  The recent discovery of super-massive black holes (SMBHs) in the centers of
high-mass ultra compact dwarf galaxies (UCDs) suggests that at least some UCDs
are the stripped nuclear star clusters of lower mass galaxies. Tracing these
former nuclei of stripped galaxies provides a unique way to track the assembly
history of a galaxy or galaxy cluster. In this paper we present a new method to
estimate how many UCDs host an SMBH in their center and thus are stripped
galaxy nuclei. We revisit the dynamical mass measurements that suggest many
UCDs have more mass than expected from stellar population estimates, which
recent observations have shown is due to the presence of an SMBH. We revise the
stellar population mass estimates using a new empirical relation between the
mass-to-light ratio (M/L) and metallicity, and use this to predict which UCDs
are most likely to host an SMBH. This enables us to calculate the fraction of
UCDs that host SMBHs across their entire luminosity range for the first time.
We then apply the SMBH occupation fraction to the observed luminosity function
of UCDs and estimate that in the Fornax and Virgo cluster alone there should be
$69^{+32}_{-25}$ stripped nuclei with SMBHs. This analysis shows that stripped
nuclei with SMBHs are almost as common in clusters as present-day galaxy
nuclei. We estimate the local SMBH number density in stripped nuclei to
$3-8\times10^{-3}Mpc^{-3}$, which represents a significant fraction (10-40\%)
of the SMBH density in the local Universe. These SMBHs hidden in stripped
nuclei will increase expected event rates for tidal disruption events and
SMBH-SMBH and SMBH-BH mergers. The existence of numerous stripped nuclei with
SMBHs are a direct consequence of hierarchical galaxy formation, but until now
their impact on the SMBH density had not been quantified.


  In this paper, we present several new linearizations of a quadratic binary
optimization problem (QBOP), primarily using the method of aggregations.
Although aggregations were studied in the past in the context of solving system
of Diophantine equations in non-negative variables, none of the approaches
developed produced practical models, particularly due to the large size of
associate multipliers. Exploiting the special structure of QBOP we show that
selective aggregation of constraints provide valid linearizations with
interesting properties. For our aggregations, multipliers can be any non-zero
real numbers. Moreover, choosing the multipliers appropriately, we demonstrate
that the resulting LP relaxations have value identical to the corresponding
non-aggregated models. We also provide a review of existing explicit
linearizations of QBOP and presents the first systematic study of such models.
Theoretical and experimental comparisons of new and existing models are also
provided.


  The calculation of imaginary time displaced correlation functions with the
auxiliary field projector quantum Monte-Carlo algorithm provides valuable
insight (such as spin and charge gaps) in the model under consideration. One of
the authors and M. Imada [F.F. Assaad and M. Imada, J. Phys. Soc. Jpn. 65 189
(1996).] have proposed a numerically stable method to compute those quantities.
Although precise this method is expensive in CPU time. Here, we present an
alternative approach which is an order of magnitude quicker, just as precise,
and very simple to implement. The method is based on the observation that for a
given auxiliary field the equal time Green function matrix, $G$, is a
projector: $G^2 = G$.


  An algorithm for unconstrained non-convex optimization is described, which
does not evaluate the objective function and in which minimization is carried
out, at each iteration, within a randomly selected subspace. It is shown that
this random approximation technique does not affect the method's convergence
nor its evaluation complexity for the search of an $\epsilon$-approximate
first-order critical point, which is $\mathcal{O}(\epsilon^{-(p+1)/p})$, where
$p$ is the order of derivatives used. A variant of the algorithm using
approximate Hessian matrices is also analyzed and shown to require at most
$\mathcal{O}(\epsilon^{-2})$ evaluations. Preliminary numerical tests show that
the random-subspace technique can significantly improve performance on some
problems, albeit, unsurprisingly, not for all.


  Many high dimensional vector distances tend to a constant. This is typically
considered a negative "contrast-loss" phenomenon that hinders clustering and
other machine learning techniques. We reinterpret "contrast-loss" as a
blessing. Re-deriving "contrast-loss" using the law of large numbers, we show
it results in a distribution's instances concentrating on a thin "hyper-shell".
The hollow center means apparently chaotically overlapping distributions are
actually intrinsically separable. We use this to develop
distribution-clustering, an elegant algorithm for grouping of data points by
their (unknown) underlying distribution. Distribution-clustering, creates
notably clean clusters from raw unlabeled data, estimates the number of
clusters for itself and is inherently robust to "outliers" which form their own
clusters. This enables trawling for patterns in unorganized data and may be the
key to enabling machine intelligence.


  Some of the operator product expansions (OPEs) between the lowest $SO(4)$
singlet higher spin-$2$ multiplet of spins $(2, \frac{5}{2}, \frac{5}{2},
\frac{5}{2}, \frac{5}{2}, 3, 3, 3, 3, 3, 3, \frac{7}{2}, \frac{7}{2},
\frac{7}{2}, \frac{7}{2}, 4)$ in an extension of the large ${\cal N}=4$
(non)linear superconformal algebra were constructed in the ${\cal N}=4$
superconformal coset $\frac{SO(N+4)}{SO(N) \times SO(4)}$ theory with $N=4$
previously. In this paper, by rewriting the above OPEs with $N=5$, the
remaining undetermined OPEs are completely determined. There exist additional
$SO(4)$ singlet higher spin-$2$ multiplet, six $SO(4)$ adjoint higher spin-$3$
multiplets, four $SO(4)$ vector higher spin-$\frac{7}{2}$ multiplets, $SO(4)$
singlet higher spin-$4$ multiplet and four $SO(4)$ vector higher
spin-$\frac{9}{2}$ multiplets in the right hand side of these OPEs.
Furthermore, by introducing the arbitrary coefficients in front of the
composite fields in the right hand sides of the above complete 136 OPEs, the
complete structures of the above OPEs are obtained by using various Jacobi
identities for generic $N$. Finally, we describe them as one single ${\cal
N}=4$ super OPE between the above lowest $SO(4)$ singlet higher spin-$2$
multiplet in the ${\cal N}=4$ superspace.


  A quantitative study of the Luminous Blue Variable NGC 2363-V1 in the
Magellanic galaxy NGC 2366 (D = 3.44 Mpc) is presented, based on ultraviolet
and optical HST/STIS spectroscopy. Contemporary WFPC2 and WHT imaging reveals a
modest V-band brightness increase of ~ 0.2 mag per year between 1996 January
and 1997 November, reaching V=17.4 mag, corresponding to Mv=-10.4 mag.
Subsequently, V1 underwent a similar decrease in V-band brightness, together
with a UV brightening of 0.35 mag from 1997 November to 1999 November.
  The optical spectrum of V1 is dominated by H emission lines, with Fe II, He I
and Na I also detected. In the ultraviolet, a forest of Fe absorption features
and numerous absorption lines typical of mid-B supergiants are observed. From a
spectral analysis with the non-LTE, line-blanketed code of Hillier & Miller
(1998), we derive stellar parameters of T*=11kK, R*=420Ro, log(L/Lo)=6.35
during 1997 November, and T*=13kK, R*=315Ro, log(L/Lo)=6.4 for 1999 July. The
wind properties of V1 are also exceptional, with Mdot ~ 4.4 x 10e-4 Mo/yr and
$v_{\infty} ~ $300 km/s, allowing for a clumped wind (filling factor = 0.3),
and assuming H/He ~ 4 by number.
  The presence of Fe lines in the UV and optical spectrum of V1 permits an
estimate of the heavy elemental abundance of NGC 2363 from our spectral
synthesis. Although some deficiencies remain, allowance for charge exchange
reactions in our calculations supports a SMC-like metallicity, that has
previously been determined for NGC 2363 from nebular oxygen diagnostics.
  Considering a variety of possible progenitor stars, V1 has definitely
undergone a giant eruption, with a substantial increase in stellar luminosity,
radius, and almost certainly mass-loss rate.


  We study the tensions of domain walls in the deconfined phase of N=4 SUSY
Yang-Mills theory on R^3 x S^1, at weak and strong coupling. We calculate the
k-wall tension at one-loop order and find that it is proportional to k(N-k)
(Casimir scaling). The two-loops analysis suggests that Casimir scaling
persists to this order. The strong coupling calculation is performed by using
the AdS/CFT correspondence. We argue that the k-wall should be identified with
an NS5-brane wrapping an S^4 inside S^5 in the AdS-Schwarzschild x S^5
background in Type IIB string theory. The tension at strong coupling is
compared with the weak coupling result. We also compare our results with those
from lattice simulations in pure Yang-Mills theory.


  The rapid development of remote sensing techniques provides rich,
large-coverage, and high-temporal information of the ground, which can be
coupled with the emerging deep learning approaches that enable latent features
and hidden geographical patterns to be extracted. This study marks the first
attempt to cross-compare performances of popular state-of-the-art deep learning
models in estimating population distribution from remote sensing images,
investigate the contribution of neighboring effect, and explore the potential
systematic population estimation biases. We conduct an end-to-end training of
four popular deep learning architectures, i.e., VGG, ResNet, Xception, and
DenseNet, by establishing a mapping between Sentinel-2 image patches and their
corresponding population count from the LandScan population grid. The results
reveal that DenseNet outperforms the other three models, while VGG has the
worst performances in all evaluating metrics under all selected neighboring
scenarios. As for the neighboring effect, contradicting existing studies, our
results suggest that the increase of neighboring sizes leads to reduced
population estimation performance, which is found universal for all four
selected models in all evaluating metrics. In addition, there exists a notable,
universal bias that all selected deep learning models tend to overestimate
sparsely populated image patches and underestimate densely populated image
patches, regardless of neighboring sizes. The methodological, experimental, and
contextual knowledge this study provides is expected to benefit a wide range of
future studies that estimate population distribution via remote sensing
imagery.


  A method to establish regions of phase space through which pass no invariant
tori transverse to a given direction field is applied to the planar circular
restricted three-body problem. Implications for the location of stable orbits
for planets around a binary star are deduced. It is expected that lessons
learnt from this problem will be useful for applications of the method to other
contexts such as flux surfaces for magnetic fields, guiding centre motion in
magnetic fields, and classical models of chemical reaction dynamics.


  In this paper we study the controllability results of impulsive neutral
stochastic functional differential equations with infinite delay driven by
fractional Brownian motion in a real separable Hilbert space. The
controllability results are obtained using stochastic analysis and a
fixed-point strategy. Finally, an illustrative example is provided to
demonstrate the effectiveness of the theoretical result.


