  Project managers need to manage risks throughout the project lifecycle and,
thus, need to know how changes in activity durations influence project duration
and risk. We propose a new indicator (the Activity Risk Index, ARI) that
measures the contribution of each activity to the total project risk while it
is underway. In particular, the indicator informs us about what activities
contribute the most to the project's uncertainty so that project managers can
pay closer attention to the performance of these activities. The main
difference between our indicator and other activity sensitivity metrics in the
literature (e.g. cruciality, criticality, significance, or schedule sensitivity
indices) is that our indicator is based on the Schedule Risk Baseline concept
instead of on cost or schedule baselines. The new metric not only provides
information at the beginning of the project, but also while it is underway.
Furthermore, the ARI is the only one to offer a normalized result: if we add
its value for each activity, the total sum is 100%.


  We carried out detailed analysis of X-ray and radio observations of a simple
flare loop that occurred on 12th August 2002, with the impulsive hard X-ray
(HXR) light curves dominated by a single pulse. The emission spectra of the
early impulsive phase are consistent with an isothermal model in the coronal
loop with a temperature reaching several keVs. A power-law high-energy spectral
tail is evident near the HXR peak time, in accordance with the appearance of
footpoints at high energies, and is well correlated with the radio emission.
The energy content of the thermal component keeps increasing gradually after
the disappearance of this nonthermal component. These results suggest that
electron acceleration only covers a central period of a longer and more gradual
energy dissipation process and that the electron transport within the loop
plays a crucial role in the formation of the inferred power-law electron
distribution. The spectral index of power-law photons shows a very gradual
evolution indicating a quasi-steady state of the electron accelerator, which is
confirmed by radio observations. These results are consistent with the theory
of stochastic electron acceleration from a thermal background. Advanced
modeling with coupled electron acceleration and spatial transport processes is
needed to explain these observations more quantitatively, which may reveal the
dependence of the electron acceleration on the spatial structure of the
acceleration region.


  In this paper, we introduce a groundbreaking end-to-end (E2E) framework for
decoding invasive brain signals, marking a significant advancement in the field
of speech neuroprosthesis. Our methodology leverages the comprehensive
reasoning abilities of large language models (LLMs) to facilitate direct
decoding. By fully integrating LLMs, we achieve results comparable to the
state-of-the-art cascade models. Our findings underscore the immense potential
of E2E frameworks in speech neuroprosthesis, particularly as the technology
behind brain-computer interfaces (BCIs) and the availability of relevant
datasets continue to evolve. This work not only showcases the efficacy of
combining LLMs with E2E decoding for enhancing speech neuroprosthesis but also
sets a new direction for future research in BCI applications, underscoring the
impact of LLMs in decoding complex neural signals for communication
restoration. Code will be made available at
https://github.com/FsFrancis15/BrainLLM.


  Let $\Omega$ be a bounded open set of $\mathbb R^{n}$, $n\ge 2$. In this
paper we mainly study some properties of the second Dirichlet eigenvalue
$\lambda_{2}(p,\Omega)$ of the anisotropic $p$-Laplacian \[ -\mathcal
Q_{p}u:=-\textrm{div} \left(F^{p-1}(\nabla u)F_\xi (\nabla u)\right), \] where
$F$ is a suitable smooth norm of $\mathbb R^{n}$ and $p\in]1,+\infty[$. We
provide a lower bound of $\lambda_{2}(p,\Omega)$ among bounded open sets of
given measure, showing the validity of a Hong-Krahn-Szego type inequality.
Furthermore, we investigate the limit problem as $p\to+\infty$.


  As power systems transit to a state of high renewable penetration, little or
no presence of synchronous generators makes the prerequisite of well-regulated
frequency for grid-following inverters unrealistic. Thus, there is a trend to
resort to grid-forming inverters which set frequency directly. We propose a
novel grid-forming frequency shaping control that is able to shape the
aggregate system frequency dynamics into a first-order one with the desired
steady-state frequency deviation and Rate of Change of Frequency (RoCoF) after
a sudden power imbalance. The no overshoot property resulting from the
first-order dynamics allows the system frequency to monotonically move towards
its new steady-state without experiencing frequency Nadir, which largely
improves frequency security. We prove that our grid-forming frequency-shaping
control renders the system internally stable under mild assumptions. The
performance of the proposed control is verified via numerical simulations on a
modified Icelandic Power Network test case.


  Bell inequality with self-testing property has played an important role in
quantum information field with both fundamental and practical applications.
However, it is generally challenging to find Bell inequalities with
self-testing property for multipartite states and actually there are not many
known candidates. In this work, we propose a systematical framework to
construct Bell inequalities from stabilizers which are maximally violated by
general stabilizer states, with two observables for each local party. We show
that the constructed Bell inequalities can self-test any stabilizer state which
is essentially device-independent, if and only if these stabilizers can
uniquely determine the state in a device-dependent manner. This bridges the gap
between device-independent and device-dependent verification methods. Our
framework can provide plenty of Bell inequalities for self-testing stabilizer
states. Among them, we give two families of Bell inequalities with different
advantages: (1) a family of Bell inequalities with a constant ratio of quantum
and classical bounds using 2N correlations, (2) Single pair inequalities
improving on all previous robustness self-testing bounds using N+1
correlations, which are both efficient and suitable for realizations in
multipartite systems. Our framework can not only inspire more fruitful
multipartite Bell inequalities from conventional verification methods, but also
pave the way for their practical applications.


