called INTERACTIVE ATTENTION, which models the interaction between the decoder
and the representation of source sentence during translation by both reading
and writing operations. INTERACTIVE ATTENTION can keep track of the interaction
history and therefore improve the translation performance. Experiments on NIST
Chinese-English translation task show that INTERACTIVE ATTENTION can achieve
significant improvements over both the previous attention-based NMT baseline
and some state-of-the-art variants of attention-based NMT (i.e., coverage
models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE
ATTENTION can outperform the open source attention-based NMT system Groundhog
by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU
points averagely on multiple test sets.


  In light of the Gravitational Waves observation and the discrete structure of
spacetime at high scales M_QG, we investigate the behavior of Gravitational
Waves and the spacetime deformation patterns in terms of gravitons and derive
the effective results with respect to photons. Using the GW150914 data and
related signals, we probe the underlying quantum scale M_QG 10_6 GeV.


  Community Question Answering (CQA) has gained increasing popularity in many
domains. Mortgage is a complex and dynamic industry, and a flexible and
efficient CQA platform can potentially enhance the quality of service for
mortgage officers significantly. We have built a dynamic CQA platform with a
state of the art semantic search engine based on recent Natural Language
Processing (NLP) techniques to dynamically and collectively capture and
transfer the maturity and tribal knowledge of the more experienced workforce to
less experienced ones. The search engine allows for both keyword and natural
language queries and is based on a fine-tuned domain-adapted Sentence-BERT
encoder linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused
with a BM25 vectorizer. Domain adaptation and fine-tuning is based on publicly
available mortgage corpora. Evaluation is performed on an internally annotated
dataset using standard information retrieval metrics such as normalized
discounted cumulative gain (nDCG), precision/recall at n, mean reciprocal rank,
and mean average precision (MAP). The results indicate that our hybrid,
fine-tuned, domain-adapted search engine is a more effective approach in
responding to the information needs of our mortgage officers compared to
traditional search techniques. We aim to publish the internally-annotated
evaluation and training datasets in the near future.


  We propose a new method for detecting high-frequency gravitational waves
(GWs) using high-energy pulsed lasers. Through the inverse Gertsenshtein
effect, the interaction between a GW and the laser beam results in the creation
of an electromagnetic signal. The latter can be detected using single-photon
counting techniques. We compute the minimal strain of a detectable GW which
only depends on the laser parameters. We find that a resonance occurs in this
process when the frequency of the GW is twice the frequency of the laser. With
this method, the frequency range $10^{13}-10^{19} $ Hz is explored
non-continuously for strains $h \gtrsim 10^{-20}$ for current laser systems and
can be extended to $h \gtrsim 10^{-26}$ with future generation facilities.


  We present a coarse-grained model of clathrin that is simple enough to be
computationally tractable yet includes key observed qualitative features: a
triskelion structure with excluded volume between legs; assembly of polymorphic
cages in the bulk; formation of buds on a membrane. We investigate the assembly
of our model using both Monte Carlo simulations and molecular dynamics with
hydrodynamic interactions, in the latter employing a new membrane boundary
condition. In the bulk, a range of known clathrin structures are assembled. A
membrane budding pathway involving the coalescence of multiple small clusters
is identified.


  SrEr$_2$O$_4$ has been characterised by low-temperature powder neutron
diffraction, as well as single crystal specific heat and magnetisation
measurements. Magnetisation measurements show that the magnetic system is
highly anisotropic at temperature above ordering. A magnetic field of 280 kOe
applied at $T=1.6$ K does not overcome the anisotropic magnetisation and fails
to fully saturate the system. Long range antiferromagnetic ordering develops
below $T_N=0.75$ K, identified by magnetic Bragg reflections with propagation
vector ${\bf k}=0$ and a lambda anomaly in the specific heat. The magnetic
structure consists of ferromagnetic chains running along the \textit{c} axis,
two adjacent chains being stacked antiferromagnetically. The moments point
along the \textit{c} direction, but only one of the two crystallographically
in-equivalent Er sites has a sizeable ordered magnetic moment, 4.5 $\rm \mu_B$
at 0.55 K. The magnetic properties of SrEr$_2$O$_4$ are discussed in terms of
the interplay between the low-dimensionality, competing exchange interactions,
dipolar interactions and low lying crystal field levels.


  The exploration of selected single nucleotide polymorphisms (SNPs) to
identify genetic diversity between different sequencing population pools
(Pool-seq) is a fundamental task in genetic research. As underlying sequence
reads and their alignment are error-prone and univariate statistical solutions
only take individual positions of the genome into account, the identification
of selected SNPs remains a challenging process. Deep learning models like
convolutional neural networks (CNNs) are able to consider large input areas in
their decisions. We suggest an unsupervised pipeline to be independent of a
rarely known ground truth. We train a supervised discriminator CNN to
distinguish alignments from different populations and utilize the model for
unsupervised SNP calling by applying explainable artificial intelligence
methods. Our proposed multivariate method is based on two main assumptions: We
assume (i) that instances having a high predictive certainty of being
distinguishable are likely to contain genetic variants, and (ii) that selected
SNPs are located at regions with input features having the highest influence on
the model's decision process. We directly compare our method with statistical
results on two different Pool-seq datasets and show that our solution is able
to extend statistical results.


  Many physical, chemical and biological processes rely on intrinsic
oscillations to employ resonance responses to external stimuli of certain
frequency. Such resonance phenomena in biological systems are typically
explained by one of two mechanisms: either a classical linear resonance of
harmonic oscillator, or entrainment and phase locking of nonlinear limit cycle
oscillators subjected to periodic forcing. Here, we discover a nonlinear
mechanism, which does not require intrinsic oscillations. Instead, the resonant
frequency dependence arises from coupling between an excitable and a monostable
region of the medium. This composite system is endowed with emergent
bistability between a stable steady state and stable spatiotemporal
oscillations. The resonant transition from stable state to oscillatory state is
induced by waves of particular frequency travelling through the medium. This
transition to the spatiotemporal oscillatory state requires accumulation of
multiple waves, resulting in the exclusion of lower frequencies. The cutting
off of high frequencies is realized by damping of wave amplitude in the
monostable zone and then by activating amplitude sensitive dynamics in the
monostable units. We demonstrate this new resonance mechanism in a simplistic
reaction-diffusion model. Also, we reveal this collective resonance mechanism
in in-vitro experiments and detailed biophysical simulations representing a
major type of arrhythmia. We further demonstrate, both experimentally and
theoretically, that the ongoing spatiotemporal oscillations, such as ectopic
activity in cardiac tissue, can be stopped by travelling waves of high
frequency. Overall, we claim the universality of this resonance mechanism in a
broad class of nonlinear biophysical systems. Specifically, we hypothesize that
such phenomena could be found in neuronal systems as an alternative to
traditional resonant processes.


  We discover a class of spacetime symmetries unique to time-periodic systems,
which we term "mixing symmetry" due to its combination of space and time
coordinates in the symmetry transformation. We systematically enumerate the
symmetry groups, and classify the corresponding Floquet-Bloch band theories by
utilizing the winding number of quasi-energy. Moreover, we provide a
comprehensive scheme for the experimental realization of these symmetries. The
particle propagator exhibits an intriguing pattern that remains invariant even
under transformations mixing space and time coordinates. We anticipate that
this distinct feature can be observed in current cold atom experiments.


  We demonstrate Tensor Query Processor (TQP): a query processor that
automatically compiles relational operators into tensor programs. By leveraging
tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools
(e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target
different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and
(3) end-to-end accelerate queries containing both relational and ML operators.
TQP is generic enough to support the TPC-H benchmark, and it provides
performance that is comparable to, and often better than, that of specialized
CPU and GPU query processors.


  Matrix and tensor operations form the basis of a wide range of fields and
applications, and in many cases constitute a substantial part of the overall
computational complexity. The ability of general-purpose GPUs to speed up many
of these operations and enable others has resulted in a widespread adaptation
of these devices. In order for tensor operations to take full advantage of the
computational power, specialized software is required, and currently there
exist several packages (predominantly in the area of deep learning) that
incorporate tensor operations on both CPU and GPU. Nevertheless, a stand-alone
framework that supports general tensor operations is still missing. In this
paper we fill this gap and propose the Ocean Tensor Library: a modular
tensor-support package that is designed to serve as a foundational layer for
applications that require dense tensor operations on a variety of device types.
The API is carefully designed to be powerful, extensible, and at the same time
easy to use. The package is available as open source.