  Photonic crystals use periodic structures to create forbidden frequency
regions for optical wave propagation, that allow for the creation and
integration of complex optical functions in small footprint devices. Such
strategy has also been successfully applied to confine mechanical waves and to
explore their interaction with light in the so-called optomechanical cavities.
Because of their challenging design, these cavities are traditionally
fabricated using dedicated high-resolution electron-beam lithography tools that
are inherently slow, limiting this solution to small-scale applications or
research. Here we show how to overcome this problem by using a deep-UV
photolithography process to fabricate optomechanical crystals on a commercial
CMOS foundry. We show that a careful design of the photonic crystals can
withstand the limitations of the photolithography process, producing cavities
with measured intrinsic optical quality factors as high as
$Q_{i}=(1.21\pm0.02)\times10^{6}$. Optomechanical crystals are also created
using phononic crystals to tightly confine the sound waves within the optical
cavity that results in a measured vacuum optomechanical coupling rate of
$g_{0}=2\pi\times(91\pm4)$ kHz. Efficient sideband cooling and amplification
are also demonstrated since these cavities are in the resolved sideband regime.
Further improvement in the design and fabrication process suggest that
commercial foundry-based optomechanical cavities could be used for quantum
ground-state cooling.


  The electronic distribution functions of two Coulomb coupled chiral edge
states forming a quasi-1D system with broken translation invariance are found
using the equation of motion approach. We find that relaxation and thereby
energy exchange between the two edge states is determined by the shot noise of
the edge states generated at a quantum point contact (QPC). In close vicinity
to the QPC, we derive analytic expressions for the distribution functions. We
further give an iterative procedure with which we can compute numerically the
distribution functions arbitrarily far away from the QPC. Our results are
compared with recent experiments of Le Sueur et al..


  Based on the Ward-Takahashi identity for the BRS symmetry, we prove to all
orders of the loop expansion the low energy theorem of hidden local symmetry
for the vector mesons (KSRF (I) relation) in the $U(N)_{\rm L}$ $\times$
$U(N)_{\rm R}$ / $U(N)_{\rm V}$ nonlinear chiral Lagrangian.


  We analytically investigate the 2-dimensional Gross-Neveu model at finite
temperature and density using Wilson fermion action. The relation between the
phase structure on the lattice and that in the continuum is clarified.


  This paper studies a variant of ramified/branched optimal transportation
problems. Given the distributions of production capacities and market sizes, a
firm looks for an allocation of productions over factories, a distribution of
sales across markets, and a transport path that delivers the product to
maximize its profit. Mathematically, given any two measures $\mu$ and $\nu$ on
$X$, and a payoff function $h$, the planner wants to minimize
$\mathbf{M}_{\alpha }(T)-\int_{X}hd(\partial T)$ among all transport paths $T$
from $\tilde{\mu}$ to $\tilde{\nu}$ with $\tilde{\mu}\leq \mu $ and
$\tilde{\nu}\leq \nu $, where $\mathbf{M}_{\alpha }$ is the standard cost
functional used in ramified transportation. After proving the existence result,
we provide a characterization of the boundary measures of the optimal solution.
They turn out to be the original measures restricted on some Borel subsets up
to a Delta mass on each connected component. Our analysis further finds that as
the boundary payoff increases, the corresponding solution of the current
problem converges to an optimal transport path, which is the solution of the
standard ramified transportation.


  Let $f:\mathbb{N}\to\{0,\pm 1\}$, for $n \in \mathbb{N}$ let $\Pi[n]$ be the
set of partitions of $n$, and for all partitions $\pi = (a_1,a_2,\ldots,a_k)
\in \Pi[n]$ let \[ f(\pi) := f(a_1)f(a_2) \cdots f(a_k). \] With this we define
the $f$-signed partition numbers \[ \mathfrak{p}(n,f) = \sum_{\pi\in\Pi[n]}
f(\pi). \] In this paper, for odd primes $p$ we derive asymptotic formulae for
$\mathfrak{p}(n,\chi_p)$ as $n\to\infty$, where $\chi_p(n)$ is the Legendre
symbol $(\frac{n}{p})$ associated $p$. A similar asymptotic formula for
$\mathfrak{p}(n,\chi_2)$ is also established, where $\chi_2(n)$ is the
Kronecker symbol $(\frac{n}{2})$. Special attention is paid to the sequence
$(\mathfrak{p}(n,\chi_5))_\mathbb{N}$, and a formula for
$\mathfrak{p}(n,\chi_5)$ supporting the recent discovery that
$\mathfrak{p}(10j+2,\chi_5)=0$ for all $j\geq 0$ is discussed. Our main results
imply, as a corollary, that the periodic vanishing displayed by
$(\mathfrak{p}(n,\chi_5))_\mathbb{N}$ does not occur in any sequence
$(\mathfrak{p}(n,\chi_p))_\mathbb{N}$ for odd primes $p \neq 5$ such that
$p\not\equiv 1\,\,(\mathrm{mod}\,8)$.
  In addition, work of Montgomery and Vaughan on exponential sums with
multiplicative coefficients is applied to establish an upper bound on certain
doubly infinite series involving multiplicative functions $f$ with $|f| \leq
1$.


  Recently, we reported [M. Wagner et al., J. Mater. Res. 26, 1886 (2011)]
transport measurements on the semiconducting intermetallic system RuIn3 and its
substitution derivatives RuIn_{3-x}A_{x} (A = Sn, Zn). Higher values of the
thermoelectric figure of merit (zT = 0.45) compared to the parent compound were
achieved by chemical substitution. Here, using density functional theory based
calculations, we report on the microscopic picture behind the measured
phenomenon. We show in detail that the electronic structure of the substitution
variants of the intermetallic system RuIn_{3-x}A_{x} (A = Sn, Zn) changes in a
rigid-band like fashion. This behavior makes possible the fine tuning of the
substitution concentration to take advantage of the sharp peak-like features in
the density of states of the semiconducting parent compound. Trends in the
transport properties calculated using the semi-classical Boltzmann transport
equations within the constant scattering time approximation are in good
agreement with the former experimental results for RuIn_{3-x}Sn_{x}. Based on
the calculated thermopower for the p-doped systems, we reinvestigated the
Zn-substituted derivative and obtained ZnO-free RuIn_{3-x}Zn_{x}. The new
experimental results are consistent with the calculated trend in thermopower
and yield large zT value of 0.8.


  Structural roles define sets of structurally similar nodes that are more
similar to nodes inside the set than outside, whereas communities define sets
of nodes with more connections inside the set than outside. Roles based on
structural similarity and communities based on proximity are fundamentally
different but important complementary notions. Recently, the notion of
structural roles has become increasingly important and has gained a lot of
attention due to the proliferation of work on learning representations
(node/edge embeddings) from graphs that preserve the notion of roles.
Unfortunately, recent work has sometimes confused the notion of structural
roles and communities (based on proximity) leading to misleading or incorrect
claims about the capabilities of network embedding methods. As such, this paper
seeks to clarify the misconceptions and key differences between structural
roles and communities, and formalize the general mechanisms (e.g., random
walks, feature diffusion) that give rise to community or role-based structural
embeddings. We theoretically prove that embedding methods based on these
mechanisms result in either community or role-based structural embeddings.
These mechanisms are typically easy to identify and can help researchers
quickly determine whether a method preserves community or role-based
embeddings. Furthermore, they also serve as a basis for developing new and
improved methods for community or role-based structural embeddings. Finally, we
analyze and discuss applications and data characteristics where community or
role-based embeddings are most appropriate.


  In this paper, we investigate in general how thermodynamic quantities such as
the polarization, magnetization and the magneto-electric tensor are affected by
the boundaries. We show that when the calculation with periodic boundary
conditions does not involve a Berry's phase, the quantity in question is
determined unambiguously by the bulk, even in the presence of gapless surface
states. When the calculation involves a Berry's phase, the bulk can only
determine the quantity up to some quantized value, given that (i) there are no
gapless surface states, (ii) the surfaces do not break the symmetries preserved
by the bulk, and (iii) the system is kept at charge neutrality. If any of the
above conditions is violated, the quantity is then determined entirely by the
details at the boundary. For components of response functions such as the
magneto-electric tensor that are entirely determined by the bulk, the value of
those components should connect smoothly to finite frequency and momentum.


  We consider the problem of soft decision fusion in a bandwidth-constrained
wireless sensor network (WSN). The WSN is tasked with the detection of an
intruder transmitting an unknown signal over a fading channel. A binary
hypothesis testing is performed using the soft decision of the sensor nodes
(SNs). Using the likelihood ratio test, the optimal soft fusion rule at the
fusion center (FC) has been shown to be the weighted distance from the soft
decision mean under the null hypothesis. But as the optimal rule requires
a-priori knowledge that is difficult to attain in practice, suboptimal fusion
rules are proposed that are realizable in practice. We show how the effect of
quantizing the test statistic can be mitigated by increasing the number of SN
samples, i.e., bandwidth can be traded off against increased latency. The
optimal power and bit allocation for the WSN is also derived. Simulation
results show that SNs with good channels are allocated more bits, while SNs
with poor channels are censored.


  Recently, it was proved by Anari-Oveis Gharan-Vinzant, Anari-Liu-Oveis
Gharan-Vinzant and Br\"{a}nd\'{e}n-Huh that, for any matroid $M$, its basis
generating polynomial and its independent set generating polynomial are
log-concave on the positive orthant. Using these, they obtain some
combinatorial inequalities on matroids including a solution of strong Mason's
conjecture. In this paper, we study the strictness of the log-concavity of
these polynomials and determine when equality holds in these combinatorial
inequalities. We also consider a generalization of our result to morphisms of
matroids.


  Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of
the most intuitive yet challenging embodied AI tasks. Agents are tasked to
navigate towards a target goal by executing a set of low-level actions,
following a series of natural language instructions. All VLN-CE methods in the
literature assume that language instructions are exact. However, in practice,
instructions given by humans can contain errors when describing a spatial
environment due to inaccurate memory or confusion. Current VLN-CE benchmarks do
not address this scenario, making the state-of-the-art methods in VLN-CE
fragile in the presence of erroneous instructions from human users. For the
first time, we propose a novel benchmark dataset that introduces various types
of instruction errors considering potential human causes. This benchmark
provides valuable insight into the robustness of VLN systems in continuous
environments. We observe a noticeable performance drop (up to -25%) in Success
Rate when evaluating the state-of-the-art VLN-CE methods on our benchmark.
Moreover, we formally define the task of Instruction Error Detection and
Localization, and establish an evaluation protocol on top of our benchmark
dataset. We also propose an effective method, based on a cross-modal
transformer architecture, that achieves the best performance in error detection
and localization, compared to baselines. Surprisingly, our proposed method has
revealed errors in the validation set of the two commonly used datasets for
VLN-CE, i.e., R2R-CE and RxR-CE, demonstrating the utility of our technique in
other tasks. Code and dataset will be made available upon acceptance at
https://intelligolabs.github.io/R2RIE-CE


  A new regime of turbulent convection has been reported nearly one decade ago,
based on global heat transfer measurements at very high Rayleigh numbers. We
examine the signature of this "Ultimate Regime" from within the flow itself. A
systematic study of probe-size corrections shows that the earlier temperature
measurements within the flow were altered by an excessive size of thermometer,
but not according to a theoretical model proposed in the literature. Using a
probe one order of magnitude smaller than the one used previously, we find
evidence that the transition to the Ultimate Regime is indeed accompanied with
a clear change in the statistics of temperature fluctuations in the flow.


  The increasing need for causal analysis in large-scale industrial datasets
necessitates the development of efficient and scalable causal algorithms for
real-world applications. This paper addresses the challenge of scaling causal
algorithms in the context of conducting causal analysis on extensive datasets
commonly encountered in industrial settings. Our proposed solution involves
enhancing the scalability of causal algorithm libraries, such as EconML, by
leveraging the parallelism capabilities offered by the distributed computing
framework Ray. We explore the potential of parallelizing key iterative steps
within causal algorithms to significantly reduce overall runtime, supported by
a case study that examines the impact on estimation times and costs. Through
this approach, we aim to provide a more effective solution for implementing
causal analysis in large-scale industrial applications.


  We consider tunnel microrefrigerators at low temperature. There is a number
of experimental studies performed on microrefrigeration in tunneling
superconductor--normal metal (SN) structures. Related to these experiments,
only the electron subsystem has been considered theoretically. Independently,
the phonon deficit effect has been studied a while ago in
superconductor-superconductor tunnel junctions. It can be regarded as a
possible prototype scheme for superconducting microrefrigerators. We try to
provide the missing link between experiments on the SN tunnel junction
refrigerators and the theory which includes microscopically phonons in
combination with the mechanism of the phonon deficit effect.


  In this contribution, we give an integral representation of the wave
functions of the quantum N-particle Toda chain with boundary interaction. In
the case of the Toda chain with one-boundary interaction, we obtain the wave
function by an integral transformation from the wave functions of the open Toda
chain. The kernel of this transformation is given explicitly in terms of
\Gamma-functions. The wave function of the Toda chain with two-boundary
interaction is obtained from the previous wave functions by an integral
transformation. In this case, the difference equation for the kernel of the
integral transformation admits separation of variables. The separated
difference equations coincide with the Baxter equation.


  We present an overview of experimental work to embed high-Q mesoscopic
mechanical oscillators in microwave and optical cavities. Based upon recent
progress, the prospect for a broad field of "cavity quantum mechanics" is very
real. These systems introduce mesoscopic mechanical oscillators as a new
quantum resource and also inherently couple their motion to photons throughout
the electromagnetic spectrum.


  We introduce a symmetric monoidal category of modules over the direct limit
queer superalgebra $\q (\infty)$. The category can be defined in two equivalent
ways with the aid of the large annihilator condition. Tensor products of copies
of the natural and the conatural representations are injective objects in this
category. We obtain the socle filtrations and formulas for the tensor products
of the indecoposable injectives. In addition, it is proven that the category is
Koszul self-dual.


  In a recent series of papers a surprisingly strong connection was discovered
between standard models of evolution in mathematical biology and Multiplicative
Weights Updates Algorithm, a ubiquitous model of online learning and
optimization. These papers establish that mathematical models of biological
evolution are tantamount to applying discrete Multiplicative Weights Updates
Algorithm, a close variant of MWUA, on coordination games. This connection
allows for introducing insights from the study of game theoretic dynamics into
the field of mathematical biology. Using these results as a stepping stone, we
show that mathematical models of haploid evolution imply the extinction of
genetic diversity in the long term limit, a widely believed conjecture in
genetics. In game theoretic terms we show that in the case of coordination
games, under minimal genericity assumptions, discrete MWUA converges to pure
Nash equilibria for all but a zero measure of initial conditions. This result
holds despite the fact that mixed Nash equilibria can be exponentially (or even
uncountably) many, completely dominating in number the set of pure Nash
equilibria. Thus, in haploid organisms the long term preservation of genetic
diversity needs to be safeguarded by other evolutionary mechanisms such as
mutations and speciation.


  In this paper we initiate the study of the heterogeneous capacitated
$k$-center problem: given a metric space $X = (F \cup C, d)$, and a collection
of capacities. The goal is to open each capacity at a unique facility location
in $F$, and also to assign clients to facilities so that the number of clients
assigned to any facility is at most the capacity installed; the objective is
then to minimize the maximum distance between a client and its assigned
facility. If all the capacities $c_i$'s are identical, the problem becomes the
well-studied uniform capacitated $k$-center problem for which constant-factor
approximations are known. The additional choice of determining which capacity
should be installed in which location makes our problem considerably different
from this problem, as well the non-uniform generalizations studied thus far in
literature. In fact, one of our contributions is in relating the heterogeneous
problem to special-cases of the classical Santa Claus problem. Using this
connection, and by designing new algorithms for these special cases, we get the
following results: (a)A quasi-polynomial time $O(\log
n/\epsilon)$-approximation where every capacity is violated by $1+\varepsilon$,
(b) A polynomial time $O(1)$-approximation where every capacity is violated by
an $O(\log n)$ factor. We get improved results for the {\em soft-capacities}
version where we can place multiple facilities in the same location.


  The ground-state degeneracy of the quantum spin system is a characteristic of
nontrivial topology, when it is gapped and robust against disordered
perturbation. The corresponding quantum phase transition (QPT) is usually
driven by a real parameter. We study a non-Hermitian Ising chain with two
transverse fields, one real and another imaginary, based on the exact solution
and numerical simulation. We show that topological degeneracy still exists and
can be obtained by an imaginary transverse field from a topologically trivial
phase of a Hermitian system. The topological degeneracy is robust against the
random imaginary field and therefore expected to be immune to disordered
dissipation from the spontaneous decay in experiment. The underlying mechanism
is the nonlocal symmetry, which emerges only in thermodynamic limit and relates
two categories of QPTs in the quantum spin system, rooted from topological
order and symmetry breaking, respectively.


  Using the submanifold quantum mechanical scheme, the restricted Dirac
operator in a submanifold is defined. Then it is shown that the zero mode of
the Dirac operator expresses the local properties of the submanifold, such as
the Frenet-Serret and generalized Weierstrass relations. In other words this
article gives a representation of a further generalized Weierstrass relations
for a general k-spin manifolds immersed in n-dimensional euclidean space
(0<k<n).


  In this paper, we explore the the phase transition, critical behavior and
microstructure of the FRW in the framework of a new higher order generalized
uncertainty principle. Our initial step involves deriving the equation of state
by defining the work density $W$ from GUP-corrected Friedmann equations as the
thermodynamic pressure $P$. Based on the modified equation of state, we conduct
an analysis of the $P-V$ phase transition in the FRW universe. Subsequently, we
obtain the critical exponents and coexistence curves for the small and large
phases of the FRW universe around the critical point. Finally, employing
Ruppeiner geometry, we derive the thermodynamic curvature scalar $R_N$,
investigating its sign-changing curve and spinodal curve. The results reveal
distinctive thermodynamic properties for FRW universes with positive and
negative GUP parameters $\beta$. In the case of $\beta>0$, the phase
transition, critical behavior and microstructure of FRW universe are consistent
with those of Van der Waals fluids. Conversely, for $\beta<0$, the results
resemble those obtained through effective scalar field theory. These findings
underscore the capacity of quantum gravity to induce phase transitions in the
universe, warranting further in-depth exploration.


  We discuss the instrumental and data reduction techniques used to suppress
speckle noise with the Simultaneous Differential Imager (SDI) implemented at
the VLT and the MMT. SDI uses a double Wollaston prism and a quad filter to
take 4 identical images simultaneously at 3 wavelengths surrounding the 1.62 um
methane bandhead found in the spectrum of cool brown dwarfs and gas giants. By
performing a difference of images in these filters, speckle noise from the
primary can be significantly attenuated, resulting in photon noise limited data
past 0.5''. Non-trivial data reduction tools are necessary to pipeline the
simultaneous differential imaging. Here we discuss a custom algorithm
implemented in IDL to perform this reduction. The script performs basic data
reduction tasks but also precisely aligns images taken in each of the filters
using a custom shift and subtract routine. In our survey of nearby young stars
at the VLT and MMT (see Biller et al., this conference), we achieved H band
contrasts >25000 (5 sigma Delta F1(1.575 um) > 10.0 mag, Delta H > 11.5 mag for
a T6 spectral type object) at a separation of 0.5" from the primary star. We
believe that our SDI images are among the highest contrast astronomical images
ever made from ground or space for methane rich companions.


  Face masks in general, and N95 filtering facepiece respirators (FRs) that
protect against SARS-Cov-2 virion in particular, have become scarce during the
ongoing COVID-19 global pandemic. This work presents practical design
principles for the fabrication of electrocharged filtration layers employed in
N95 FRs using commonly available materials and easily replicable methods. The
input polymer is polypropylene or polystyrene, and can include discarded
plastic containers of these materials, and the fabrication setup is based on
the cotton candy (CC) principle. The primary parameters underlying the CC
principle are translated to simple design rules that allow anyone to construct
their own fabrication system from common parts, or employ a commercial cotton
candy machine with minimal modifications. Finally, basic characterization
results for structural and filtration properties of electrocharged fabrics made
using the CC principle are detailed.


  Off-resonant optical imaging is the most popular method for continuous
monitoring of a Bose-Einstein condensate (BEC). However, the disturbance caused
by scattered photons places a serious limitation on the lifetime of such
continuously-monitored condensates. In this paper, we demonstrate that a new
choice of feedback control can overcome the heating effects of the measurement
backaction. In particular, we show that the measurement backaction caused by
off-resonant optical imaging is a multimode quantum-field effect, as the entire
heating process is not seen in single-particle or mean-field models of the
system. Correctly simulating such continuously-monitored systems is only
possible using the number-phase Wigner (NPW) particle filter, which is a hybrid
between the leading techniques for simulating non-equilibrium dynamics in
condensates and particle filters for simulating high-dimensional non-Gaussian
filters in the field of engineering. The new control scheme will enable
long-term continuous measurement and feedback on one of the leading platforms
for precision measurement and the simulation of quantum fields, allowing for
the possibility of single-shot experiments, adaptive measurements and robust
state-preparation and manipulation.


