  VLBA and EVN radio observations of H2O masers at 22 GHz and methanol masers
at 6.7 GHz have been used to obtain images of the maser spots in the infrared
object GL2789, which is associated with the young stellar object V645Cyg. The
position of these masers coincides with that of the optical object to within
0.2 arcsec. The maser spots are located in a line oriented north--south, and
their positions and radial velocities can be described by a model with a
Keplerian disk with maximum radius 40 AU for the H2O masers and 800 AU for the
methanol masers. The H2O and methanol masers spots are unresolved, and the
lower limits for their brightness temperatures are 2x10^{13} K and 1.4x10^9 K,
respectively. A model in which the maser radiation is formed in extended
water-methanol clouds associated with ice planets forming around the young star
is proposed.


  A $z$-matching on a bipartite graph is a set of edges, among which each
vertex of two types of the graph is adjacent to at most $1$ and at most $z$
($\geqslant 1$) edges, respectively. The $z$-matching problem concerns finding
$z$-matchings with the maximum size. Our approach to this combinatorial
optimization problem is twofold. From an algorithmic perspective, we adopt a
local algorithm as a linear approximate solver to find $z$-matchings on any
graph instance, whose basic component is a generalized greedy leaf removal
procedure in graph theory. From a theoretical perspective, on uncorrelated
random bipartite graphs, we develop a mean-field theory for percolation
phenomenon underlying the local algorithm, leading to an analytical estimation
of $z$-matching sizes on random graphs. Our analytical theory corrects the
prediction by belief propagation algorithm at zero-temperature limit in
(Krea\v{c}i\'{c} and Bianconi 2019 \textsl{EPL} \textbf{126} 028001). Besides,
our theoretical framework extends a core percolation analysis of $k$-XORSAT
problems to a general context of uncorrelated random hypergraphs with arbitrary
degree distributions of factor and variable nodes.


  In stochastic decision problems, one often wants to estimate the underlying
probability measure statistically, and then to use this estimate as a basis for
decisions. We shall consider how the uncertainty in this estimation can be
explicitly and consistently incorporated in the valuation of decisions, using
the theory of nonlinear expectations.


  In this paper, we propose the use of a black-box optimization method called
deterministic Mesh Adaptive Direct Search (MADS) algorithm with orthogonal
directions (Ortho-MADS) for the selection of hyperparameters of Support Vector
Machines with a Gaussian kernel. Different from most of the methods in the
literature that exploit the properties of the data or attempt to minimize the
accuracy of a validation dataset over the first quadrant of (C, gamma), the
Ortho-MADS provides convergence proof. We present the MADS, followed by the
Ortho-MADS, the dynamic stopping criterion defined by the MADS mesh size and
two different search strategies (Nelder-Mead and Variable Neighborhood Search)
that contribute to a competitive convergence rate as well as a mechanism to
escape from undesired local minima. We have investigated the practical
selection of hyperparameters for the Support Vector Machine with a Gaussian
kernel, i.e., properly choose the hyperparameters gamma (bandwidth) and C
(trade-off) on several benchmark datasets. The experimental results have shown
that the proposed approach for hyperparameter tuning consistently finds
comparable or better solutions, when using a common configuration, than other
methods. We have also evaluated the accuracy and the number of function
evaluations of the Ortho-MADS with the Nelder-Mead search strategy and the
Variable Neighborhood Search strategy using the mesh size as a stopping
criterion, and we have achieved accuracy that no other method for
hyperparameters optimization could reach.


  We propose the cascade attribute learning network (CALNet), which can learn
attributes in a control task separately and assemble them together. Our
contribution is twofold: first we propose attribute learning in reinforcement
learning (RL). Attributes used to be modeled using constraint functions or
terms in the objective function, making it hard to transfer. Attribute
learning, on the other hand, models these task properties as modules in the
policy network. We also propose using novel cascading compensative networks in
the CALNet to learn and assemble attributes. Using the CALNet, one can zero
shoot an unseen task by separately learning all its attributes, and assembling
the attribute modules. We have validated the capacity of our model on a wide
variety of control problems with attributes in time, position, velocity and
acceleration phases.


  This work is devoted to demonstration of the analysis on optimizing the
output power harvested from vibration energy harvester.


  It is pointed out that quantum vacuum fluctuations may give rise to a
curvature of space-time equivalent to the curvature currently attributed to
dark energy. A simple calculation is made, which suggests that the value of the
dark energy density is roughly given by the product of Newton constant time the
quantity m^6 c^4 h^-4, m being a typical mass of elementary particles. The
estimate is compatible with observations.


  Small objects have relatively low resolution, the unobvious visual features
which are difficult to be extracted, so the existing object detection methods
cannot effectively detect small objects, and the detection speed and stability
are poor. Thus, this paper proposes a small object detection algorithm based on
FSSD, meanwhile, in order to reduce the computational cost and storage space,
pruning is carried out to achieve model compression. Firstly, the semantic
information contained in the features of different layers can be used to detect
different scale objects, and the feature fusion method is improved to obtain
more information beneficial to small objects; secondly, batch normalization
layer is introduced to accelerate the training of neural network and make the
model sparse; finally, the model is pruned by scaling factor to get the
corresponding compressed model. The experimental results show that the average
accuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is
59.5 FPS on GTX1080ti. After pruning, the compressed model can reach 79.9% mAP,
and 79.5 FPS in detection speed. On MS COCO, the best detection accuracy (APs)
is 12.1%, and the overall detection accuracy is 49.8% AP when IoU is 0.5. The
algorithm can not only improve the detection accuracy of small objects, but
also greatly improves the detection speed, which reaches a balance between
speed and accuracy.


  In this paper we develop Poisson geometry for non-commutative algebras. This
generalizes the bi-symplectic geometry which was recently, and independently,
introduced by Crawley-Boevey, Etingof and Ginzburg.
  Our (quasi-)Poisson brackets induce classical (quasi-)Poisson brackets on
representation spaces. As an application we show that the moduli spaces of
representations associated to the deformed multiplicative preprojective
algebras recently introduced by Crawley-Boevey and Shaw carry a natural Poisson
structure.


  DP-coloring of a simple graph is a generalization of list coloring, and also
a generalization of signed coloring of signed graphs. It is known that for each
$k \in \{3, 4, 5, 6\}$, every planar graph without $C_k$ is 4-choosable.
Furthermore, Jin, Kang, and Steffen \cite{JKS} showed that for each $k \in \{3,
4, 5, 6\}$, every signed planar graph without $C_k$ is signed 4-choosable. In
this paper, we show that for each $k \in \{3, 4, 5, 6\}$, every planar graph
without $C_k$ is 4-DP-colorable, which is an extension of the above results.


  We study spherically symmetric spacetimes for matter distributions with
isotropic pressures. We generate new exact solutions to the Einstein field
equations which also contains isotropic pressures. We develop an algorithm that
produces a new solution if a particular solution is known. The algorithm leads
to a nonlinear Bernoulli equation which can be integrated in terms of arbitrary
functions. We use a conformally flat metric to show that the integrals may be
expressed in terms of elementary functions. It is important to note that we
utilise isotropic coordinates unlike other treatments.


  Current analytic and semi-analytic dark matter halo models distinguish
between the central galaxy in a halo and the satellite galaxies in halo
substructures. Using a recent halo-model description of the color dependence of
galaxy clustering (Skibba & Sheth 2008), we investigate the colors of central
and satellite galaxies predicted by the model and compare them to those of two
galaxy group catalogs constructed from the Sloan Digital Sky Survey (Yang et
al. 2007, Berlind et al. 2006a). In the model, the environmental dependence of
galaxy color is determined by that of halo mass, and the predicted color mark
correlations were shown to be consistent with SDSS measurements. The model
assumes that satellites tend to follow a color-magnitude sequence that
approaches the red sequence at bright luminosities; the model's success
suggests that bright satellites tend to be `red and dead' while the star
formation in fainter ones is in the process of being quenched. In both the
model and the SDSS group catalogs, we find that at fixed luminosity or stellar
mass, central galaxies tend to be bluer than satellites. In contrast, at fixed
group richness or halo mass, central galaxies tend to be redder than
satellites, and galaxy colors become redder with increasing mass. We also
compare the central and satellite galaxy color distributions, as a function of
luminosity and as a function of richness, in the model and in the two group
catalogs. Except for faint galaxies and small groups, the model and both group
catalogs are in very good agreement.


  The lanthanide (Ln) copper oxides of the general chemical formula Ln2CuO4
take two different crystal structures: K2NiF4(T) and Nd2CuO4(T'). La2CuO4 takes
the T structure by high-temperature bulk processes. The "thermal expansion
mismatch" between the La-O and Cu-O bonds predicts that the T' phase of La2CuO4
can be stabilized at synthesis temperatures below 425 deg. Such low synthesis
temperatures are difficult to access by bulk processes, but easy by thin-film
processes. We have surveyed growth parameters in molecular beam epitaxy, and
succeeded in the selective stabilization of T- and T'-La2CuO4. From our
observations, it turns out that the growth temperature as well as the substrate
play a crucial role in the selective stabilization: the T' structure is
stabilized at low growth temperatures (< 600 deg.) and with substrates of a <
3.70 Ang or a > 3.90 Ang, while the T structure is stabilized at high growth
temperatures (> 650 deg) or with substrates of a ~ 3.70 - 3.85 Ang. We have
also been attempting hole (Ca, Sr, and Ba) and electron (Ce) doping into both
of T- and T'-La2CuO4. In T-La2CuO4, hole doping produces the well-known LSCO
and LBCO. Surprisingly, contrary to the empirical law, electron doping is also
possible up to x ~ 0.06 - 0.08, although the films do not show
superconductivity. In T'-La2CuO4, electron doping produces superconducting
T'-(La,Ce)2CuO4 with Tc ~ 30 K, although hole doping has as yet been
unsuccessful.


  This paper presents a new way of describing cross fields based on fourth
order tensors. We prove that the new formulation is forming a linear space in
$\mathbb{R}^9$. The algebraic structure of the tensors and their projections on
$\mbox{SO}(3)$ are presented. The relationship of the new formulation with
spherical harmonics is exposed. This paper is quite theoretical. Due to pages
limitation, few practical aspects related to the computations of cross fields
are exposed. Nevetheless, a global smoothing algorithm is briefly presented and
computation of cross fields are finally depicted.