  We describe here the realization of a single electron source similar to
single photon sources in optics. On-demand single electron injection is
obtained using a quantum dot connected to the conductor via a tunnel barrier of
variable transmission (quantum point contact). Electron emission is triggered
by a sudden change of the dot potential which brings a single energy level
above the Fermi energy in the conductor. A single charge is emitted on an
average time ranging from 100 ps to 10 ns ultimately determined by the barrier
transparency and the dot charging energy. The average single electron emission
process is recorded with a 0.5 ns time resolution using a real-time fast
acquisition card. Single electron signals are compared tosimulation based on
scattering theory approach adapted for finite excitation energies.


  The small-world property is known to have a profound effect on the navigation
efficiency of complex networks [J. M. Kleinberg, Nature 406, 845 (2000)].
Accordingly, the proper addition of shortcuts to a regular substrate can lead
to the formation of a highly efficient structure for information propagation.
Here we show that enhanced flow properties can also be observed in these
complex topologies. Precisely, our model is a network built from an underlying
regular lattice over which long-range connections are randomly added according
to the probability, $P_{ij}\sim r_{ij}^{-\alpha}$, where $r_{ij}$ is the
Manhattan distance between nodes $i$ and $j$, and the exponent $\alpha$ is a
controlling parameter. The mean two-point global conductance of the system is
computed by considering that each link has a local conductance given by
$g_{ij}\propto r_{ij}^{-\delta}$, where $\delta$ determines the extent of the
geographical limitations (costs) on the long-range connections. Our results
show that the best flow conditions are obtained for $\delta=0$ with $\alpha=0$,
while for $\delta \gg 1$ the overall conductance always increases with
$\alpha$. For $\delta\approx 1$, $\alpha=d$ becomes the optimal exponent, where
$d$ is the topological dimension of the substrate. Interestingly, this exponent
is identical to the one obtained for optimal navigation in small-world networks
using decentralized algorithms.


  LUX-ZEPLIN (LZ) is a next generation dark matter direct detection experiment
that will operate 4850 feet underground at the Sanford Underground Research
Facility (SURF) in Lead, South Dakota, USA. Using a two-phase xenon detector
with an active mass of 7~tonnes, LZ will search primarily for low-energy
interactions with Weakly Interacting Massive Particles (WIMPs), which are
hypothesized to make up the dark matter in our galactic halo. In this paper,
the projected WIMP sensitivity of LZ is presented based on the latest
background estimates and simulations of the detector. For a 1000~live day run
using a 5.6~tonne fiducial mass, LZ is projected to exclude at 90\% confidence
level spin-independent WIMP-nucleon cross sections above $1.4 \times
10^{-48}$~cm$^{2}$ for a 40~$\mathrm{GeV}/c^{2}$ mass WIMP. Additionally, a
$5\sigma$ discovery potential is projected reaching cross sections below the
exclusion limits of recent experiments. For spin-dependent
WIMP-neutron(-proton) scattering, a sensitivity of $2.3 \times
10^{-43}$~cm$^{2}$ ($7.1 \times 10^{-42}$~cm$^{2}$) for a
40~$\mathrm{GeV}/c^{2}$ mass WIMP is expected. With underground installation
well underway, LZ is on track for commissioning at SURF in 2020.


  Leveled Homomorphic Encryption (LHE) offers a potential solution that could
allow sectors with sensitive data to utilize the cloud and securely deploy
their models for remote inference with Deep Neural Networks (DNN). However,
this application faces several obstacles due to the limitations of LHE. One of
the main problems is the incompatibility of commonly used nonlinear functions
in DNN with the operations supported by LHE, i.e. addition and multiplication.
As common in LHE approaches, we train a model with a nonlinear function, and
replace it with a low-degree polynomial approximation at inference time on
private data. While this typically leads to approximation errors and loss in
prediction accuracy, we propose a method that reduces this loss to small values
or eliminates it entirely, depending on simple hyper-parameters. This is
achieved by the introduction of a novel and elegantly simple Min-Max
normalization scheme, which scales inputs to nonlinear functions into ranges
with low approximation error. While being intuitive in its concept and trivial
to implement, we empirically show that it offers a stable and effective
approximation solution to nonlinear functions in DNN. In return, this can
enable deeper networks with LHE, and facilitate the development of security-
and privacy-aware analytics applications.


  The farming domain has seen a tremendous shift towards digital solutions.
However, capturing farmers' requirements regarding Digital Farming (DF)
technology remains a difficult task due to domain-specific challenges. Farmers
form a diverse and international crowd of practitioners who use a common pool
of agricultural products and services, which means we can consider the
possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF:
CrowdRE4DF. We found that online user feedback in this domain is limited,
necessitating a way of capturing user feedback from farmers in situ. Our
solution, the Farmers' Voice application, uses speech-to-text, Machine Learning
(ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed
good technology acceptance, and accurate transcription and ML analysis even in
noisy farm settings. Our findings help to drive the development of DF
technology through in-situ requirements elicitation.


  Magnetorotational dynamo action in Keplerian shear flow is a
three-dimensional, nonlinear magnetohydrodynamic process whose study is
relevant to the understanding of accretion and magnetic field generation in
astrophysics. Transition to this form of dynamo is subcritical and shares many
characteristics of transition to turbulence in non-rotating hydrodynamic shear
flows. This suggests that these different fluid systems become active through
similar generic bifurcation mechanisms, which in both cases have eluded
detailed understanding so far. In this paper, we investigate numerically the
bifurcation mechanisms at work in the incompressible Keplerian
magnetorotational dynamo problem in the shearing box framework. Using numerical
techniques imported from dynamical systems research, we show that the onset of
chaotic dynamo action at magnetic Prandtl numbers larger than unity is
primarily associated with global homoclinic and heteroclinic bifurcations of
nonlinear magnetorotational dynamo cycles. These global bifurcations are
supplemented by local bifurcations of cycles marking the beginning of
period-doubling cascades. This suggests that nonlinear magnetorotational dynamo
cycles provide the pathway to turbulent injection of both kinetic and magnetic
energy in incompressible magnetohydrodynamic Keplerian shear flow in the
absence of an externally imposed magnetic field. Studying the nonlinear physics
and bifurcations of these cycles in different regimes and configurations may
subsequently help to better understand the conditions of excitation of
magnetohydrodynamic turbulence and instability-driven dynamos in various
astrophysical systems and laboratory experiments. The detailed characterization
of global bifurcations provided for this three-dimensional subcritical fluid
dynamics problem may also prove useful for the problem of transition to
turbulence in hydrodynamic shear flows.


  Let M be a closed hyperbolic three manifold. We construct closed surfaces
which map by immersions into M so that for each one the corresponding mapping
on the universal covering spaces is an embedding, or, in other words, the
corresponding induced mapping on fundamental groups is an injection.


  This paper discusses algorithms for solving Markov decision processes (MDPs)
that have monotone optimal policies. We propose a two-stage alternating convex
optimization scheme that can accelerate the search for an optimal policy by
exploiting the monotone property. The first stage is a linear program
formulated in terms of the joint state-action probabilities. The second stage
is a regularized problem formulated in terms of the conditional probabilities
of actions given states. The regularization uses techniques from
nearly-isotonic regression. While a variety of iterative method can be used in
the first formulation of the problem, we show in numerical simulations that, in
particular, the alternating method of multipliers (ADMM) can be significantly
accelerated using the regularization step.


  We have analysed new R-band photometry of globular clusters in NGC 6166, the
cD galaxy in the cooling flow cluster A2199. In combination with the earlier B
photometry of Pritchet \& Harris (1990), we obtain B$-$R colours for $\sim$ 40
globular clusters in NGC 6166. The mean B$-$R is 1.26 $\pm$ 0.11, corresponding
to a mean [Fe/H] = $-$1 $\pm$ 0.4. Given that NGC 6166 is one of the most
luminous cD galaxies studied to date, our result implies significant scatter in
the relationship between mean cluster [Fe/H] and parent galaxy luminosity. We
obtain a globular cluster specific frequency of S$_N$ $\sim$ 9, with a possible
range between 5 and 18. This value is inconsistent with the value of S$_N$
$\leq$ 4 determined earlier by Pritchet \& Harris (1990) from B-band
photometry, and we discuss possible reasons for the discrepancy. Finally, we
reassess whether or not cooling flows are an important mechanism for forming
globular clusters in gE/cD galaxies.