  Previous learning-based vulnerability detection methods relied on either
medium-sized pre-trained models or smaller neural networks from scratch. Recent
advancements in Large Pre-Trained Language Models (LLMs) have showcased
remarkable few-shot learning capabilities in various tasks. However, the
effectiveness of LLMs in detecting software vulnerabilities is largely
unexplored. This paper aims to bridge this gap by exploring how LLMs perform
with various prompts, particularly focusing on two state-of-the-art LLMs:
GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves
competitive performance with the prior state-of-the-art vulnerability detection
approach and GPT-4 consistently outperformed the state-of-the-art.


  This paper introduces a method of identifying a maximal set of safe
strategies from data for stochastic systems with unknown dynamics using barrier
certificates. The first step is learning the dynamics of the system via
Gaussian process (GP) regression and obtaining probabilistic errors for this
estimate. Then, we develop an algorithm for constructing piecewise stochastic
barrier functions to find a maximal permissible strategy set using the learned
GP model, which is based on sequentially pruning the worst controls until a
maximal set is identified. The permissible strategies are guaranteed to
maintain probabilistic safety for the true system. This is especially important
for learning-enabled systems, because a rich strategy space enables additional
data collection and complex behaviors while remaining safe. Case studies on
linear and nonlinear systems demonstrate that increasing the size of the
dataset for learning the system grows the permissible strategy set.


  We report unidirectional charge transport in a $\mathrm{NbSe_2}$
noncentrosymmetric superconductor, which is exchange-coupled with a
$\mathrm{CrPS_4}$ van der Waals layered antiferromagnetic insulator. The
$\mathrm{NbSe_2/CrPS_4}$ bilayer device exhibits bias-dependent superconducting
critical-current variations of up to $16\%$, with the magnetochiral anisotropy
reaching $\sim 10^5\mathrm{\ T^{-1}A^{-1}}$. Furthermore, the
$\mathrm{CrPS_4/NbSe_2/CrPS_4}$ spin-valve structure exhibits the
superconducting diode effect with critical-current variations of up to $40\%$.
We also utilize the magnetic proximity effect to induce switching in the
superconducting state of the spin-valve structure. It exhibits an infinite
magnetoresistance ratio depending on the field sweep direction and
magnetization configuration. Our result demonstrates a novel route for
enhancing the nonreciprocal response in the weak external field regime
($<50\mathrm{\ mT}$) by exploiting the magnetic proximity effect.


  Many recent works show that a spatial manipulation module could boost the
performances of deep neural networks (DNNs) for 3D point cloud analysis. In
this paper, we aim to provide an insight into spatial manipulation modules.
Firstly, we find that the smaller the rotational degree of freedom (RDF) of
objects is, the more easily these objects are handled by these DNNs. Then, we
investigate the effect of the popular T-Net module and find that it could not
reduce the RDF of objects. Motivated by the above two issues, we propose a
rotation transformation network for point cloud analysis, called RTN, which
could reduce the RDF of input 3D objects to 0. The RTN could be seamlessly
inserted into many existing DNNs for point cloud analysis. Extensive
experimental results on 3D point cloud classification and segmentation tasks
demonstrate that the proposed RTN could improve the performances of several
state-of-the-art methods significantly.


  Deep inference is a proof theoretic methodology that generalizes the standard
notion of inference of the sequent calculus, whereby inference rules become
applicable at any depth inside logical expressions. Deep inference provides
more freedom in the design of deductive systems for different logics and a rich
combinatoric analysis of proofs. In particular, construction of exponentially
shorter analytic proofs becomes possible, however with the cost of a greater
nondeterminism than in the sequent calculus. In this paper, we show that the
nondeterminism in proof search can be reduced without losing the shorter proofs
and without sacrificing proof theoretic cleanliness. For this, we exploit an
interaction and depth scheme in the logical expressions. We demonstrate our
method on deep inference systems for multiplicative linear logic and classical
logic, discuss its proof complexity and its relation to focusing, and present
implementations.


  Flag domains are open orbits of noncompact real forms of complex semisimple
Lie groups acting on flag manifolds. To each flag domain one can associate a
compact complex manifold called the base cycle. The ampleness of the normal
bundle of the base cycle in a flag domain measures the concavity near the base
cycle. In this paper we compute the ampleness of normal bundles of base cycles
in flag domains in various cases, including flag domains in the full flag
manifolds $G/B$ when $G$ is classical, and period domains parameterizing
polarized Hodge structures with fixed Hodge numbers.


  Galactic dynamo models have generally relied on input parameters that are
very challenging to constrain. We address this problem by developing a model
that uses observable quantities as input: the galaxy rotation curve, the
surface densities of the gas, stars and star formation rate, and the gas
temperature. The model can be used to estimate parameters of the random and
mean components of the magnetic field, as well as the gas scale height,
root-mean-square velocity and the correlation length and time of the
interstellar turbulence, in terms of the observables. We use our model to
derive theoretical scaling relations for the quantities of interest, finding
reasonable agreement with empirical scaling relations inferred from
observation. We assess the dependence of the results on different assumptions
about turbulence driving, finding that agreement with observations is improved
by explicitly modeling the expansion and energetics of supernova remnants. The
model is flexible enough to include alternative prescriptions for the physical
processes involved, and we provide links to two open-source PYTHON programs
that implement it.


  Electrically driven adiabatic changes of temperature are identified in the
archetypal electrocaloric material PbSc0.5Ta0.5O3 by comparing isothermal
changes of electrical polarization due to slow variation of electric field, and
adiabatic changes of electrical polarization due to fast variation of electric
field. By obtaining isothermal (adiabatic) electrical polarization data at
measurement (starting) temperatures separated by <0.4 K, we identify a maximum
temperature change of ~2 K due to a maximum field change of 26 kV cm-1, for
starting temperatures in the range 300 - 315 K. These quasi indirect
measurements combine with their direct, indirect and quasi-direct counterparts
to complete the set, and could find routine use in future.


  In the last 20 years we have accumulated an enormous amount of data on
elementary particles and their interactions. This data serves two purposes: to
fix the phenomenological parameters of the Standard Model [SM] and to verify
that the SM is an excellent description of nature. It is our goal to understand
the origin of these many arbitrary parameters. In this talk we consider a
supersymmetric [SUSY] SO(10) grand unified theory [GUT]. We present a
straightforward procedure, incorporating a general operator analysis, which
allows us to use low energy data to determine the fermionic sector of the
theory at the GUT scale.


  The effect of spatial modulation of the single-site anisotropy D on the
ground state of the S=1 Heisenberg chains is investigated. In the case of
period 2 modulation, it is found that the phase diagram contains the Haldane
phase, large-D phase, Neel phase of udud-type and u0d0-type. It is shown that
the hidden antiferromagnetic order in the Haldane phase compatible with the
spatial modulation of D-term get frozen resulting in the emergence of various
types of Neel orders. The investigation of the model with longer period
D-modulation also confirms this picture.


  The FLASHForward experimental facility is a high-performance test-bed for
precision plasma-wakefield research, aiming to accelerate high-quality electron
beams to GeV-levels in a few centimetres of ionised gas. The plasma is created
by ionising gas in a gas cell either by a high-voltage discharge or a
high-intensity laser pulse. The electrons to be accelerated will either be
injected internally from the plasma background or externally from the FLASH
superconducting RF front end. In both cases the wakefield will be driven by
electron beams provided by the FLASH gun and linac modules operating with a 10
Hz macro-pulse structure, generating 1.25 GeV, 1 nC electron bunches at up to 3
MHz micro-pulse repetition rates. At full capacity, this FLASH bunch-train
structure corresponds to 30 kW of average power, orders of magnitude higher
than drivers available to other state-of-the-art LWFA and PWFA experiments.
This high-power functionality means FLASHForward is the only plasma-wakefield
facility in the world with the immediate capability to develop, explore, and
benchmark high-average-power plasma-wakefield research essential for
next-generation facilities. The operational parameters and technical highlights
of the experiment are discussed, as well as the scientific goals and
high-average-power outlook.


  Pi Fractions are used to create deterministic uniformly distributed
pseudorandom decision space sample points for a global search and optimization
algorithm. These fractions appear to be uniformly distributed on [0,1] and can
be used in any stochastic algorithm rendering it effectively deterministic
without compromising its ability to explore the decision space. Pi Fractions
are generated using the BBP Pi digit extraction algorithm. The Pi Fraction
approach is tested using genetic algorithm Pi-GASR with very good results. A Pi
Fraction data file is available upon request.


  Widespread development of driverless vehicles has led to the formation of
autonomous racing, where technological development is accelerated by the high
speeds and competitive environment of motorsport. A particular challenge for an
autonomous vehicle is that of identifying a target trajectory - or, in the case
of a competition vehicle, the racing line. Many existing approaches to finding
the racing line are either not time-optimal solutions, or are computationally
expensive - rendering them unsuitable for real-time application using on-board
processing hardware. This study describes a machine learning approach to
generating an accurate prediction of the racing line in real-time on desktop
processing hardware. The proposed algorithm is a feed-forward neural network,
trained using a dataset comprising racing lines for a large number of circuits
calculated via traditional optimal control lap time simulation. The network
predicts the racing line with a mean absolute error of +/-0.27m, and just
+/-0.11m at corner apex - comparable to human drivers, and autonomous vehicle
control subsystems. The approach generates predictions within 33ms, making it
over 9,000 times faster than traditional methods of finding the optimal
trajectory. Results suggest that for certain applications data-driven
approaches to find near-optimal racing lines may be favourable to traditional
computational methods.