  Modern data analysis frequently relies on the use of large datasets, often
constructed as amalgamations of diverse populations or data-sources.
Heterogeneity across these smaller datasets constitutes two major challenges
for causal inference: (1) the source of each sample can introduce latent
confounding between treatment and effect, and (2) diverse populations may
respond differently to the same treatment, giving rise to heterogeneous
treatment effects (HTEs). The issues of latent confounding and HTEs have been
studied separately but not in conjunction. In particular, previous works only
report the conditional average treatment effect (CATE) among similar
individuals (with respect to the measured covariates). CATEs cannot resolve
mixtures of potential treatment effects driven by latent heterogeneity, which
we call mixtures of treatment effects (MTEs). Inspired by method of moment
approaches to mixture models, we propose "synthetic potential outcomes" (SPOs).
Our new approach deconfounds heterogeneity while also guaranteeing the
identifiability of MTEs. This technique bypasses full recovery of a mixture,
which significantly simplifies its requirements for identifiability. We
demonstrate the efficacy of SPOs on synthetic data.


  The tunneling conductance spectra of a
normal-metal/insulator/quasi-one-dimensional superconductor is calculated by
using the Blonder-Tinkham-Klapwijk formulation. The pairing symmetry of the
superconductor is assumed to be $p$, $d$, and $f$-wave. It is found that there
is a well defined zero energy peak in electron tunneling along the direction
parallel to the chains or normal to these when the transmitted quasiparticles
feel different sign of the pair potential. The actual line shape of the spectra
is sensitive to the nodes of the pair potential on the Fermi surface.


  For every non-autonomous system, there is the related family of Koopman
operators $\mathcal{K}^{(t,t_0)}$, parameterized by the time pair $(t,t_0)$. In
this paper we are investigating the time dependency of the spectral properties
of the Koopman operator family in the linear non-autonomous case and we propose
an algorithm for computation of its spectrum from observed data only. To build
this algorithm we use the concept of the fundamental matrix of linear
non-autonomous systems and some specific aspects of Arnoldi-like methods. In
particular, we use Arnoldi-like methods on local data stencils, we exploit the
information contained in the Krylov subspace projection error, and discover
limitations in the application of Arnoldi-like methods to cases with continous
time dependency. We present results of this data-driven algorithm on various
linear non-autonomous systems, hybrid as well as continuous in time. In all the
examples comparison with exact eigenvalues and eigenfunctions shows excellent
performance of the proposed algorithm.


  The proliferation of mobile apps and reduced time in mobile app releases
mandates the need for faster and efficient testing of mobile apps, their GUI
and functional capabilities. Though, there are wide variety of open source
tools and frameworks that are developed to provide automated test
infrastructure for testing mobile apps. Each of these automation tools supports
different scripting languages for automating the app testing. These frameworks
fundamentally lacks the ability to directly capture the intent of the users who
intend to effectively test the mobile app and its cross-app functional
capabilities and performance without worrying about the low-level scripting
language associated with each tool. Hence, to address this limitation, we
propose a high-level intent-based automation specification language and APIs
that could effectively address following aspects: (i)capture the test
automation steps to be captured as high-level intents using intuitive
automation specification language, and (ii) provides framework support to
effectively capture the users behavior patterns effectively for testing the
apps. We develop, AppIntent a high level automation specification
language-based framework that directly captures the test automation intents
with in and across multiple apps using high-level and intuitive language
without worrying about how to actually develop scripts for automation.


  Since the federated learning, which makes AI learning possible without moving
local data around, was introduced by google in 2017 it has been actively
studied particularly in the field of medicine. In fact, the idea of machine
learning in AI without collecting data from local clients is very attractive
because data remain in local sites. However, federated learning techniques
still have various open issues due to its own characteristics such as non
identical distribution, client participation management, and vulnerable
environments. In this presentation, the current issues to make federated
learning flawlessly useful in the real world will be briefly overviewed. They
are related to data/system heterogeneity, client management, traceability, and
security. Also, we introduce the modularized federated learning framework, we
currently develop, to experiment various techniques and protocols to find
solutions for aforementioned issues. The framework will be open to public after
development completes.


  In this paper, motivated by a work of Luk and Yau, and Huneke and Wiegand, we
study various aspects of the cohomological rigidity property of tensor product
of modules over commutative Noetherian rings. We determine conditions under
which the vanishing of a single local cohomology module of a tensor product
implies the vanishing of all the lower ones, and obtain new connections between
the local cohomology modules of tensor products and the Tate homology. Our
argument yields bounds for the depth of tensor products of modules, as well as
criteria for freeness of modules over complete intersection rings. Along the
way, we also give a splitting criteria for vector bundles on smooth complete
intersections.


  We study exact solutions of the steady state behaviour of several non-linear
open quantum systems which can be applied to the field of circuit quantum
electrodynamics. Using Fokker-Planck equations in the generalised
P-representation we investigate the analytical solutions of two fundamental
models. First, we solve for the steady-state response of a linear cavity that
is coupled to an approximate transmon qubit and use this solution to study both
the weak and strong driving regimes, using analytical expressions for the
moments of both cavity and transmon fields, along with the Husimi Q-function
for the transmon. Second, we revisit exact solutions of quantum Duffing
oscillator which is driven both coherently and parametrically while also
experiencing decoherence by the loss of single and pairs of photons. We use
this solution to discuss both stabilisation of Schroedinger cat states and the
generation of squeezed states in parametric amplifiers, in addition to studying
the Q-functions of the different phases of the quantum system. The field of
superconducting circuits, with its strong nonlinearities and couplings, has
provided access to a new parameter regime in which returning to these exact
quantum optics methods can provide valuable insights.


  This paper presents a new AI challenge, the Tales of Tribute AI Competition
(TOTAIC), based on a two-player deck-building card game released with the High
Isle chapter of The Elder Scrolls Online. Currently, there is no other AI
competition covering Collectible Card Games (CCG) genre, and there has never
been one that targets a deck-building game. Thus, apart from usual CCG-related
obstacles to overcome, like randomness, hidden information, and large branching
factor, the successful approach additionally requires long-term planning and
versatility. The game can be tackled with multiple approaches, including
classic adversarial search, single-player planning, and Neural Networks-based
algorithms. This paper introduces the competition framework, describes the
rules of the game, and presents the results of a tournament between sample AI
agents.