  This paper investigates the performance of open-loop multi-antenna
point-to-point links in ad hoc networks with slotted ALOHA medium access
control (MAC). We consider spatial multiplexing transmission with linear
maximum ratio combining and zero forcing receivers, as well as orthogonal space
time block coded transmission. New closed-form expressions are derived for the
outage probability, throughput and transmission capacity. Our results
demonstrate that both the best performing scheme and the optimum number of
transmit antennas depend on different network parameters, such as the node
intensity and the signal-to-interference-and-noise ratio operating value. We
then compare the performance to a network consisting of single-antenna devices
and an idealized fully centrally coordinated MAC. These results show that
multi-antenna schemes with a simple decentralized slotted ALOHA MAC can
outperform even idealized single-antenna networks in various practical
scenarios.


  Every second flat Reidemeister move of knot projections can be decomposed
into two types thorough an inverse or direct self-tangency modification,
respectively called strong or weak, when orientations of the knot projections
are arbitrarily provided. Further, we introduce the notions of strong and weak
(1, 2) homotopies; we define that two knot projections are strongly (resp.
weakly) (1, 2) homotopic if and only if two knot projections are related by a
finite sequence of first and strong (resp. weak) second flat Reidemeister
moves. This paper gives a new necessary and sufficient condition that two knot
projections are not strongly (1, 2) homotopic. Similarly, we obtain a new
necessary and sufficient condition in the weak (1, 2) homotopy case. We also
define a new integer-valued strong (1, 2) homotopy invariant. Using it, we show
that the set of the non-trivial prime knot projections without 1-gons that can
be trivialized under strong (1, 2) homotopy is disjoint from that of weak (1,
2) homotopy. We also investigate topological properties of the new invariant
and give its generalization, a comparison of our invariants and Arnold
invariants, and a table of invariants.


  In this paper, we introduce the fundamental notion of a Markov basis, which
is one of the first connections between commutative algebra and statistics. The
notion of a Markov basis is first introduced by Diaconis and Sturmfels (1998)
for conditional testing problems on contingency tables by Markov chain Monte
Carlo methods. In this method, we make use of a connected Markov chain over the
given conditional sample space to estimate the P-values numerically for various
conditional tests. A Markov basis plays an importance role in this arguments,
because it guarantees the connectivity of the chain, which is needed for
unbiasedness of the estimate, for arbitrary conditional sample space. As
another important point, a Markov basis is characterized as generators of the
well-specified toric ideals of polynomial rings. This connection between
commutative algebra and statistics is the main result of Diaconis and Sturmfels
(1998). After this first paper, a Markov basis is studied intensively by many
researchers both in commutative algebra and statistics, which yields an
attractive field called computational algebraic statistics. In this paper, we
give a review of the Markov chain Monte Carlo methods for contingency tables
and Markov bases, with some fundamental examples. We also give some
computational examples by algebraic software Macaulay2 and statistical software
R. Readers can also find theoretical details of the problems considered in this
paper and various results on the structure and examples of Markov bases in
Aoki, Hara and Takemura (2012).


  In this article, we report the process induced variation in the
characteristics of PECVD deposited and thermally grown silicon dioxide (SiO2)
thin film. We find key differences in the porosity, arrangement of the
nano-pores, surface roughness, refractive index and electrical resistivity of
the SiO2 thin films obtained by the two methods. While the occurrence of the
nanoporous structure is an inherent property of the material and independent of
the process of film growth or deposition, the arrangements of these nano-pores
in the oxide film is process dependent. The distinct arrangements of the
nano-pores are signatures of the deposition/growth processes. Morphological
analysis has been carried out to demonstrate the difference between oxides
either grown by thermal oxidation or through PECVD deposition. The tunable
conductive behavior of the metal filled nano-porous oxides is also
demonstrated, which has potential to be used as conductive oxides in various
applications.


  In supervised machine learning, an agent is typically trained once and then
deployed. While this works well for static settings, robots often operate in
changing environments and must quickly learn new things from data streams. In
this paradigm, known as streaming learning, a learner is trained online, in a
single pass, from a data stream that cannot be assumed to be independent and
identically distributed (iid). Streaming learning will cause conventional deep
neural networks (DNNs) to fail for two reasons: 1) they need multiple passes
through the entire dataset; and 2) non-iid data will cause catastrophic
forgetting. An old fix to both of these issues is rehearsal. To learn a new
example, rehearsal mixes it with previous examples, and then this mixture is
used to update the DNN. Full rehearsal is slow and memory intensive because it
stores all previously observed examples, and its effectiveness for preventing
catastrophic forgetting has not been studied in modern DNNs. Here, we describe
the ExStream algorithm for memory efficient rehearsal and compare it to
alternatives. We find that full rehearsal can eliminate catastrophic forgetting
in a variety of streaming learning settings, with ExStream performing well
using far less memory and computation.


  We study in a model-independent way new neutral and charged vector bosons
that could give observable signals with leptonic final states at the LHC. We
show, in particular, that a charged vector W' decaying into lepton plus
neutrino is accompanied by at least an extra neutral vector boson Z', nearly
degenerate with the charged one. Conversely, a Z' boson with significant
isospin violation cannot exist without a companion W'. To take advantage of
these generic correlations, we perform a combined analysis of LHC data in the
dilepton and lepton-plus-missing-energy channels, which allows us to improve
the limits from independent analyses. We also develop some tools to easily deal
with cases in which several heavy vector bosons with similar masses interfere.
Finally, we develop a theoretically consistent framework for the study of the
sequential Z' and W' benchmarks.


  Based on the hypothesis that the (non-reversible) arrow of time is intrinsic
in any system, no matter how small, the consequences are discussed. Within the
framework of local quantum physics it is shown how such a semi-group action of
time can consistently be extended to that of the group of spacetime
translations in Minkowski space. In presence of massless excitations, however,
there arise ambiguities in the theoretical extensions of the time translations
to the past. The corresponding loss of quantum information on states upon time
is determined. Finally, it is explained how the description of operations in
classical terms combined with constraints imposed by the arrow of time leads to
a quantum theoretical framework. These results suggest that the arrow of time
is fundamental in nature and not merely a consequence of statistical effects on
which the Second Law is based.


  In this note, we provide convergence results for the proximal point algorithm
and a splitting variant thereof in the setting of CAT$(\kappa)$ spaces with
$\kappa > 0$ using a recent definition for the resolvent of a convex, lower
semi-continuous function due to Kimura and Kohsaka (J. Fixed Point Theory Appl.
18 (2016), 93-115).


  Generating high-resolution images with generative models has recently been
made widely accessible by leveraging diffusion models pre-trained on
large-scale datasets. Various techniques, such as MultiDiffusion and
SyncDiffusion, have further pushed image generation beyond training
resolutions, i.e., from square images to panorama, by merging multiple
overlapping diffusion paths or employing gradient descent to maintain
perceptual coherence. However, these methods suffer from significant
computational inefficiencies due to generating and averaging numerous
predictions, which is required in practice to produce high-quality and seamless
images. This work addresses this limitation and presents a novel approach that
eliminates the need to generate and average numerous overlapping denoising
predictions. Our method shifts non-overlapping denoising windows over time,
ensuring that seams in one timestep are corrected in the next. This results in
coherent, high-resolution images with fewer overall steps. We demonstrate the
effectiveness of our approach through qualitative and quantitative evaluations,
comparing it with MultiDiffusion, SyncDiffusion, and StitchDiffusion. Our
method offers several key benefits, including improved computational efficiency
and faster inference times while producing comparable or better image quality.


  The mass spectra of $D_s$ meson is obtained in the framework of relativistic
independent quark model using Martin like potential for the quark confinement.
The predicted excited states are in good agreement with the experimental
results as well as with the lattice and other theoretical predictions. The
spectroscopic parameters are employed further to compute the decay constant,
electromagnetic transition and leptonic decay widths. The present result for
its decay constant, $f_P$ (252.82 MeV) is in excellent agreement with the value
252.6 $\pm$ 11.1 MeV reported by CLEO-c and the predicted branching ratios for
$(D_s \rightarrow \tau \bar{\nu}_\tau, \mu \bar{\nu}_\mu)$ ($5.706 \times
10^{-2}, 5.812 \times 10^{-3}$) are in close agreement with the PDG values ($
(5.43 \pm 0.31)\times 10^{-2}, (5.90 \pm 0.33)\times 10^{-3} $) respectively.


  This work represents integration of MTJ with 30nm FinFET for low voltage
analog write operations and readout optimization for the p-bit or true random
number generator (TRNG), where the induced p-bit, the probabilistic state of
the magnetic tunnel junction (MTJ), is detected within only a single
computational period. The period contains two sub-cycles: write and joined read
& reset cycles. The operation with MTJ becomes stochastic, independent after
calibrating at the desired working point against the factors, which can induce
the signal deviations, e.g. temperature, material degradation or external
magnetic field.


  Extending the celebrated results of Alexandrov (1958) and Korevaar-Ros (1988)
for smooth sets, as well as the results of Schneider (1979) and the first
author (1999) for arbitrary convex bodies, we obtain for the first time the
characterization of the isoperimetric sets of a uniformly convex smooth
finite-dimensional normed space (i.e. Wulff shapes) in the non-smooth and
non-convex setting, based on the natural geometric condition involving the
curvature measures. More specifically we show, under a natural mean-convexity
assumption, that finite unions of disjoint Wulff shapes are the only sets of
positive reach $ A \subseteq \mathbf{R}^{n+1} $ with finite and positive volume
such that, for some $ k \in \{0, \ldots , n-1\}$, the $ k $-th generalized
curvature measure $ \Theta^\phi_{k}(A, \cdot) $, which is defined on the unit
normal bundle of $ A $ with respect to the relative geometry induced by $ \phi
$, is proportional to $ \Theta^\phi_{n}(A, \cdot)$. If $ k = n-1 $ the
conclusion holds for all sets of positive reach with finite and positive
volume. We also prove a related sharp result about the removability of the
singularities. This result is based on the extension of the notion of a normal
boundary point, originally introduced by Busemann and Feller (1936) for
arbitrary convex bodies, to sets of positive reach.
  These results are new even in the Euclidean space.
  Several auxiliary and related results are also proved, which are of
independent interest. They include the extension of the classical Steiner--Weyl
tube formula to arbitrary closed sets in a finite dimensional uniformly convex
normed vector space and a general formula for the derivative of the localized
volume function, which extends and complements recent results of
Chambolle--Lussardi--Villa (2021).