  We study the quantum behaviour of a binary mixture of Bose-Einstein
condensates (BEC) in a double-well potential starting from a two-mode
Bose-Hubbard Hamiltonian. We focus on the small tunneling amplitude regime and
apply perturbation theory up to second order. Analytical expressions for the
energy eigenvalues and eigenstates are obtained. Then the quantum evolution of
the number difference of bosons between the two potential wells is fully
investigated for two different initial conditions: completely localized states
and coherent spin states. In the first case both the short and the long time
dynamics is studied and a rich behaviour is found, ranging from small amplitude
oscillations and collapses and revivals to coherent tunneling. In the second
case the short-time scale evolution of number difference is determined and a
more irregular dynamics is evidenced. Finally, the formation of Schroedinger
cat states is considered and shown to affect the momentum distribution.


  Emoji is an essential component in dialogues which has been broadly utilized
on almost all social platforms. It could express more delicate feelings beyond
plain texts and thus smooth the communications between users, making dialogue
systems more anthropomorphic and vivid. In this paper, we focus on
automatically recommending appropriate emojis given the contextual information
in multi-turn dialogue systems, where the challenges locate in understanding
the whole conversations. More specifically, we propose the hierarchical long
short-term memory model (H-LSTM) to construct dialogue representations,
followed by a softmax classifier for emoji classification. We evaluate our
models on the task of emoji classification in a real-world dataset, with some
further explorations on parameter sensitivity and case study. Experimental
results demonstrate that our method achieves the best performances on all
evaluation metrics. It indicates that our method could well capture the
contextual information and emotion flow in dialogues, which is significant for
emoji recommendation.


  Doubly heavy tetraquark states are the prime candidates for tightly bound
exotic states. We present a systematic study of the mass spectra of the
$S$-wave doubly heavy tetraquark states $QQ\bar{q}\bar{q}$ ($q=u, d, s$ and
$Q=c, b$) with different quantum numbers $J^P=0^+$, $1^+$, and $2^+$ in the
framework of the improved chromomagnetic interaction (ICMI) model. The
parameters in the ICMI model are obtained by fitting the conventional hadron
spectra and are used directly to predict the masses of the tetraquark states.
For heavy quarks, the uncertainties of the parameters are obtained by comparing
the masses of doubly and triply heavy baryons with those given by lattice QCD,
QCD sum rules, and potential models. Several compact and stable bound states
are found in both the doubly charmed and doubly bottomed tetraquark systems.
The predicted mass of the $cc\bar u\bar d$ state is consistent with the recent
measurement from the LHCb collaboration.


  We analyse the <VAP> three-point function of vector, axial-vector and
pseudoscalar currents. In the spirit of large N_C, a resonance dominated Green
function is confronted with the leading high-energy behaviour from the operator
product expansion. The matching is shown to be fully compatible with a chiral
resonance Lagrangian and it allows to determine some of the chiral low-energy
constants of O(p^6).


  Propagation of an extraordinary electromagnetic wave in the vicinity of
electron cyclotron resonance surface in an open linear trap is studied
analytically, taking into account inhomogeneity of the magnetic field in
paraxial approximation. Ray trajectories are derived from a reduced dispersion
equation that makes it possible to avoid the difficulty associated with a
transition from large propagation angles to the case of strictly longitudinal
propagation. Our approach is based on the theory, originally developed by the
Zvonkov and Timofeev [1], who used the paraxial approximation for the magnetic
field strength, but did not consider the slope of the magnetic field lines,
which led to considerable error, as has been recently noted by Gospodchikov and
Smolyakova [2]. We have found ray trajectories in analytic form and
demonstrated that the inhomogeneity of both the magnetic field strength and the
field direction can qualitatively change the picture of wave propagation and
significantly affect the efficiency of electron cyclotron heating of a plasma
in a linear magnetic trap. Analysis of the ray trajectories has revealed a
criterion for the resonance point on the axis of the trap to be an attractor
for the ray trajectories. It is also shown that a family of ray trajectories
can still reach the resonance point on the axis if the latter generally repels
the ray trajectories.
  As an example, results of general theory are applied to the electron
cyclotron resonance heating experiment which is under preparation on the Gas
Dynamic Trap in the Budker Institute of Nuclear Physics [3].


  The Zamolodchikov $\mathcal{W}_3$-algebra $\mathcal{W}^c_3$ with central
charge $c$ has full automorphism group $\mathbb{Z}_2$. It was conjectured in
the physics literature over 20 years ago that the orbifold
$(\mathcal{W}^c_3)^{\mathbb{Z}_2}$ is of type $\mathcal{W}(2,6,8,10,12)$ for
generic values of $c$. We prove this conjecture for all $c \neq \frac{559 \pm 7
\sqrt{76657}}{95}$, and we show that for these two values, the orbifold is of
type $\mathcal{W}(2,6,8,10,12,14)$. This paper is part of a larger program of
studying orbifolds and cosets of vertex algebras that depend continuously on a
parameter. Minimal strong generating sets for orbifolds and cosets are often
easy to find for generic values of the parameter, but determining which values
are generic is a difficult problem. In the example of
$(\mathcal{W}^c_3)^{\mathbb{Z}_2}$, we solve this problem using tools from
algebraic geometry.


  We prove Lieb-Robinson bounds and the existence of the thermodynamic limit
for a general class of irreversible dynamics for quantum lattice systems with
time-dependent generators that satisfy a suitable decay condition in space.


  We prove an equivalence between the existence of the first structure relation
satisfied by a sequence of monic orthogonal polynomials
$\{P_n\}_{n=0}^{\infty}$, the orthogonality of the second derivatives
$\{\mathbb{D}_{x}^2P_n\}_{n= 2}^{\infty}$ and a generalized Sturm-Liouville
type equation. Our treatment of the generalized Bochner theorem leads to
explicit solutions of the difference equation [Vinet L., Zhedanov A., J.
Comput. Appl. Math. 211 (2008), 45-56], which proves that the only monic
orthogonal polynomials that satisfy the first structure relation are Wilson
polynomials, continuous dual Hahn polynomials, Askey-Wilson polynomials and
their special or limiting cases as one or more parameters tend to $\infty$.
This work extends our previous result [arXiv:1711.03349] concerning a
conjecture due to Ismail. We also derive a second structure relation for
polynomials satisfying the first structure relation.


  The excess of solar-neutrino events above 13 MeV that has been recently
observed by Superkamiokande can be explained by the vacuum oscillation solution
to the Solar Neutrino Problem (SNP). If the boron neutrino flux is 20% smaller
than the standard solar model (SSM) prediction and the chlorine signal is
assumed 30% (or 3.4 sigmas) higher than the measured one, there exists a vacuum
oscillation solution to SNP that reproduces both the observed spectrum of the
recoil electrons, including the high energy distortion, and the other measured
neutrino rates. The most distinct signature of this solution is a semi-annual
seasonal variation of the Be7 neutrino flux with maximal amplitude. While the
temporal series of the GALLEX and Homestake signals suggest that such a
seasonal variation could be present, future detectors (BOREXINO, LENS and
probably GNO) will be able to test it.


  The local structures and optical absorption characteristic of Fe doped TiO2
nanoparticles synthesized by the sol-gel method were characterized by X-ray
Diffraction (XRD), X-ray absorption fine structure spectroscopy (XAFS) and
UV-Vis absorption spectroscopy (UV-Vis). XRD patterns show that all Fe-doped
TiO2 samples have the characteristic anatase structure. Accurate Fe and Ti
K-edge EXAFS analysis further reveal that all Fe atoms replace Ti atoms in the
anatase lattice. The analysis of UV-Vis data shows a red shift to the visible
range. According to the above results, we claim that substitutional Fe atoms
lead to the formation of structural defects and new intermediate energy levels
appear, narrowing the band gap and extending the optical absorption edge
towards the visible region.


