  Code review is an effective software quality assurance activity; however, it
is labor-intensive and time-consuming. Thus, a number of generation-based
automatic code review (ACR) approaches have been proposed recently, which
leverage deep learning techniques to automate various activities in the code
review process (e.g., code revision generation and review comment generation).
  We find the previous works carry three main limitations. First, the ACR
approaches have been shown to be beneficial in each work, but those methods are
not comprehensively compared with each other to show their superiority over
their peer ACR approaches. Second, general-purpose pre-trained models such as
CodeT5 are proven to be effective in a wide range of Software Engineering (SE)
tasks. However, no prior work has investigated the effectiveness of these
models in ACR tasks yet. Third, prior works heavily rely on the Exact Match
(EM) metric which only focuses on the perfect predictions and ignores the
positive progress made by incomplete answers. To fill such a research gap, we
conduct a comprehensive study by comparing the effectiveness of recent ACR
tools as well as the general-purpose pre-trained models. The results show that
a general-purpose pre-trained model CodeT5 can outperform other models in most
cases. Specifically, CodeT5 outperforms the prior state-of-the-art by
13.4\%--38.9\% in two code revision generation tasks. In addition, we introduce
a new metric namely Edit Progress (EP) to quantify the partial progress made by
ACR tools. The results show that the rankings of models for each task could be
changed according to whether EM or EP is being utilized. Lastly, we derive
several insightful lessons from the experimental results and reveal future
research directions for generation-based code review automation.


  We explore the timing and spectral properties of GRS 1915+105 based on X-ray
observations of NICER and Insight-HXMT during the long outburst from 2017 to
2021. We find a new class of variability in the rising stage of the outburst
that differs from the formerly reported patterns of light curves. This new
variability pattern, which we name class $\psi$, is characterized by several
periodic mini pulses superposed on another longer periodic pulse. The periods
are around $\sim$130 seconds and $\sim$10 seconds for the main pulses and mini
pulses respectively based on the analysis of power spectrum density (PSD) and
step-wise filter correlation (SFC), where the SFC method has an advantage in
finding the superimposed periodic components. The mini pulses become weak or
disappear when the luminosity increases and the light curves change into the
classical class $\kappa$. The class $\psi$ shows a softer spectrum with lower
count rates compared to the class $\kappa$ during the main pulse. The new class
$\psi$ shows peculiar timing and spectral properties compared to those of
classic class $\kappa$, which can help us to explore the class transition
mechanism in GRS 1915+105.


  The form of resonance line-shapes unveils information about its
nonperturbative properties and formation mechanisms. Here, we study the
non-Breit-Wigner energy distribution of the resonance $\psi (3770)$ using an
unitarized effective Lagrangian approach, that includes the effect of the
nearby threshold $D^{+}D^{-}$. Two poles are found in the second Riemann sheet
near the resonance amplitude. We discuss the setting of the free parameters and
possible effects contributing to the signal.


  Let $\Uq$ be a quantum group. Regarding a (noncommutative) space with
$\Uq$-symmetry as a $\Uq$-module algebra $A$, we may think of equivariant
vector bundles on $A$ as projective $A$-modules with compatible $\Uq$-action.
We construct an equivariant K-theory of such quantum vector bundles using
Quillen's exact categories, and provide means for its compution. The
equivariant K-groups of quantum homogeneous spaces and quantum symmetric
algebras of classical type are computed.


  (Abridged) We investigate the ionized environment of the Seyfert 1 galaxy NGC
985 with a new Chandra-HETGS observation and an archival BeppoSAX observation.
Two absorption components are clearly required to fit absorption features
observed in the Chandra spectrum. The components have a difference of 29 in
ionization parameter and 3 in column density. The presence of the low
ionization component is evidenced by an Fe M-shell unresolved transition array
(UTA) produced by charge states VII to XIII. The high ionization phase is
required by the presence of broad absorption features arising from several
blends of Fe L-shell transitions (Fe XVII-XXII). A third highly ionized
component might also be present, but the data does not allow to constrain its
properties. An X-ray luminosity variation by a factor 2.3 is observed between
the BeppoSAX and Chandra observations (separated by almost 3 years).
Variability in the opacity of the absorbers is detected in response to the
continuum variation, but while the colder component is consistent with a simple
picture of photoionization equilibrium, the ionization state of the hotter
component seems to increase while the continuum flux drops. The most striking
result in our analysis is that during both the Chandra and the BeppoSAX
observations, the two absorbing components appear to have the same pressure.
Thus, we suggest that the absorption arises from a multi-phase wind. Such
scenario can explain the change in opacity of both absorption components during
the observations, but requires that a third hotter component is
pressure-confining the two phases. Hence, our analysis points to a 3-phase
medium similar to the wind found in NGC 3783, and further suggests that such a
wind might be a common characteristic in AGN.


  In this paper, we establish the curvature estimates for a class of Hessian
type equations. Some applications are also discussed.


  The central result of this paper is the small-is-very-small principle for
restricted sequential theories. The principle says roughly that whenever the
given theory shows that a property has a small witness, i.e. a witness in every
definable cut, then it shows that the property has a very small witness: i.e. a
witness below a given standard number.
  We draw various consequences from the central result. For example (in rough
formulations): (i) Every restricted, recursively enumerable sequential theory
has a finitely axiomatized extension that is conservative w.r.t. formulas of
complexity $\leq n$. (ii) Every sequential model has, for any $n$, an extension
that is elementary for formulas of complexity $\leq n$, in which the
intersection of all definable cuts is the natural numbers. (iii) We have
reflection for $\Sigma^0_2$-sentences with sufficiently small witness in any
consistent restricted theory $U$. (iv) Suppose $U$ is recursively enumerable
and sequential. Suppose further that every recursively enumerable and
sequential $V$ that locally inteprets $U$, globally interprets $U$. Then, $U$
is mutually globally interpretable with a finitely axiomatized sequential
theory.
  The paper contains some careful groundwork developing partial satisfaction
predicates in sequential theories for the complexity measure depth of
quantifier alternations.


  We use the technique of Harish-Chandra bimodules to prove that regular
strongly typical blocks of the category $\mathcal{O}$ for the queer Lie
superalgebra $\mathfrak{q}_n$ are equivalent to the corresponding blocks of the
category $\mathcal{O}$ for the Lie algebra $\mathfrak{gl}_n$.


  Radiofrequency (RF) catheter ablation has transformed treatment for
tachyarrhythmias and has become first-line therapy for some tachycardias. The
precise localization of the arrhythmogenic site and the positioning of the RF
catheter over that site are problematic: they can impair the efficiency of the
procedure and are time consuming (several hours). Electroanatomic mapping
technologies are available that enable the display of the cardiac chambers and
the relative position of ablation lesions. However, these are expensive and use
custom-made catheters. The proposed methodology makes use of standard catheters
and inexpensive technology in order to create a 3D volume of the heart chamber
affected by the arrhythmia. Further, we propose a novel method that uses a
priori 3D information of the mapping catheter in order to estimate the 3D
locations of multiple electrodes across single view C-arm images. The monoplane
algorithm is tested for feasibility on computer simulations and initial canine
data.


  Several experimental measurements are expressed in the form of
one-dimensional profiles, for which there is a scarcity of methodologies able
to classify the pertinence of a given result to a specific group. The
polarization curves that evaluate the corrosion kinetics of electrodes in
corrosive media are an application where the behavior is chiefly analyzed from
profiles. Polarization curves are indeed a classic method to determine the
global kinetics of metallic electrodes, but the strong nonlinearity from
different metals and alloys can overlap and the discrimination becomes a
challenging problem. Moreover, even finding a typical curve from replicated
tests requires subjective judgement. In this paper we used the so-called
multi-q approach based on the Tsallis statistics in a classification engine to
separate multiple polarization curve profiles of two stainless steels. We
collected 48 experimental polarization curves in aqueous chloride medium of two
stainless steel types, with different resistance against localized corrosion.
Multi-q pattern analysis was then carried out on a wide potential range, from
cathodic up to anodic regions. An excellent classification rate was obtained,
at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and
both potential ranges, respectively, using only 2% of the original profile
data. These results show the potential of the proposed approach towards
efficient, robust, systematic and automatic classification of highly non-linear
profile curves.


  The significance to which the cosmic microwave background observations by the
satellite COBE can be used to refute a specific observationally based
hypothesis for the global topology (3-manifold) of the Universe is
investigated, by a new method of applying the principle of matched circle
pairs. Moreover, it is shown that this can be done without assuming Gaussian
distributions for the density perturbation spectrum.
  The Universe is assumed to correspond to a flat Friedmann-Lema\^{\i}tre model
with a zero value of the cosmological constant. The 3-manifold is hypothesised
to be a 2-torus in two directions, with a third axis larger than the horizon
diameter. The positions and lengths of the axes are determined by the relative
positions of the galaxy clusters Coma, RX~J1347.5-1145 and CL 09104+4109,
assumed to be multiple topological images of a single, physical cluster.
  If the following two assumptions are valid: (i) the error estimates in the
COBE DMR data are accurate estimates of the total random plus systematic error;
and (ii) the temperature fluctuations are dominated by the na\"{\i}ve
Sachs-Wolfe effect; then the distribution of the temperature differences
between multiply imaged pixels is significantly wider than the uncertainty in
the differences, and the candidate is rejected at the 94% level.
  This result is valid for either the `subtracted' or `combined' Analysed
Science Data Sets, for either $10\deg$ or $20\deg$ smoothing, and is
strengthened if suspected contaminated regions from the galactic centre and the
Ophiuchus and Orion complexes are removed.


  Inverse phase transitions are striking phenomena in which an apparently more
ordered state disorders under cooling. This behavior can naturally emerge in
tricritical systems on heterogeneous networks and it is strongly enhanced by
the presence of disassortative degree correlations. We show it both
analytically and numerically, providing also a microscopic interpretation of
inverse transitions in terms of freezing of sparse subgraphs and coupling
renormalization.