  Background: The stochastic behavior of patient arrival at an emergency
department (ED) complicates the management of an ED. More than 50% of hospitals
ED capacity tends to operate beyond its normal capacity and eventually fails to
deliver high-quality care. To address the concern of stochastics ED arrivals,
many types of research has been done using yearly, monthly and weekly time
series forecasting. Aim: Our research team believes that hourly time-series
forecasting of the load can improve ED management by predicting the arrivals of
future patients, and thus, can support strategic decisions in terms of quality
enhancement. Methods: Our research does not involve any human subject, only ED
admission data from January 2014 to August 2017 retrieved from the UnityPoint
Health database. Autoregressive integrated moving average (ARIMA), Holt
Winters, TBATS, and neural network methods were implemented to forecast hourly
ED patient arrival. Findings: ARIMA (3,0,0) (2,1,0) was selected as the best
fit model with minimum Akaike information criterion and Schwartz Bayesian
criterion. The model was stationary and qualified the Box Ljung correlation
test and the Jarque Bera test for normality. The mean error (ME) and root mean
square error (RMSE) were selected as performance measures. An ME of 1.001 and
an RMSE of 1.55 was obtained. Conclusions: ARIMA can be used to provide hourly
forecasts for ED arrivals and can be utilized as a decision support system in
the healthcare industry. Application: This technique can be implemented in
hospitals worldwide to predict ED patient arrival.


  We report CMOS-compatible quantized current sources (electron pumps)
fabricated with nanowires (NWs) on 300 mm SOI wafers. Unlike other Al, GaAs or
Si based metallic or semiconductor pumps, the fabrication does not rely on
electron-beam lithography. The structure consists of two gates in series on the
nanowire and the only difference with the SOI nanowire process lies in long (40
nm) nitride spacers. As a result a single, silicided island gets isolated
between the gates and transport is dominated by Coulomb blockade at cryogenic
temperatures thanks to the small size and therefore capacitance of this island.
Operation and performances comparable to devices featuring e-beam lithography
is demonstrated in the non-adiabatic pumping regime, with a pumping frequency
up to 300 MHz. We also identify and model signatures of charge traps affecting
charge pumping in the adiabatic regime. The availability of quantized current
references in a process close to the 28FDSOI technology could trigger new
applications for these pumps and allow to cointegrate them with cryogenic CMOS
circuits, for instance in the emerging field of interfaces with quantum bits.


  Machine learning (ML) based time series forecasting models often require and
assume certain degrees of stationarity in the data when producing forecasts.
However, in many real-world situations, the data distributions are not
stationary and they can change over time while reducing the accuracy of the
forecasting models, which in the ML literature is known as concept drift.
Handling concept drift in forecasting is essential for many ML methods in use
nowadays, however, the prior work only proposes methods to handle concept drift
in the classification domain. To fill this gap, we explore concept drift
handling methods in particular for Global Forecasting Models (GFM) which
recently have gained popularity in the forecasting domain. We propose two new
concept drift handling methods, namely: Error Contribution Weighting (ECW) and
Gradient Descent Weighting (GDW), based on a continuous adaptive weighting
concept. These methods use two forecasting models which are separately trained
with the most recent series and all series, and finally, the weighted average
of the forecasts provided by the two models are considered as the final
forecasts. Using LightGBM as the underlying base learner, in our evaluation on
three simulated datasets, the proposed models achieve significantly higher
accuracy than a set of statistical benchmarks and LightGBM baselines across
four evaluation metrics.


  A new scheme of approximation in quantum theory is proposed which is
potentially applicable to arbtrary interacting systems. The method consists in
in approximating the original Hamiltonian by one corresponding to a suitable
exactly solvable system (with interaction) such that the "quantum average" of
both are equal, thus forcing self-consistency.The method transcends the
limitations of the variational method and the perturbation theory.The results
are systematically improvable by the construction of a improved perturbation
theory (IPT) which automatically satisfies the condition of convergence.
Uniformly accurate results are obtained for the case of the quartic-,sextic-
and octic anharmonic oscillators as well as the quartic and sextic double well
oscillators. The leading order results reproduce earlier results by different
methods using different input assumptions.The results for the sextic
oscillators agree well with exact prediction of supersymmetry. We also discuss
the stability and structure of the effective vacuum state of the approximation.


  Recent measurements suggest free electrons created in ultra-high energy
cosmic ray extensive air showers (EAS) can interact with neutral air molecules
producing Bremsstrahlung radiation in the microwave regime. The microwave
radiation produced is expected to scale with the number of free electrons in
the shower, which itself is a function of the energy of the primary particle
and atmospheric depth. Using these properties a calorimetric measurement of the
EAS is possible. This technique is analogous to fluorescence detection with the
added benefit of a nearly 100% duty cycle and practically no atmospheric
attenuation. The Microwave Detection of Air Showers (MIDAS) prototype is
currently being developed at the University of Chicago. MIDAS consists of a 53
feed receiver operating in the 3.4 to 4.2 GHz band. The camera is deployed on a
4.5 meter parabolic reflector and is instrumented with high speed power
detectors and autonomous FPGA trigger electronics. We present the current
status of the MIDAS instrument and an outlook for future development.


  Small amplitude quasi-periodic pulsations (QPPs) detected in soft X-ray
emission are commonplace in many flares. To date, the underpinning processes
resulting in the QPPs are unknown. In this paper, we attempt to constrain the
prevalence of \textit{stationary} QPPs in the largest statistical study to
date, including a study of the relationship of QPP periods to the properties of
the flaring active region, flare ribbons, and CME affiliation. We build upon
the work of \cite{inglis2016} and use a model comparison test to search for
significant power in the Fourier spectra of lightcurves of the GOES 1--8~\AA\
channel. We analyze all X-, M- and C- class flares of the past solar cycle, a
total of 5519 flares, and search for periodicity in the 6-300~s timescale
range. Approximately 46\% of X-class, 29\% of M-class and 7\% of C-class flares
show evidence of stationary QPPs, with periods that follow a log-normal
distribution peaked at 20~s. The QPP periods were found to be independent of
flare magnitude, however a positive correlation was found between QPP period
and flare duration. No dependence of the QPP periods to the global active
region properties was identified. A positive correlation was found between QPPs
and ribbon properties including unsigned magnetic flux, ribbon area and ribbon
separation distance. We found that both flares with and without an associated
CME can host QPPs. Furthermore, we demonstrate that for X- and M- class flares,
decay phase QPPs have statistically longer periods than impulsive phase QPPs.


  We use the clathrate hydrate trapping theory to calculate the enrichments in
O, N, S, Xe, Ar and Kr compared to solar in Saturn's atmosphere. For this, we
calibrate our calculations using two different carbon abundance determinations
that cover the domain of measurements published in the last decades: one
derived from the NASA $Kuiper$ Airborne Observatory measurements and the other
obtained from the Cassini spacecraft observations. We show that these two
different carbon abundances imply quite a different minimum heavy element
content for Saturn. Using the Kuiper Airborne Observatory measurement for
calibration, the amount of ices accreted by Saturn is found to be consistent
with current interior models of this planet. On the other hand, using the
Cassini measurement for calibration leads to an ice content in the planet's
envelope which is higher than the one derived from the interior models. In this
latter case, reconciling the interior models with the amount of C measured by
the Cassini spacecraft requires that significant differential sedimentation of
water and volatile species have taken place in Saturn's interior during its
lifetime.


  We seek to establish qualitative convergence results to a general class of
evolution PDEs described by gradient flows in optimal transportation distances.
These qualitative convergence results come from dynamical systems under the
general name of LaSalle Invariance Principle. By combining some of the basic
notions of gradient flow theory and dynamical systems, we are able to reproduce
this invariance principle in the setting of evolution PDEs under general
assumptions. We apply this abstract theory to a non-exhaustive list of examples
that recover, simplify, and even extend the results in their respective
literatures.


  Finding an approximate second-order stationary point (SOSP) is a well-studied
and fundamental problem in stochastic nonconvex optimization with many
applications in machine learning. However, this problem is poorly understood in
the presence of outliers, limiting the use of existing nonconvex algorithms in
adversarial settings.
  In this paper, we study the problem of finding SOSPs in the strong
contamination model, where a constant fraction of datapoints are arbitrarily
corrupted. We introduce a general framework for efficiently finding an
approximate SOSP with \emph{dimension-independent} accuracy guarantees, using
$\widetilde{O}({D^2}/{\epsilon})$ samples where $D$ is the ambient dimension
and $\epsilon$ is the fraction of corrupted datapoints.
  As a concrete application of our framework, we apply it to the problem of low
rank matrix sensing, developing efficient and provably robust algorithms that
can tolerate corruptions in both the sensing matrices and the measurements. In
addition, we establish a Statistical Query lower bound providing evidence that
the quadratic dependence on $D$ in the sample complexity is necessary for
computationally efficient algorithms.


  We show that SIS Josephson junctions have a threshold resistance, above which
the Josephson coupling and the supercurrents become extremely small, due to the
shrinking of the Cooper pair size during the Josephson tunneling. Accordingly,
the threshold resistance is smaller for higher Tc superconductors with small
Cooper pair size and for the insulating barrier with higher resistance. This
understanding agrees with the observations in SIS junctions of low Tc
superconductors, such as Sn, Pb, and Nb. For MgB2 it explains why the big gap
does not show the supercurrents, unlike the small gap. Furthermore, it is
consistent with the fact that high Tc cuprates show the Josephson effects only
for SNS type junctions, including the intrinsic Josephson effects.


  Quantum digital signatures (QDS) provide a means for signing electronic
communications with informationtheoretic security. However, all previous
demonstrations of quantum digital signatures assume trusted measurement
devices. This renders them vulnerable against detector side-channel attacks,
just like quantum key distribution. Here, we exploit a
measurement-device-independent (MDI) quantum network, over a
200-square-kilometer metropolitan area, to perform a field test of a
three-party measurement-device-independent quantum digital signature (MDI-QDS)
scheme that is secure against any detector side-channel attack. In so doing, we
are able to successfully sign a binary message with a security level of about
1E-7. Remarkably, our work demonstrates the feasibility of MDI-QDS for
practical applications.