  We present an algorithm for re-rendering a person from a single image under
arbitrary poses. Existing methods often have difficulties in hallucinating
occluded contents photo-realistically while preserving the identity and fine
details in the source image. We first learn to inpaint the correspondence field
between the body surface texture and the source image with a human body
symmetry prior. The inpainted correspondence field allows us to transfer/warp
local features extracted from the source to the target view even under large
pose changes. Directly mapping the warped local features to an RGB image using
a simple CNN decoder often leads to visible artifacts. Thus, we extend the
StyleGAN generator so that it takes pose as input (for controlling poses) and
introduces a spatially varying modulation for the latent space using the warped
local features (for controlling appearances). We show that our method compares
favorably against the state-of-the-art algorithms in both quantitative
evaluation and visual comparison.


  The offset linear canonical transform encompassing the numerous integral
transforms, is a promising tool for analyzing non-stationary signals with more
degrees of freedom. In this paper, we generalize the windowed offset linear
canonical transform for quaternion-valued signals, by introducing a novel
time-frequency transform namely the quaternion windowed offset linear canonical
transform of 2D quaternion-valued signals. We initiate our investigation by
studying some fundamental properties of the proposed transform including inner
product relation, energy conservation, and reproducing formula by employing the
machinery of quaternion offset linear canonical transforms. Some uncertainty
principles such as Heisenberg-Weyl, logarithmic and local uncertainty principle
are also derived for quaternion windowed offset linear canonical transform.
Finally, we gave an example of quaternion windowed offset linear canonical
transform.


  Thermal channels -- the free processes allowed in the resource theory of
quantum thermodynamics -- are generalised to thermal instruments, which we
interpret as implementing thermodynamically free quantum measurements; a
Maxwellian demon using such measurements never violates the second law of
thermodynamics. Further properties of thermal instruments are investigated and,
in particular, it is shown that they only measure observables commuting with
the Hamiltonian, and they thermalise the measured system when performing a
complete measurement, the latter of which indicates a thermodynamically induced
information-disturbance trade-off. The demarcation of measurements that are not
thermodynamically free paves the way for a resource-theoretic quantification of
their thermodynamic cost.


  We prove a range of critical Hardy inequalities and uncertainty type
principles on one of most general subclasses of nilpotent Lie groups, namely
the class of homogeneous groups. Moreover, we establish a new type of critical
Hardy inequality and prove Hardy-Sobolev type inequalities. Most of the
obtained estimates are new already for the case of $\mathbb R^{n}$. For
example, for any $f\in C_{0}^{\infty}(\mathbb{R}^{n}\backslash\{0\})$ our
results imply the range of critical Hardy inequalities of the form $$\qquad
\underset{R>0}{\sup}\left\|\frac{f-f_{R}}{|x|^{\frac{n}{p}}{\log}\frac{R}{|x|}}\right\|_{L^{p}(\mathbb{R}^{n})}\leq
\frac{p}{p-1}\left\| \frac{1}{|x|^{\frac{n}{p}-1}} \nabla
f\right\|_{L^{p}(\mathbb{R}^{n})},\quad 1<p<\infty,$$ where
$f_{R}=f(R\frac{x}{|x|})$, with sharp constant $\frac{p}{p-1}$, recovering the
known cases of $p=n$ and $p=2$. Moreover, our results also imply a new type of
a critical Hardy inequality of the form
$$\left\|\frac{f}{|x|}\right\|_{L^{n}(\mathbb{R}^{n})}\leq
n\left\|(\log|x|)\nabla f\right\|_{L^{n}\mathbb{R}^{n})}, $$ for all $f\in
C_{0}^{\infty}(\mathbb{R}^{n}\backslash\{0\}),$ where the constant $n$ is
sharp. However, homogeneous groups provide a perfect degree of generality to
talk about such estimates without using specific properties of $\mathbb R^n$ or
of the Euclidean distance.


  Using realistic cosmological simulations of Milky Way sized haloes, we study
their dynamical state and the accuracy of inferring their mass profiles with
steady-state models of dynamical tracers. We use a new method that describes
the phase-space distribution of a steady-state tracer population in a spherical
potential without any assumption regarding the distribution of their orbits.
Applying the method to five haloes from the Aquarius $\Lambda$CDM N-body
simulation, we find that dark matter particles are an accurate tracer that
enables the halo mass and concentration parameters to be recovered with an
accuracy of $5\%$. Assuming a potential profile of the NFW form does not
significantly affect the fits in most cases, except for halo A whose density
profile differs significantly from the NFW form, leading to a $30\%$ bias in
the dynamically fitted parameters. The existence of substructures in the dark
matter tracers only affects the fits by $\sim 1\%$. Applying the method to mock
stellar haloes generated by a particle-tagging technique, we find the stars are
farther from equilibrium than dark matter particles, yielding a systematic bias
of $\sim 20\%$ in the inferred mass and concentration parameter. The level of
systematic biases obtained from a conventional distribution function fit to
stars is comparable to ours, while similar fits to DM tracers are significantly
biased in contrast to our fits. In line with previous studies, the mass bias is
much reduced near the tracer half-mass radius.


  I discuss the origin of the ``Sakharov oscillations'' (or ``secondary Doppler
peaks'') in standard angular power spectra of the Cosmic Microwave Background
anisotropies calculated for inflationary models. I contrast this with the
production of CMB anisotropies in `active' models and argue that the presence
or absence of Sakharov oscillations will reveal fundamental properties of the
primordial perturbations.


  This paper focuses on the spatial-spectral terahertz (THz) networks, where
transmitters equipped with leaky-wave antennas send information to their
receivers at the THz frequency bands. As a directional and nearly planar
antenna, the leaky-wave antenna allows for information transmissions with
narrow beams and high antenna gains. The conventional large antenna arrays are
confronted with challenging issues such as scaling limits and path discovery in
the THz frequencies. Therefore, this work exploits the potential of leaky-wave
antennas in the dense THz networks, to establish low-complexity THz links. By
addressing the propagation angle-frequency coupling effects, the transmission
rate is analyzed. The results show that the leaky-wave antenna is efficient for
achieving the high-speed transmission rate. The co-channel interference
management is unnecessary when the THz transmitters with large subchannel
bandwidths are not extremely dense. A simple subchannel allocation solution is
proposed, which enhances the transmission rate compared with the same number of
subchannels with the equal allocation of the frequency band. After subchannel
allocation, a low-complexity power allocation method is proposed to improve the
energy efficiency.


  Message-passing based concurrent languages are widely used in developing
large distributed and coordination systems. This paper presents the buffered
$\pi$-calculus --- a variant of the $\pi$-calculus where channel names are
classified into buffered and unbuffered: communication along buffered channels
is asynchronous, and remains synchronous along unbuffered channels. We show
that the buffered $\pi$-calculus can be fully simulated in the polyadic
$\pi$-calculus with respect to strong bisimulation. In contrast to the
$\pi$-calculus which is hard to use in practice, the new language enables easy
and clear modeling of practical concurrent languages. We encode two real-world
concurrent languages in the buffered $\pi$-calculus: the (core) Go language and
the (Core) Erlang. Both encodings are fully abstract with respect to weak
bisimulations.


  We study four problems in the dynamics of a body moving about a fixed point,
providing a non-complex, analytical solution for all of them. For the first
two, we will work on the motion first integrals. For the symmetrical heavy
body, that is the Lagrange-Poisson case, we compute the second and third Euler
angles in explicit and real forms by means of multiple hypergeometric functions
(Lauricella, functions). Releasing the weight load but adding the complication
of the asymmetry, by means of elliptic integrals of third kind, we provide the
precession angle completing some previous treatments of the Euler-Poinsot case.
Integrating then the relevant differential equation, we reach the finite polar
equation of a special trajectory named the {\it herpolhode}. In the last
problem we keep the symmetry of the first problem, but without the weight, and
take into account a viscous dissipation. The approach of first integrals is no
longer practicable in this situation and the Euler equations are faced directly
leading to dumped goniometric functions obtained as particular occurrences of
Bessel functions of order $-1/2$.