  We present the results from a $\sim500$ ks Chandra observation of the
$z=6.31$ QSO SDSS J1030+0524. This is the deepest X-ray observation to date of
a $z\sim6$ QSO. The QSO is detected with a total of 125 net counts in the full
($0.5-7$ keV) band and its spectrum can be modeled by a single power-law model
with photon index of $\Gamma = 1.81 \pm 0.18$ and full band flux of
$f=3.95\times 10^{-15}$ erg s$^{-1}$ cm$^{-2}$. When compared with the data
obtained by XMM-Newton in 2003, our Chandra observation in 2017 shows a harder
($\Delta \Gamma \approx -0.6$) spectrum and a 2.5 times fainter flux. Such a
variation, in a timespan of $\sim2$ yrs rest-frame, is unexpected for such a
luminous QSO powered by a $> 10^9 \: M_{\odot}$ black hole. The observed source
hardening and weakening could be related to an intrinsic variation in the
accretion rate. However, the limited photon statistics does not allow us to
discriminate between an intrinsic luminosity and spectral change, and an
absorption event produced by an intervening gas cloud along the line of sight.
We also report the discovery of diffuse X-ray emission that extends for 30"x20"
southward the QSO with a signal-to-noise ratio of $\sim$6, hardness ratio of
$HR=0.03_{-0.25}^{+0.20}$, and soft band flux of $f_{0.5-2 \: keV}=
1.1_{-0.3}^{+0.3} \times 10^{-15}$ erg s$^{-1}$ cm$^{-2}$, that is not
associated to a group or cluster of galaxies. We discuss two possible
explanations for the extended emission, which may be either associated with the
radio lobe of a nearby, foreground radio galaxy (at $z \approx 1-2$), or
ascribed to the feedback from the QSO itself acting on its surrounding
environment, as proposed by simulations of early black hole formation.


  We show that two particles interacting via spin exchange exhibit topological
features found in one-dimensional single particle lattice models. This is
accomplished by absorbing all of the spatial degrees of freedom of the lattices
into the spin degrees of freedom of the two particles. Comparing the spin
system with the Su-Schrieffer-Heeger model, we show the existence of
topologically protected edge spin states and establish the bulk-edge
correspondence. Modifying the spin system with a chiral symmetry breaking term
results in it resembling the Rice-Mele model and can therefore act as a
Thouless spin pump of one of the particles when periodically and adiabatically
driven. By using the spin states as a synthetic spatial dimension, we show two
particles are enough to simulate well known topological properties in condensed
matter physics.


  In this paper we present a self consistent theoretical approach for the
calculation of the Survival Probability for central dijet production . These
calculations are performed in a model of high energy soft interactions based on
two ingredients:(i) the results of N=4 SYM, which at the moment is the only
theory that is able to deal with a large coupling constant; and (ii) the
required matching with high energy QCD. Assuming, in accordance with these
prerequisites, that soft Pomeron intercept is rather large and the slope of the
Pomeron trajectory is equal to zero, we derive analytical formulae that sum
both enhanced and semi-enhanced diagrams for elastic and diffractive
amplitudes. Using parameters obtained from a fit to the available experimental
data, we calculate the Survival Probability for central dijet production at
energies accessible at the LHC. The results presented here which include the
contribution of semi-enhanced and net diagrams, are considerably larger than
our previous estimates.


  We give a uniform bound of the bounded geodesic image theorem for the closed
oriented surfaces. The proof utilizes the bicorn curves introduced by Przytycki
and Sisto (see arXiv:1502.02176). With the uniformly bounded Hausdorff distance
of the bicorn paths and 1-slimness of the bicorn curve triangles, we are able
to show the bound is 44 for both non-annular and annular subsurfaces. In a
particular case when the distance between a geodesic and an essential boundary
component of subsurface (or core if it is annular) is $\geq 18$, then the bound
can be as small as 3, which is comparable to the bound 4 in the motivating
examples by Masur and Minsky (see arXiv:9807150), and is same as the bound
given by Webb for non-annular subsurfaces.


  Analyzing the order of unobservable impulse in descriptor system leads to a
new testing criterion for impulse observability, both the statement and the
proof of which use only the original system data.


  Natural language processing (NLP) can be done using either top-down (theory
driven) and bottom-up (data driven) approaches, which we call mechanistic and
phenomenological respectively. The approaches are frequently considered to
stand in opposition to each other. Examining some recent approaches in deep
learning we argue that deep neural networks incorporate both perspectives and,
furthermore, that leveraging this aspect of deep learning may help in solving
complex problems within language technology, such as modelling language and
perception in the domain of spatial cognition.


  Berry phase effect plays a central role in many mesoscale condensed matter
and quantum chemical systems that are naturally under the environmental
influence of dissipation. We propose and microscopically derive a prototypical
quantum coherent tunneling model around a monopole or conical potential
intersection in order to address the intriguing but overlooked interplay
between dissipation and topologically nontrivial Berry phase effect. We adopt
the instanton approach with both symmetry analysis and accurate numerical
solutions that consistently incorporate nonperturbative dissipation and Berry
phase. It reveals a novel dissipative quantum interference phenomenon with
Berry phase effect. The phase diagram of this tunneling exhibits Kramers
degeneracy, nonmonotonic dependence on dissipation and a generic
dissipation-driven phase transition of quantum interference, before which an
unconventional dissipation-enhanced regime of quantum tunneling persists.


  Artifact-centric business process models have gained increasing momentum
recently due to their ability to combine structural (i.e., data related) with
dynamical (i.e., process related) aspects. In particular, two main lines of
research have been pursued so far: one tailored to business artefact modeling
languages and methodologies, the other focused on the foundations for their
formal verification. In this paper, we merge these two lines of research, by
showing how recent theoretical decidability results for verification can be
fruitfully transferred to a concrete UML-based modeling methodology. In
particular, we identify additional steps in the methodology that, in
significant cases, guarantee the possibility of verifying the resulting models
against rich first-order temporal properties. Notably, our results can be
seamlessly transferred to different languages for the specification of the
artifact lifecycles.


  We study the effect of spatial heterogeneity on the collective motion of
self-propelled particles (SPPs). The heterogeneity is modeled as a random
distribution of either static or diffusive obstacles, which the SPPs avoid
while trying to align their movements. We find that such obstacles have a
dramatic effect on the collective dynamics of usual SPP models. In particular,
we report about the existence of an optimal (angular) noise amplitude that
maximizes collective motion. We also show that while at low obstacle densities
the system exhibits long-range order, in strongly heterogeneous media
collective motion is quasi-long-range and exists only for noise values in
between two critical noise values, with the system being disordered at both,
large and low noise amplitudes. Since most real system have spatial
heterogeneities, the finding of an optimal noise intensity has immediate
practical and fundamental implications for the design and evolution of
collective motion strategies.


  In this paper, we investigate CoFeCrAl alloy by means of various experimental
techniques and ab-initio calculations to look for half-metallic nature. The
alloy is found to exist in the cubic Heusler structure, with presence of B2
ordering. Saturation magnetization (MS) value of about 2 Bohr magneton/f.u. is
observed at 8 K under ambient pressure, which is in good agreement with the
Slater-Pauling rule. MS values are found to be independent of pressure, which
is a prerequisite for half-metals. The ab-initio electronic structure
calculations predict half-metallic nature for the alloy with a spin slitting
energy of 0.31 eV. Importantly, this system shows a high current spin
polarization value of 0.67 [with error of 0.02], as deduced from the point
contact Andreev reflection (PCAR) measurements. Linear dependence of electrical
resistivity with temperature indicates the possibility of reasonably high spin
polarization at elevated temperatures (~150 K) as well. All these suggest that
CoFeCrAl is a promising material for the spintronic devices.


  A common first step in time series signal analysis involves digitally
filtering the data to remove linear correlations. The residual data is
spectrally white (it is ``bleached''), but in principle retains the nonlinear
structure of the original time series. It is well known that simple linear
autocorrelation can give rise to spurious results in algorithms for estimating
nonlinear invariants, such as fractal dimension and Lyapunov exponents. In
theory, bleached data avoids these pitfalls. But in practice, bleaching
obscures the underlying deterministic structure of a low-dimensional chaotic
process. This appears to be a property of the chaos itself, since nonchaotic
data are not similarly affected. The adverse effects of bleaching are
demonstrated in a series of numerical experiments on known chaotic data. Some
theoretical aspects are also discussed.


  Radio mini-haloes are poorly-understood, moderately-extended diffuse radio
sources that trace the presence of magnetic fields and relativistic electrons
on scales of hundreds of kiloparsecs, predominantly in relaxed clusters. With
relatively few confirmed detections to-date, many questions remain unanswered.
This paper presents new radio observations of the galaxy cluster
MS1455.0$+$2232 performed with MeerKAT (covering the frequency range 872$-$1712
MHz) and LOFAR (covering 120$-$168 MHz), the first results from a homogeneously
selected mini-halo census. We find that this mini-halo extends for $\sim590$
kpc at 1283 MHz, significantly larger than previously believed, and has a
flatter spectral index ($\alpha = -0.97 \pm 0.05$) than typically expected. Our
X-ray analysis clearly reveals a large-scale (254 kpc) sloshing spiral in the
intracluster medium. We perform a point-to-point analysis, finding a tight
single correlation between radio and X-ray surface brightness with a
super-linear slope of $b_{\rm 1283~MHz} = 1.16^{+0.06}_{-0.07}$ and $b_{\rm
145~MHz} = 1.15^{+0.09}_{-0.08}$; this indicates a strong link between the
thermal and non-thermal components of the intracluster medium. Conversely, in
the spectral index/X-ray surface brightness plane, we find that regions inside
and outside the sloshing spiral follow different correlations. We find
compelling evidence for multiple sub-components in this mini-halo for the first
time. While both the turbulent (re-)acceleration and hadronic scenarios are
able to explain some observed properties of the mini-halo in MS1455.0$+$2232,
neither scenario is able to account for all the evidence presented by our
analysis.


  Recently developed numerical methods have enabled the explicit construction
of the superconducting state of the Hubbard model of strongly correlated
electrons in parameter regimes where the model also exhibits a pseudogap and a
Mott insulating phase. $d_{x^2-y^2}$ symmetry superconductivity is found to
occur in proximity to the Mott insulator, but separated from it by a
pseudogapped nonsuperconducting phase. The superconducting transition
temperature and order parameter amplitude are found to be maximal at the onset
of the normal-state pseudogap. The emergence of superconductivity from the
normal state pseudogap leads to a decrease in the excitation gap. All of these
features are consistent with the observed behavior of the copper-oxide
superconductors.