  We present a novel framework for applying deep neural networks (DNN) to soft
decoding of linear codes at arbitrary block lengths. Unlike other approaches,
our framework allows unconstrained DNN design, enabling the free application of
powerful designs that were developed in other contexts. Our method is robust to
overfitting that inhibits many competing methods, which follows from the
exponentially large number of codewords required for their training. We achieve
this by transforming the channel output before feeding it to the network,
extracting only the syndrome of the hard decisions and the channel output
reliabilities. We prove analytically that this approach does not involve any
intrinsic performance penalty, and guarantees the generalization of performance
obtained during training. Our best results are obtained using a recurrent
neural network (RNN) architecture combined with simple preprocessing by
permutation. We provide simulation results that demonstrate performance that
sometimes approaches that of the ordered statistics decoding (OSD) algorithm.


  A novel concept of multiconfigurational self-consistent field method, the
generalized occupation-restricted-multiple-active-space (GORMAS) is presented.
GORMAS wave functions are defined by substituting the complete active space
(CAS) in ORMAS, given a pre- or a post-restriction. The GORMAS approach shows a
flexible selection of active space, reduce the ineffective reference
configurations from CAS dramatically. Test calculations in molecule or complex
systems, CH$_2$O, (H$_2$O)$_2$ molecule, and oxoMn(salen) are presented. They
show the GORMAS wave functions achieve the similar accuracy with under 15\%
dimension of reference spaces.


  The effect of central, tensor and spin-isospin nucleon-nucleon correlations
upon semi-exclusive ^{16}O(e,e'p) reactions is studied for Q^2 and Bjorken x
values in the range $0.2 \lesssim Q^2 \lesssim 1.1 (GeV/c)^2$ and 0.15
$\lesssim$ x $\lesssim$ 2. The fully unfactorized calculations are performed in
a framework that accounts not only for the dynamical coupling of virtual
photons to correlated nucleon pairs but also for meson-exchange and
$\Delta_{33}$-isobar currents. Tensor correlations are observed to produce
substantially larger amounts of semi-exclusive ^{16}O(e,e'p) strength than
central correlations do and are predominantly manifest in the proton-neutron
knockout channel. With the exception of the $x \approx 2$ case, in all
kinematical situations studied the meson-exchange and isobar currents are a
strong source of A(e,e'p) strength at deep missing energies. This feature gives
the A(e,e'p) strength at deep missing energies a pronounced transverse
character.


  A new iterative image reconstruction algorithm for electrical capacitance
tomography (ECT) is proposed that is based on iterative soft thresholding of a
total variation penalty and adaptive reweighted compressive sensing. This
algorithm encourages sharp changes in the ECT image and overcomes the
disadvantage of the $l_1$ minimization by equipping the total variation with an
adaptive weighting depending on the reconstructed image. Moreover, the
non-linear effect is also partially reduced due to the adoption of an updated
sensitivity matrix. Simulation results show that the proposed algorithm
recovers ECT images more precisely than existing state-of-the-art algorithms
and therefore is suitable for the imaging of multiphase systems in industrial
or medical applications.


  Recent experiments, in which Terahertz (THz) light has been used to induce
nonequilibrium superconducting states, have raised a number of intriguing
fundamental questions. Theoretically, these experiments are most often
described within the Floquet formalism, which suffers a number of well-known
limitations (e.g. Floquet heating). Alternative approaches rely on heavy
numerical methods. In this Article we develop an analytical theory of
nonequilibrium superconductivity that combines path integrals on the
Kostantinov-Perel' time contour with adiabatic perturbation theory [G. Rigolin,
G. Ortiz, and V.H. Ponce, Phys. Rev. A~{\bf 78}, 052508 (2008)]. We consider a
general system of electrons and Raman phonons coupled by the Fr\"ohlich
interaction, in the presence of a time-dependent external field which acts on
the phonon subsystem. The latter is supposed to model the THz light-induced
excitation of nonlinear interactions between infrared and Raman phonons.
Assuming that the external field has a slow dependence on time, we derive
equations for the dynamical superconducting gap, calculating the leading
adiabatic term and the first non-adiabatic correction. Our nonequilibrium
formulas can be solved numerically with a minimal increase of computational
complexity with respect to that needed to calculate the superconducting gap at
equilibrium.


  We carried out two dimensional high-resolution magnetohydrodynamic (MHD)
simulations of an accretion disk around a weakly magnetized neutron star.
General relativistic effects are taken into account by using pseudo-Newtonian
potential of Pacz\'{y}nski, B., P. J. Witta (1980). When magnetic loops connect
the neutron star and the accretion disk, the twist injection from the disk or
from the rotating neutron star triggers expansion of the loops. Since the
expanding magnetic loops prevent inflow toward the magnetic poles of the
neutron star, disk matter accumulates on the boundary between the magnetosphere
and the disk. Magnetic reconnection taking place in the loops creates a channel
along which the disk matter can accrete and unloads the magnetosphere. This
process produces quasi-periodic variation of the accretion flow in the
innermost region of the disk. We found two kinds of oscillations. One is the
magnetospheric oscillation regulated by magnetic reconnection. The other is the
radial disk oscillation. The typical frequency of the oscillations is 100 Hz to
2 kHz. Furthermore, we predict that QPO sources inevitably accompany X-ray
flares by magnetic reconnection and bipolar outflows of hot X-ray emitting
plasma similar to the optical jets in protostars.


  In quantum information theory, there is an explicit mapping between general
unitary dynamics and Hermitian ground state eigenvalue problems known as the
Feynman-Kitaev Clock. A prominent family of methods for the study of quantum
ground states are quantum Monte Carlo methods, and recently the full
configuration interaction quantum Monte Carlo (FCIQMC) method has demonstrated
great promise for practical systems. We combine the Feynman-Kitaev Clock with
FCIQMC to formulate a new technique for the study of quantum dynamics problems.
Numerical examples using quantum circuits are provided as well as a technique
to further mitigate the sign problem through time-dependent basis rotations.
Moreover, this method allows one to combine the parallelism of Monte Carlo
techniques with the locality of time to yield an effective parallel-in-time
simulation technique.


  This papers focuses on symbol spotting on real-world digital architectural
floor plans with a deep learning (DL)-based framework. Traditional on-the-fly
symbol spotting methods are unable to address the semantic challenge of
graphical notation variability, i.e. low intra-class symbol similarity, an
issue that is particularly important in architectural floor plan analysis. The
presence of occlusion and clutter, characteristic of real-world plans, along
with a varying graphical symbol complexity from almost trivial to highly
complex, also pose challenges to existing spotting methods. In this paper, we
address all of the above issues by leveraging recent advances in DL and
adapting an object detection framework based on the You-Only-Look-Once (YOLO)
architecture. We propose a training strategy based on tiles, avoiding many
issues particular to DL-based object detection networks related to the relative
small size of symbols compared to entire floor plans, aspect ratios, and data
augmentation. Experiments on real-world floor plans demonstrate that our method
successfully detects architectural symbols with low intra-class similarity and
of variable graphical complexity, even in the presence of heavy occlusion and
clutter. Additional experiments on the public SESYD dataset confirm that our
proposed approach can deal with various degradation and noise levels and
outperforms other symbol spotting methods.


  Class of spherically symmetric Stephani cosmological models is examined in
the context of evolution type. It is assumed that the equation of state at the
symmetry center of the models is barotropic. Classification of cosmological
models is performed depending on different values and signs of two free
parameters. It is shown that for (hyperbolic geometry) dust-like cosmological
model exhibits accelerated expansion at later stages of evolution. The Hubble
and deceleration parameters are defined in the model and it is shown that the
deceleration parameter decreases with the distance becoming negative for
sufficiently distant galaxies. Redshift-magnitude relation is calculated and
discussed in the context of SnIa observational data. It is noticed that the
most distant supernovae of type Ia fit quite well to the redshift-magnitude
relation calculated in the considered model without introducing the
cosmological constant. It is also shown that the age of the universe in the
model is longer than in the Friedmann model corresponding to the same Hubble
and energy density parameters.


  Consider a pair of plane straight-line graphs, whose edges are colored red
and blue, respectively, and let n be the total complexity of both graphs. We
present a O(n log n)-time O(n)-space technique to preprocess such pair of
graphs, that enables efficient searches among the red-blue intersections along
edges of one of the graphs. Our technique has a number of applications to
geometric problems. This includes: (1) a solution to the batched red-blue
search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an
algorithm to compute the maximum vertical distance between a pair of 3D
polyhedral terrains one of which is convex in O(n log n) time, where n is the
total complexity of both terrains; (3) an algorithm to construct the Hausdorff
Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n)
time and O(n+m) space, where n is the total number of points in all clusters
and m is the number of crossings between all clusters; (4) an algorithm to
construct the farthest-color Voronoi diagram of the corners of n axis-aligned
rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle
problem for n parallel line segments in the plane in optimal O(n log n) time.
All these results are new or improve on the best known algorithms.


  Sensitive dependence of nonlinear systems on initial conditions or parameters
can be useful in applications. We propose in this paper that bubbling behavior
in simple driven symmetrical maps may be used as a working principle of
sensitive devices. The system is stable when there is no input and displays
bursting behavior when there is small input. The symmetrical property of the
bursting pattern is very sensitive to the bias of the noisy inputs, which makes
the system promising for detecting weak signals among noisy environment.


  A Quadratic Diffusion Monte Carlo method has been used to obtain the equation
of state of liquid $^4$He including the negative pressure region down to the
spinodal point. The atomic interaction used is a renewed version (HFD-B(HE)) of
the Aziz potential, which reproduces quite accurately the features of the
experimental equation of state. The spinodal pressure has been calculated and
the behavior of the sound velociy around the spinodal density has been
analyzed.


  Inelastic neutron scattering is used to study transverse-polarized magnetic
excitations in the quasi-one-dimensional S=1/2 antiferromagnet BaCu_2Si_2O_7,
where the saturation value for the N\'eel order parameter is $m_0=0.12 \mu_{\rm
B}$ per spin. At low energies the spectrum is totally dominated by
resolution-limited spin wave-like excitations. An excitation continuum sets in
above a well-defined threshold frequency. Experimental results are discussed in
the context of current theories for weakly-interacting quantum half-integer
spin chains.