  The emergence of new applications brings multi-class traffic with diverse
quality of service (QoS) requirements to wide area networks (WANs), motivating
research in traffic engineering (TE). In recent years, novel centralized and
hierarchical TE schemes have used heuristic or machine learning techniques to
orchestrate resources in closed systems such as datacenter networks. However,
these schemes suffer from long delivery delays and high control overhead when
applied to general WANs. To provide low-delay services, this paper proposes an
asynchronous multi-class traffic management (AMTM) scheme. We first establish
an asynchronous TE paradigm in which distributed nodes locally perform
low-complexity and low-delay traffic control based on link prices, and the TE
server updates link prices to eliminate decision conflicts between edge nodes.
By modeling the asynchronous TE paradigm as a control system with
non-negligible control loop delay, we find that the traditional pricing
strategy cannot simultaneously achieve a low packet loss rate and a low flow
delivery delay. To address this issue, we propose a new pricing strategy based
on the observations of virtual queues in intermediate nodes. We also present a
system design and related algorithms that utilize a dynamic step size mechanism
of link price update. Simulation results show that AMTM can effectively reduce
the end-to-end flow delivery delay.


  We define and study a series indexed by rooted trees and with coefficients in
Q(q). We show that it is related to a family of Lie idempotents. We prove that
this series is a q-deformation of a more classical series and that some of its
coefficients are Carlitz q-Bernoulli numbers.


  We show a new property of sonic black holes. After deriving the metric of a
sonic black hole from the Schrodinger equation and quantizing the perturbation
fields near the sonic event horizon, we show particles of Hawking radiation can
act as a source of entanglement: two-mode squeezed entanglement is produced
near the event horizon, which can be used in quantum teleportation. The
fidelity of the teleportation is closely related to the temperature of the
sonic black holes, but high fidelity seems difficult to reach in our case.


  We investigate the recently proposed possibility of chaotic inflation with
respect to the imaginary part of the field T in a supersymmetric embedding of
the Starobinsky model. We show that the stage of rapid expansion driven by Im T
in this model ends almost instantly, and the subsequent stages of inflation are
driven by the real part of the field T, as in the standard Starobinsky model.
Thus, the Starobinsky model and its supersymmetric generalizations remain
disfavored by the recent BICEP2 data.


  Floating point multiplication is a crucial operation in high power computing
applications such as image processing, signal processing etc. And also
multiplication is the most time and power consuming operation. This paper
proposes an efficient method for IEEE 754 floating point multiplication which
gives a better implementation in terms of delay and power. A combination of
Karatsuba algorithm and Urdhva-Tiryagbhyam algorithm (Vedic Mathematics) is
used to implement unsigned binary multiplier for mantissa multiplication. The
multiplier is implemented using Verilog HDL, targeted on Spartan-3E and
Virtex-4 FPGA.


  Spin dynamics is usually described as massless or, more precisely, as free of
inertia. Recent experiments, however, found direct evidence for inertial spin
dynamics. In turn, it is necessary to rethink the basics of spin dynamics.
Focusing on a macrospin in an environment (bath), we show that the spin-to-bath
coupling gives rise to spin inertia. This bath-induced spin inertia appears
universally from all the high-frequency bath modes. We expect our results to
provide new insights into recent experiments on spin inertia. Moreover, they
indicate that any channel for spin dissipation should also be accompanied by a
term accounting for bath-induced spin inertia. As an illustrative example, we
consider phonon-bath-induced spin inertia in a YIG/GGG stack.


  The boundary energy - momentum tensors for a static observer in the
conformally flat Rindler geometry are considered. We found the surface energy
is positive far form the Planck world but the transversal pressures are
negative. The kinematical parameters associated to a nongeodesic congruence of
static observers are computed. The entropy $S$ corresponding to the degrees of
freedom on the two surface of constant $\rho$ and $t$ equals the horizon
entropy of a black hole with a time dependent mass and the Padmanabhan
expression $E = 2 S T$ is obeyed. The two surface shear tensor is vanishing but
the coefficient of the bulk viscosity $\zeta$ is $1/16 \pi$ and therefore the
negative pressure due to it acts as a surface tension.


  The local linear embedding algorithm (LLE) is a non-linear dimension-reducing
technique, widely used due to its computational simplicity and intuitive
approach. LLE first linearly reconstructs each input point from its nearest
neighbors and then preserves these neighborhood relations in the
low-dimensional embedding. We show that the reconstruction weights computed by
LLE capture the high-dimensional structure of the neighborhoods, and not the
low-dimensional manifold structure. Consequently, the weight vectors are highly
sensitive to noise. Moreover, this causes LLE to converge to a linear
projection of the input, as opposed to its non-linear embedding goal. To
overcome both of these problems, we propose to compute the weight vectors using
a low-dimensional neighborhood representation. We prove theoretically that this
straightforward and computationally simple modification of LLE reduces LLE's
sensitivity to noise. This modification also removes the need for
regularization when the number of neighbors is larger than the dimension of the
input. We present numerical examples demonstrating both the perturbation and
linear projection problems, and the improved outputs using the low-dimensional
neighborhood representation.


  A subclass of nondeterministic Finite Automata generated by means of regular
Grammars (GFAs, for short) is introduced. A process algebra is proposed, whose
semantics maps a term to a GFA. We prove a representability theorem: for each
GFA $N$, there exists a process algebraic term $p$ such that its semantics is a
GFA isomorphic to $N$. Moreover, we provide a concise axiomatization of
language equivalence: two GFAs $N_1$ and $N_2$ recognize the same regular
language if and only if the associated terms $p_1$ and $p_2$, respectively, can
be equated by means of a set of axioms, comprising 6 axioms plus 2 conditional
axioms, only.