  In this paper we establish a general inequality involving the Laplacian of
the warping functions and the squared mean curvature of any doubly warped
product isometrically immersed in a Riemannian manifold. Moreover, we obtain
some geometric inequalities for C-totally real doubly warped product
submanifolds of generalized $(\kappa,\mu)$-space forms.


  In this paper, we propose a generalized intelligent quality-based approach
for fusing multi-source information. The goal of the proposed approach intends
to fuse the multi-complex-valued distribution information while maintaining a
high quality of the fused result by considering the usage of credible
information sources.


  Let k be a field of characteristic zero, V a smooth, positive-dimensional,
quasiprojective variety over k, and D a nonempty effective divisor on V. Let K
be the function field of V, and A the semilocal ring of D in K.
  In this paper, we prove the Diophantine undecidability of:
  (1) A, in all cases;
  (2) K, when k is (formally) real and V has a real point;
  (3) K, when k is a subfield of a p-adic field, for some odd prime p.
  To achieve this, we use Denef's method: from an elliptic curve E over Q,
without complex multiplication, one constructs a quadratic twist E' of E over
Q(t), which has Mordell-Weil rank one. Most of the paper is devoted to proving
(using a theorem of R. Noot) that one can choose f in K, vanishing at D, such
that the group E'(K) deduced from the field extension K/Q(f)=Q(t) is equal to
E'(Q(t)). Then we mimic the arguments of Denef (for the real case) and of Kim
and Roush (for the p-adic case).


  Learning to see through data is central to contemporary forms of algorithmic
knowledge production. While often represented as a mechanical application of
rules, making algorithms work with data requires a great deal of situated work.
This paper examines how the often-divergent demands of mechanization and
discretion manifest in data analytic learning environments. Drawing on research
in CSCW and the social sciences, and ethnographic fieldwork in two data
learning environments, we show how an algorithm's application is seen sometimes
as a mechanical sequence of rules and at other times as an array of situated
decisions. Casting data analytics as a rule-based (rather than rule-bound)
practice, we show that effective data vision requires would-be analysts to
straddle the competing demands of formal abstraction and empirical contingency.
We conclude by discussing how the notion of data vision can help better
leverage the role of human work in data analytic learning, research, and
practice.


  This paper presents a canonical dual method for solving a quadratic discrete
value selection problem subjected to inequality constraints. The problem is
first transformed into a problem with quadratic objective and 0-1 integer
variables. The dual problem of the 0-1 programming problem is thus constructed
by using the canonical duality theory. Under appropriate conditions, this dual
problem is a maximization problem of a concave function over a convex
continuous space. Numerical simulation studies, including some large scale
problems, are carried out so as to demonstrate the effectiveness and efficiency
of the method proposed.


  The classical tests of general relativity (perihelion precession, deflection
of light, and the radar echo delay) are considered for several spherically
symmetric static vacuum solutions in brane world models. Generally, the
spherically symmetric vacuum solutions of the brane gravitational field
equations have properties quite distinct as compared to the standard black hole
solutions of general relativity. As a first step a general formalism that
facilitates the analysis of general relativistic Solar System tests for any
given spherically symmetric metric is developed. It is shown that the existing
observational Solar System data on the perihelion shift of Mercury, on the
light bending around the Sun (obtained using long-baseline radio
interferometry), and ranging to Mars using the Viking lander, constrain the
numerical values of the parameters of the specific models.


  In the Horava-Lifshitz theory of quantum gravity, two conditions -- detailed
balance and projectability -- are usually assumed. The breaking of
projectability simplifies the theory, but it leads to serious problems with the
theory. The breaking of detailed balance leads to a more complicated form of
the theory, but it appears to resolve some of the problems. Sotiriou, Visser
and Weinfurtner formulated the most general theory of Horava-Lifshitz type
without detailed balance. We compute the linear scalar perturbations of the FRW
model in this form of HL theory. We show that the higher-order curvature terms
in the action lead to a gravitational effective anisotropic stress on small
scales. Specializing to a Minkowski background, we study the spin-0 scalar mode
of the graviton, using a gauge-invariant analysis, and find that it is stable
in both the infrared and ultraviolet regimes for $0 \le \xi \le 2/3$. However,
in this parameter range the scalar mode is a ghost.


  We present the results of a spectroscopic analysis of rotational velocities
in 714 M dwarf stars observed by the SDSS III Apache Point Galactic Evolution
Experiment (APOGEE) survey. We use a template fitting technique to estimate
$v\sin{i}$ while simultaneously estimating $\log{g}$, $[\text{M}/\text{H}]$,
and $T_{\text{eff}}$. We conservatively estimate that our detection limit is 8
km s$^{-1}$. We compare our results to M dwarf rotation studies in the
literature based on both spectroscopic and photometric measurements. Like other
authors, we find an increase in the fraction of rapid rotators with decreasing
stellar temperature, exemplified by a sharp increase in rotation near the M$4$
transition to fully convective stellar interiors, which is consistent with the
hypothesis that fully convective stars are unable to shed angular momentum as
efficiently as those with radiative cores. We compare a sample of targets
observed both by APOGEE and the MEarth transiting planet survey and find no
cases were the measured $v\sin{i}$ and rotation period are physically
inconsistent, requiring $\sin{i}>1$. We compare our spectroscopic results to
the fraction of rotators inferred from photometric surveys and find that while
the results are broadly consistent, the photometric surveys exhibit a smaller
fraction of rotators beyond the M$4$ transition by a factor of $\sim 2$. We
discuss possible reasons for this discrepancy. Given our detection limit, our
results are consistent with a bi-modal distribution in rotation that is seen in
photometric surveys.


  In this paper we quantify the notion of antisymmetry of the Fourier transform
of certain vector valued measures. The introduced scale is related to the
condition appearing in Uchiyama's theorem and is used to give a lower bound for
the rectifiable dimension of those measures. Moreover, we obtain an estimate of
the lower Hausdorff dimension assuming certain more restrictive version of the
$2$-wave cone condition. Results of our considerations can be viewed as an
uncertainty-type principle in the following way: it is impossible to
simultaneously localize a (bundle) measure and a direction of its Fourier
transform on small sets. The investigated class is modeled on the example of
gradients of BV functions. The article contains also a theorem concerning
regularity: we prove that elements of considered class vanish on 1-purely
unrectifiable sets. Our results can be applied to studying the properties of
PDE-constrainted measures.


  We demonstrate a single-parameter route for reproducing higher mass objects
as observed in the LIGO--Virgo mass distribution, using only the isolated
binary stellar evolution channel. This single parameter encodes the
cosmological mass growth of compact stellar remnants that exceed the
Tolman-Oppenheimer-Volkoff limit. Cosmological mass growth appears in known
solutions to General Relativity with cosmological boundary conditions. We
consider the possibility of solutions with cosmological boundary conditions,
which reduce to Kerr on timescales short compared to the Hubble time. We
discuss complementary observational signatures of these solutions that can
confirm or invalidate their astrophysical relevance.


  We study the singularities of algebraic difference equations on curves from
the point of view of equivariant sheaves. We propose a definition for the
formal local type of an equivariant sheaf at a point in the case of a reduced
curve acted on by a group which is virtually the integers. We show that with
this definition, equivariant sheaves can be glued from an "open cover".
Precisely, we show that an equivariant sheaf can be uniquely recovered from the
following data: the restriction to the complement of a point, the local type at
the point itself, and an isomorphism between the two over the punctured
neighborhood of said point.
  We study symmetric elliptic difference equations ("elliptic equations" from
now on) from this point of view. We consider several natural notions for an
algebraic version of symmetric elliptic difference equations, i.e. symmetric
elliptic difference modules ("elliptic modules"). We show that different
versions are not equivalent, but we detail how they are related: all the
versions embed fully faithfully into the same category of equivariant sheaves.
This implies that we can use the theory for equivariant sheaves to study
singularities of elliptic equations as well.
  One reason to study elliptic equations is that they generalize, and
degenerate to, (q-)difference equations (i.e. equivariant sheaves) and
differential equations (i.e. D-modules) on the projective line. We discuss this
from the elliptic module point of view, which requires studying elliptic
modules on singular curves. We study the relation between elliptic modules on
singular curves and their normalization. We show that for modules which are
flat at the singular points there is an equivalence and we give examples
showing that this cannot be improved upon.


  We apply machine learning to the problem of finding numerical Calabi-Yau
metrics. We extend previous work on learning approximate Ricci-flat metrics
calculated using Donaldson's algorithm to the much more accurate "optimal"
metrics of Headrick and Nassar. We show that machine learning is able to
predict the K\"ahler potential of a Calabi-Yau metric having seen only a small
sample of training data.


  Spin coating technique was employed to fabricate CuO films at different spin
speeds for different time duration, and they were annealed at different
temperatures for different time durations in air. These thin films were
characterized using UV Visible spectrometer and solar cell simulator. As the
spinning rate increases, the corresponding band gap energy of the thin film
increases. However, the annealing temperature does not affect the optical band
gap. Moreover, the optical band gap does not depend on the annealing time and
spin time. It was observed that the optical band gap decreases with the number
of CuO layers. The optical band gap values of CuO thin films were between 1.843
and 1.869 eV. Inclusion of the two additives enhances the photocurrent,
photovoltage and efficiency. Photocurrent density can be enhanced from 0.1464
to 1.172 mA per cm2 using the additive ethylene glycol. Properties of CuO films
synthesized using sol gel dip coating technique were compared with those of CuO
films grown using spin coating method.