  We address the effects of disorder on the conducting properties of metal and
semiconducting carbon nanotubes. Experimentally, the mean free path is found to
be much larger in metallic tubes than in doped semiconducting tubes. We show
that this result can be understood theoretically if the disorder potential is
long-ranged. The effects of a pseudospin index that describes the internal
sublattice structure of the states lead to a suppression of scattering in
metallic tubes, but not in semiconducting tubes. This conclusion is supported
by tight-binding calculations.


  We give a global version of Le-Ramanujam mu-constant theorem for polynomials.
Let f_t, (t in [0,1]), be a family of polynomials of n complex variables with
isolated singularities, whose coefficients are polynomials in t. We consider
the case where some numerical invariants are constant (the affine Milnor
number, the Milnor number at infinity, the number of critical values, the
number of affine critical values, the number of critical values at infinity).
Let n=2, we also suppose the degree of the f_t is a constant, then the
polynomials f_0 and f_1 are topologically equivalent. For n>3 we suppose that
critical values at infinity depend continuously on t, then we prove that the
geometric monodromy representations of the f_t, are all equivalent.


  The HI disk of the blue compact dwarf (BCD) galaxy NGC 2915 extends to
  22 optical scalelengths and shows spiral arms reaching far beyond the optical
component. None of the previous theories for spiral structure provide likely
explanations for these very extended spiral arms. Our numerical simulations
first demonstrate that such large spiral arms can form in an extended gas disk
embedded in a massive triaxial dark matter halo with slow figure rotation,
through the strong gravitational torque of the rotating halo. We then show that
the detailed morphological properties of the developed spirals and rings depend
strongly on the pattern speed of the figure rotation, the shape of the triaxial
halo, and the inclination of the disk with respect to the plane including the
triaxial halo's long and middle axes. These results strongly suggest that the
dark matter halo of NGC 2915 is triaxial and has figure rotation. Based on
these results, we also suggest that dynamical effects of triaxial halos with
figure rotation are important in various aspect of galaxy formation and
evolution, such as formation of polar ring galaxies, excitation of
non-axisymmetric structures in low surface-brightness galaxies, and gas fueling
to the central starburst regions of BCDs.


  We compute flavor non-singlet meson screening masses in the chiral limit of
QCD with $N_f=3$ quarks. The calculation is carried out at 12 temperatures from
$T\approx 1$ GeV up to the electroweak scale. For each temperature we simulated
several lattice spacings, so as to be able to perform the continuum limit
extrapolation with confidence at a few permille accuracy. In the entire range
of temperatures explored, the meson screening masses deviate from the free
theory result $2\pi T$ by at most a few percent. Their values, however, cannot
be explained by one-loop perturbation theory up to the electroweak scale, where
the pseudoscalar and the vector screening masses are still significantly
different within our precision. Chiral symmetry restoration manifests itself
through the degeneracy of the pseudoscalar and the scalar channels and of the
vector and the axial ones.


  Coronal mass ejections (CMEs) can create significant disruption to human
activities and systems on Earth, much of which can be mitigated with prior
warning of the upstream solar wind conditions. However, it is currently
extremely challenging to accurately predict the arrival time and internal
structure of a CME from coronagraph images alone. In this study, we take
advantage of a rare opportunity to use Solar Orbiter, at 0.5\,AU upstream of
Earth, as an upstream solar wind monitor. We were able to use real time science
quality magnetic field measurements, taken only 12 minutes earlier, to predict
the arrival time of a CME prior to reaching Earth. We used measurements at
Solar Orbiter to constrain an ensemble of simulation runs from the ELEvoHI
model, reducing the uncertainty in arrival time from 10.4\,hours to 2.5\,hours.
There was also an excellent agreement in the $B_z$ profile between Solar
Orbiter and Wind spacecraft, despite being separated by 0.5\,AU and
10$^{\circ}$ longitude. Therefore, we show that it is possible to predict not
only the arrival time of a CME, but the sub-structure of the magnetic field
within it, over a day in advance. The opportunity to use Solar Orbiter as an
upstream solar wind monitor will repeat once a year, which should further help
assess the efficacy upstream in-situ measurements in real time space weather
forecasting.


  The correlation function measured in ultrarelativistic nuclear collisions is
non-Gaussian. By making use of models we discuss and assess how much various
effects can influence its shape. In particular, we focus on the
parametrisations expressed with the help of L\'evy-stable distributions. We
show that the L\'evy index may deviate substantially from 2 due to non-critical
effects such as non-spherical shape, resonance decays, event-by-event
fluctuations and functional dependence on $Q_{inv}$ or similar.


  A stable 50 mJ three-channel optical waveform synthesizer is demonstrated and
used to reproducibly generate a high-order harmonics supercontinuum in the
soft-x-ray region. This synthesizer is composed of pump pulses from a
10-Hz-repetition-rate Ti:sapphire pump laser and signal and idler pulses from
an infrared two-stage optical parametric amplifier driven by this pump laser.
With the full active stabilization of all relative time delays, relative
phases, and the carrier-envelope phase, a shot-to-shot stable intense continuum
harmonic spectrum is obtained around 60 eV with pulse energy above 0.24 $\mu$J.
The peak power of the soft-x-ray continuum is evaluated to be beyond 1 GW with
a 140 as transform limit duration. Furthermore, we found a characteristic delay
dependence of the multi-cycle waveform synthesizer and established its control
scheme. Compared with the one-color case, we experimentally observe an
enhancement of the cut-off spectrum intensity by one to two orders of magnitude
through the three-color waveform synthesis.


  We describe a procedure to identify and remove a class of interference lines
from gravitational wave interferometer data. We illustrate the usefulness of
this technique applying it to prototype interferometer data and removing all
those lines corresponding to the external electricity main supply and related
features.


  In the era of big data astronomy, next generation telescopes and large sky
surveys produce data sets at the TB or even PB level. Due to their large data
volumes, these astronomical data sets are extremely difficult to transfer and
analyze using personal computers or small clusters. In order to offer better
access to data, data centers now generally provide online science platforms
that enable analysis close to the data. The Chinese Virtual Observatory
(China-VO) is one of the member projects in the International Virtual
Observatory Alliance and it is dedicated to providing a research and education
environment where globally distributed astronomy archives are simple to find,
access, and interoperate. In this study, we summarize highlights of the work
conducted at the China-VO, as well the experiences and lessons learned during
the full life-cycle management of astronomical data. Finally, We discuss the
challenges and future trends for astronomical science platforms.


  A time-dependent Hermite-Galerkin spectral method (THGSM) is investigated in
this paper for the nonlinear convection-diffusion equations in the unbounded
domains. The time-dependent scaling factor and translating factor are
introduced in the definition of the generalized Hermite functions (GHF). As a
consequence, the THGSM based on these GHF has many advantages, not only in
theorethical proofs, but also in numerical implementations. The stability and
spectral convergence of our proposed method have been established in this
paper. The Korteweg-de Vries-Burgers (KdVB) equation and its special cases,
including the heat equation and the Burgers' equation, as the examples, have
been numerically solved by our method. The numerical results are presented, and
it surpasses the existing methods in accuracy. Our theoretical proof of the
spectral convergence has been supported by the numerical results.


  The status of dark matter search with the HDMS experiment is reviewed. After
one year of running the HDMS prototype detector in the Gran Sasso Underground
Laboratory, the inner crystal of the detector has been replaced with a HPGe
crystal of enriched 73Ge. The results of the operation of the HDMS prototype
detector are discussed.


  Corresponding to optical Fresnel transformation characteristic of ray
transfer matrix elements (A;B;C;D); AD-BC = 1, there exists Fresnel operator
F(A;B;C;D) in quantum optics, we show that under the Fresnel transformation the
pure position density |x><x| becomes the tomographic density |x>_rs,rs_<x|,
which is just the Radon transform of the Wigner operator, i.e.,
  F|x><x|F^(+) = |x>_rs,rs_<x|= \int dx'dp'delta[x-(Dx'-Bp')]*Wigner operator
where s, r are the complex-value expression of (A;B;C;D). So the probability
distribution for the Fresnel quadrature phase is the tomography (Radon
transform of Wigner function), and the tomogram of a state |phi> is just the
wave function of its Fresnel transformed state F|phi>, i.e. rs_<x||phi>=
<x|F^(+)|phi>. Similarly, we find F|p><p|F^(+) = |p>_rs,rs_<p|= \int
dx'dp'delta[x-(Ap'-Cx')]*Wigner operator.


  This paper presents a telepresence interaction framework based on touchscreen
and telepresence-robot technologies. The core of the framework is a new user
interface, Touchable live video Image based User Interface, called TIUI. The
TIUI allows a remote operator to not just drive the telepresence robot but
operate and interact with real objects by touching their live video images on a
pad with finger touch gestures. We implemented a telepresence interaction
system which is composed of a telepresence robot and tele-interactive objects
located in a local space, the TIUI of a pad located in a remote space, and the
wireless networks connecting the two spaces. Our system can be a perfect
embodiment of a remote operator to do most of daily living tasks, such as
opening a door, drawing a curtain, pushing a wheelchair, and other like tasks.
The evaluation and demonstration results show the effectiveness and promising
applications of our system.


  The spinning down (up) of a superfluid is associated with a radial motion of
its quantized vortices. In the presence of pinning barriers against the motion
of the vortices, a spin-down may be still realized through ``random unpinning''
and ``vortex motion,'' as two physically separate processes, as suggested
recently. The spin-down rate of a pinned superfluid is calculated, in this
framework, by directly solving the equation of motion applicable to only the
unpinned moving vortices, at any given time. The results indicate that the
pinned superfluid in the crust of a neutron star may as well spin down at the
same steady-state rate as the rest of the star, through random unpinning
events, while pinning conditions prevail and the superfluid rotational lag is
smaller than the critical lag value.


  We study SU(5) Grand Unified Theories within a local framework in F-theory
with multiple extra U(1) symmetries arising from a small monodromy group. The
use of hypercharge flux for doublet-triplet splitting implies massless exotics
in the spectrum that are protected from obtaining a mass by the U(1)
symmetries. We find that lifting the exotics by giving vacuum expectation
values to some GUT singlets spontaneously breaks all the U(1) symmetries which
implies that proton decay operators are induced. If we impose an additional
R-parity symmetry by hand we find all the exotics can be lifted while proton
decay operators are still forbidden. These models can retain the gauge coupling
unification accuracy of the MSSM at 1-loop. For models where the generations
are distributed across multiple curves we also present a motivation for the
quark-lepton mass splittings at the GUT scale based on a Froggatt-Nielsen
approach to flavour.