  Electronic health records (EHRs) offer great promises for advancing precision
medicine and, at the same time, present significant analytical challenges.
Particularly, it is often the case that patient-level data in EHRs cannot be
shared across institutions (data sources) due to government regulations and/or
institutional policies. As a result, there are growing interests about
distributed learning over multiple EHRs databases without sharing patient-level
data. To tackle such challenges, we propose a novel communication efficient
method that aggregates the local optimal estimates, by turning the problem into
a missing data problem. In addition, we propose incorporating posterior samples
of remote sites, which can provide partial information on the missing
quantities and improve efficiency of parameter estimates while having the
differential privacy property and thus reducing the risk of information
leaking. The proposed approach, without sharing the raw patient level data,
allows for proper statistical inference and can accommodate sparse regressions.
We provide theoretical investigation for the asymptotic properties of the
proposed method for statistical inference as well as differential privacy, and
evaluate its performance in simulations and real data analyses in comparison
with several recently developed methods.


  We have successfully grown epitaxial La$_{1.67}$Sr$_{0.33}$NiO$_4$ films with
a small crystalline mosaic using pulsed laser deposition. With synchrotron
radiation, the x-ray diffraction peaks associated with charge stripes have been
successfully observed for relatively thick films. Anomalies due to the
charge-ordering transition have been examined using four-point probe
resistivity measurements. X-ray scattering provides direct evidence for
suppression of the stripe phase in thin samples; the phase disappears for film
thicknesses $\leqslant$ 2600 ~\AA{}. The suppression appears to be a result of
shrinking the stripe phase domains. This may reflect the stripe phase
progressing from nematic to isotropic.


  The theory of quantum computation is presented in a self contained way from a
computer science perspective. The basics of classical computation and quantum
mechanics is reviewed. The circuit model of quantum computation is presented in
detail. Throughout there is an emphasis on the physical as well as the abstract
aspects of computation and the interplay between them.
  This report is presented as a Master's thesis at the department of Computer
Science and Engineering at G{\"o}teborg University, G{\"o}teborg, Sweden.
  The text is part of a larger work that is planned to include chapters on
quantum algorithms, the quantum Turing machine model and abstract approaches to
quantum computation.


  The Grassmann convexity conjecture gives a conjectural formula for the
maximal total number of real zeros of the consecutive Wronskians of an
arbitrary fundamental solution to a disconjugate linear ordinary differential
equation with real time. The conjecture can be reformulated in terms of convex
curves in the nilpotent lower triangular group. The formula has already been
shown to be a correct lower bound and to give a correct upper bound in several
small dimensional cases. In this paper we obtain a general explicit upper
bound.


  Let f be a polynomial with irrational leading coefficient. We obtain
inequalities for the distance from the nearest integer of f(p) that hold for
infinitely many primes p. These results improve work of Harman in 1981 and 1983
and Wong in 1997.


  In this paper we present an active-set method for the solution of
$\ell_1$-regularized convex quadratic optimization problems. It is derived by
combining a proximal method of multipliers (PMM) strategy with a standard
semismooth Newton method (SSN). The resulting linear systems are solved using a
Krylov-subspace method, accelerated by certain general-purpose preconditioners
which are shown to be optimal with respect to the proximal parameters.
Practical efficiency is further improved by warm-starting the algorithm using a
proximal alternating direction method of multipliers. We show that the outer
PMM achieves global convergence under mere feasibility assumptions. Under
additional standard assumptions, the PMM scheme achieves global linear and
local superlinear convergence. The SSN scheme is locally superlinearly
convergent, assuming that its associated linear systems are solved accurately
enough, and globally convergent under certain additional regularity
assumptions. We provide numerical evidence to demonstrate the effectiveness of
the approach by comparing it against OSQP and IP-PMM (an ADMM and a regularized
IPM solver, respectively) on several elastic-net linear regression and
$L^1$-regularized PDE-constrained optimization problems.


  Generalizing a result in the theory of finite fields we prove that, apart
from a couple of exceptions that can be classified, for any elements
$a_1,...,a_m$ of the cyclic group of order $m$, there is a permutation $\pi$
such that $1a_{\pi(1)}+...+ma_{\pi(m)}=0$.


  It has been shown in literature that a possible mechanism of mass generation
for gauge fields is through a topological coupling of vector and tensor fields.
After integrating over the tensor degrees of freedom, one arrives at an
effective massive theory that, although gauge invariant, is nonlocal. Here we
quantize this nonlocal resulting theory both by path integral and canonical
procedures. This system can be considered as equivalent to one with an infinite
number of time derivatives and consequently an infinite number of momenta. This
means that the use of the canonical formalism deserves some care. We show the
consistency of the formalism we use in the canonical procedure by showing that
the obtained propagators are the same as those of the (Lagrangian) path
integral approach. The problem of nonlocality appears in the obtainment of the
spectrum of the theory. This fact becomes very transparent when we list the
infinite number of commutators involving the fields and their velocities.


  We follow the example of Cabibbo by revising the Standard Model (SM) to
present a universal mass structure for fermions. A universal Higgs coupling for
each species of fundamental fermions moves the SM towards a Theory of Matter,
albeit without correctly describing the observed mass spectrum. It exposes a
need for a complete Theory of Matter to include components from physics beyond
the Standard Model (BSM). Describing the effect of these components
phenomenologically provides a means to infer the nature of some of the BSM
physics required. Our results also provide constraints on some BSM matrix
elements. Here we apply this concept to quarks; the application to leptons will
appear in a separate paper. An immediate benefit for theory is the reduction of
the largest fine structure constant for the Higgs coupling to fermions by an
order of magnitude, which improves the perturbative appearance of the weak
interactions. The small mixing of the third generation of each fermion in the
fermion families to the others is attributed to the small BSM perturbations
that produce the small mass ratio of the lighter generations to the most
massive one.


  We find a family of 3d steady gradient Ricci solitons that are flying wings.
This verifies a conjecture by Hamilton. For a 3d flying wing, we show that the
scalar curvature does not vanish at infinity. The 3d flying wings are
collapsed.
  For dimension $n\ge 4$, we find a family of $\mathbb{Z}_2\times
O(n-1)$-symmetric but non-rotationally symmetric n-dimensional steady gradient
solitons with positive curvature operator. We show that these solitons are
non-collapsed.


  Gaussian processes and random fields have a long history, covering multiple
approaches to representing spatial and spatio-temporal dependence structures,
such as covariance functions, spectral representations, reproducing kernel
Hilbert spaces, and graph based models. This article describes how the
stochastic partial differential equation approach to generalising Mat\'ern
covariance models via Hilbert space projections connects with several of these
approaches, with each connection being useful in different situations. In
addition to an overview of the main ideas, some important extensions, theory,
applications, and other recent developments are discussed. The methods include
both Markovian and non-Markovian models, non-Gaussian random fields,
non-stationary fields and space-time fields on arbitrary manifolds, and
practical computational considerations.


  The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.


  Consider the switch chain on the set of $d$-regular bipartite graphs on $n$
vertices with $3\leq d\leq n^{c}$, for a small universal constant $c>0$. We
prove that the chain satisfies a Poincar\'e inequality with a constant of order
$O(nd)$; moreover, when $d$ is fixed, we establish a log-Sobolev inequality for
the chain with a constant of order $O_d(n\log n)$. We show that both results
are optimal. The Poincar\'e inequality implies that in the regime $3\leq d\leq
n^c$ the mixing time of the switch chain is at most $O\big((nd)^2
\log(nd)\big)$, improving on the previously known bound $O\big((nd)^{13}
\log(nd)\big)$ due to Kannan, Tetali and Vempala and $O\big(n^7d^{18}
\log(nd)\big)$ obtained by Dyer et al. The log-Sobolev inequality that we
establish for constant $d$ implies a bound $O(n\log^2 n)$ on the mixing time of
the chain which, up to the $\log n$ factor, captures a conjectured optimal
bound. Our proof strategy relies on building, for any fixed function on the set
of $d$-regular bipartite simple graphs, an appropriate extension to a function
on the set of multigraphs given by the configuration model. We then establish a
comparison procedure with the well studied random transposition model in order
to obtain the corresponding functional inequalities. While our method falls
into a rich class of comparison techniques for Markov chains on different state
spaces, the crucial feature of the method - dealing with chains with a large
distortion between their stationary measures - is a novel addition to the
theory.