  Social intelligence manifests the capability, often referred to as the Theory
of Mind (ToM), to discern others' behavioral intentions, beliefs, and other
mental states. ToM is especially important in multi-agent and human-machine
interaction environments because each agent needs to understand the mental
states of other agents in order to better respond, interact, and collaborate.
Recent research indicates that the ToM model possesses the capability to infer
beliefs, intentions, and anticipate future observations and actions;
nonetheless, its deployment in tackling intricate tasks remains notably
limited. The challenges arise when the number of agents increases, the
environment becomes more complex, and interacting with the environment and
predicting the mental state of each other becomes difficult and time consuming.
To overcome such limits, we take inspiration from the Theory of Collective Mind
(ToCM) mechanism, predicting observations of all other agents into a unified
but plural representation and discerning how our own actions affect this mental
state representation. Based on this foundation, we construct an imaginative
space to simulate the multi-agent interaction process, thus improving the
efficiency of cooperation among multiple agents in complex decision-making
environments. In various cooperative tasks with different numbers of agents,
the experimental results highlight the superior cooperative efficiency and
performance of our approach compared to the Multi-Agent Reinforcement Learning
(MARL) baselines. We achieve consistent boost on SNN- and DNN-based decision
networks, and demonstrate that ToCM's inferences about others' mental states
can be transferred to new tasks for quickly and flexible adaptation.


  We propose an online form of the cake cutting problem. This models situations
where players arrive and depart during the process of dividing a resource. We
show that well known fair division procedures like cut-and-choose and the
Dubins-Spanier moving knife procedure can be adapted to apply to such online
problems. We propose some desirable properties that online cake cutting
procedures might possess like online forms of proportionality and
envy-freeness, and identify which properties are in fact possessed by the
different online cake procedures.


  A sizable transverse thermoelectric coefficient N , large to the extent that
it potentially serves applications, is predicted to arise, by means of
first-principles calculations, in a Skyrmion crystal assumed on EuO monolayer
where carrier electrons are introduced upon a quantum anomalous Hall insulating
phase of Chern number C = 2. This encourages future experiments to pursue such
an effect.


  Two of recent progress in lattice QCD approach to nuclear force are reported.
(i) Tensor force from quenched lattice QCD: By truncating the derivative
expansion of inter-nucleon potential to the strictly local terms, we obtain
central force V_C(r) and tensor force V_T(r) separately from s-wave and d-wave
components of Bethe-Salpeter wave function for two nucleon state with J^P=1^+.
Numerical calculation is performed with quenched QCD on 32^4 lattice using the
standard plaquette action at beta=5.7 with the standard Wilson quark action
with kappa=0.1640, 0.1665, 0.1678. Preliminary results show that the depths of
the resulting tensor force amount to 20 to 40 MeV, which is enhanced in the
light quark mass region. (ii) Nuclear force from 2+1 flavor QCD with PACS-CS
gauge configuration: Preliminary full QCD results are obtained by using 2+1
flavor gauge configurations generated by PACS-CS collaboration. The resulting
potential has the midium range attraction of about 30 MeV similar to the
preceding quenched calculations. However, the repulsive core at short distance
is significantly stronger than the corresponding quenched QCD result.


  This paper considers the problem of detecting topology variations in
dynamical networks. We consider a network whose behavior can be represented via
a linear dynamical system. The problem of interest is then that of finding
conditions under which it is possible to detect node or link disconnections
from prior knowledge of the nominal network behavior and on-line measurements.
The considered approach makes use of analysis tools from switching systems
theory. A number of results are presented along with examples.


  We study some topological aspects of non-abelian gauge theories intimately
connected to the Lie algebras of the gauge groups and the homotopy theory in
the generalized gauge orbit space. The physics connection to the
non-perturbative solution to strong CP problem as originally proposed by the
author is also discussed. Some relevant topological formulas are also given and
discussed. A result from the physics application is that the usual gauge orbit
space on the compactified space can contain at most a $Z_2$ monopole structure
in the SP(2N) gauge theories. Some relevance to the open universe is also
discussed. We expect that our results may also be useful to the other studies
of non-abelian gauge theories in general.


  We give lower bounds for various natural node- and edge-based local
strategies for exploring a graph. We consider this problem both in the setting
of an arbitrary graph as well as the abstraction of a geometric exploration of
a space by a robot, both of which have been extensively studied. We consider
local exploration policies that use time-of-last- visit or alternatively
least-frequently-visited local greedy strategies to select the next step in the
exploration path. Both of these strategies were previously considered by Cooper
et al. (2011) for a scenario in which counters for the last visit or visit
frequency are attached to the edges. In this work we consider the case in which
the counters are associated with the nodes, which for the case of dual graphs
of geometric spaces could be argued to be intuitively more natural and likely
more efficient. Surprisingly, these alternate strategies give worst-case
superpolynomial/ exponential time for exploration, whereas the least-frequently
visited strategy for edges has a polynomially bounded exploration time, as
shown by Cooper et al. (2011).


  We use non-equilibrium dynamical mean-field theory with iterative
perturbation theory as an impurity solver to study the recovery of $SU(2)$
symmetry in real-time following a hopping integral parameter quench from a
mass-imbalanced to a mass-balanced single-band Hubbard model at half-filling. A
dynamical order parameter $\gamma(t)$ is defined to characterize the evolution
of the system towards $SU(2)$ symmetry. By comparing the momentum dependent
occupation from an equilibrium calculation (with the $SU(2)$ symmetric
Hamiltonian after the quench at an effective temperature) with the data from
our non-equilibrium calculation, we conclude that the $SU(2)$ symmetry
recovered state is a thermalized state. Further evidence from the evolution of
the density of states supports this conclusion. At the same time, we find the
order parameter in the weak Coulomb interaction regime undergoes an approximate
exponential decay. We numerically investigate the interplay of the relevant
parameters (initial temperature, Coulomb interaction strength, initial
mass-imbalance ratio) and their combined effect on the thermalization behavior.
Finally, we study evolution of the order parameter as the hopping parameter is
changed with either a linear ramp or a pulse. Our results can be useful in
strategies to engineer the relaxation behavior of interacting, quantum
many-particle systems.


  We consider a smooth system of the form $\dot q=f_0(q)+\sum\limits_{i=1}^k
u_i f_i(q)$, $q\in M,\ u_i\in\mathbb R,$ and study controllability issues on
the group of diffeomorphisms of $M$. It is well-known that the system can
arbitrarily well approximate the movement in the direction of any Lie bracket
polynomial of $f_1,\ldots,f_k$. Any Lie bracket polynomial of $f_1,\ldots,f_k$
is good in this sense. Moreover, some combinations of Lie brackets which
involve the drift term $f_0$ are also good but surely not all of them. In this
paper we try to characterize good ones and, in particular, all universal good
combinations, which are good for any nilpotent truncation of any system.


  This paper extends the work in [Suzuki, 1996] and presents an efficient
depth-first branch-and-bound algorithm for learning Bayesian network
structures, based on the minimum description length (MDL) principle, for a
given (consistent) variable ordering. The algorithm exhaustively searches
through all network structures and guarantees to find the network with the best
MDL score. Preliminary experiments show that the algorithm is efficient, and
that the time complexity grows slowly with the sample size. The algorithm is
useful for empirically studying both the performance of suboptimal heuristic
search algorithms and the adequacy of the MDL principle in learning Bayesian
networks.


  Activation cross-sections for deuteron induced reactions on Sm are presented
for the first time for
$^{nat}$Sm(d,xn)$^{155,154,152m2,152m1,152g,150m,150g,149,148,147,146}$Eu,
$^{nat}$Sm(d,x)$^{153,145}$Sm and $^{nat}$Sm(d,x)$^{151,150,149,145,144,143}$Pm
up to 50 MeV. The cross-sections were measured by the stacked-foil irradiation
technique and high resolution $\gamma$-ray spectrometry. The results were
compared with results of nuclear reaction codes ALICE-D, EMPIRE-D and TALYS
(from TENDL libraries). Integral yields of the products were calculated from
the excitation functions.


  Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequential Skip Prediction Challenge', where the task was to
predict the skip behaviour of the second half in a music listening session
conditioned on the first half. We model this task using a Multi-RNN approach
consisting of two distinct stacked recurrent neural networks, where one network
focuses on encoding the first half of the session and the other network focuses
on utilizing the encoding to make sequential skip predictions. The encoder
network is initialized by a learned session-wide music encoding, and both of
them utilize a learned track embedding. Our final model consists of a majority
voted ensemble of individually trained models, and ranked 2nd out of 45
participating teams in the competition with a mean average accuracy of 0.641
and an accuracy on the first skip prediction of 0.807. Our code is released at
https://github.com/Varyn/WSDM-challenge-2019-spotify.


  Kaon production, in particular $K^+$ production in heavy ion collisions at
intermediate energies is discussed. Main emphasis is put on the question if
subthreshold $K^+$ production can serve as a suitable tool to test the high
density phase of such reactions and to deliver information on the high density
behavior of the nuclear equation of state. It is shown that the $K^+$
excitation function in heavy ($Au+Au$) over light ($C+C$) systems provides a
robust observable which, by comparison to data, strongly favors a soft equation
of state. A second question of interest is the existence of an in-medium kaon
potential as predicted by effective chiral Lagrangiens. Here it is argued that
transport calculations support this scenario with, in the meantime, a
significant level of consistency.


  We studied the dynamics of S-stars in the Galactic center using the
physics-informed neural networks. The neural networks are considered for both,
Keplerian and the General Relativity dynamics, the orbital parameters for stars
S1, S2, S9, S13, S31, and S54 are obtained and the regression problem is
solved. It is shown that the neural network is able to detect the Schwarzschild
precession for S2 star, while the regressed part revealed an additional
precession. Attributing the latter to a possible contribution of a modified
gravity, we obtain a constraint for the weak-field modified General Relativity
involving the cosmological constant which also deals with the Hubble tension.
Our analysis shows the efficiency of neural networks in revealing the S-star
dynamics and the prospects upon the increase of the amount and the accuracy of
the observational data.


  Cosmic reionization holds the key to understand structure formation in the
Universe, and can inform us about the properties of the first sources, as their
star formation efficiency and escape fraction of ionizing photons. By combining
the recent release of Planck electron scattering optical depth data with
observations of high-redshift quasar absorption spectra, we obtain strong
constraints on viable reionization histories. We show that inclusion of Planck
data favors a reionization scenario with a single stellar population. The mean
$x_{\rm HI}$ drops from $\sim0.8$ at $z=10.6$ to $\sim10^{-4}$ at $z=5.8$ and
reionization is completed around $5.8\lesssim z\lesssim8.5$ (2-$\sigma$), thus
indicating a significant reduction in contributions to reionization from high
redshift sources. We can put independent constraints on the escape fraction
$f_{\rm esc}$ of ionizing photons by incorporating the high-redshift galaxy
luminosity function data into our analysis. We find a non-evolving $f_{\rm
esc}$ of $\sim10\%$ in the redshift range $z=6-9$.