  Structural features of ferroic domains are fundamentally important for the
understanding of microstructure and physical properties of ferroic materials,
and they are also of technological significance for information storage and
electronic switches. There are three well-known forms of ferroic orders in
correlation with the spontaneous magnetization, electric polarization, and
spontaneous strain, respectively. Ferroelectricity often accompanies
ferroelastic strain, so ferroelectric domains tend to form in simple elongated
configurations. However, due to strong boundary conditions, ferroelectric
domains in nano-materials or near defects can have unusual configurations. In
addition, vortex domains where multiple ferroelectric domains merge at one
point have been observed in YMnO3. Herein, we report the discovery of unique
ferroelectric domains with a concentric cylindrical shape in hexagonal
manganites. We have also observed remarkable domains with multiple annular
patterns. Our findings could open a new avenue for comprehensive studies of the
ferroic domains, and this novel pattern could also leads to possibilities for
new technologic applications.


  We determine the shear viscosity and the dc electrical conductivity of
interacting three-dimensional Luttinger semimetals, which have a quadratic band
touching point in the energy spectrum, in the hydrodynamic regime. It is
well-known that when the chemical potential is right at the band touching point
the long-range Coulomb interaction induces the Luttinger-Abrikosov-Beneslavskii
(LAB) phase at T = 0, which is an interacting, scale-invariant,
non-Fermi-liquid state of electrons. Upon combining the renormalization-group
(RG) analysis near the upper critical spatial dimension of four with the
Boltzmann kinetic equation, we determine the universal ratio of viscosity over
entropy, and the electrical dc conductivity of the system at the interacting
LAB fixed point of the RG flow. The projection of the Coulomb interaction on
the eigenstates of the system is found to play an important quantitative role
for the scattering amplitude in the collision integral, and the so-called
Auger-processes make a large numerical contribution to the inverse scattering
time in the transport quantities. The obtained leading order result suggests
that the universal ratio of the viscosity over entropy, when extrapolated to
the physical three spatial dimensions, is above, but could be rather close to,
the Kovtun-Son-Starinets lower bound.


  A reinsurance contract should address the conflicting interests of the
insurer and reinsurer. Most of existing optimal reinsurance contracts only
considers the interests of one party. This article combines the proportional
and stop-loss reinsurance contracts and introduces a new reinsurance contract
called proportional-stop-loss reinsurance. Using the balanced loss function,
unknown parameters of the proportional-stop-loss reinsurance have been
estimated such that the expected surplus for both the insurer and reinsurer are
maximized. Several characteristics for the new reinsurance are provided.


  Software configuration tuning is essential for optimizing a given performance
objective (e.g., minimizing latency). Yet, due to the software's intrinsically
complex configuration landscape and expensive measurement, there has been a
rather mild success, particularly in preventing the search from being trapped
in local optima. To address this issue, in this paper we take a different
perspective. Instead of focusing on improving the optimizer, we work on the
level of optimization model and propose a meta multi-objectivization (MMO)
model that considers an auxiliary performance objective (e.g., throughput in
addition to latency). What makes this model distinct is that we do not optimize
the auxiliary performance objective, but rather use it to make
similarly-performing while different configurations less comparable (i.e.
Pareto nondominated to each other), thus preventing the search from being
trapped in local optima. Importantly, by designing a new normalization method,
we show how to effectively use the MMO model without worrying about its weight
-- the only yet highly sensitive parameter that can affect its effectiveness.
Experiments on 22 cases from 11 real-world software systems/environments
confirm that our MMO model with the new normalization performs better than its
state-of-the-art single-objective counterparts on 82% cases while achieving up
to 2.09x speedup. For 68% of the cases, the new normalization also enables the
MMO model to outperform the instance when using it with the normalization from
our prior FSE work under pre-tuned best weights, saving a great amount of
resources which would be otherwise necessary to find a good weight. We also
demonstrate that the MMO model with the new normalization can consolidate
recent model-based tuning tools on 68% of the cases with up to 1.22x speedup in
general.


  This paper aims at generalizing some geometric properties of Grassmannians of
finite dimensional vector spaces to the case of Grassmannnians of infinite
dimensional ones, in particular for that of $k((z))$. It is shown that the
Determinant Line Bundle generates its Picard Group and that the Pl\"ucker
equations define it as closed subscheme of a infinite projective space.
Finally, a characterization of finite dimensional projective spaces in
Grassmannians allows us to offer an approach to the study of the automorphism
group.


  In this short paper, we will review the proposal of a correspondence between
the doubled geometry of Double Field Theory and the higher geometry of bundle
gerbes. Double Field Theory is T-duality covariant formulation of the
supergravity limit of String Theory, which generalises Kaluza-Klein theory by
unifying metric and Kalb-Ramond field on a doubled-dimensional space. In light
of the proposed correspondence, this doubled geometry is interpreted as an
atlas description of the higher geometry of bundle gerbes. In this sense,
Double Field Theory can be interpreted as a field theory living on the total
space of the bundle gerbe, just like Kaluza-Klein theory is set on the total
space of a principal bundle. This correspondence provides a higher geometric
interpretation for para-Hermitian geometry which opens the door to its
generalisation to the other Extended Field Theories. This review is based on,
but not limited to, my talk at the workshop Generalized Geometry and
Applications at Universit\"at Hamburg on 3rd of March 2020.


  This paper discusses an electron energy analyzer with a cylindrically
symmetrical electrostatic field, designed for rapid Auger analysis. The device
was designed and built. The best parameters of the analyzer were estimated and
then experimentally verified.


  After an introduction to the QCD phase diagram, the nuclear equations of
state, and neutron star mergers, I discuss three projects related to transport
and nuclear matter in neutron star mergers. The first is the nature of beta
equilibrium in the portion of a merger that is transparent to neutrinos. We
calculate the weak interaction (Urca) rates and find that the beta equilibrium
condition needs to be modified by adding an additional chemical potential,
which changes slightly the particle content in neutrino-transparent beta
equilibrium. Secondly, we calculate the bulk viscosity in neutrino-transparent
nuclear matter in conditions encountered in neutron star mergers. Bulk
viscosity arises from a phase lag between the pressure and density in the
nuclear matter, which is due to the finite rate of beta equilibration. When
bulk viscosity is sufficiently strong, which happens when the equilibration
rate nearly matches the frequency of the density oscillation, it can noticeably
dampen the oscillation. We find that in certain thermodynamic conditions likely
encountered in mergers, oscillations in nuclear matter can be damped on
timescales on the order of 10 milliseconds, so we conclude that bulk viscosity
should be included in merger simulations. Finally, we study thermal transport
due to axions in neutron star mergers. We conclude that axions are never
trapped in mergers, but instead escape, carrying energy away from the merger.
We calculate the cooling time due to the energy carried away by axions and find
that within current constraints on the axion-nucleon coupling, axions could
cool fluid elements in mergers on timescales which could affect the dynamics of
the merger.


  Multi-hop random access networks have received much attention due to their
distributed nature which facilitates deploying many new applications over the
sensor and computer networks. Recently, utility maximization framework is
applied in order to optimize performance of such networks, however proposed
algorithms result in large transmission delays. In this paper, we will analyze
delay in random access multi-hop networks and solve the delay-constrained
utility maximization problem. We define the network utility as a combination of
rate utility and energy cost functions and solve the following two problems:
'optimal medium access control with link delay constraint' and, 'optimal
congestion and contention control with end-to-end delay constraint'. The
optimal tradeoff between delay, rate, and energy is achieved for different
values of delay constraint and the scaling factors between rate and energy.
Eventually linear and super-linear distributed optimization solutions are
proposed for each problem and their performance are compared in terms of
convergence and complexity.


  We obtain charged rotating black hole solutions to the theory of
Einstein-Maxwell gravity with cosmological constant in five dimensions. Some of
the physical properties of these black holes are discussed.


  Generalized autoregressive moving average (GARMA) models are a class of
models that was developed for extending the univariate Gaussian ARMA time
series model to a flexible observation-driven model for non-Gaussian time
series data. This work presents Bayesian approach for GARMA models with
Poisson, binomial and negative binomial distributions. A simulation study was
carried out to investigate the performance of Bayesian estimation and Bayesian
model selection criteria. Also three real datasets were analysed using the
Bayesian approach on GARMA models.


  With the growth of wireless networks, security has become a fundamental issue
in wireless communications due to the broadcast nature of these networks. In
this work, we consider MIMO wiretap channels in a fast fading environment, for
which the overall performance is characterized by the ergodic MIMO secrecy
rate. Unfortunately, the direct solution to finding ergodic secrecy rates is
prohibitive due to the expectations in the rates expressions in this setting.
To overcome this difficulty, we invoke the large-system assumption, which
allows a deterministic approximation to the ergodic mutual information.
Leveraging results from random matrix theory, we are able to characterize the
achievable ergodic secrecy rates. Based on this characterization, we address
the problem of covariance optimization at the transmitter. Our numerical
results demonstrate a good match between the large-system approximation and the
actual simulated secrecy rates, as well as some interesting features of the
precoder optimization.


  Shell models are simplified models of hydrodynamic turbulence, retaining only
some essential features of the original equations, such as the non-linearity,
symmetries and quadratic invariants. Yet, they were shown to reproduce the most
salient properties of developed turbulence, in particular universal statistics
and multi-scaling. We set up the functional renormalisation group (RG)
formalism to study generic shell models. In particular, we formulate an inverse
RG flow, which consists in integrating out fluctuation modes from the large
scales (small wavenumbers) to the small scales (large wavenumbers), which is
physically grounded and has long been advocated in the context of turbulence.
Focusing on the Sabra shell model, we study the effect of both a large-scale
forcing, and a power-law forcing exerted at all scales. We show that these two
types of forcing yield different fixed points, and thus correspond to distinct
universality classes, characterised by different scaling exponents. We find
that the power-law forcing leads to dimensional (K41-like) scaling, while the
large-scale forcing entails anomalous scaling.