  The HI line at 21 cm is a tracer of circumstellar matter around AGB stars,
and especially of the matter located at large distances (0.1-1 pc) from the
central stars. It can give unique information on the kinematics and on the
physical conditions in the outer parts of circumstellar shells and in the
regions where stellar matter is injected into the interstellar medium. However
this tracer has not been much used up to now, due to the difficulty of
separating the genuine circumstellar emission from the interstellar one.
  With the Nancay Radiotelescope we are carrying out a survey of the HI
emission in a large sample of evolved stars. We report on recent progresses of
this long term programme, with emphasis on S-type stars.


  Two-view relative pose estimation and structure reconstruction is a classical
problem in computer vision. The typical methods usually employ the singular
value decomposition of the essential matrix to get multiple solutions of the
relative pose, from which the right solution is picked out by reconstructing
the three-dimension (3D) feature points and imposing the constraint of positive
depth. This paper revisits the two-view geometry problem and discovers that the
two-view imaging geometry is equivalently governed by a Pair of new Pose-Only
(PPO) constraints: the same-side constraint and the intersection constraint.
From the perspective of solving equation, the complete pose solutions of the
essential matrix are explicitly derived and we rigorously prove that the
orientation part of the pose can still be recovered in the case of pure
rotation. The PPO constraints are simplified and formulated in the form of
inequalities to directly identify the right pose solution with no need of 3D
reconstruction and the 3D reconstruction can be analytically achieved from the
identified right pose. Furthermore, the intersection inequality also enables a
robust criterion for pure rotation identification. Experiment results validate
the correctness of analyses and the robustness of the derived pose
solution/pure rotation identification and analytical 3D reconstruction.


  We study fundamental graph problems such as graph connectivity, minimum
spanning forest (MSF), and approximate maximum (weight) matching in a
distributed setting. In particular, we focus on the Adaptive Massively Parallel
Computation (AMPC) model, which is a theoretical model that captures
MapReduce-like computation augmented with a distributed hash table.
  We show the first AMPC algorithms for all of the studied problems that run in
a constant number of rounds and use only $O(n^\epsilon)$ space per machine,
where $0 < \epsilon < 1$. Our results improve both upon the previous results in
the AMPC model, as well as the best-known results in the MPC model, which is
the theoretical model underpinning many popular distributed computation
frameworks, such as MapReduce, Hadoop, Beam, Pregel and Giraph.
  Finally, we provide an empirical comparison of the algorithms in the MPC and
AMPC models in a fault-tolerant distriubted computation environment. We
empirically evaluate our algorithms on a set of large real-world graphs and
show that our AMPC algorithms can achieve improvements in both running time and
round-complexity over optimized MPC baselines.


  Does the Heisenberg uncertainty principle (HUP) apply along the time
dimension in the same way it applies along the three space dimensions?
Relativity says it should; current practice says no. With recent advances in
measurement at the attosecond scale it is now possible to decide this question
experimentally.
  The most direct test is to measure the time-of-arrival of a quantum particle:
if the HUP applies in time, then the dispersion in the time-of-arrival will be
measurably increased.
  We develop an appropriate metric of time-of-arrival in the standard case;
extend this to include the case where there is uncertainty in time; then
compare. There is -- as expected -- increased uncertainty in the
time-of-arrival if the HUP applies along the time axis. The results are fully
constrained by Lorentz covariance, therefore uniquely defined, therefore
falsifiable.
  So we have an experimental question on our hands. Any definite resolution
would have significant implications with respect to the role of time in quantum
mechanics and relativity. A positive result would also have significant
practical applications in the areas of quantum communication, attosecond
physics (e.g. protein folding), and quantum computing.


  Quantum Optimal Control is an established field of research which is
necessary for the development of Quantum Technologies. In recent years, Machine
Learning techniques have been proved usefull to tackle a variety of quantum
problems. In particular, Reinforcement Learning has been employed to address
typical problems of control of quantum systems. In this tutorial we introduce
the methods of Quantum Optimal Control and Reinforcement Learning by applying
them to the problem of three-level population transfer. The jupyter notebooks
to reproduce some of our results are open-sourced and available on github.


  These are the lecture notes for the Morningside Center of Mathematics
Geometry Summer School on August 15-20, 2022. These lectures sketch the results
by Yau, Demailly-Paun, the author, and Datar-Pingali about generalized
Monge-Amp\`ere equations and subvarieties and aim to use these results to study
the Hodge conjecture.


  We present the method of moments approach to pricing barrier-type options
when the underlying is modelled by a general class of jump diffusions. By
general principles the option prices are linked to certain infinite dimensional
linear programming problems. Subsequently approximating those systems by finite
dimensional linear programming problems, upper and lower bounds for the prices
of such options are found. As numerical illustration we apply the method to the
valuation of several barrier-type options (double barrier knockout option,
American corridor and double no touch) under a number of different models,
including a case with deterministic interest rates, and compare with Monte
Carlo simulation results. In all cases we find tight bounds with short
execution times. Theoretical convergence results are also provided.


  Labeling large image datasets with attributes such as facial age or object
type is tedious and sometimes infeasible. Supervised machine learning methods
provide a highly accurate solution, but require manual labels which are often
unavailable. Zero-shot models (e.g., CLIP) do not require manual labels but are
not as accurate as supervised ones, particularly when the attribute is numeric.
We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot
models for regression and classification on unlabelled datasets. Our method
does not use any annotated images. Instead, we assume a prior over the label
distribution in the dataset. We then train an adapter network on top of CLIP
under two competing objectives: i) minimal change of predictions from the
original CLIP model ii) minimal distance between predicted and prior
distribution of labels. Additionally, we present a novel approach for selecting
prompts for Vision & Language models using a distributional prior. Our method
is effective and presents a significant improvement over the original model. We
demonstrate an improvement of 28% in mean absolute error on the UTK age
regression task. We also present promising results for classification
benchmarks, improving the classification accuracy on the ImageNet dataset by
2.83%, without using any labels.


  We introduce Language-Informed Latent Actions (LILA), a framework for
learning natural language interfaces in the context of human-robot
collaboration. LILA falls under the shared autonomy paradigm: in addition to
providing discrete language inputs, humans are given a low-dimensional
controller $-$ e.g., a 2 degree-of-freedom (DoF) joystick that can move
left/right and up/down $-$ for operating the robot. LILA learns to use language
to modulate this controller, providing users with a language-informed control
space: given an instruction like "place the cereal bowl on the tray," LILA may
learn a 2-DoF space where one dimension controls the distance from the robot's
end-effector to the bowl, and the other dimension controls the robot's
end-effector pose relative to the grasp point on the bowl. We evaluate LILA
with real-world user studies, where users can provide a language instruction
while operating a 7-DoF Franka Emika Panda Arm to complete a series of complex
manipulation tasks. We show that LILA models are not only more sample efficient
and performant than imitation learning and end-effector control baselines, but
that they are also qualitatively preferred by users.


  In this paper, using pseudo-holomorphic curve method, one proves the
Weinstein conjecture in the product $P_1\times P_2$ of two strongly
geometrically bounded symplectic manifolds under some conditions with $P_1$. In
particular, if $N$ is a closed manifold or a noncompact manifold of finite
topological type, our result implies that the Weinstein conjecture in
$\mathbb{C}\mathbb{P}^2\times T^*N$ holds.


  We consider an operator $P_V=(1+V)P$ on $\ell^2(Z^d)$, where $P$ is the
transition operator of a symmetric irreducible random walk, and $V$ is a
``sparse'' potential. We first characterize the essential spectra of this
operator. Secondly, we prove that all the eigenfunctions which correspond to
discrete spectra decay exponentially fast. Thirdly, we give a sufficient
condition for this operator to have an absolute spectral gap at the right edge
of the spectra. Finally, as an application of the absolute spectral gap and the
exponential decay of the eigenfunctions, we prove a limit theorem for the
random walk under the Gibbs measure associated to the potential $V$.