  This is a survey of the author's book "D-manifolds and d-orbifolds: a theory
of derived differential geometry", available at
http://people.maths.ox.ac.uk/~joyce/dmanifolds.html
  We introduce a 2-category dMan of "d-manifolds", new geometric objects which
are 'derived' smooth manifolds, in the sense of the 'derived algebraic
geometry' of Toen and Lurie. They are a 2-category truncation of the 'derived
manifolds' of Spivak (see arXiv:0810.5174, arXiv:1212.1153). The category of
manifolds Man embeds in dMan as a full subcategory. We also define 2-categories
dMan^b,dMan^c of "d-manifolds with boundary" and "d-manifolds with corners",
and orbifold versions of these dOrb,dOrb^b,dOrb^c, "d-orbifolds". For brevity,
this survey concentrates mostly on d-manifolds without boundary. A longer and
more detailed summary of the book is given in arXiv:1208.4948.
  Much of differential geometry extends very nicely to d-manifolds and
d-orbifolds -- immersions, submersions, submanifolds, transverse fibre
products, orientations, etc. Compact oriented d-manifolds and d-orbifolds have
virtual classes.
  There are truncation functors to d-manifolds and d-orbifolds from essentially
every geometric structures on moduli spaces used in enumerative invariant
problems in differential geometry or complex algebraic geometry, including
Fredholm sections of Banach vector bundles over Banach manifolds, the
"Kuranishi spaces" of Fukaya, Oh, Ohta and Ono and the "polyfolds" of Hofer,
Wysocki and Zehnder in symplectic geometry, and C-schemes with perfect
obstruction theories in algebraic geometry. Thus, results in the literature
imply that many important classes of moduli spaces are d-manifolds or
d-orbifolds, including moduli spaces of J-holomorphic curves in symplectic
geometry.
  D-manifolds and d-orbifolds will have applications in symplectic geometry,
and elsewhere.


  In this manuscript, we present modified Feldman-katok metric and modified
max-mean metric, then apply them to examine the measure-theoretic pressure and
topological pressure.


  We present a theoretical description for circuits consisting of weak
anharmonic qubits coupled to cavity multimodes. We obtain a unitary
transformation that diagonalizes harmonic sector of the circuit. Weak
anharmonicity does not alter the normal mode basis, however it can modify
energy levels. We study two examples of a transmon and two transmons coupled to
bus resonator, and we determine dressed frequencies and Kerr nonlinearities in
closed form formulas. Our results are valid for arbitrary frequency detuning
and coupling within and beyond dispersive regime.


  Using the lattice QCD equation of state for an isentropically expanding
fireball we follow the evolution of the effective number of particles in an
ideal gas pV/T. This number reduces roughly to its third around the crossover
temperature, which helps to circumvent the entropy obstacle inherent in quark
coalescence models of the hadronization.


  We develop a concept of surface plasmon polaritons (SPPs) based four-wave
mixing (4WM), in which a laser-launched evanescent SPP field is utilized as a
coherent pumping source to involve directly in a nonlinear 4WM process at the
dielectric/metal interface. Conversion efficiency of the resulting 4WM
radiation is expected to be dramatically increased due to the local-field
enhancement effect. Feasibility of implementing this concept at the air/gold
film and graphene flake/gold film interfaces is further examined by numerical
simulations. The concept shows intriguing promise for applications in newly
emerging nanophotonics, optoelectronics, and active plasmonics.


  Hybrid quantum systems that unite laser-cooled trapped ions and ultracold
quantum gases in a single experimental setup have opened a rapidly advancing
field of study, including Quantum chemistry, polaron physics, quantum
information processing and quantum simulations. We present a fully developed
and tested ion trap chip and propose a flat chip trap that can be placed
beneath the ion trap. This design substantially addresses the difficulties
specific to hybrid traps and features well-aligned chips that allow for
independent adjustment of the depth of the atomic trap and the confinement and
positioning of ions. The ion trap has been successfully tested with linear ion
crystals of Yb$^{+}$ and neutral $^{87}$Rb were also loaded into a mMOT a few
millimeters under the ion trapping region.


  We report the results of an [O III] lambda 5007 survey for planetary nebulae
(PN) in five galaxies that were hosts of well-observed Type Ia supernovae: NGC
524, NGC 1316, NGC 1380, NGC 1448 and NGC 4526. The goals of this survey are to
better quantify the zero-point of the maximum magnitude versus decline rate
relation for supernovae Type Ia and to validate the insensitivity of Type Ia
luminosity to parent stellar population using the host galaxy Hubble type as a
surrogate. We detected a total of 45 planetary nebulae candidates in NGC 1316,
44 candidates in NGC 1380, and 94 candidates in NGC 4526. From these data, and
the empirical planetary nebula luminosity function (PNLF), we derive distances
of 17.9 +0.8/-0.9 Mpc, 16.1 +0.8/-1.1 Mpc, and 13.6 +1.3/-1.2 Mpc respectively.
Our derived distance to NGC 4526 has a lower precision due to the likely
presence of Virgo intracluster planetary nebulae in the foreground of this
galaxy. In NGC 524 and NGC 1448 we detected no planetary nebulae candidates
down to the limiting magnitudes of our observations. We present a formalism for
setting realistic distance limits in these two cases, and derive robust lower
limits of 20.9 Mpc and 15.8 Mpc, respectively.
  After combining these results with other distances from the PNLF, Cepheid,
and Surface Brightness Fluctuations distance indicators, we calibrate the
optical and near-infrared relations for supernovae Type Ia and we find that the
Hubble constants derived from each of the three methods are broadly consistent,
implying that the properties of supernovae Type Ia do not vary drastically as a
function of stellar population. We determine a preliminary Hubble constant of
H_0 = 77 +/- 3 (random) +/- 5 (systematic) km/s/Mpc for the PNLF, though more
nearby galaxies with high-quality observations are clearly needed.