  We investigate the effects of a novel approach to diamond nanofabrication and
nitrogen vacancy (NV) center formation on the optical linewidth of the NV
zero-phonon line (ZPL). In this post-implantation method, nitrogen is implanted
after all fabrication processes have been completed. We examine three
post-implanted samples, one implanted with $^{14}$N and two with $^{15}$N
isotopes. We perform photoluminescence excitation (PLE) spectroscopy to assess
optical linewidths and optically detected magnetic resonance (ODMR)
measurements to isotopically classify the NV centers. From this, we find that
NV centers formed from nitrogen naturally occuring in the diamond lattice are
characterized by a linewidth distribution peaked at an optical linewidth nearly
two orders of magnitude smaller than the distribution characterizing most of
the NV centers formed from implanted nitrogen. Surprisingly, we also observe a
number of $^{15}$NV centers with narrow ($<500\,\mathrm{MHz}$) linewidths,
implying that implanted nitrogen can yield NV centers with narrow optical
linewidths. We further use a Bayesian approach to statistically model the
linewidth distributions, to accurately quantify the uncertainty of fit
parameters in our model, and to predict future linewidths within a particular
sample. Our model is designed to aid comparisons between samples and research
groups, in order to determine the best methods of achieving narrow NV
linewidths in structured samples.


  Owing to their high magnon frequencies, antiferromagnets are key materials
for future high-speed spintronics. Picosecond switching of antiferromagnetic
order has been viewed a milestone for decades and pursued only by using
ultrafast external perturbations. Here, we show that picosecond spin switching
occurs spontaneously due to thermal fluctuations in the antiferromagnetic
orthoferrite Sm0.7Er0.3FeO3. By analysing the correlation between the
pulse-to-pulse polarisation fluctuations of two femtosecond optical probes, we
extract the autocorrelation of incoherent magnon fluctuations. We observe a
strong enhancement of the magnon fluctuation amplitude and the coherence time
around the critical temperature of the spin reorientation transition. The
spectrum shows two distinct modes, one corresponding to the quasi-ferromagnetic
mode and another one which has not been previously reported in pump-probe
experiments. Comparison to a stochastic spin dynamics simulation reveals this
new mode as smoking gun of ultrafast spontaneous spin switching within the
double-well anisotropy potential.


  We propose a simple method to construct staples in lattice gauge theory with
Wilson action on a parallel computer. This method can be applicable to any
dimensional system and to any dimensional division without difficulty.
Furthermore this requires rather small working area to realize gauge simulation
on a parallel computer.


  Various kinds of heterogeneity in solids including atomistic discreteness
affect the fracture strength as well as the failure dynamics remarkably. Here
we study the effects of an initial crack in a discrete model for fracture in
heterogeneous materials, known as the fiber bundle model. We find three
distinct regimes for fracture dynamics depending on the initial crack size. If
the initial crack is smaller than a certain value, it does not affect the
rupture dynamics and the critical stress. While for a larger initial crack, the
growth of the crack leads to a breakdown of the entire system, and the critical
stress depends on the crack size in a power-law manner with a nontrivial
exponent. The exponent, as well as the limiting crack size, depend on the
strength of heterogeneity and the range of stress relaxation in the system.


  Photon coincidence spectroscopy is a promising technique for probing the
nonlinear regime of cavity quantum electrodynamics in the optical domain,
however its accuracy is mitigated by two factors: higher-order photon
correlations, which contribute to an enhanced pair count rate, and
non-simultaneity of emitted photon pairs from the optical cavity. We show that
the technique of photon coincidence spectroscopy is effective in the presence
of these effects if the quantitative predictions are adjusted to include
non-simultaneity and higher-order correlations.


  This paper investigates the classification of the Audio Set dataset. Audio
Set is a large scale weakly labelled dataset of sound clips. Previous work used
multiple instance learning (MIL) to classify weakly labelled data. In MIL, a
bag consists of several instances, and a bag is labelled positive if at least
one instances in the audio clip is positive. A bag is labelled negative if all
the instances in the bag are negative. We propose an attention model to tackle
the MIL problem and explain this attention model from a novel probabilistic
perspective. We define a probability space on each bag, where each instance in
the bag has a trainable probability measure for each class. Then the
classification of a bag is the expectation of the classification output of the
instances in the bag with respect to the learned probability measure.
Experimental results show that our proposed attention model modeled by fully
connected deep neural network obtains mAP of 0.327 on Audio Set dataset,
outperforming the Google's baseline of 0.314 and recurrent neural network of
0.325.


  A fundamental aspect of behaviour is the ability to encode salient features
of experience in memory and use these memories, in combination with current
sensory information, to predict the best action for each situation such that
long-term objectives are maximized. The world is highly dynamic, and
behavioural agents must generalize across a variety of environments and
objectives over time. This scenario can be modeled as a partially-observable
multi-task reinforcement learning problem. We use genetic programming to evolve
highly-generalized agents capable of operating in six unique environments from
the control literature, including OpenAI's entire Classic Control suite. This
requires the agent to support discrete and continuous actions simultaneously.
No task-identification sensor inputs are provided, thus agents must identify
tasks from the dynamics of state variables alone and define control policies
for each task. We show that emergent hierarchical structure in the evolving
programs leads to multi-task agents that succeed by performing a temporal
decomposition and encoding of the problem environments in memory. The resulting
agents are competitive with task-specific agents in all six environments.
Furthermore, the hierarchical structure of programs allows for dynamic run-time
complexity, which results in relatively efficient operation.


  We describe the offline computing system of the Belle experiment, consisting
of a computing farm with one thousand IA-32 CPUs. Up to now, the Belle
experiment has accumulated more than 120 fb$^{-1}$ of data, which is the world
largest $B\bar{B}$ sample at the $\Upsilon(4S)$ energy. The data have to be
processed with a single version of reconstruction software and calibration
constants to perform precise measurements of $B$ meson decays. In addition,
Monte Carlo samples three times larger than the real beam data are generated.
To fullfill our computing needs, we have constructed the computing system with
90(300) quad(dual) CPU PC servers from multiple vendors as a central processing
system. The details of this computing system and performance of data processing
with the current model are presented.


  We study one dimensional clean systems with few channels and strong
electron-electron interactions. We find that in several circumstances, even
when time reversal symmetry holds, they may lead to two terminal fractional
quantized conductance and fractional shot noise. The condition on the
commensurability of the Fermi momenta of the different channels and the
strength of interactions resulting in such remarkable phenomena are explored
using abelian bosonization. Finite temperature and length effects are accounted
for by a generalization of the Luther-Emery re-fermionization at specific
values of the interaction strength. We discuss the connection of our model to
recent experiments in confined 2DEG, featuring possible fractional conductance
plateaus. One of the most dominant observed fractions, with two terminal
conductance equals to $\frac{2}{5}\frac{e^{2}}{h}$, is found in several
scenarios of our model. Finally, we discuss how at very small energy scales the
conductance returns to an integer value and the role of disorder.


  Ab initio investigations of structural, electronic, and dynamical properties
of the high-temperature $\beta$ phase of copper pyrophosphate were performed
using density functional theory. The electronic band structure shows the Mott
insulating state due to electron correlations in copper ions. By calculating
phonon dispersion relations, the soft mode at the A point of the Brillouin zone
was revealed, showing the dynamical instability of the $\beta$ phase at low
temperatures. The double-well potential connected with the soft mode is derived
and the mechanism of the structural phase transition to the $\alpha$ phase is
discussed. The self-consistent phonon calculations based on the
temperature-dependent effective potential show the stabilization of the $\beta$
phase at high temperatures, due to the anharmonic effects. The pronounced
temperature dependence and the large line width of the soft mode indicate an
essential role of anharmonicity in the structural phase transition.


  Absorption-line spectroscopy of 23 background QSOs and numerous background
galaxies has let us measure the spatial distribution of metals and neutral
hydrogen around 1044 UV-selected galaxies at redshifts 1.8<z<3.3. The typical
galaxy is surrounded to radii r~40 proper kpc by gas that has a large velocity
spread (dv>260 km/s) and produces very strong absorption lines (N_CIV >> 10^14
cm^-2) in the spectra of background objects. Absorption with an average column
density of N_CIV ~ 10^14 cm^-2 extends to 80 kpc, a radius large enough to
imply that most strong intergalactic CIV absorption is associated with
star-forming galaxies like those in our sample. We find that the galaxy-CIV
cross-correlation length increases with CIV column density and is similar to
the galaxy-galaxy length (r_0 ~ 4 h^-1 Mpc) for N_CIV > 10^12.5 cm^-2.
Distortions in the redshift-space galaxy-CIV correlation function on small
scales may imply that some of the CIV systems have large peculiar velocities.
Four of the five detected OVI absorption systems in our sample lie within 400
proper kpc of a known galaxy. Strong Lyman-a absorption is produced by the
intergalactic gas within 1 h^-1 comoving Mpc of most galaxies, but for a
significant minority (~1/3) the absorption is weak or absent. We were unable to
identify any statistically significant differences between galaxies with weak
nearby HI absorption and the rest, although galaxies with weak absorption may
have higher star-formation rates. Galaxies near intergalactic CIV systems
appear to reside in relatively dense environments and to have distinctive
spectral energy distributions that are characterized by blue colors and young
ages. (abridged)


  Age related macular degeneration (AMD) is one of the major causes for
blindness in the elderly population. In this report, we propose deep learning
based methods for retinal analysis using color fundus images for computer aided
diagnosis of AMD. We leverage the recent state of the art deep networks for
building a single fundus image based AMD classification pipeline. We also
propose methods for the other directly relevant and auxiliary tasks such as
lesions detection and segmentation, fovea detection and optic disc
segmentation. We propose the use of generative adversarial networks (GANs) for
the tasks of segmentation and detection. We also propose a novel method of
fovea detection using GANs.