  Recognizing entity synonyms from text has become a crucial task in many
entity-leveraging applications. However, discovering entity synonyms from
domain-specific text corpora (e.g., news articles, scientific papers) is rather
challenging. Current systems take an entity name string as input to find out
other names that are synonymous, ignoring the fact that often times a name
string can refer to multiple entities (e.g., "apple" could refer to both Apple
Inc and the fruit apple). Moreover, most existing methods require training data
manually created by domain experts to construct supervised-learning systems. In
this paper, we study the problem of automatic synonym discovery with knowledge
bases, that is, identifying synonyms for knowledge base entities in a given
domain-specific corpus. The manually-curated synonyms for each entity stored in
a knowledge base not only form a set of name strings to disambiguate the
meaning for each other, but also can serve as "distant" supervision to help
determine important features for the task. We propose a novel framework, called
DPE, to integrate two kinds of mutually-complementing signals for synonym
discovery, i.e., distributional features based on corpus-level statistics and
textual patterns based on local contexts. In particular, DPE jointly optimizes
the two kinds of signals in conjunction with distant supervision, so that they
can mutually enhance each other in the training stage. At the inference stage,
both signals will be utilized to discover synonyms for the given entities.
Experimental results prove the effectiveness of the proposed framework.


  During the COVID-19 pandemic, we witnessed a disproportionate infection rate
among marginalized and low-income groups. Despite empirical evidence suggesting
that structural inequalities in society contribute to health disparities, there
has been little attempt to offer a computational and theoretical explanation to
establish its plausibility and quantitative impact. This paper combines an
agent-based network model and a compartmental susceptible-infectious-removed
model (SIR) to explain how socioeconomic inequalities affect the dynamics of
disease spreading. Specifically, we focus on two aspects of structural
inequalities: wealth inequality and social segregation. Our computational model
demonstrates that under high income inequality, the infection gap widens
between the low-income and high-income groups, and also the overall infected
cases increase. We also observed that social segregation between different
socioeconomic status (SES) groups intensifies the spreading and hence mortality
rates. Furthermore, we explain the second peak of the infectious cases during a
pandemic as a result of a false sense of safety and loosening the quarantine,
among the higher SES individuals. These findings send a strong message to
policymakers; confinement measures must be accompanied by substantial financial
assistance to those from lower-income groups so that the people regardless of
their socioeconomic status can afford to stay in. Without financial assistance,
lower-income individuals will remain in circulation, which will prolong the
duration and magnitude of the infection.


  The motion of an ion in a coherent lower hybrid wave (characterized by
|k_parallel| << |k_perp| and omega >> Omega_i) in a tokamak plasma is studied.
For ions satisfying v_perp > omega/k_perp, the Lorentz force law for the ions
is reduced to a set of difference equations which give the Larmor radius and
phase of an ion on one cyclotron orbit in terms of these quantities a cyclotron
period earlier. From these difference equations an earlier result [Phys. Fluids
21, 1584 (1978)] that above a certain wave amplitude the ion motion is
stochastic, is readily obtained. The stochasticity threshold is given a simple
physical interpretation. In addition, the difference equations are used to
derive a diffusion equation governing the heating of the ions above the
stochasticity threshold. By including the effects of collisions, the heating
rate for the bulk ions is obtained.


  The Douglas-Rachford reflection method is a general purpose algorithm useful
for solving the feasibility problem of finding a point in the intersection of
finitely many sets. In this chapter we demonstrate that applied to a specific
problem, the method can benefit from heuristics specific to said problem which
exploit its special structure. In particular, we focus on the problem of
protein conformation determination formulated within the framework of matrix
completion, as was considered in a recent paper of the present authors.


  A graph profile records all possible densities of a fixed finite set of
graphs. Profiles can be extremely complicated; for instance the full profile of
any triple of connected graphs is not known, and little is known about
hypergraph profiles. We introduce the tropicalization of graph and hypergraph
profiles. Tropicalization is a well-studied operation in algebraic geometry,
which replaces a variety (the set of real or complex solutions to a finite set
of algebraic equations) with its "combinatorial shadow". We prove that the
tropicalization of a graph profile is a closed convex cone, which still
captures interesting combinatorial information. We explicitly compute these
tropicalizations for arbitrary sets of complete and star hypergraphs. We show
they are rational polyhedral cones even though the corresponding profiles are
not even known to be semialgebraic in some of these cases. We then use
tropicalization to prove strong restrictions on the power of the sums of
squares method, equivalently Cauchy-Schwarz calculus, to test (which is weaker
than certification) the validity of graph density inequalities. In particular,
we show that sums of squares cannot test simple binomial graph density
inequalities, or even their approximations. Small concrete examples of such
inequalities are presented, and include the famous Blakley-Roy inequalities for
paths of odd length. As a consequence, these simple inequalities cannot be
written as a rational sum of squares of graph densities.


  Machine learning (ML) has been widely used in the literature to automate
software engineering tasks. However, ML outcomes may be sensitive to
randomization in data sampling mechanisms and learning procedures. To
understand whether and how researchers in SE address these threats, we surveyed
45 recent papers related to three predictive tasks: defect prediction (DP),
predictive mutation testing (PMT), and code smell detection (CSD). We found
that less than 50% of the surveyed papers address the threats related to
randomized data sampling (via multiple repetitions); only 8% of the papers
address the random nature of ML; and parameter values are rarely reported (only
18% of the papers). To assess the severity of these threats, we conducted an
empirical study using 26 real-world datasets commonly considered for the three
predictive tasks of interest, considering eight common supervised ML
classifiers. We show that different data resamplings for 10-fold
cross-validation lead to extreme variability in observed performance results.
Furthermore, randomized ML methods also show non-negligible variability for
different choices of random seeds. More worryingly, performance and variability
are inconsistent for different implementations of the conceptually same ML
method in different libraries, as also shown through multi-dataset pairwise
comparison. To cope with these critical threats, we provide practical
guidelines on how to validate, assess, and report the results of predictive
methods.


  Diffusion models can learn strong image priors from underlying data
distribution and use them to solve inverse problems, but the training process
is computationally expensive and requires lots of data. Such bottlenecks
prevent most existing works from being feasible for high-dimensional and
high-resolution data such as 3D images. This paper proposes a method to learn
an efficient data prior for the entire image by training diffusion models only
on patches of images. Specifically, we propose a patch-based position-aware
diffusion inverse solver, called PaDIS, where we obtain the score function of
the whole image through scores of patches and their positional encoding and
utilize this as the prior for solving inverse problems. First of all, we show
that this diffusion model achieves an improved memory efficiency and data
efficiency while still maintaining the capability to generate entire images via
positional encoding. Additionally, the proposed PaDIS model is highly flexible
and can be plugged in with different diffusion inverse solvers (DIS). We
demonstrate that the proposed PaDIS approach enables solving various inverse
problems in both natural and medical image domains, including CT
reconstruction, deblurring, and superresolution, given only patch-based priors.
Notably, PaDIS outperforms previous DIS methods trained on entire image priors
in the case of limited training data, demonstrating the data efficiency of our
proposed approach by learning patch-based prior.


  Even a century after the formulation of Quantum Mechanics (QM), the wave
function collapse (WFC) remains a contentious aspect of the theory.
Environment-induced decoherence has offered a partial resolution by
illustrating how unitary evolution in an open quantum system can lead to
effective WFC within its components. However, this approach by itself does not
lead to a fully self-consistent reformulation of QM. We introduce a modified
set of QM postulates, which exclude both WFC and Born's probability rule. They
are replaced with the Maximum Entropy Principle, a weaker postulate that
specifies conditional probabilities for mutually compatible observations.
Within this formulation, both WFC and Born's rule become emerging properties.


  AdS/CFT predicts a precise relation between the central charge a, the scaling
dimensions of some operators in the CFT on D3-branes at conical singularities
and the volumes of the horizon and of certain cycles in the supergravity dual.
We review how a quantitative check of this relation can be performed for all
toric singularities. In addition to the results presented in hep-th/0506232, we
also discuss the relation with the recently discovered map between toric
singularities and tilings; in particular, we discuss how to find the precise
distribution of R-charges in the quiver gauge theory using dimers technology.


  This paper proposes a new technique based on nonlinear Adaptive Median filter
(AMF) for image restoration. Image denoising is a common procedure in digital
image processing aiming at the removal of noise, which may corrupt an image
during its acquisition or transmission, while retaining its quality. This
procedure is traditionally performed in the spatial or frequency domain by
filtering. The aim of image enhancement is to reconstruct the true image from
the corrupted image. The process of image acquisition frequently leads to
degradation and the quality of the digitized image becomes inferior to the
original image. Filtering is a technique for enhancing the image. Linear filter
is the filtering in which the value of an output pixel is a linear combination
of neighborhood values, which can produce blur in the image. Thus a variety of
smoothing techniques have been developed that are non linear. Median filter is
the one of the most popular non-linear filter. When considering a small
neighborhood it is highly efficient but for large window and in case of high
noise it gives rise to more blurring to image. The Centre Weighted Median (CWM)
filter has got a better average performance over the median filter [8]. However
the original pixel corrupted and noise reduction is substantial under high
noise condition. Hence this technique has also blurring affect on the image. To
illustrate the superiority of the proposed approach by overcoming the existing
problem, the proposed new scheme (AMF) Adaptive Median Filter has been
simulated along with the standard ones and various performance measures have
been compared.


  Preliminary results from two recent CDF b physics analysis are presented. The
first obtains sin(2beta) = 0.79 + 0.41 -0.44 from a measurement of the
asymmetry in B0, B0bar to J/psi K_short decays, providing the best direct
indication so far that CP invariance is violated in the b sector. The second
obtains new results on the parity even (A_0 and A_par) and odd (A_perp)
polarization amplitudes from full angular analyses of B0 to J/psi K*0 and B_s
to J/psi phi decays.


  Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for
various tasks such as building inspections, rescue operations, deliveries, and
warehousing. However, there are two main limitations to previous approaches:
they may not be able to provide a complete map of the environment and assume
that the map built during exploration is accurate enough for safe navigation,
which is usually not the case. To address these limitations, a novel
exploration method is proposed that combines frontier-based exploration with a
collector strategy that achieves global exploration and complete map creation.
In each iteration, the collector strategy stores and validates frontiers
detected during exploration and selects the next best frontier to navigate to.
The collector strategy ensures global exploration by balancing the exploitation
of a known map with the exploration of unknown areas. In addition, the online
path replanning ensures safe navigation through the map created during motion.
The performance of the proposed method is verified by exploring 3D simulation
environments in comparison with the state-of-the-art methods. Finally, the
proposed approach is validated in a real-world experiment.


  It is of great importance to develop a photodetector system with an ultralow
radioactivity background in rare event searches. Silicon photomultipliers
(SiPMs) and application-specific integrated circuits (ASICs) are two ideal
candidates for low background photosensors and readout electronics,
respectively, because they are mainly composed of silicon, which can achieve
good radio-purity without considerable extra effort. However, interposers, used
to provide mechanical support and signal routes between the photosensor and the
electronics, are a bottleneck in building ultralow background photodetectors.
Silicon and quartz are two candidates to construct the low background
interposer because of their good radio-purity; nevertheless, it is non-trivial
to produce through silicon vias (TSV) or through quartz vias (TQV) on the large
area silicon or quartz wafer. In this work, based on double-sided TSV
interconnect technology, we developed the first prototype of a silicon
interposer with a size of 10~cm$\times$10~cm and a thickness of 320~$\mu$m. The
electrical properties of the interposer are carefully evaluated at room
temperature, and its performance is also examined at -110~$^\circ$C with an
integrated SiPM on the interposer. The testing results reveal quite promising
performance of the prototype, and the single photoelectron signals can be
clearly observed from the SiPM. The features of the observed signals are
comparable with those from the SiPM mounted on a normal FR4-based PCB. Based on
the success of the silicon interposer prototype, we started the follow-up
studies that aimed to further improve the performance and yield of the silicon
interposer, and eventually to provide a solution for building an ultralow
background photodetector system.


  Quantum correlations and coherence generated between two free spinless
particles in the lattice, interacting with a common quantum phonon bath, are
studied. The reduced density matrix is solved using the Markov approach. We
show that the bath induces correlations between the particles. The coherence
induced by the bath is studied, calculating off-diagonal elements of the
density matrix, spatiotemporal dispersion, purity and quantum mutual
information. We find a characteristic time-scale pointing out when this
coherence is maximum. In addition, a Wigner-like distribution in the
phase-space (lattice) is introduced as an indirect indicator of the quantumness
of total correlations and coherence induced by the thermal bath. The negative
volume of the Wigner function also shows a behavior which is in agreement with
the time-scale that we have found. A Gaussian distribution for the profile of
particles is not obtained, and interference patterns are observed as the result
of bath-induced coherence. As the temperature of the bath vanishes, the
ballistic behavior of the tight-binding model is recovered. The geometric
quantum discord is calculated, to characterize the nature of the correlations.


  The MuCool Test Area (MTA) at Fermilab is a facility to develop the
technology required for ionization cooling for a future Muon Collider and/or
Neutrino Factory. As part of this research program, we have tested two 805 MHz
vacuum RF cavities in a multi-Tesla magnetic field to study the effects of the
static magnetic field on the cavity operation. This study gives useful
information on field emitters in the cavity, dark current, surface
conditioning, breakdown mechanisms and material properties of the cavity. All
these factors determine the maximum accelerating gradient in the cavity. This
paper discusses the image processing technique for quantitative estimation of
spark damage spot distribution on cavity interior surfaces. The distribution is
compared with the electric field distribution predicted by a computer code
calculation. The local spark density is proportional to probability of surface
breakdown and shows a power law dependence on the maximum electric field (E).
This E dependence is consistent with the dark current calculated from the
Fowler-Nordheim equation.


  Splicing systems are generative mechanisms introduced by Tom Head in 1987 to
model the biological process of DNA recombination. The computational engine of
a splicing system is the "splicing operation", a cut-and-paste binary string
operation defined by a set of "splicing rules" $r = (\alpha_1, \alpha_2 ;
\alpha_3, \alpha_4)$ where $\alpha_1, \alpha_2, \alpha_3, \alpha_4$ are words
over an alphabet $\Sigma$. For two strings $x = x_1 \alpha_1 \alpha_2 x_2$ and
$y = y_1 \alpha_3 \alpha_4 y_2$, applying the splicing rule $r$ produces the
string $z = x_1 \alpha_1 \alpha_4 y_2$.
  In this paper we focus on a particular type of splicing systems, called $(i,
j)$ semi-simple splicing systems, $i = 1,2$ and $j = 3, 4$, wherein all
splicing rules have the property that the two strings in positions $i$ and $j$
are singleton letters, while the other two strings are empty. The language
generated by such a system consists of the set of words that are obtained
starting from an initial set called "axiom set", by iteratively applying the
splicing rules to strings in the axiom set as well as to intermediately
produced strings. We consider semi-simple splicing systems where the axiom set
is a regular language, and investigate the descriptional complexity of such
systems in terms of the size of the minimal deterministic finite automata that
recognize the languages they generate.


  There is an observational indication of extragalactic magnetic fields. No
known astrophysical process can explain the origin of such large scale magnetic
fields, which motivates us to look for their origin in primordial inflation. By
solving the linearized Einstein equations, we study metric perturbations
sourced by magnetic fields that are produced during inflation. This leads to a
simple but robust bound on the inflation models by requiring that the induced
metric perturbation should not exceed the observed value 10^-5. In case of the
standard single field inflation model, the bound can be converted into a lower
bound on the Hubble parameter during inflation.


  We explore possible extensions of the Weak Gravity Conjecture (WGC) to scalar
field theories. To avoid charged black hole remnants, the WGC requires the
existence of a particle with a mass $m < g q M_P$, with charge $q$ and U(1)
gauge coupling $g$, allowing the decay to shed the black hole charge. Although
there is no obvious problem that arises in the absence of a U(1) charge, it has
been postulated that gravity must remain the weakest force even when extended
to scalar interactions. Quantifying this conjecture may be done by comparing
scalar and gravitational amplitudes, or as we advocate here by comparing
scattering cross sections. In theories with non-trivial field space geometries,
by working out examples with perturbation theory around arbitrary field values
and performing tadpole resummations, we argue that the conjecture must be
applied only at the extrema of the scalar potential (when expressed in locally
canonical coordinates). We consider several toy models in the context of
no-scale supergravity and also consider examples of inflationary models.


  The geometry of a light wavefront, evolving from a initial flat wavefront in
the 3-space associated with a post-Newtonian relativistic spacetime, is studied
numerically by means of the ray tracing method. For a discretization of the
bidimensional light wavefront, a surface fitting technique is used to determine
the curvature of this surface. The relationship between the intrinsic curvature
of the wavefront and the change of the arrival time at different points on the
Earth is also numerically discussed.


  We show that up to commensurability there are only finitely many cocompact
arithmetic Kleinian groups generated by rotations. This implies, in particular,
that there exist only finitely many conjugacy classes of cocompact two
generated arithmetic Kleinian groups. The proof of the main result is based on
a generalized Gromov--Guth inequality and bounds for the hyperbolic and tube
volumes of the quotient orbifolds.


  Big data analytic applications using phasor measurements help improve the
situation awareness of grid operators to better operate and control the system.
Phasor measurement unit (PMU) data from actual grids is viewed as highly
confidential and is not publicly available to researchers and educators. This
paper develops a methodology to synthesize PMU data that can be accessed and
shared freely, with a focus on input data preparation. Time series of demand-
and generation-side input data are generated using public data from different
utilities and statistical analysis methods. Detailed load dynamics modeling is
also performed in this paper to extend the synthetic electric network models.


  This paper introduces a quantile regression estimator for panel data models
with individual heterogeneity and attrition. The method is motivated by the
fact that attrition bias is often encountered in Big Data applications. For
example, many users sign-up for the latest program but few remain active users
several months later, making the evaluation of such interventions inherently
very challenging. Building on earlier work by Hausman and Wise (1979), we
provide a simple identification strategy that leads to a two-step estimation
procedure. In the first step, the coefficients of interest in the selection
equation are consistently estimated using parametric or nonparametric methods.
In the second step, standard panel quantile methods are employed on a subset of
weighted observations. The estimator is computationally easy to implement in
Big Data applications with a large number of subjects. We investigate the
conditions under which the parameter estimator is asymptotically Gaussian and
we carry out a series of Monte Carlo simulations to investigate the finite
sample properties of the estimator. Lastly, using a simulation exercise, we
apply the method to the evaluation of a recent Time-of-Day electricity pricing
experiment inspired by the work of Aigner and Hausman (1980).


  Parrondo games with one-dimensional spatial dependence were introduced by
Toral and extended to the two-dimensional setting by Mihailovi\'c and
Rajkovi\'c. $MN$ players are arranged in an $M\times N$ array. There are three
games, the fair, spatially independent game $A$, the spatially dependent game
$B$, and game $C$, which is a random mixture or nonrandom pattern of games $A$
and $B$. Of interest is $\mu_B$ (or $\mu_C$), the mean profit per turn at
equilibrium to the set of $MN$ players playing game $B$ (or game $C$). Game $A$
is fair, so if $\mu_B\le0$ and $\mu_C>0$, then we say the Parrondo effect is
present.
  We obtain a strong law of large numbers and a central limit theorem for the
sequence of profits of the set of $MN$ players playing game $B$ (or game $C$).
The mean and variance parameters are computable for small arrays and can be
simulated otherwise. The SLLN justifies the use of simulation to estimate the
mean. The CLT permits evaluation of the standard error of a simulated estimate.
We investigate the presence of the Parrondo effect for both small arrays and
large ones. One of the findings of Mihailovi\'c and Rajkovi\'c was that
"capital evolution depends to a large degree on the lattice size." We provide
evidence that this conclusion is incorrect. Part of the evidence is that, under
certain conditions, the means $\mu_B$ and $\mu_C$ converge as $M,N\to\infty$.
Proof requires that a related spin system on ${\bf Z}^2$ be ergodic. However,
our sufficient conditions for ergodicity are rather restrictive.