  Two pseudo-Newtonian potentials, which approximate the angular and epicyclic
frequencies of the relativistic accretion disk around rotating (and counter
rotating) compact objects, are presented. One of them, the Logarithmically
Modified Potential, is a better approximation for the frequencies while the
other, the Second-order Expanded potential, also reproduces the specific energy
for circular orbits in close agreement with the General Relativistic values.
These potentials may be included in time dependent hydrodynamical simulations
to study the temporal behavior of such accretion disks.


  Domain generalization (DG) remains a significant challenge for perception
based on deep neural networks (DNN), where domain shifts occur due to lighting,
weather, or geolocation changes. In this work, we propose VLTSeg to enhance
domain generalization in semantic segmentation, where the network is solely
trained on the source domain and evaluated on unseen target domains. Our method
leverages the inherent semantic robustness of vision-language models. First, by
substituting traditional vision-only backbones with pre-trained encoders from
CLIP and EVA-CLIP as transfer learning setting we find that in the field of DG,
vision-language pre-training significantly outperforms supervised and
self-supervised vision pre-training. We thus propose a new vision-language
approach for domain generalized segmentation, which improves the domain
generalization SOTA by 7.6% mIoU when training on the synthetic GTA5 dataset.
We further show the superior generalization capabilities of vision-language
segmentation models by reaching 76.48% mIoU on the popular Cityscapes-to-ACDC
benchmark, outperforming the previous SOTA approach by 6.9% mIoU on the test
set at the time of writing. Additionally, our approach shows strong in-domain
generalization capabilities indicated by 86.1% mIoU on the Cityscapes test set,
resulting in a shared first place with the previous SOTA on the current
leaderboard at the time of submission.


  A study of the gas content in 1038 interacting galaxies, essentially selected
from Arp, Arp and Madore, Vorontsov-Velyaminov catalogues and some of the
published literature, is presented here. The data on the interstellar medium
have been extracted from a number of sources in the literature and compared
with a sample of 1916 normal galaxies. The mean values for each of the
different ISM tracers (FIR, 21 cm, CO lines, X-ray) have been estimated by
means of survival analysis techniques, in order to take into account the
presence of upper limits. From the data it appears that interacting galaxies
have a higher gas content than normal ones. Galaxies classified as ellipticals
have both a dust and gas content one order of magnitude higher than normal.
Spirals have in most part a normal dust and HI content but an higher molecular
gas mass. The X-ray luminosity also appears higher than that of normal galaxies
of same morphological type, both including or excluding AGNs. We considered the
alternative possibilities that the molecular gas excess may derive from the
existence of tidal torques which produce gas infall from the surrounding
regions or from a different metallicity which affects the X conversion factor
between the observed CO line luminosity and the H$_2$ calculated mass.
According to our tests, it appears that interacting galaxies possess a higher
molecular mass than normal galaxies but with a similar star formation
efficiency.


  User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the
core of high rate data-oriented downlink schemes of the next-generation of
cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users
according to their channels vector directions and SINR levels. However, when
scheduling is applied independently in each cell, the inter-cell interference
(ICI) power at each user receiver is not known in advance since it changes at
each new scheduling slot depending on the scheduling decisions of all
interfering base stations. In order to cope with this uncertainty, we consider
the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat
reQuest (ARQ). We develop a game-theoretic framework for this problem and build
on stochastic optimization techniques in order to find optimal scheduling and
ARQ schemes. Particularizing our framework to the case of "outage service
rates", we obtain a scheme based on adaptive variable-rate coding at the
physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then,
we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ)
that is able to achieve a throughput performance arbitrarily close to the
"genie-aided service rates", with no need for a genie that provides
non-causally the ICI power levels. The novel HARQ scheme is both easier to
implement and superior in performance with respect to the conventional
combination of adaptive variable-rate coding and ARQ-LLC.


  We present the results of the comparative analysis of the most known
semi-empirical and empirical spectral atlases that was carried out using the
data from the WBVR photometric catalogue. The results show that standard error
of synthesized stellar magnitudes calculated with SEDs from best spectral
atlases reaches 0.02 mag. It has been also found out that some of modern
spectral atlases are burdened with significant systematic errors. The agreement
for the 5000-10000 A spectral range is rather satisfactory, while there are
problems for wavelengths shorter than 4400 A.


  Extending a fundamental result for (indefinite) quadratic programs, this
paper shows that certain non-convex piecewise programs have only a finite
number of directional stationary values, and thus, possess only finitely many
locally minimum values. We present various special cases of our main results,
in particular, an application to a least-squares piecewise affine regression
problem for which every directional stationary point is locally minimizing.