  In this paper we present a re-analysis of the criteria used to characterize
the Peimbert classes I, IIa, IIb, III and IV, through a statistical study of a
large sample of planetary nebulae previously classified according to these
groups. In the original classification, it is usual to find planetary nebulae
that cannot be associated with a single type; these most likely have dubious
classifications into two or three types. Statistical methods can greatly
contribute in providing a better characterization of planetary nebulae groups.
We use the Bayes Theorem to calculate the posterior probabilities for an object
to be member of each of the types I, IIa, IIb, III and IV. This calculation is
particularly important for planetary nebulae that are ambiguously classified in
the traditional method. The posterior probabilities are defined from the
probability density function of classificatory parameters of a well-defined
sample, composed only by planetary nebulae unambiguously fitted into the
Peimbert types. Because the probabilities depend on the available observational
data, they are conditional probabilities, and, as new observational data are
added to the sample, the classification of the nebula can be improved, to take
into account this new information. This method differs from the original
classificatory scheme, because it provides a quantitative result of the
representativity of the object within its group. Also, through the use of
marginal distributions it is possible to extend the Peimbert classification
even to those objects for which only a few classificatory parameters are known.


  We consider ideals in a polynomial ring that are generated by regular
sequences of homogeneous polynomials and are stable under the action of the
symmetric group permuting the variables. In previous work, we determined the
possible isomorphism types for these ideals. Following up on that work, we now
analyze the possible degrees of the elements in such regular sequences. For
each case of our classification, we provide some criteria guaranteeing the
existence of regular sequences in certain degrees.


  In this paper we study the dynamics in the general case for a Tavis Cummings
atom in a non-uniform cavity. In addition to the dynamical Stark shift, the
center-of-mass motion of the atom and the recoil effect are considered in both
- the weak and the strong cavity atom coupling regimes. It is shown that the
spatial motion of the atom inside the cavity in the resonant case leads to a
transition between topologically different solutions. This effect is manifested
by a singularity in the inter-level transition spectrum. In the non-resonant
case, the spatial motion of the atom leads to a switching of the spin
orientation. In both effects, the key factor is the relation between the values
of the Stark shift and the cavity field coupling constant. We also investigate
the entanglement of an atom in the cavity with the radiation field. It is shown
that the entanglement between the atom and the field, usually quantified in
terms of purity, decreases with increasing the Stark shift.


  Relativistic effects in the non-resonant two-photon K-shell ionization of
neutral atoms are studied theoretically within the framework of second-order
perturbation theory. The non-relativistic results are compared with the
relativistic calculations in the dipole and no-pair approximations as well as
with the complete relativistic approach. The calculations are performed in both
velocity and length gauges. Our results show a significant decrease of the
total cross section for heavy atoms as compared to the non-relativistic
treatment, which is mainly due to the relativistic wavefunction contraction.
The effects of higher multipoles and negative continuum energy states
counteract the relativistic contraction contribution, but are generally much
weaker. While the effects beyond the dipole approximation are equally important
in both gauges, the inclusion of negative continuum energy states visibly
contributes to the total cross section only in the velocity gauge.


  We construct fixed point lattice models for group supercohomology symmetry
protected topological (SPT) phases of fermions in 2+1D. A key feature of our
approach is to construct finite depth circuits of local unitaries that
explicitly build the ground states from a tensor product state. We then recover
the classification of fermionic SPT phases, including the group structure under
stacking, from the algebraic composition rules of these circuits. Furthermore,
we show that the circuits are symmetric, implying that the group
supercohomology phases can be many body localized. Our strategy involves first
building an auxiliary bosonic model, and then fermionizing it using the duality
of Chen, Kapustin, and Radicevic. One benefit of this approach is that it
clearly disentangles the role of the algebraic group supercohomology data,
which is used to build the auxiliary bosonic model, from that of the spin
structure, which is combinatorially encoded in the lattice and enters only in
the fermionization step. In particular this allows us to study our models on 2d
spatial manifolds of any topology and to define a lattice-level procedure for
ungauging fermion parity.


  Spatial separation of suspended particles based on contrast in their physical
or chemical properties forms the basis of various biological assays performed
on lab-on-achip devices. To electronically acquire this information, we have
recently introduced a microfluidic sensing platform, called Microfluidic CODES,
which combines the resistive pulse sensing with the code division multiple
access in multiplexing a network of integrated electrical sensors. In this
paper, we enhance the multiplexing capacity of the Microfluidic CODES by
employing sensors that generate non-orthogonal code waveforms and a new
decoding algorithm that combines machine learning techniques with minimum
mean-squared error estimation. As a proof of principle, we fabricated a
microfluidic device with a network of 10 code-multiplexed sensors and
characterized it using cells suspended in phosphate buffer saline solution.


  We study two criterions of cyclicity for divisor class groups of functions
fields, the first one involves Artin L-functions and the second one involves
"affine" class groups. We show that, in general, these two criterions are not
linked.


  We present an in-depth analysis of the spectral and temporal behavior of a
long almost uninterrupted INTEGRAL observation of Vela X-1 in Nov/Dec 2003. In
addition to an already high activity level, Vela X-1 exhibited several very
intense flares with a maximum intensity of more than 5 Crab in the 20-40 keV
band. Furthermore Vela X-1 exhibited several off states where the source became
undetectable with ISGRI. We interpret flares and off states as being due to the
strongly structured wind of the optical companion: when Vela X-1 encounters a
cavity in the wind with strongly reduced density, the flux drops, thus
potentially triggering the onset of the propeller effect which inhibits further
accretion, thus giving rise to the off states. The required drop in density to
trigger the propeller effect in Vela X-1 is of the same order as predicted by
theoretical papers for the densities in the OB star winds. The same structured
wind can give rise to the giant flares when Vela X-1 encounters a dense blob in
the wind. Further temporal analysis reveals that a short lived QPO with a
period of ~6800 sec is present. The part of the light curve during which the
QPO is present is very close to the off states and just following a high
intensity state, thus showing that all these phenomena are related.


  State of the art quantum computers have very limited applicability for
accurate calculations. Here we report the first experimental demonstration of
qubit-based matched filtering for a detection of the gravitational-wave signal
from a binary black hole merger. With our implementation on noisy
superconducting qubits, we obtained a similar signal-to-noise ratio for the
binary black hole merger as achievable with classical computation, providing
evidence for the utility of qubits for practically relevant tasks. The
algorithm we invented for this application is a Monte Carlo algorithm which
uses quantum and classical computation together. It provides a quasi-quadartic
speed-up for time-domain convolution, similar to achievable with fast Fourier
transform.


  The Schmidt law (SF law) in the Milky Way was investigated using 3D
distribution maps of HII regions, HI and molecular (\Htwo) gases with spatial
resolutions of $\sim 1$ kpc in the Galactic plane and a few tens of pc in the
vertical direction. HII regions were shown to be distributed in a star-forming
(SF) disk with nearly constant vertical full thickness 92 pc in spatial
coincidence with the molecular gas disk. The vertically averaged volume star
formation rate (SFR) $\rho_{\rm SFR}$ in the SF disk is related to the surface
SFR $\Sigma_{\rm SFR}$ by $\rho_{\rm SFR} /[{\rm M_\odot y^{-1} kpc^{-3}}]
=9.26\times \Sigma_{\rm SFR}/[{\rm M_\odot y^{-1} kpc^{-2}}]$. The SF law
fitted by a single power law of gas density in the form of $\Sigma_{\rm SFR}
\propto \rho_{\rm SFR} \propto \rho_{\rm gas}^\alpha$ and $\propto \Sigma_{\rm
gas}^\beta$ showed indices of $\alpha=0.78 \pm 0.05$ for $\rho_{\rm H_2}$ and
$2.15 \pm 0.08$ for $\rho_{\rm total}$, and $\beta=1.14\pm 0.23$ for
$\Sigma_{\rm total}$, where $\rho$ and $\Sigma$ denote volume and surface
densities, respectively. The star formation rate is shown to be directly
related to the molecular gas, but indirectly to HI and total gas densities. The
dependence of the SF law on the gaseous phase is explained by the phase
transition theory between HI and H$_2$ gases.


  A short review on electromagnetic properties of neutrinos is presented. In
spite of many efforts in the theoretical and experimental studies of neutrino
electromagnetic properties, they still remain one of the main puzzles related
to neutrinos.


  Neural networks (NN) achieve remarkable results in various tasks, but lack
key characteristics: interpretability, support for categorical features, and
lightweight implementations suitable for edge devices. While ongoing efforts
aim to address these challenges, Gradient Boosting Trees (GBT) inherently meet
these requirements. As a result, GBTs have become the go-to method for
supervised learning tasks in many real-world applications and competitions.
However, their application in online learning scenarios, notably in
reinforcement learning (RL), has been limited. In this work, we bridge this gap
by introducing Gradient-Boosting RL (GBRL), a framework that extends the
advantages of GBT to the RL domain. Using the GBRL framework, we implement
various actor-critic algorithms and compare their performance with their NN
counterparts. Inspired by shared backbones in NN we introduce a tree-sharing
approach for policy and value functions with distinct learning rates, enhancing
learning efficiency over millions of interactions. GBRL achieves competitive
performance across a diverse array of tasks, excelling in domains with
structured or categorical features. Additionally, we present a
high-performance, GPU-accelerated implementation that integrates seamlessly
with widely-used RL libraries (available at https://github.com/NVlabs/gbrl).
GBRL expands the toolkit for RL practitioners, demonstrating the viability and
promise of GBT within the RL paradigm, particularly in domains characterized by
structured or categorical features.


  In this paper, cyclic codes are established over some finite quaternion
integer rings with respect to the quaternion Mannheim distance, and de- coding
algorithm for these codes is given.


  We study parity violation in graviton non-gaussianity generated during
inflation. We develop a useful formalism to calculate graviton non-gaussianity.
Using this formalism, we explicitly calculate the parity violating part of the
bispectrum for primordial gravitational waves in the exact de Sitter spacetime
and prove that no parity violation appears in the non-gaussianity. We also
extend the analysis to slow-roll inflation and find that the parity violation
of the bispectrum is proportional to the slow-roll parameter. We argue that
parity violating non-gaussianity can be tested by the CMB. Our results are also
useful for calculating three-point function of the stress tensor in the
non-conformal field theory through the gravity/field theory correspondence.


  We present an efficient implementation of analytical non-adiabatic derivative
coupling elements for the coupled cluster singles and doubles model. The
derivative coupling elements are evaluated in a biorthonormal formulation in
which the nuclear derivative acts on the right electronic state, where this
state is biorthonormal with respect to the set of left states. This stands in
contrast to earlier implementations based on normalized states and a gradient
formula for the derivative coupling. As an illustration of the implementation,
we determine a minimum energy conical intersection between the n{\pi}* and
{\pi}{\pi}* states in the nucleobase thymine.