  By comparing positions on a spectral color-color diagram from 10 black hole
candidates (BHCs) observed with Ginga (1354-64, 1826-24, 1630-47, LMC X-1, LMC
X-3, GS 2000+25, GS 2023+33, GS 1124-68, Cyg X-1, and GX 339-4) with the
observed broad-band noise (0.001-64 Hz; BBN) and quasi-periodic oscillation
(QPO) variability, we find that the so-called ``Very High State'' is spectrally
intermediate to the Soft/High-State and Hard/Low-State. We find a transition
point in spectral hardness where the dependence of the BHC QPO centroid
frequency (of GS 1124-68 and GX 339-4) on spectral hardness switches from a
correlation to an anti-correlation; where the BBN variability switches from
High-State to Low-State; and where the spectral hardness of the QPO relative to
that of the BBN variability is a maximum. This coincidence of changing behavior
in both the QPO and the broad-band variability leads us to hypothesize that the
QPO is due to interaction between the physical components which dominate the
behaviors of BHCs when they occupy the Hard/Low and Soft/High States. We
conclude that these QPO should be observed from BHCs during transition between
these two states. Comparison with QPO and BBN behavior observed during the 1996
transition of Cyg X-1 supports this hypothesis.
  We also report 1-3 Hz QPO observed in GS 2000+25 and Cyg X-1 in the Hard/Low
State, and we compare these to the QPO observed in GS 1124-68 and GX 339-4.


  The emergence of large language models (LLMs) has revolutionized machine
learning and related fields, showcasing remarkable abilities in comprehending,
generating, and manipulating human language. However, their conventional usage
through API-based text prompt submissions imposes certain limitations in terms
of context constraints and external source availability. To address these
challenges, we propose a novel framework called Reinforced Retrieval Augmented
Machine Learning (RRAML). RRAML integrates the reasoning capabilities of LLMs
with supporting information retrieved by a purpose-built retriever from a vast
user-provided database. By leveraging recent advancements in reinforcement
learning, our method effectively addresses several critical challenges.
Firstly, it circumvents the need for accessing LLM gradients. Secondly, our
method alleviates the burden of retraining LLMs for specific tasks, as it is
often impractical or impossible due to restricted access to the model and the
computational intensity involved. Additionally we seamlessly link the
retriever's task with the reasoner, mitigating hallucinations and reducing
irrelevant, and potentially damaging retrieved documents. We believe that the
research agenda outlined in this paper has the potential to profoundly impact
the field of AI, democratizing access to and utilization of LLMs for a wide
range of entities.


  We report on the growth and electrical characterization of a series of
two-dimensional hole systems (2DHSs) used to study the density dependence of
low temperature mobility in 20 nm GaAs/AlGaAs quantum wells. The hole density
was controlled by changing the Al mole fraction and the setback of the
delta-doping layer. We varied the density over a range from 1.8 $\times$
10$^{10}$ cm$^{-2}$ to 1.9 $\times$ 10$^{11}$ cm$^{-2}$ finding a nonmonotonic
dependence of mobility on density at T = 0.3 K. Surprisingly, a peak mobility
of 2.3 $\times$ 10$^6$ cm$^2$/Vs was measured at a density of 6.5 $\times$
10$^{10}$ cm$^{-2}$ with further increase in density resulting in reduced
mobility. We discuss possible mechanisms leading to the observed non-monotonic
density dependence of the mobility. Relying solely on interface roughness
scattering to explain the observed drop in mobility at high density requires
roughness parameters which are not consistent with measurements of similar
electron structures. This leaves open the possibility of contributions from
other scattering mechanisms at high density.


  This paper investigates type I error violations that occur when blinded
sample size reviews are applied in equivalence testing. We give a derivation
which explains why such violations are more pronounced in equivalence testing
than in the case of superiority testing. In addition, the amount of type I
error inflation is quantified by simulation as well as by some theoretical
considerations. Non-negligible type I error violations arise when blinded
interim re-assessments of sample sizes are performed particularly if sample
sizes are small, but within the range of what is practically relevant.


  We prove that a H-surface M in H^2xR, |H| <= 1/2, inherits the symmetries of
its boundary when the boundary is either a horizontal curve with curvature
greater than one or two parallel horizontal curves with curvature greater than
one, whose distance is greater or equal to \pi Furthermore we prove that the
asymptotic boundary of a surface with mean curvature bounded away from zero
consists of parts of straight lines, provided it is sufficiently regular


  This is a short survey illustrating some of the essential aspects of the
theory of canonical extensions. In addition some topological results about
canonical extensions of lattices with additional operations in finitely
generated varieties are given. In particular, they are doubly algebraic
lattices and their interval topologies agree with their double Scott topologies
and make them Priestley topological algebras.


  Incompressible Navier-Stokes equations on a thin spherical domain
$Q_\varepsilon$ along with free boundary conditions under a random forcing are
considered. The convergence of the martingale solution of these equations to
the martingale solution of the stochastic Navier-Stokes equations on a sphere
$\mathbb{S}^2$ as the thickness converges to zero is established.


  Reconfigurable intelligent surface (RIS) has been identified as a promising
technology for future wireless communication systems due to its ability to
manipulate the propagation environment intelligently. RIS is a
frequency-selective device, thus it can only effectively manipulate the
propagation of signals within a specific frequency band. This frequency
selective characteristic can make deploying RIS in wireless cellular networks
more challenging, as adjacent base stations (BSs) operate on different
frequency bands. In addition, rate-splitting multiple access (RSMA) scheme has
been shown to enhance the performance of RIS-aided multi-user communication
systems. Accordingly, this work considers a more practical reflection model for
RIS-aided RSMA communication systems, which accounts for the responses of
signals across different frequency bands. To that end, new analytical
expressions for the ergodic sum-rate are derived using the moment generating
function (MGF) and Jensen inequality. Based on these analytical sum-rate
expressions, novel practical RIS reflection designs and power allocation
strategies for the RSMA scheme are proposed and investigated to maximize the
achievable sum-rate in RIS-assisted multi-cell, multi-band cellular networks.
Simple sub-optimal designs are also introduced and discussed. The results
validate the significant gains of our proposed reflection design algorithms
with RSMA over conventional schemes in terms of achievable sum-rate.
Additionally, the power allocation strategy for the RSMA scheme is shown to
offer superior performance compared to conventional precoding schemes that do
not rely on RSMA.


  In the literature, there is no consensus on the origin of the relativistic
Sagnac effect, particularly from the standpoint of the rotating observer. The
experiments of Wang et al. \cite{wang2003modified,wang2004generalized} has,
however, questioned the pivotal role of rotation of the platform in Sagnac
effect. Recently, the relative motion between the reflectors which force light
to propagate along a closed path and the observer has been ascribed as the
cause of the Sagnac effect. Here, we propose a thought experiment on linear
Sagnac effect and explore another one proposed earlier to demonstrate that the
origin of the Sagnac effect is neither the rotation of frame affecting clock
synchronization nor the relative motion between the source and the observer;
Sagnac effect originates purely due to asymmetric position of the observer with
respect to the light paths. Such a conclusion is validated by analysis of a
gedanken Sagnac kind experiment involving rotation.


  We present a Riemann-Hilbert problem formalism for the initial-boundary value
problem for the three-wave equation:
  \[p_{ij,t}-\frac{b_i-b_j}{a_i-a_j}p_{ij,x}+\sum_k(\frac{b_k-b_j}{a_k-a_j}-\frac{b_i-b_k}{a_i-a_k})p_{ik}p_{kj}=0,\quad
i,j,k=1,2,3.\] on the half-line.


  Using the nonlinear Schroedinger equation (Gross-Pitaevskii equation), the
dynamics of a macroscopic wave packet for Bose-Einstein condensates falling
through double slits is analyzed. This problem is identified with a search for
the fate of a soliton showing a head-on collision with a hard-walled obstacle
of finite size. We explore the splitting of the wave packet and its
reorganization to form an interference pattern. Particular attention is paid to
the role of gravity (g) and repulsive nonlinearity (u_0) in the fringe pattern.
The peak-to-peak distance in the fringe pattern and the number of interference
peaks are found to be proportional to g^(-1/2) and u_0^(1/2)g^(1/4),
respectively. We suggest a way of designing an experiment under controlled
gravity and nonlinearity.


  We discuss the determination of the lowest Form Factors relative to the trace
operators of N=1 Super Sinh-Gordon Model. Analytic continuations of these Form
Factors as functions of the coupling constant allows us to study a series of
models in a uniform way, among these the latest model of the Roaming Series and
a class of minimal supersymmetric models.


  Establishing voxelwise semantic correspondence across distinct imaging
modalities is a foundational yet formidable computer vision task. Current
multi-modality registration techniques maximize hand-crafted inter-domain
similarity functions, are limited in modeling nonlinear intensity-relationships
and deformations, and may require significant re-engineering or underperform on
new tasks, datasets, and domain pairs. This work presents ContraReg, an
unsupervised contrastive representation learning approach to multi-modality
deformable registration. By projecting learned multi-scale local patch features
onto a jointly learned inter-domain embedding space, ContraReg obtains
representations useful for non-rigid multi-modality alignment. Experimentally,
ContraReg achieves accurate and robust results with smooth and invertible
deformations across a series of baselines and ablations on a neonatal T1-T2
brain MRI registration task with all methods validated over a wide range of
deformation regularization strengths.


  We define the notion of equivariant Hopf Galois extension and apply it as a
functor between category of SAYD modules of the Hopf algebras involving in the
extension. This generalizes the result of Jara-Stefan and B\"ohm-Stefan on
associating a SAYD modules to any ordinary Hopf Galois extension.


  The equilibrium properties of polymer droplets on a soft deformable surface
are investigated by molecular dynamics simulations of a bead-spring model. The
surface consists of a polymer brush with irreversibly end-tethered linear
homopolymer chains onto a flat solid substrate. We tune the softness of the
surface by varying the grafting density. Droplets are comprised of bead-spring
polymers of various chain lengths. First, both systems, brush and polymer
liquid, are studied independently in order to determine their static and
dynamic properties. In particular, using a numerical implementation of an AFM
experiment, we measure the shear modulus of the brush surface and compare the
results to theoretical predictions. Then, we study the wetting behavior of
polymer droplets with different contact angles and on substrates that differ in
softness. Density profiles reveal, under certain conditions, the formation of a
wetting ridge beneath the three-phase contact line. Cap-shaped droplets and
cylindrical droplets are also compared to estimate the effect of the line
tension with respect to the droplet size. Finally, the results of the
simulations are compared to a phenomenological free-energy calculation that
accounts for the surface tensions and the compliance of the soft substrate.
Depending on the surface/drop compatibility, surface softness and drop size, a
transition between two regimes is observed: from one where the drop surface
energy balances the adhesion with the surface, which is the classical
Young-Dupr\'e wetting regime, to another one where a coupling occurs between
adhesion, droplet and surface elastic energies.