  A fundamental problem from invariant theory is to describe the endomorphism
algebra of multilinear functions on a representation V invariant under the
action of a group G. According to Weyl's classic, a first main (later:
fundamental) theorem of invariant theory provides a finite spanning set for
this algebra, whereas a a second main theorem describes the linear relations
between those basic invariants.
  We use diagrammatic methods to carry Weyl's programme a step further,
providing explicit bases for the subspace of the r-th tensor powers of V
invariant under the action of G, that are additionally preserved by the action
of the long cycle of the symmetric group on r letters. The representations we
study are essentially those that occur in Weyl's book: the defining
representations of the symplectic groups, the defining representations of the
symmetric groups considered as linear representations, and the adjoint
representations of the linear groups.
  In particular, we present a transparent, combinatorial proof of a second
fundamental theorem for the defining representation of the symplectic groups
Sp(2n). Our formulation is explicit and provides a very precise link to
(n+1)-noncrossing perfect matchings, going beyond a dimension count. Extending
our argument to the k-th symmetric powers of these representations, the
combinatorial objects involved turn out to be (n+1)-noncrossing k-regular
graphs. As corollaries we obtain instances of the cyclic sieving phenomenon for
these objects and the natural rotation action.
  In general, we derive branching rules for the diagram algebras corresponding
to the representations in a uniform way. We also compute the Frobenius
characteristics of modules of the diagram algebras restricted to the action of
the symmetric group and obtain the isotypic decomposition of the r-th tensor
power of V when n is large enough in comparison to r.


  With the newest version of our Monte Carlo code for ultra-high-energy cosmic
ray (UHECR) propagation, CRPropa 3, the flux of neutrinos and photons due to
interactions of UHECRs with extragalactic background light can be predicted.
Together with the recently updated data for the isotropic diffuse gamma-ray
background (IGRB) by Fermi LAT, it is now possible to severely constrain UHECR
source models. The evolution of the UHECR sources especially plays an important
role in the determination of the expected secondary photon spectrum. Pure
proton UHECR models are already strongly constrained, primarily by the highest
energy bins of Fermi LAT's IGRB, as long as their number density is not
strongly peaked at recent times.


  In order to protect brokers from customer defaults in a volatile market, an
active margin system is proposed for the transactions of margin lending in
China. The probability of negative return under the condition that collaterals
are liquidated in a falling market is used to measure the risk associated with
margin loans, and a recursive algorithm is proposed to calculate this
probability under a Markov chain model. The optimal maintenance margin ratio
can be given under the constraint of the proposed risk measurement for a
specified amount of initial margin. An example of such a margin system is
constructed and applied to $26,800$ margin loans of 134 stocks traded on the
Shanghai Stock Exchange. The empirical results indicate that the proposed
method is an operational method for brokers to set margin system with a clearly
specified target of risk control.


  Theoretical physics suffered a major loss with the death of my dear friend
Jan Zaanen on January 18. This note is my remembrance of him.


  Caching is a promising solution to satisfy the ever increasing demands for
the multi-media traffics. In caching networks, coded caching is a recently
proposed technique that achieves significant performance gains over the uncoded
caching schemes. However, to implement the coded caching schemes, each file has
to be split into $F$ packets, which usually increases exponentially with the
number of users $K$. Thus, designing caching schemes that decrease the order of
$F$ is meaningful for practical implementations. In this paper, by reviewing
the Ali-Niesen caching scheme, the placement delivery array (PDA) design
problem is firstly formulated to characterize the placement issue and the
delivery issue with a single array. Moreover, we show that, through designing
appropriate PDA, new centralized coded caching schemes can be discovered.
Secondly, it is shown that the Ali-Niesen scheme corresponds to a special class
of PDA, which realizes the best coding gain with the least $F$. Thirdly, we
present a new construction of PDA for the centralized caching system, wherein
the cache size of each user $M$ (identical cache size is assumed at all users)
and the number of files $N$ satisfies $M/N=1/q$ or ${(q-1)}/{q}$ ($q$ is an
integer such that $q\geq 2$). The new construction can decrease the required
$F$ from the order $O\left(e^{K\cdot\left(\frac{M}{N}\ln \frac{N}{M}
+(1-\frac{M}{N})\ln \frac{N}{N-M}\right)}\right)$ of Ali-Niesen scheme to
$O\left(e^{K\cdot\frac{M}{N}\ln \frac{N}{M}}\right)$ or
$O\left(e^{K\cdot(1-\frac{M}{N})\ln\frac{N}{N-M}}\right)$ respectively, while
the coding gain loss is only $1$.


  Transient microstructure in the diffuse interstellar medium has been observed
towards galactic and extragalactic sources for decades, usually in lines of
atoms and ions, and, more recently, in molecular lines. Evidently, there is a
molecular component to the transient microstructure. In this paper we explore
the chemistry that may arise in such microstructure. We use a PDR code to model
the conditions of relatively high density, low temperature, very low visual
extinction and very short elapsed time that are appropriate for these objects.
We find that there is a well defined region of parameter space where detectable
abundances of molecular species might be found. The best matching models are
those where the interstellar microstructure is young (<100 yr), small (~100
AU), and dense (>10^4 cm^-3).


  We prove that each coarsely homogenous separable metric space $X$ is coarsely
equivalent to one of the spaces: the sigleton, the Cantor macro-cube or the
Baire macro-space. This classification is derived from coarse characterizations
of the Cantor macro-cube and of the Baire macro-space given in this paper.
Namely, we prove that a separable metric space $X$ is coarsely equivalent to
the Baire macro-space if any only if $X$ has asymptotic dimension zero and has
unbounded geometry in the sense that for every $\delta$ there is $\epsilon$
such that no $\epsilon$-ball in $X$ can be covered by finitely many sets of
diameter $\le \delta$.