  In this memo we investigate the applicability of NVIDIA Graphics Processing
Units (GPUs) for SKA1-Low station and Central Signal Processing (CSP)-level
processing. Station-level processing primarily involves generating a single
station beam which will then be correlated with other beams in CSP. Fine
channelisation can be performed either at the station of CSP-level, while
coarse channelisation is assumed to be performed on FPGA-based Tile Processors,
together with A/D conversion, equilisation and other processes. Rough estimates
for number of GPUs required and power requirements will also be provided.


  Numerous applications in data mining and machine learning require recovering
a matrix of minimal rank. Robust principal component analysis (RPCA) is a
general framework for handling this kind of problems. Nuclear norm based convex
surrogate of the rank function in RPCA is widely investigated. Under certain
assumptions, it can recover the underlying true low rank matrix with high
probability. However, those assumptions may not hold in real-world
applications. Since the nuclear norm approximates the rank by adding all
singular values together, which is essentially a $\ell_1$-norm of the singular
values, the resulting approximation error is not trivial and thus the resulting
matrix estimator can be significantly biased. To seek a closer approximation
and to alleviate the above-mentioned limitations of the nuclear norm, we
propose a nonconvex rank approximation. This approximation to the matrix rank
is tighter than the nuclear norm. To solve the associated nonconvex
minimization problem, we develop an efficient augmented Lagrange multiplier
based optimization algorithm. Experimental results demonstrate that our method
outperforms current state-of-the-art algorithms in both accuracy and
efficiency.


  On the bases of the improved approximation for the spectral function of
one-particle states the Landau-Silin kinetic equations for the normal Fermi
liquids of neutral and electrically charged particles are shown to be valid at
finite temperature above the temperature of superfluid transition.


  We utilize the eigenvalue filtering technique combined with the stochastic
estimate of the mode number to determine the eigenvalue spectrum. Simulations
of (2 + 1)-flavor QCD are performed using the Highly Improved Staggered Quarks
(HISQ/tree) action on $N_{\tau}$ = 8 lattices with aspect ratios
$N_{\sigma}/N_{\tau}$ ranging from 5 to 7. The strange quark mass is fixed to
its physical value $m_{s}^{\rm phy}$, and the light quark masses $m_{l}$ are
varied from $m_{s}^{\rm phy}/40$ to $m_{s}^{\rm phy}/160$ which correspond to
pion mass $m_{\pi}$ ranging from 110 MeV to 55 MeV in the continuum limit. We
compute the chiral condensate and $\chi_{\pi} - \chi_{\delta}$ through the
eigenvalue spectrum obtained from the the eigenvalue filtering method. We
compare these results with those obtained from a direct calculation of the
observables which involves inversions of the fermion matrix using the
stochastic "noise vector" method. We find that these approaches yield
consistent results. Furthermore, we also investigate the quark mass and
temperature dependences of the Dirac eigenvalue density at zero eigenvalues to
gain more insights about the $U_A(1)$ symmetry breaking in QCD.


  Cosmological data provide a powerful tool in the search for physics beyond
the Standard Model (SM). An interesting target are light relics, new degrees of
freedom which decoupled from the SM while relativistic. Nearly massless relics
contribute to the radiation energy budget, and are commonly parametrized as
variations in the effective number $N_{\rm eff}$ of neutrino species.
Additionally, relics with masses greater than $10^{-4}$ eV become
non-relativistic before today, and thus behave as matter instead of radiation.
This leaves an imprint in the clustering of the large-scale structure of the
universe, as light relics have important streaming motions, mirroring the case
of massive neutrinos. Here we forecast how well current and upcoming
cosmological surveys can probe light massive relics (LiMRs). We consider
minimal extensions to the SM by both fermionic and bosonic relic degrees of
freedom. By combining current and upcoming cosmic-microwave-background and
large-scale-structure surveys, we forecast the significance at which each LiMR,
with different masses and temperatures, can be detected. We find that a very
large coverage of parameter space will be attainable by upcoming experiments,
opening the possibility of exploring uncharted territory for new physics beyond
the SM.


  This Ph.D. thesis is devoted to the constructions of Lagrangian formulation
on Finsler and Kawaguchi manifolds. While Finsler geometry is a natural
extension of Riemannian geometry, Kawaguchi geometry is the extension of
Finsler geometry to higher order derivatives and to k-dimensional parameter
space. The latter extension is also called areal geometry in some references.
On Finsler (Kawaguchi) manifold, we can define a reparameterisation invariant 1
(k)-dimensional area by the Hilbert (Kawaguchi) form, which we take as an
action. The equation of motion obtained from such action also has the property
of reparameterisation invariance. In this framework, the solution manifold of
the Euler-Lagrange equation is realised as a submanifold of Finsler/Kawaguchi
manifold, and no fibered structure over the parameter space is needed. We also
show that for the case of first order k-dimensional parameter space and second
order 1-dimensional parameter space, a global Lagrangian could be constructed.
For second order k-dimensional parameter space, Lagrangian is not global but it
still has the reparameterisation invariant property. Such theory is expected to
provide the ideal stage for formulating fundamental theories of physics,
especially for cases such as when one needs to consider the mixing of spacetime
and field variables. It is shown that locally, any conventional Lagrangian
could be reformulated by the parameter independent Lagrangian. Furthermore, the
parameter independent property will gives us the freedom of choosing a
parameter, which in some cases turns out to be useful in finding solutions and
symmetries.


  We present a large set of new self-dual N=1 SUSY gauge theories. Examples
include SU(N) theories with tensors and SO(N) theories with spinors. Using
these dualities as starting points, new non-trivial duals can be derived by
higgsing the gauge group or by integrating out matter. General lessons that can
be learned from these duals are: ``accidental" infrared symmetries play an
important role in duality, many theories have more than one ``dual", and there
seems to be no simple organizing pattern which relates duals of theories with
different number of flavors.


  The conventional understanding has always been that noble gases are
chemically inert and do not affect materials properties. This belief has led to
their use as a standard reference in various experimental applications through
noble gas implantation. However, in our research, using first-principles
calculations, we delve into the effects of noble gas defects on the properties
of several functional oxides, thereby questioning this long-held assumption. We
provide evidence that noble gases can indeed serve as functional defects. They
have the potential to decentralize the localized defect states and prompt a
shift of electrons from a localized state to the main conduction band. Our
investigation unveils that noble gas defects can indeed significantly alter
material properties. Thus, we underscore the importance of factoring in such
defects when assessing material properties.


  Generative adversarial networks (GANs) are frequently utilized in astronomy
to construct an emulator of numerical simulations. Nevertheless, training GANs
can prove to be a precarious task, as they are prone to instability and often
lead to mode collapse problems. Conversely, the diffusion model also has the
ability to generate high-quality data without adversarial training. It has
shown superiority over GANs with regard to several natural image datasets. In
this study, we undertake a quantitative comparison between the denoising
diffusion probabilistic model (DDPM) and StyleGAN2 (one of the most robust
types of GANs) via a set of robust summary statistics from scattering
transform. In particular, we utilize both models to generate the images of 21
cm brightness temperature mapping, as a case study, conditionally based on
astrophysical parameters that govern the process of cosmic reionization. Using
our new Fr\'echet Scattering Distance (FSD) as the evaluation metric to
quantitatively compare the sample distribution between generative models and
simulations, we demonstrate that DDPM outperforms StyleGAN2 on varied sizes of
training sets. Through Fisher forecasts, we demonstrate that on our datasets,
StyleGAN2 exhibits mode collapses in varied ways, while DDPM yields a more
robust generation. We also explore the role of classifier-free guidance in DDPM
and show the preference for a non-zero guidance scale only when the training
data is limited. Our findings indicate that the diffusion model presents a
promising alternative to GANs in the generation of accurate images. These
images can subsequently provide reliable parameter constraints, particularly in
the realm of astrophysics.


  Recent measurements revealed an anomalous Coulomb drag in graphene, hinting
at new physics at charge neutrality. The anomalous drag is explained by a new
mechanism based on energy transport, which involves interlayer energy transfer,
coupled to charge flow via lateral heat currents and thermopower. The old and
new drag mechanisms are governed by distinct physical effects, resulting in
starkly different behavior, in particular for drag magnitude and sign near
charge neutrality. The new mechanism explains the giant enhancement of drag
near charge neutrality, as well as its sign and anomalous sensitivity to
magnetic field. Under realistic conditions, energy transport dominates in a
wide temperature range, giving rise to a universal value of drag which is
essentially independent of the electron-electron interaction strength.