  Sensor-to-segment calibration is a crucial step in inertial motion tracking.
When two segments are connected by a hinge joint, for example in human knee and
finger joints as well as in many robotic limbs, then the joint axis vector must
be identified in the intrinsic sensor coordinate systems. There exist methods
that identify these coordinates by solving an optimization problem that is
based on kinematic joint constraints, which involve either the measured
accelerations or the measured angular rates. In the current paper we
demonstrate that using only one of these constraints leads to inaccurate
estimates at either fast or slow motions. We propose a novel method based on a
cost function that combines both constraints. The restrictive assumption of a
homogeneous magnetic field is avoided by using only accelerometer and gyroscope
readings. To combine the advantages of both sensor types, the residual weights
are adjusted automatically based on the estimated signal variances and a
nonlinear weighting of the acceleration norm difference. The method is
evaluated using real data from nine different motions of an upper limb
exoskeleton. Results show that, unlike previous approaches, the proposed method
yields accurate joint axis estimation after only five seconds for all fast and
slow motions.


  When pre-trained on large unsupervised textual corpora, language models are
able to store and retrieve factual knowledge to some extent, making it possible
to use them directly for zero-shot cloze-style question answering. However,
storing factual knowledge in a fixed number of weights of a language model
clearly has limitations. Previous approaches have successfully provided access
to information outside the model weights using supervised architectures that
combine an information retrieval system with a machine reading component. In
this paper, we go a step further and integrate information from a retrieval
system with a pre-trained language model in a purely unsupervised way. We
report that augmenting pre-trained language models in this way dramatically
improves performance and that the resulting system, despite being unsupervised,
is competitive with a supervised machine reading baseline. Furthermore,
processing query and context with different segment tokens allows BERT to
utilize its Next Sentence Prediction pre-trained classifier to determine
whether the context is relevant or not, substantially improving BERT's
zero-shot cloze-style question-answering performance and making its predictions
robust to noisy contexts.


  Conservation laws strongly influence production of particles in high-energy
particle collisions. Effects connected to these mechanisms were studied in
details using correlation techniques in \ee\ collisions. At the time, models
were tuned to correctly reproduce the measurements. Similar studies for
hadron-hadron collisions have never been performed, until recent ALICE
measurements. ALICE has reported on studies of untriggered two-particle angular
correlations of identified particles ($\pi$, $K$, and p) measured in pp
collisions at center-of-mass energy of $\sqrt{s}=$ 7 TeV. Those preliminary
results confirm that also in hadron-hadron collisions, at much higher energies,
conservation laws strongly influence the shape of the correlation functions for
different particle types and must be taken into account while analysing the
data. Moreover, they show that the contemporary models (PYTHIA, PHOJET) no
longer reproduce the experimental data well.


  We study the motion of vitreous humour in a deforming eyeball. From the
mechanical and computational perspective this is a task to solve a
fluid-structure interaction problem between a complex viscoelastic fluid
(vitreour humour) and a nonlinear elastic solid (sclera and lens). We propose a
numerical methodology capable of handling the fluid-structure interaction
problem, and we demonstrate its applicability via solving the corresponding
governing equations in a realistic geometrical setting and for realistic
parameter values. It is shown that the choice of the rheological model for the
vitreous humour has a negligible influence on the overall flow pattern in the
domain of interest, whilst it is has a significant impact on the mechanical
stress distribution in the domain of interest.


  Color centers are versatile systems that generate quantum light, sense
magnetic fields and produce spin-photon entanglement. We review how these
properties have pushed the limits of fundamental knowledge in a variety of
scientific disciplines, from rejecting local-realistic theories to sensing
superconducting phase transitions. In the light of recent progress in material
processing and device fabrication, we identify new opportunities for
interdisciplinary fundamental discoveries in physics and geochemistry.


  We study aspects of Calabi-Yau four-folds as compactification manifolds of
F-theory, using mirror symmetry of toric hypersurfaces. Correlation functions
of the topological field theory are determined directly in terms of a natural
ring structure of divisors and the period integrals, and subsequently used to
extract invariants of moduli spaces of rational curves subject to certain
conditions. We then turn to the discussion of physical properties of the
space-time theories, for a number of examples which are dual to $E_8\times E_8$
heterotic N=1 theories. Non-critical strings of various kinds, with low tension
for special values of the moduli, lead to interesting physical effects. We give
a complete classification of those divisors in toric manifolds that contribute
to the non-perturbative four-dimensional superpotential; the physical
singularities associated to it are related to the apppearance of tensionless
strings. In some cases non-perturbative effects generate an everywhere non-zero
quantum tension leading to a combination of a conventional field theory with
light strings hiding at a low energy scale related to supersymmetry breaking.


  The number of users that can be spatially multiplexed by a wireless access
point depends on the aperture of its antenna array. When the aperture increases
and wavelength shrinks, "new" electromagnetic phenomena can be utilized to
further enhance network capacity. In this chapter, we describe how extremely
large aperture arrays (ELAA) can extend the radiative near-field region to
kilometer distances. We demonstrate how this affects the propagation models in
line-of-sight (LoS) scenarios and enables finite-depth beamforming. In
particular, it becomes possible to simultaneously serve users that are located
in the same direction but at different distances.


  We present a new method that investigates the evolutionary history of the
pulsating DB white dwarf GD358 using asteroseismology. This is done considering
the internal C/O profile, which describes the relative abundances of carbon and
oxygen from the core of the star to its surface. Different evolutionary
channels lead to the generation of different C/O profiles, and these affect the
pulsation periods. We used C/O profiles associated with white dwarfs that
evolved through binary evolution channels where the progenitor experienced one
or two episodes of mass loss during one or two common envelope (CE) phases, and
two profiles from single star evolution. We computed models using these
different profiles and used a genetic algorithm (GA) to optimize the search in
the parameter space in order to find the best-fit to the observed pulsation
periods. We used three-parameter models, adjusting the stellar mass, the
effective temperature, and the helium mass of the external layer. Our results
suggest that binary evolution profiles may provide a better match to the
pulsation periods of GD 358. The best-fit to the observations is obtained using
a profile related to an evolutionary history where two episodes of mass loss
happen during two CE phases. The best-fit model has a mass close to the mean
mass for DB white dwarfs found in various works, and a temperature consistent
with UV spectra obtained with the IUE satellite.


  Quantum states of gravitational source masses can lead to experimental
outcomes that are inconsistent with the predictions of a purely classical field
theory of gravity. Environmental decoherence places strict boundary conditions
to the potential realization of such experiments: sufficiently mild not to act
as a fundamental show-stopper, yet sufficiently demanding to represent a
formidable challenge to the next generation of quantum experiment(er)s.


  Originally, the Carnot cycle is a theoretical thermodynamic cycle that
provides an upper limit on the efficiency that any classical thermodynamic
engine can achieve during the conversion of heat into work, or conversely, the
efficiency of a refrigeration system in creating a temperature difference by
the application of work to the system. The aim of this paper is to introduce
and study the economic Carnot cycles into a Roegenian economy, using our
Thermodynamic-Economic Dictionary. Of course, the most difficult questions are:
what is the economic significance of such a cycle? Roegenian economics is
acceptable or not, in terms of practical applications? Our answer is yes for
both questions.


  A transient femtosecond population inversion in graphene was recently
reported by Li et al., Phys. Rev. Lett. 108, 167401 (2012). Based on a
microscopic theory we clarify the underlying microscopic mechanism: Transient
gain and population inversion in graphene occurs due to a complex interplay of
strong optical pumping and carrier cooling that fills states close to the Dirac
point giving rise to a relaxation bottleneck. The subsequent femtosecond decay
of the optical gain is mainly driven by Coulomb-induced Auger recombination.


  A majority of real life networks are weighted and sparse. The present article
aims at characterization of weighted networks based on sparsity, as a measure
of inherent diversity, of different network parameters. It utilizes sparsity
index defined on ordered degree sequence of simple networks and derives further
properties of this index. The range of possible values of sparsity index of any
connected network, with edge-count in specific intervals, is worked out
analytically in terms of node-count; a pattern is uncovered in corresponding
degree sequences to produce highest sparsities. Given the edge-weight frequency
distribution of a network, we have formulated an expression of the sparsity
index of edge-weights. Its properties are analyzed under different
distributions of edge-weights. For example, the upper and lower bounds of
sparsity index of edge-weights of a network, having all distinct edge-weights,
is determined in terms of its node-count and edge density. The article
highlights that this summary index with low computational cost, computed on
different network parameters, is useful to reveal different structural and
organizational aspects of networks for performing analysis. An application of
this index has been demonstrated through overlapping community detection of
networks. The results validated on artificial and real-world networks show its
efficacy.


  We consider C^r-diffeomorphisms of a compact smooth manifold having a pair of
robust heterodimensional cycles where r is a positive integer or infinity. We
prove that if certain conditions about the signatures of non-linearities and
Schwarzian derivatives of the transition maps are satisfied, then by giving C^r
arbitrarily small perturbation, we can produce a periodic point at which the
first return map in the center direction is C^r-flat. As a consequence, we will
prove that C^r-generic diffeomorphisms in the neighborhood of the initial
diffeomorphism exhibit super-exponential growth of number of periodic points.
We also give examples which show the necessity of the conditions on
non-linearities and the Schwarzian derivatives.