  Lee, Abramowicz & Kluzniak (2004) demonstrated numerically that rotation of
neutron star couples with oscillations of its accretion disk, and excites
resonances. No specific coupling was assumed, but magnetic field was suggested
as the most likely one. Following this idea, we show (Petri 2005) that if the
neutron star is non-axially symmetric and rotating, its gravity may provide the
coupling and excite resonances. Here, we return to the original suggestion that
the coupling is of a magnetic origin, and demonstrate how does it work in terms
of a simple, analytic toy-model.


  We present a deep learning approach to estimation of the bead parameters in
welding tasks. Our model is based on a four-hidden-layer neural network
architecture. More specifically, the first three hidden layers of this
architecture utilize Sigmoid function to produce their respective intermediate
outputs. On the other hand, the last hidden layer uses a linear transformation
to generate the final output of this architecture. This transforms our deep
network architecture from a classifier to a non-linear regression model. We
compare the performance of our deep network with a selected number of results
in the literature to show a considerable improvement in reducing the errors in
estimation of these values. Furthermore, we show its scalability on estimating
the weld bead parameters with same level of accuracy on combination of datasets
that pertain to different welding techniques. This is a nontrivial result that
is counter-intuitive to the general belief in this field of research.


  We construct an extension of the Abelian Higgs model, which consists of a
complex scalar field by including an additional real, electromagnetically
neutral scalar field. We couple this real scalar field to the complex scalar
field via a quartic coupling and investigate $U(1)$ vortex solutions in this
"extended Abelian Higgs Model". Since this model has two additional homogeneous
ground states, the $U(1)$ vortices that can form in this model have a richer
structure than in the Abelian Higgs Model. We also find the "phase diagram" of
the model showing the parameter space in which the real scalar particle
condenses in the vortex state while having a zero vacuum expectation value in
the homogeneous ground state.


  In multiprocessor systems, various problems are treated with Lamport's
logical clock and the resultant logical time orders between operations.
However, one often needs to face the high complexities caused by the lack of
logical time order information in practice. In this paper, we utilize the
\emph{global clock} to infuse the so-called \emph{pending period} to each
operation in a multiprocessor system, where the pending period is a time
interval that contains the performed time of the operation. Further, we define
the \emph{physical time order} for any two operations with disjoint pending
periods. The physical time order is obeyed by any real execution in
multiprocessor systems due to that it is part of the truly happened operation
orders restricted by global clock, and it is then proven to be independent and
consistent with traditional logical time orders. The above novel yet
fundamental concepts enables new effective approaches for analyzing
multiprocessor systems, which are named \emph{pending period analysis} as a
whole. As a consequence of pending period analysis, many important problems of
multiprocessor systems can be tackled effectively. As a significant application
example, complete memory consistency verification, which was known as an
NP-hard problem, can be solved with the complexity of $O(n^2)$ (where $n$ is
the number of operations). Moreover, the two event ordering problems, which
were proven to be Co-NP-Hard and NP-hard respectively, can both be solved with
the time complexity of O(n) if restricted by pending period information.


  Stencil loops are a common motif in computations including convolutional
neural networks, structured-mesh solvers for partial differential equations,
and image processing. Stencil loops are easy to parallelise, and their fast
execution is aided by compilers, libraries, and domain-specific languages.
Reverse-mode automatic differentiation, also known as algorithmic
differentiation, autodiff, adjoint differentiation, or back-propagation, is
sometimes used to obtain gradients of programs that contain stencil loops.
Unfortunately, conventional automatic differentiation results in a memory
access pattern that is not stencil-like and not easily parallelisable.
  In this paper we present a novel combination of automatic differentiation and
loop transformations that preserves the structure and memory access pattern of
stencil loops, while computing fully consistent derivatives. The generated
loops can be parallelised and optimised for performance in the same way and
using the same tools as the original computation. We have implemented this new
technique in the Python tool PerforAD, which we release with this paper along
with test cases derived from seismic imaging and computational fluid dynamics
applications.


  Gastrointestinal (GI) cancers account for 1.5 million deaths worldwide.
Endoscopic Submucosal Dissection (ESD) is an advanced therapeutic endoscopy
technique with superior clinical outcome due to the minimally invasive and en
bloc removal of tumours. In the western world, ESD is seldom carried out, due
to its complex and challenging nature. Various surgical systems are being
developed to make this therapy accessible, however, these solutions have shown
limited operational workspace, dexterity, or low force exertion capabilities.
The current paper shows the ESD CYCLOPS system, a bimanual surgical robotic
attachment that can be mounted at the end of any flexible endoscope. The system
is able to achieve forces of up to 46N, and showed a mean error of 0.217mm
during an elliptical tracing task. The workspace and instrument dexterity is
shown by pre-clinical ex vivo trials, in which ESD is succesfully performed by
a GI surgeon. The system is currently undergoing pre-clinical in vivo
validation.


  Quantum state tomography, an important task in quantum information
processing, aims at reconstructing a state from prepared measurement data.
Bayesian methods are recognized to be one of the good and reliable choice in
estimating quantum states~\cite{blume2010optimal}. Several numerical works
showed that Bayesian estimations are comparable to, and even better than other
methods in the problem of $1$-qubit state recovery. However, the problem of
choosing prior distribution in the general case of $n$ qubits is not
straightforward. More importantly, the statistical performance of Bayesian type
estimators have not been studied from a theoretical perspective yet. In this
paper, we propose a novel prior for quantum states (density matrices), and we
define pseudo-Bayesian estimators of the density matrix. Then, using
PAC-Bayesian theorems, we derive rates of convergence for the posterior mean.
The numerical performance of these estimators are tested on simulated and real
datasets.


  This paper focuses on the tracking performance limitation for a class of
networked control systems(NCSs) with two-channel constraints. In communication
channels, we consider bandwidth, energy constraints and additive colored
Gaussian noise(ACGN) simultaneously. In plant, non-minimal zeros and unstable
poles are considered; multi-repeated zeros and poles are also applicable. To
obtain the optimal performance, the two-parameter controller is adopted. The
theoretical results show that the optimal tracking performance is influenced by
the non-minimum phase zeros, unstable poles, gain at all frequencies of the
given plant, and the reference input signal for NCSs. Moreover, the performance
limitation is also affected by the limited bandwidth, additive colored Gaussian
noise, and the corresponding multiples for the non-minimum phase zeros and
unstable poles. Additionally, the channel minimal input power constraints are
given under the condition ensuring the stability of the system and acquiring
system performance limitation. Finally, simulation examples are given to
illustrate the theoretical results.


  Just as computational simulations of atoms, molecules and cells have shaped
the way we study the sciences, true-to-life simulations of human-like agents
can be valuable tools for studying human behavior. We propose Humanoid Agents,
a system that guides Generative Agents to behave more like humans by
introducing three elements of System 1 processing: Basic needs (e.g. hunger,
health and energy), Emotion and Closeness in Relationships. Humanoid Agents are
able to use these dynamic elements to adapt their daily activities and
conversations with other agents, as supported with empirical experiments. Our
system is designed to be extensible to various settings, three of which we
demonstrate, as well as to other elements influencing human behavior (e.g.
empathy, moral values and cultural background). Our platform also includes a
Unity WebGL game interface for visualization and an interactive analytics
dashboard to show agent statuses over time. Our platform is available on
https://www.humanoidagents.com/ and code is on
https://github.com/HumanoidAgents/HumanoidAgents


  Modern deep learning architectures produce highly accurate results on many
challenging semantic segmentation datasets. State-of-the-art methods are,
however, not directly transferable to real-time applications or embedded
devices, since naive adaptation of such systems to reduce computational cost
(speed, memory and energy) causes a significant drop in accuracy. We propose
ContextNet, a new deep neural network architecture which builds on factorized
convolution, network compression and pyramid representation to produce
competitive semantic segmentation in real-time with low memory requirement.
ContextNet combines a deep network branch at low resolution that captures
global context information efficiently with a shallow branch that focuses on
high-resolution segmentation details. We analyse our network in a thorough
ablation study and present results on the Cityscapes dataset, achieving 66.1%
accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps
with pipelined computations for streamed data).


  We show that a compact orientable 4-manifold M has a CR regular immersion
into C3 if and only if both its first Pontryagin class and its Euler
characteristic vanish, and has a CR regular embedding into C3 if and only if in
addition the second Stiefel-Whitney class of M vanishes.


  We present the results of simultaneous high resolution observations of v=1
and v=2, J=1-0 SiO masers toward TX Cam at four epochs covering a stellar
cycle. Near maser maximum (Epochs III and IV), the individual components of
both masers are distributed in ring-like structures but the ring is severely
disrupted near stellar maser minimum (Epochs I and II). In Epochs III and IV
there is a large overlap between the radii at which the two maser transitions
occur. However in both epochs the average radius of the v=2 maser ring is
smaller than for the v=1 maser ring, the difference being larger for Epoch IV.
The observed relative ring radii in the two transitions, and the trends on the
ring thickness, are close to those predicted by the model of Humphreys et al.
(\cite{humphreys02}). In many individual features there is an almost exact
overlap in space and velocity of emission from the two transitions, arguing
against pure radiative pumping. At both Epochs III and IV in many spectral
features only 50% of the flux density is recovered in our images, implying
significant smooth maser structure. For both transitions we find that red- and
blue-shifted masers occur in all parts of the rings, with relatively few masers
at the systemic velocity. Thus there is no evidence for rotation, although the
blue-shifted masers are somewhat more prominent to the west. At all four epochs
red-shifted components are generally brighter than blue-shifted ones. At Epochs
III and IV, we see many filamentary or spoke-like features in both v=1 and v=2
masers, especially in the red-shifted gas. These spokes show systematic
velocity gradients consistent with a decelerating outward flow with increasing
radius. We outline a possible model to explain why, given the presence of these
spokes, there is a deficit of maser features at the systemic velocity.