  We continue to explore, in the context of a toy model, the hypothesis that
the interacting universe we see around us could result from single particle
(undergraduate) quantum mechanics via a novel spontaneous symmetry breaking
(SSB) acting at the level of probability distributions on Hamiltonians (rather
than on states as is familiar from both Ginzburg-Landau superconductivity and
the Higgs mechanism). In an earlier paper [7] we saw qubit structure emerge
spontaneously on $\mathbb{C}^4$ and $\mathbb{C}^8$, and in this work we see
$\mathbb{C}^6$ spontaneously decomposing as $\mathbb{C}^2\otimes \mathbb{C}^3$
and very curiously $\mathbb{C}^5$ (and $\mathbb{C}^7$) splitting off one (one
or three) directions and then factoring. This evidence provides additional
support for the broad hypothesis: Nature will seek out tensor decompositions
where none are present. We consider how this finding may form a basis for the
origins of interaction and ask if it can be related to established foundational
discussions such as string theory.


  We present a consistent truncation of IIB supergravity on manifolds admitting
a Sasaki-Einstein structure, which keeps the metric and five real scalar
fields. This theory can be further truncated to a constrained one-parameter
family that depends on only the metric and one scalar, as well as to a theory
with a metric and three scalars. The reduced theory admits supersymmetric and
non-supersymmetric AdS_5 and AdS_4 x R solutions. We analyze the spectrum
around the AdS critical points and identify the dual operators.


  We examine the existence of the interaction-generated quantum anomalous Hall
phase on the honeycomb lattice. For the spinless model at half filling, the
existence of a quantum anomalous Hall phase (Chern insulator phase) has been
predicted using mean-field methods. However, recent exact diagonalization
studies for small clusters with periodic boundary condition have not found a
clear sign of an interaction-driven Chern insulator phase. We use exact
diagonalization method to study properties of small clusters with open boundary
condition and, contrary to previous studies, we find clear signatures of the
topological phase transition for finite size clusters. We also examine
applicability of the entangled-plaquette state (correlator-product state)
ansatz to describe the ground states of the system. Within this approach the
lattice is covered with plaquettes and the ground state wave-function is
written in terms of the plaquette coefficients. Configurational weights can
then be optimized using a variational Monte Carlo algorithm. Using the
entangled-plaquette state ansatz we study the ground state properties of the
system for larger system sizes and show that the results agree with the exact
diagonalization results for small clusters. This confirms validity of the
entangled-plaquette state ansatz to describe the ground states of the system
and provides further confirmation of the existence of the quantum anomalous
Hall phase in the thermodynamic limit, as predicted by the mean-field theory
calculations.


  Strong-field mid-infrared pump--terahertz (THz) probe spectroscopy has been
proven as a powerful tool for light control of different orders in strongly
correlated materials. We report the construction of an ultrafast broadband
infrared pump--THz probe system in reflection geometry. A two-output optical
parametric amplifier is used for generating mid-infrared pulses with GaSe as
the nonlinear crystal. The setup is capable of pumping bulk materials at
wavelengths ranging from 1.2 $\mu$m to 15 $\mu$m and beyond, and detecting the
subtle, transient photoinduced changes in the reflected electric field of the
THz probe at different temperatures. As a demonstration, we present 15 $\mu$m
pump--THz probe measurements of a bulk EuSbTe$_{3}$ single crystal. A $0.5\%$
transient change in the reflected THz electric field can be clearly resolved.
The widely tuned pumping energy could be used in mode-selective excitation
experiments and applied to many strongly correlated electron systems.


  We present a detailed description of the drift chambers used as an active
target and a tracking device in the NOMAD experiment at CERN. The main
characteristics of these chambers are a large area, a self supporting structure
made of light composite materials and a low cost. A spatial resolution of 150
microns has been achieved with a single hit efficiency of 97%.


  R parity violation can induce mixing of the supersymmetric Higgs bosons with
the sneutrinos at the tree level. We study the effect of this mixing on the
decays of Higgs scalars as well as sneutrinos in an effective model where the
violation of R parity is included in the minimal supersymmetric model through
bilinear lepton number violating superpotential terms. We show that a small
violation of R parity can lead to a sizeable branching ratio for the
supersymmetric Higgs boson decay mode $H \to \chi \ell$ (where $\chi$ denotes
an electroweak gaugino and $\ell$ is either a tau neutrino or a tau lepton).
Relevant constraints on R parity violation as well as those coming from SUSY
particle searches still allow the decay $H \to \chi \ell$ to compete with the
conventional decay $H\ra b \bar{b}$, at least for some ranges of parameters of
the model. Moreover, the tau sneutrino will have dominant R parity violating
decays to standard model fermions $b \bar{b}$, $\tau^+ \tau^-$ or to the
invisible mode $\nu \bar{\nu}$ whenever the phase space for R parity conserving
channels is closed.


  YZ Phe is a very short-period contact binary (Sp.= $K2\,V$) with an orbital
period of 0.2347 days near the short period limit (0.22 d). We present the
complete light curves in $VRI$ bands, which photometric data were obtained with
the 0.61-m telescope of PROMPT-8 at CTIO in Chile during June to October 2016
and August 2017. The photometric solutions were determined by using the W-D
method and the results reveal that YZ Phe is a W-subtype shallow contact binary
($f\sim$ 10%, $q$ = 2.635 or $1/q$ = 0.379 for W subtype) with rotational
motion of a large hot spot on the more massive component, showing a strong
O'Connell effect with variation of maxima in photometric time series at period
of 4.20 yr and stellar cycle at period of 1.28 yr. By compiling all available
eclipse times, the result shows a long-term period decrease at a rate of
$\mathrm{d}P/\mathrm{d}t = -2.64(\pm 0.02)\times 10^{-8}$ d $yr^{-1}$,
superimposed on a cyclic variation ($A_3$ = 0.0081 days and $P_3$ = 40.76
years). This variation cannot be explained by Applegate mechanism. Thus, the
cyclic change may be interpreted as light-travel time effect via the presence
of a cool third body. Based on photometric solutions, the third light was
detected with 2% contribution of total light in $V$ and $I$ bands. Those
support the existence of a third body. For the long-term period decrease, it
can be explained by mass transfer from the more massive component ($M_2 \sim
0.74 M_{\odot}$) to the less massive one ($M_1 \sim 0.28 M_{\odot}$) or plus
AML via magnetic braking. With $1/q$ $<$ 0.4 and long-term period decrease, all
suggest that YZ Phe is on the AML-controlled state and its fill-out factor will
increase, as well as the system will evolve into a deeper normal contact
binary.


  We present optical and near-infrared photometry and spectroscopy of the type
Ia SN 1998bu in the Leo I Group galaxy M96 (NGC 3368). The data set consists of
356 photometric measurements and 29 spectra of SN 1998bu between UT 1998 May 11
and July 15. The well-sampled light curve indicates the supernova reached
maximum light in B on UT 1998 May 19.3 (JD 2450952.8 +/- 0.8) with B = 12.22
+/- 0.03 and V = 11.88 +/- 0.02. Application of a revised version of the
Multicolor Light Curve Shape (MLCS) method yields an extinction toward the
supernova of A_V = 0.94 +/- 0.15 mag, and indicates the supernova was of
average luminosity compared to other normal type Ia supernovae. Using the HST
Cepheid distance modulus to M96 (Tanvir et al. 1995) and the MLCS fit
parameters for the supernova, we derive an extinction-corrected absolute
magnitude for SN 1998bu at maximum, M_V = -19.42 +/- 0.22. Our independent
results for this supernova are consistent with those of Suntzeff et al. (1999).
Combining SN 1998bu with three other well-observed local calibrators and 42
supernovae in the Hubble flow yields a Hubble constant, H_0 = 64^{+8}_{-6}
km/s/Mpc, where the error estimate incorporates possible sources of systematic
uncertainty including the calibration of the Cepheid period-luminosity
relation, the metallicity dependence of the Cepheid distance scale, and the
distance to the LMC.