  This study of 117 low-redshift Im and Sm galaxies investigates the star
formation rates of late-type galaxies, to determine whether they are
quasi-continuous or dominated by bursts with quiescent interludes. We analyse
the distribution of star formation timescales (stellar masses/star formation
rates) for the entire sample, and of gas depletion timescales for those
galaxies with gas mass measurements. We find that, on average, the late-type
galaxies studied could have produced their total stellar masses by an
extrapolation of their current star formation activity over a period of just
under a Hubble time. This is not the case for a comparison sample of
earlier-type galaxies, even those with disk-dominated morphologies and similar
total stellar masses to the late-type galaxies. The earlier-type galaxies are
on average forming their stars more slowly at present than the average rate
over their past histories. No totally quiescent Im or Sm galaxies are found,
and although some evidence of intrinsic variation in the star formation rate
with time is found, this is typically less than a factor of 2 increase or
decrease relative to the mean level. The Im and Sm galaxies have extensive gas
reservoirs and can maintain star formation at the current rate for more than
another Hubble time. The average spatial distribution of star formation in the
Im galaxies, and to a lesser extent the Sm galaxies, is very similar to that of
the older stellar population traced by the red light.


  We consider the task of designing Local Computation Algorithms (LCA) for
applications of the Lov\'{a}sz Local Lemma (LLL). LCA is a class of sublinear
algorithms proposed by Rubinfeld et al.~\cite{Ronitt} that have received a lot
of attention in recent years. The LLL is an existential, sufficient condition
for a collection of sets to have non-empty intersection (in applications,
often, each set comprises all objects having a certain property). The
ground-breaking algorithm of Moser and Tardos~\cite{MT} made the LLL fully
constructive, following earlier results by Beck~\cite{beck_lll} and
Alon~\cite{alon_lll} giving algorithms under significantly stronger LLL-like
conditions. LCAs under those stronger conditions were given in~\cite{Ronitt},
where it was asked if the Moser-Tardos algorithm can be used to design LCAs
under the standard LLL condition. The main contribution of this paper is to
answer this question affirmatively. In fact, our techniques yield LCAs for
settings beyond the standard LLL condition.


  There is a mathematical error in the first version of this paper. A new
corrected version will be posted when the error is fixed, possibly with a
modified title.


  One of the suggested thick disc formation mechanisms is that they were born
quickly and in situ from a turbulent clumpy disc. Subsequently, thin discs
formed slowly within them from leftovers of the turbulent phase and from
material accreted through cold flows and minor mergers. In this letter, I
propose an observational test to verify this hypothesis. By combining thick
disc and total stellar masses of edge-on galaxies with galaxy stellar mass
functions calculated in the redshift range of $z\leq3.0$, I derived a positive
correlation between the age of the youngest stars in thick discs and the
stellar mass of the host galaxy; galaxies with a present-day stellar mass of
$\mathcal{M}_\star(z=0)<10^{10}\,\mathcal{M}_\odot$ have thick disc stars as
young as $4-6\,{\rm Gyr}$, whereas the youngest stars in the thick discs of
Milky-Way-like galaxies are $\sim10\,{\rm Gyr}$ old. I tested this prediction
against the scarcely available thick disc age estimates, all of them are from
galaxies with $\mathcal{M}_\star(z=0)\gtrsim10^{10}\,\mathcal{M}_\odot$, and I
find that field spiral galaxies seem to follow the expectation. On the other
hand, my derivation predicts ages that are too low for the thick discs in
lenticular galaxies, indicating a fast early evolution for S0 galaxies. I
propose the idea of conclusively testing whether thick discs formed quickly and
in situ by obtaining the ages of thick discs in field galaxies with masses of
$\mathcal{M}_\star(z=0)\sim10^{9.5}\,\mathcal{M}_\odot$ and by checking whether
they contain $\sim5\,{\rm Gyr}$-old stars.


  In the present work we compute the spectrum of quasinormal frequencies of
four-dimensional charged black holes in Einstein-power-Maxwell theory. In
particular we study scalar perturbations adopting the 6th order WKB
approximation. We analyze in detail the behaviour of the spectrum depending on
the charge of the black hole, the quantum number of angular momentum and the
overtone number. In addition, a comparison is made between the results obtained
here and the results valid for charged black holes in other theories as well as
for the Reissner-Nordstr${\"o}$m black hole. Finally, we have provided an
analytical expression for the quasinormal spectrum in the eikonal limit.


  A compact manifold is called Bieberbach if it carries a flat Riemannian
metric. Bieberbach manifolds are aspherical, therefore the supremum of their
systolic ratio, over the set of Riemannian metrics, is finite by a fundamental
result of M. Gromov.
  We study the optimal systolic ratio of compact of $3$-dimensional orientable
Bieberbach manifolds which are not tori, and prove that it cannot be realized
by a flat metric. We also highlight a metric that we construct on one type of
such manifolds ($C_2$) which has interesting geometric properties : it is
extremal in its conformal class and the systole is realized by "very many"
geodesics.


  The recently proposed optical elements containing semitransparent wavelike
films embedded into the transparent material are investigated. Such optical
elements do not distort a wave transmitted through them. Novel optical elements
can be fabricated using well-known embossing methods. Their possible
applications are discussed. The optical elements containing holographic
wavelike films can be used for color holography, for augmented reality wearable
glasses, as a beam combiner for head-up displays. The optical elements
containing diffuse wavelike films can be used as an alternative for tinted car
windows, instead of mirror-glass windows in high-rise buildings, for
shop-windows, for one-way privacy windows, for printing images on eyeglasses.
The scheme of a waveguide display with a single-layer holographic wavelike
films is analyzed. The feasibility of the proposed applications is verified by
estimation of the optical elements parameters and computer simulation, and
measurements conducted on an experimental specimen.


  We prove a sharp quantitative form of the classical isocapacitary inequality.
Namely, we show that the difference between the capacity of a set and that of a
ball with the same volume bounds the square of the Fraenkel asymmetry of the
set. This provides a positive answer to a conjecture of Hall, Hayman, and
Weitsman (J. d' Analyse Math. '91).


  Microcavity polaritons in the lasing regime undergo a spontaneous symmetry
breaking transition resulting in coherent emission with a well defined
polarization. The order parameter is thus a vector describing both the laser
global phase and polarization. Using an ultrafast single-shot detection
technique we show that polariton lasing in GaAs-based microcavities presents a
high degree of second order coherence ($g^{(2)}(\tau=0) \approx 1$) above
threshold, and that the initial polarization is stochastic, taking any possible
direction in the Poincar\'e sphere (linear, elliptical or circular). Once the
polarization direction is established, subsequent oscillations of the emission
probability witness the presence of an intrinsic polarization splitting. Our
results show the negligible role of polariton interactions in the total
emission statistics and in the establishment of the initial polarization.


  The influence of Coulomb distortion on the polarization transfer in elastic
proton and antiproton electron scattering at low energies is calculated in a
distorted wave Born approximation. For antiproton electron scattering Coulomb
effects reduce substantially the spin transfer cross section compared to the
plane wave Born approximation whereas for proton electron scattering they lead
to a dramatic increase for kinetic proton lab energies below about 20 keV.


  Signal-dependent beamformers are advantageous over signal-independent
beamformers when the acoustic scenario - be it real-world or simulated - is
straightforward in terms of the number of sound sources, the ambient sound
field and their dynamics. However, in the context of augmented reality audio
using head-worn microphone arrays, the acoustic scenarios encountered are often
far from straightforward. The design of robust, high-performance, adaptive
beamformers for such scenarios is an on-going challenge. This is due to the
violation of the typically required assumptions on the noise field caused by,
for example, rapid variations resulting from complex acoustic environments,
and/or rotations of the listener's head. This work proposes a multi-channel
speech enhancement algorithm which utilises the adaptability of
signal-dependent beamformers while still benefiting from the computational
efficiency and robust performance of signal-independent super-directive
beamformers. The algorithm has two stages. (i) The first stage is a hybrid
beamformer based on a dictionary of weights corresponding to a set of noise
field models. (ii) The second stage is a wide-band subspace post-filter to
remove any artifacts resulting from (i). The algorithm is evaluated using both
real-world recordings and simulations of a cocktail-party scenario. Noise
suppression, intelligibility and speech quality results show a significant
performance improvement by the proposed algorithm compared to the baseline
super-directive beamformer. A data-driven implementation of the noise field
dictionary is shown to provide more noise suppression, and similar speech
intelligibility and quality, compared to a parametric dictionary.


  We present an alternate formula for calculating gravitational time delay. We
use this formula to reduce geodetic Very Long Baseline Interferometry (VLBI)
data, taking into account gravitational effects within the solar system, and to
test general relativity. The alternate formula was obtained by expanding the
conventional formula in a Taylor series. We show that the gravitational delay
can be split into several terms including a term due to the coordinate
transformation and terms that are explicitly linked to the light deflection
angle. Our formula is compared numerically with the conventional formula, and
difference in arrival times within 1 ps are found at 1$^\circ$ from the Sun for
a full range of baseline lengths. We conclude that the standard reduction of
geodetic VLBI data for the effects of general relativity is equivalent to
displacing the reference radio sources from their original catalogue positions
in accordance with the classical light deflection formula across the whole sky.


  We calculate the $O(\alpha m_t^2/m^2_W)$ corrections arising from diagrams
involving the top-quark loops to the light neutral Higgs boson production via
$q\bar q'\to WH$ at the Fermilab Tevatron in both the standard model and the
minimal supersymmetric model. In contrast to the QCD correction which increases
the tree-level cross section, the corrections imply a few percent reduction in
the production cross section relative to the tree-leve results.


  Let X be a set and Bin(X) the set of all binary operations on X. We say that
a subset of Bin(X) is a distributive set of operations if all pairs of elements
are right distributive. J.Przytycki posed the question of which groups can be
realized as distributive sets. The initial guess that any group may be embedded
into Bin(X) for some X was complicated by an observation that if a binary
operation is idempotent (a*a=a), then it commutes with every element of S. The
first noncommutative subgroup of Bin(X), the symmetric group on three elements,
was found in October of 2011 by Y.Berman. We show that any group can be
embedded in Bin(X) for X=G (as a set). We also discuss minimality of embeddings
observing that, in particular, X with six elements is the smallest set such
that Bin(X) contains a non-abelian subgroup.