  This work introduces Physics-informed State-space neural network Models
(PSMs), a novel solution to achieving real-time optimization, flexibility, and
fault tolerance in autonomous systems, particularly in transport-dominated
systems such as chemical, biomedical, and power plants. Traditional data-driven
methods fall short due to a lack of physical constraints like mass
conservation; PSMs address this issue by training deep neural networks with
sensor data and physics-informing using components' Partial Differential
Equations (PDEs), resulting in a physics-constrained, end-to-end differentiable
forward dynamics model. Through two in silico experiments -- a heated channel
and a cooling system loop -- we demonstrate that PSMs offer a more accurate
approach than a purely data-driven model. In the former experiment, PSMs
demonstrated significantly lower average root-mean-square errors across test
datasets compared to a purely data-driven neural network, with reductions of 44
%, 48 %, and 94 % in predicting pressure, velocity, and temperature,
respectively.
  Beyond accuracy, PSMs demonstrate a compelling multitask capability, making
them highly versatile. In this work, we showcase two: supervisory control of a
nonlinear system through a sequentially updated state-space representation and
the proposal of a diagnostic algorithm using residuals from each of the PDEs.
The former demonstrates PSMs' ability to handle constant and time-dependent
constraints, while the latter illustrates their value in system diagnostics and
fault detection. We further posit that PSMs could serve as a foundation for
Digital Twins, constantly updated digital representations of physical systems.


  Using arguments parallel to those used in support of using H2CO as a
sensitive probe of temperature and density in molecular clouds, we measured the
J=7-6 and J=10-9 transitions of thioformaldehyde (H2CS) in several hot core
sources. The goal here was to investigate more closely the conditions giving
rise to H2CS emission in cloud cores containing young stars by modelling
several transitions. The H2CS molecule is a slightly asymmetric rotor, a
heavier analogue to H2CO. As in H2CO, transitions occur closely spaced in
frequency, though they are substantially separated in energy. Transitions of
H2CS originating from the K=0, 1, 2, 3, and 4 ladders in the 230 and 345 GHz
windows can productively be used to constrain densities and temperatures. As a
first step in developing the use of these transitions as thermometers and
densitometers, we surveyed and modeled the emission from well known warm dense
cores.


  Machine learning and deep learning methods have been widely explored in
understanding the chaotic behavior of the atmosphere and furthering weather
forecasting. There has been increasing interest from technology companies,
government institutions, and meteorological agencies in building digital twins
of the Earth. Recent approaches using transformers, physics-informed machine
learning, and graph neural networks have demonstrated state-of-the-art
performance on relatively narrow spatiotemporal scales and specific tasks. With
the recent success of generative artificial intelligence (AI) using pre-trained
transformers for language modeling and vision with prompt engineering and
fine-tuning, we are now moving towards generalizable AI. In particular, we are
witnessing the rise of AI foundation models that can perform competitively on
multiple domain-specific downstream tasks. Despite this progress, we are still
in the nascent stages of a generalizable AI model for global Earth system
models, regional climate models, and mesoscale weather models. Here, we review
current state-of-the-art AI approaches, primarily from transformer and operator
learning literature in the context of meteorology. We provide our perspective
on criteria for success towards a family of foundation models for nowcasting
and forecasting weather and climate predictions. We also discuss how such
models can perform competitively on downstream tasks such as downscaling
(super-resolution), identifying conditions conducive to the occurrence of
wildfires, and predicting consequential meteorological phenomena across various
spatiotemporal scales such as hurricanes and atmospheric rivers. In particular,
we examine current AI methodologies and contend they have matured enough to
design and implement a weather foundation model.


  A classical theorem by Jacobson says that a ring in which every element $x$
satisfies the equation $x^n=x$ for some $n>1$ is commutative. According to
Birkhoff's Completeness Theorem, if $n$ is fixed, there must be an equational
proof of this theorem. But equational proofs have only appeared for some values
of $n$ so far. This paper is about finding such a proof in general. We are able
to make a reduction to the case that $n$ is a prime power $p^k$ and the ring
has characteristic $p$. We then prove the special cases $k=1$ and $k=2$. The
general case is reduced to a series of constructive Wedderburn Theorems, which
we can prove in many special cases. Several examples of equational proofs are
discussed in detail.


  We present a calculation of the phase-shift for $\pi$-$\pi$ scattering in
isospin-1, spin-1 channel in the elastic region. The mass and width of the rho
resonance is extracted by fitting these phaseshifts. To vary the scattering
momentum we employ asymmetric boxes. We use $N_f = 2$ nHYP-smeared clover
fermions and generate two sets of ensembles with pion masses about 315MeV and
227MeV. To determine the phase shifts we compute the energy spectrum both for
states at rest and boosted. We employ a variational analysis with interpolating
fields including several $q$-$\bar{q}$ and $\pi$-$\pi$ interpolating fields
with different scattering momenta.


  The Sun Watcher with Active Pixels and Image Processing (SWAP) is an EUV
solar telescope on board ESA's Project for Onboard Autonomy 2 (PROBA2) mission
launched on 2 November 2009. SWAP has a spectral bandpass centered on 17.4 nm
and provides images of the low solar corona over a 54x54 arcmin field-of-view
with 3.2 arcsec pixels and an imaging cadence of about two minutes. SWAP is
designed to monitor all space-weather-relevant events and features in the low
solar corona. Given the limited resources of the PROBA2 microsatellite, the
SWAP telescope is designed with various innovative technologies, including an
off-axis optical design and a CMOS-APS detector. This article provides
reference documentation for users of the SWAP image data.


  We study the super dense coding capacity in the presence of quantum channels
with correlated noise. We investigate both the cases of unitary and non-unitary
encoding. Pauli channels for arbitrary dimensions are treated explicitly. The
super dense coding capacity for some special channels and resource states is
derived for unitary encoding. We also provide an example of a memory channel
where non-unitary encoding leads to an improvement in the super dense coding
capacity.