  Synchrotron X-ray diffraction measurements have been conducted on liquid
phosphorus trichloride, tribromide and triiodide. Molecular Dynamics
simulations for these molecular liquids were performed with a dual purpose: (1)
to establish whether existing intermolecular potential functions can provide a
picture that is consistent with diffraction data; (2) to generate reliable
starting configurations for subsequent Reverse Monte Carlo modelling.
Structural models (i.e., sets of coordinates of thousands of atoms) that were
fully consistent with experimental diffraction information, within errors, have
been prepared by means of the Reverse Monte Carlo method. Comparison with
reference systems, generated by hard sphere-like Monte Carlo simulations, was
also carried out to demonstrate the extent to which simple space filling
effects determine the structure of the liquids (and thus, also estimating the
information content of measured data). Total scattering structure factors,
partial radial distribution functions and orientational correlations as a
function of distances between the molecular centres have been calculated from
the models. In general, more or less antiparallel arrangements of the primary
molecular axes that are found to be the most favourable orientation of two
neighbouring molecules. In liquid PBr3 electrostatic interactions seem to play
a more important role in determining intermolecular correlations than in the
other two liquids; molecular arrangements in both PCl3 and PI3 are largely
driven by steric effects.


  We study a semigroup $\phi$ of linear operators acting on a Banach space $X$
which satisfies the condition $\codim X_0<\infty$, where $X_0=\{x\in X \mid
\phi_t(x)\underset{t\to\infty}\longrightarrow 0\}.$ We show that $X_0$ is
closed under these conditions. We establish some properties concerning the
asymptotic behavior of subspaces which complement $X_0$ in $X$.


  This paper considers a full-duplex (FD) multiuser multiple-input
single-output system where a base station simultaneously serves both uplink
(UL) and downlink (DL) users on the same time-frequency resource. The crucial
barriers in implementing FD systems reside in the residual self-interference
and co-channel interference. To accelerate the use of FD radio in future
wireless networks, we aim at managing the network interference more effectively
by jointly designing the selection of half-array antenna modes (in the transmit
or receive mode) at the BS with time phases and user assignments. The first
problem of interest is to maximize the overall sum rate subject to
quality-of-service requirements, which is formulated as a highly non-concave
utility function followed by non-convex constraints. To address the design
problem, we propose an iterative low-complexity algorithm by developing new
inner approximations, and its convergence to a stationary point is guaranteed.
To provide more insights into the solution of the proposed design, a general
max-min rate optimization is further considered to maximize the minimum
per-user rate while satisfying a given ratio between UL and DL rates.
Furthermore, a robust algorithm is devised to verify that the proposed scheme
works well under channel uncertainty. Simulation results demonstrate that the
proposed algorithms exhibit fast convergence and substantially outperform
existing schemes.


  Context: Researchers from different groups and institutions are collaborating
towards the construction of groups of interrelated replications. Applying
unsuitable techniques to aggregate interrelated replications' results may
impact the reliability of joint conclusions.
  Objectives: Comparing the advantages and disadvantages of the techniques
applied to aggregate interrelated replications' results in Software Engineering
(SE).
  Method: We conducted a literature review to identify the techniques applied
to aggregate interrelated replications' results in SE. We analyze a
prototypical group of interrelated replications in SE with the techniques that
we identified. We check whether the advantages and disadvantages of each
technique -according to mature experimental disciplines such as medicine-
materialize in the SE context.
  Results: Narrative synthesis and Aggregation of p-values do not take
advantage of all the information contained within the raw-data for providing
joint conclusions. Aggregated Data (AD) meta-analysis provides visual summaries
of results and allows assessing experiment-level moderators. Individual
Participant Data (IPD) meta-analysis allows interpreting results in natural
units and assessing experiment-level and participant-level moderators.
  Conclusion: All the information contained within the raw-data should be used
to provide joint conclusions. AD and IPD, when used in tandem, seem suitable to
analyze groups of interrelated replications in SE.


  A vertex-colored graph $G$ is {\it rainbow vertex-connected} if any pair of
vertices in $G$ are connected by a path whose internal vertices have distinct
colors, which was introduced by Krivelevich and Yuster. The {\it rainbow
vertex-connection number} of a connected graph $G$, denoted by $rvc(G)$, is the
smallest number of colors that are needed in order to make $G$ rainbow
vertex-connected. In a previous paper we showed that it is NP-Complete to
decide whether a given graph $G$ has $rvc(G)=2$. In this paper we show that for
every integer $k\geq 2$, deciding whether $rvc(G)\leq k$ is NP-Hard. We also
show that for any fixed integer $k\geq 2$, this problem belongs to NP-class,
and so it becomes NP-Complete.


  We approach the development of models and control strategies of
susceptible-infected-susceptible (SIS) epidemic processes from the perspective
of marked temporal point processes and stochastic optimal control of stochastic
differential equations (SDEs) with jumps. In contrast to previous work, this
novel perspective is particularly well-suited to make use of fine-grained data
about disease outbreaks and lets us overcome the shortcomings of current
control strategies. Our control strategy resorts to treatment intensities to
determine who to treat and when to do so to minimize the amount of infected
individuals over time. Preliminary experiments with synthetic data show that
our control strategy consistently outperforms several alternatives. Looking
into the future, we believe our methodology provides a promising step towards
the development of practical data-driven control strategies of epidemic
processes.


  The response functions for the unpolarized $(e,e'p)$ and polarized
$(\vec{e},e'p)$ reaction are calculated for medium-heavy nuclei under quasifree
conditions. The formalism presented here incorporates two-body currents related
to meson-exchange and the $\Delta (1232)$ excitation. The final-state
interaction of the outgoing nucleon with the residual nucleus is handled in a
HF-RPA formalism. The sensitivity of the results to the two-body currents is
discussed for the five structure functions in quasielastic $(\vec{e},e'p)$
scattering off the target nuclei $^{16}O$ and $^{40}Ca$. A selective
sensitivity to the two-body currents is obtained in the longitudinal-transverse
interference term $W_{LT}$ where two-body currents can explain part of the
discrepancy between the impulse-approximation calculations and the data.


  The method of active day fraction (ADF) was proposed recently to calibrate
different solar observers to the standard observational conditions. The result
of the calibration may depend on the overall level of solar activity during the
observational period. This dependency is studied quantitatively using data of
the Royal Greenwich Observatory, by formally calibrating synthetic
pseudo-observers to the full reference dataset. It is shown that the sunspot
group number is precisely estimated by the ADF method for periods of moderate
activity, may be slightly underestimated by 0.5\,--\,1.5 groups ($\leq$10\,\%)
for strong and very strong activity, and is strongly overestimated by up to 2.5
groups ($\leq$30\,\%) for weak--moderate activity. The ADF method becomes
unapplicable for the periods of grand minima of activity. In general, the ADF
method tends to overestimate the overall level of activity and to reduce the
long-term trends.


  By combining data from the text, citation, and reference databases with data
from the ADS readership logs we have been able to create Second Order
Bibliometric Operators, a customizable class of collaborative filters which
permits substantially improved accuracy in literature queries.
  Using the ADS usage logs along with membership statistics from the
International Astronomical Union and data on the population and gross domestic
product (GDP) we develop an accurate model for world-wide basic research where
the number of scientists in a country is proportional to the GDP of that
country, and the amount of basic research done by a country is proportional to
the number of scientists in that country times that country's per capita GDP.
  We introduce the concept of utility time to measure the impact of the
ADS/URANIA and the electronic astronomical library on astronomical research. We
find that in 2002 it amounted to the equivalent of 736 FTE researchers, or $250
Million, or the astronomical research done in France.
  Subject headings: digital libraries; bibliometrics; sociology of science;
information retrieval


  In biochemical networks, reactions often occur on disparate timescales and
can be characterized as either "fast" or "slow." The quasi-steady state
approximation (QSSA) utilizes timescale separation to project models of
biochemical networks onto lower-dimensional slow manifolds. As a result, fast
elementary reactions are not modeled explicitly, and their effect is captured
by non-elementary reaction rate functions (e.g. Hill functions). The accuracy
of the QSSA applied to deterministic systems depends on how well timescales are
separated. Recently, it has been proposed to use the non-elementary rate
functions obtained via the deterministic QSSA to define propensity functions in
stochastic simulations of biochemical networks. In this approach, termed the
stochastic QSSA, fast reactions that are part of non-elementary reactions are
not simulated, greatly reducing computation time. However, it is unclear when
the stochastic QSSA provides an accurate approximation of the original
stochastic simulation. We show that, unlike the deterministic QSSA, the
validity of the stochastic QSSA does not follow from timescale separation
alone, but also depends on the sensitivity of the non-elementary reaction rate
functions to changes in the slow species. The stochastic QSSA becomes more
accurate when this sensitivity is small. Different types of QSSAs result in
non-elementary functions with different sensitivities, and the total QSSA
results in less sensitive functions than the standard or the pre-factor QSSA.
We prove that, as a result, the stochastic QSSA becomes more accurate when
non-elementary reaction functions are obtained using the total QSSA. Our work
provides a novel condition for the validity of the QSSA in stochastic
simulations of biochemical reaction networks with disparate timescales.


  An active method for long time storage of quantum superposition state in
atomic system using the Oscillating Dark States (ODS) is presented. Quantum
state of a three-level $\Lambda$ configuration atomic system oscillates
periodically between two ground levels, when two pairs of classical detuning
laser fields driving the system into the ODS under evolving adiabatic
conditions. When considering another uploading/unloading adiabatic conditions
and applying the oscillation of the ODS to quantum state storage, surprisingly,
we can obtain the greatly suppressed decoherence of the system and high
fidelity of the retrieved state, even if decay factor of coherence term of the
system density matrix $\gamma_{21}$$\cdot$ $t$$ \gg$1. The storage time is not
limited by coherence decay time of the atomic system any longer, and can be
thousands of times longer than that in those passive schemes without additional
laser fields.


  We show that global de Sitter space is unstable to particle creation, even
for a massive free field theory with no self-interactions. The O(4,1) de Sitter
invariant state is a definite phase coherent superposition of particle and
anti-particle solutions in both the asymptotic past and future, and therefore
is not a true vacuum state. In the closely related case of particle creation by
a constant, uniform electric field, a time symmetric state analogous to the de
Sitter invariant one is constructed, which is also not a stable vacuum state.
We provide the general framework necessary to describe the particle creation
process, the mean particle number, and dynamical quantities such as the
energy-momentum tensor and current of the created particles in both the de
Sitter and electric field backgrounds in real time, establishing the connection
to kinetic theory. We compute the energy-momentum tensor for adiabatic vacuum
states in de Sitter space initialized at early times in global S^3 sections,
and show that particle creation in the contracting phase results in
exponentially large energy densities at later times, necessitating an inclusion
of their backreaction effects, and leading to large deviation of the spacetime
from global de Sitter space before the expanding phase can begin.