  We present SUSY_FLAVOR version 2.5 - program that calculates over 30
low-energy flavor observables in the general $R$-parity conserving MSSM.
Comparing to previous versions, in SUSY_FLAVOR v2.5 parameter initialization in
SLHA2 formats has been significantly generalized, so that the program accepts
most of the output files produced by other libraries analyzing the MSSM
phenomenology. Number of bugs and inconsistencies have been fixed, based on
users feedback. Calculations of several processes implemented in earlier
versions have been corrected. New processes of rare decays of the top quark to
Higgs boson have been included. Variables controlling inclusion of
contributions from various MSSM sectors have been added. Full updated manual of
SUSY_FLAVOR v2.5 integrating the details of the modifications listed below can
be found at {\tt arxiv.org/abs/arXiv:1203.5023}.


  We revisit the challenging problem of resolving prepositional-phrase (PP)
attachment ambiguity. To date, proposed solutions are either rule-based, where
explicit grammar rules direct how to resolve ambiguities; or statistical, where
the decision is learned from a corpus of labeled examples. We argue that
explicit commonsense knowledge bases can provide an essential ingredient for
making good attachment decisions. We implemented a module, named Patch-Comm,
that can be used by a variety of conventional parsers, to make attachment
decisions. Where the commonsense KB does not provide direct answers, we fall
back on a more general system that infers "out-of-knowledge-base" assertions in
a manner similar to the way some NLP systems handle out-of-vocabulary words.
Our results suggest that the commonsense knowledge-based approach can provide
the best of both worlds, integrating rule-based and statistical techniques. As
the field is increasingly coming to recognize the importance of explainability
in AI, a commonsense approach can enable NLP developers to better understand
the behavior of systems, and facilitate natural dialogues with end users.


  Learning from implicit user feedback is challenging as we can only observe
positive samples but never access negative ones. Most conventional methods cope
with this issue by adopting a pairwise ranking approach with negative sampling.
However, the pairwise ranking approach has a severe disadvantage in the
convergence time owing to the quadratically increasing computational cost with
respect to the sample size; it is problematic, particularly for large-scale
datasets and complex models such as neural networks. By contrast, a pointwise
approach does not directly solve a ranking problem, and is therefore inferior
to a pairwise counterpart in top-K ranking tasks; however, it is generally
advantageous in regards to the convergence time. This study aims to establish
an approach to learn personalised ranking from implicit feedback, which
reconciles the training efficiency of the pointwise approach and ranking
effectiveness of the pairwise counterpart. The key idea is to estimate the
ranking of items in a pointwise manner; we first reformulate the conventional
pointwise approach based on density ratio estimation and then incorporate the
essence of ranking-oriented approaches (e.g. the pairwise approach) into our
formulation. Through experiments on three real-world datasets, we demonstrate
that our approach not only dramatically reduces the convergence time (one to
two orders of magnitude faster) but also significantly improving the ranking
performance.


  This thesis deals with the enumerative study of combinatorial maps, and its
application to the enumeration of other combinatorial objects.
  Combinatorial maps, or simply maps, form a rich combinatorial model. They
have an intuitive and geometric definition, but are also related to some deep
algebraic structures. For instance, a special type of maps called
constellations provides a unifying framework for some enumeration problems
concerning factorizations in the symmetric group. Standing on a position where
many domains meet, maps can be studied using a large variety of methods, and
their enumeration can also help us count other combinatorial objects. This
thesis is a sampling from the rich results and connections in the enumeration
of maps.
  This thesis is structured into four major parts. The first part, including
Chapter 1 and 2, consist of an introduction to the enumerative study of maps.
The second part, Chapter 3 and 4, contains my work in the enumeration of
constellations, which are a special type of maps that can serve as a unifying
model of some factorizations of the identity in the symmetric group. The third
part, composed by Chapter 5 and 6, shows my research on the enumerative link
from maps to other combinatorial objects, such as generalizations of the Tamari
lattice and random graphs embeddable onto surfaces. The last part is the
closing chapter, in which the thesis concludes with some perspectives and
future directions in the enumerative study of maps.


  The spectroscopy measurement is one of main pathways for exploring and
understanding the nature. Today, it seems that racing artificial intelligence
will remould its styles. The algorithms contained in huge neural networks are
capable of substituting many of expensive and complex components of spectrum
instruments. In this work, we presented a smart machine learning strategy on
the measurement of absorbance curves, and also initially verified that an
exceedingly-simplified equipment is sufficient to meet the needs for this
strategy. Further, with its simplicity, the setup is expected to infiltrate
into many scientific areas in versatile forms.


  Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art
Imitation Learning algorithms commonly used in robotics. In AIL, an artificial
adversary's misclassification is used as a reward signal that is optimized by
any standard Reinforcement Learning (RL) algorithm. Unlike most RL settings,
the reward in AIL is $differentiable$ but current model-free RL algorithms do
not make use of this property to train a policy. The reward is AIL is also
shaped since it comes from an adversary. We leverage the differentiability
property of the shaped AIL reward function and formulate a class of Actor
Residual Critic (ARC) RL algorithms. ARC algorithms draw a parallel to the
standard Actor-Critic (AC) algorithms in RL literature and uses a residual
critic, $C$ function (instead of the standard $Q$ function) to approximate only
the discounted future return (excluding the immediate reward). ARC algorithms
have similar convergence properties as the standard AC algorithms with the
additional advantage that the gradient through the immediate reward is exact.
For the discrete (tabular) case with finite states, actions, and known
dynamics, we prove that policy iteration with $C$ function converges to an
optimal policy. In the continuous case with function approximation and unknown
dynamics, we experimentally show that ARC aided AIL outperforms standard AIL in
simulated continuous-control and real robotic manipulation tasks. ARC
algorithms are simple to implement and can be incorporated into any existing
AIL implementation with an AC algorithm. Video and link to code are available
at: https://sites.google.com/view/actor-residual-critic.


  Existing data-dependent hashing methods use large backbone networks with
millions of parameters and are computationally complex. Existing knowledge
distillation methods use logits and other features of the deep (teacher) model
and as knowledge for the compact (student) model, which requires the teacher's
network to be fine-tuned on the context in parallel with the student model on
the context. Training teacher on the target context requires more time and
computational resources. In this paper, we propose context unaware knowledge
distillation that uses the knowledge of the teacher model without fine-tuning
it on the target context. We also propose a new efficient student model
architecture for knowledge distillation. The proposed approach follows a
two-step process. The first step involves pre-training the student model with
the help of context unaware knowledge distillation from the teacher model. The
second step involves fine-tuning the student model on the context of image
retrieval. In order to show the efficacy of the proposed approach, we compare
the retrieval results, no. of parameters and no. of operations of the student
models with the teacher models under different retrieval frameworks, including
deep cauchy hashing (DCH) and central similarity quantization (CSQ). The
experimental results confirm that the proposed approach provides a promising
trade-off between the retrieval results and efficiency. The code used in this
paper is released publicly at \url{https://github.com/satoru2001/CUKDFIR}.


  We study the effect of magnetic anisotropy in a single electron transistor
with ferromagnetic electrodes and a non-magnetic island. We identify the
variation $\delta \mu$ of the chemical potential of the electrodes as a
function of the magnetization orientation as a key quantity that permits to
tune the electrical properties of the device. Different effects occur depending
on the relative size of $\delta \mu$ and the charging energy. We provide
preliminary quantitative estimates of $\delta \mu$ using a very simple toy
model for the electrodes.


  We minimally coupled a scalar potential $V(\phi)$ with asymmetric vacua to
the Einstein gravity to numerically construct the hairy Reissner-Nordstrom
black hole (RNBH) as a direct generalization of RNBHs to possess scalar hair.
By fixing the electric charge to mass ratio $q$, a branch of hairy RNBHs
bifurcates from the RNBH when the scalar field $\phi_H$ is non-trivial at the
horizon. The values of $q$ are bounded for $0 \leq q \leq 1$, which contrast to
a class of hairy black holes with $q>1$ in the Einstein-Maxwell-scalar theory.
We find that the profiles of solutions affected by the competition between the
strength of $\phi_H$ and $q$, for instance, the gradient of scalar field at the
horizon can increase very sharply when $q \rightarrow 1$ and $\phi_H$ is small
but its gradient can be very small which independent of $q$ when $\phi_H$ is
large. Furthermore, the weak energy condition of hairy RNBHs, particularly at
the horizon can be satisfied when $q>0$.


  We calculate the covariant one-loop quantum gravitational effective action
for a scalar field model inspired by the recently proposed nonminimal natural
inflation model. Our calculation is perturbative, in the sense that the
effective action is evaluated in orders of background field, around a Minkowski
background. The effective potential has been evaluated taking into account the
finite corrections. An order-of-magnitude estimate of the one-loop corrections
reveals that gravitational and non-gravitational corrections have same or
comparable magnitudes.


  We present a Newtonian multi-fluid formalism for superfluid neutron star
cores, focussing on the additional dissipative terms that arise when one takes
into account the individual dynamical degrees of freedom associated with the
coupled "fluids". The problem is of direct astrophysical interest as the nature
of the dissipative terms can have significant impact on the damping of the
various oscillation modes of the star and the associated gravitational-wave
signatures. A particularly interesting application concerns the
gravitational-wave driven instability of f- and r-modes. We apply the developed
formalism to two specific three-fluid systems: (i) a hyperon core in which both
Lambda and Sigma^- hyperons are present, and (ii) a core of deconfined quarks
in the colour-flavour-locked phase in which a population of neutral K^0 kaons
is present. The formalism is, however, general and can be applied to other
problems in neutron-star dynamics (such as the effect of thermal excitations
close to the superfluid transition temperature) as well as laboratory
multi-fluid systems.


  Using a recursion formula due to Dvir, we obtain information on maximal and
almost maximal components in Kronecker products of characters of the symmetric
groups. This is applied to confirm a conjecture made by Bessenrodt and
Kleshchev in 1999, which classifies all such Kronecker products with only three
or four components.