  The Sum-of-Squares (SoS) hierarchy of semidefinite programs is a powerful
algorithmic paradigm which captures state-of-the-art algorithmic guarantees for
a wide array of problems. In the average case setting, SoS lower bounds provide
strong evidence of algorithmic hardness or information-computation gaps. Prior
to this work, SoS lower bounds have been obtained for problems in the "dense"
input regime, where the input is a collection of independent Rademacher or
Gaussian random variables, while the sparse regime has remained out of reach.
We make the first progress in this direction by obtaining strong SoS lower
bounds for the problem of Independent Set on sparse random graphs. We prove
that with high probability over an Erdos-Renyi random graph $G\sim
G_{n,\frac{d}{n}}$ with average degree $d>\log^2 n$, degree-$D_{SoS}$ SoS fails
to refute the existence of an independent set of size $k =
\Omega\left(\frac{n}{\sqrt{d}(\log n)(D_{SoS})^{c_0}} \right)$ in $G$ (where
$c_0$ is an absolute constant), whereas the true size of the largest
independent set in $G$ is $O\left(\frac{n\log d}{d}\right)$.
  Our proof involves several significant extensions of the techniques used for
proving SoS lower bounds in the dense setting. Previous lower bounds are based
on the pseudo-calibration heuristic of Barak et al [FOCS 2016] which produces a
candidate SoS solution using a planted distribution indistinguishable from the
input distribution via low-degree tests. In the sparse case the natural planted
distribution does admit low-degree distinguishers, and we show how to adapt the
pseudo-calibration heuristic to overcome this.
  Another notorious technical challenge for the sparse regime is the quest for
matrix norm bounds. In this paper, we obtain new norm bounds for graph matrices
in the sparse setting.


  We construct and fully characterize a scalar boundary conformal field theory
on a triangulated Riemann surface. The results are analyzed from a string
theory perspective as tools to deal with open/closed string dualities.


  We propose a simple common framework for Risk-Limiting and Bayesian (polling)
audits for two-candidate plurality elections. Using it, we derive an expression
for the general Bayesian audit; in particular, we do not restrict the prior to
a beta distribution. We observe that the decision rule for the Bayesian audit
is a simple comparison test, which enables the use of pre-computation---without
simulations---and greatly increases the computational efficiency of the audit.
Our main contribution is a general form for an audit that is both Bayesian and
risk-limiting: the {\em Bayesian Risk-Limiting Audit}, which enables the use of
a Bayesian approach to explore more efficient Risk-Limiting Audits.


  It has recently been established that quantum many-body scarring can prevent
the thermalisation of some isolated quantum systems, starting from certain
initial states. One of the first models to show this was the so-called PXP
Hamiltonian, which was used to theoretically model an experiment on a chain of
strongly interacting Rydberg atoms. A defining feature of the PXP Hamiltonian
is a set of dynamical constraints that make certain states inaccessible to the
dynamics. In this paper we construct a class of spin chain models that are
parameterised by a discrete variable $\ell$ that controls the "strength" of a
dynamical constraint. We show that by increasing $\ell$ the constraint becomes
weaker, in the sense that fewer states are excluded from the dynamics. The PXP
Hamiltonian is special case for $\ell = 2$. By weakening the constraint to
$\ell \geq 4$, however, we find a more extreme version of quantum scarring than
in the PXP Hamiltonian, with the number of scar states growing exponentially in
the system size.


  The question of the existence of an analogue, in the framework of central
simple algebras with involution, of the notion of Pfister form is raised. In
particular, algebras with orthogonal involution which split as a tensor product
of quaternion algebras with involution are studied. It is proven that, up to
degree 16, over any extension over which the algebra splits, the involution is
adjoint to a Pfister form. Moreover, cohomological invariants of those algebras
with involution are discussed.


  We propose a novel prescription to take off the square root of Nambu-Goto
action for a p-brane, which generalizes the Brink-Di Vecchia-Howe-Tucker or
also known as Polyakov method. With an arbitrary decomposition as d+n=p+1, our
resulting action is a modified d-dimensional Polyakov action which is gauged
and possesses a Nambu n-bracket squared potential. We first spell out how the
(p+1)-dimensional diffeomorphism is realized in the lower dimensional action.
Then we discuss a possible gauge fixing of it to a direct product of
$d$-dimensional diffeomorphism and n-dimensional volume preserving
diffeomorphism. We show that the latter naturally leads to a novel Filippov-Lie
n-algebra based gauge theory action in d-dimensions.


  Observations of the X-ray band wavelength reveal an evident ellipticity of
many galaxy clusters atmospheres. The modeling of the intracluster gas with an
ellipsoidal $\beta$-model leads to different estimates for the total
gravitational mass and the gas mass fraction of the cluster than those one
finds for a spherical beta-model. An analysis of a recent Chandra image of the
galaxy cluster RBS797 indicates a strong ellipticity and thus a pronounced
aspherical geometry. A preliminary investigation which takes into account an
ellipsoidal shape for this cluster gives different mass estimates than by
assuming spherical symmetry. We have also investigated the influence of
aspherical geometries of galaxy clusters, and of polytropic profiles of the
temperature on the estimate of the Hubble constant through the
Sunyaev-Zel'dovich effect. We find that the non-inclusion of such effects can
induce errors up to 40 per cent on the Hubble constant value.


  We use the extended Barabasi model without the rewired process and show that
the degree distribution for the corresponding networks is the Tsallis
distribution. We offer an analysis of the novel "The Sound and the Fury" by W.
Faulkner in English and in Russian, and show that the degree distributions of
the relevant word networks are described with the Tsallis distribution. We have
constructed degree distributions for each of the relevant word networks and
defined the value of the nonextensivity parameter with the maximum likelihood
method. For the novel text in English qB = 1.57; qK = 1.49; qJ = 1.53; qA =
1.47; qT = 1.54, and for the translation into Russian qB = 1.50; qK = 1.42; qD
= 1.46; qA = 1.40; qT = 1.47. Therefore, if the translation of the novel is
regarded as mapping, the nonextensivity parameters ordering qB > qT > qD > qK >
qA is an invariant of this mapping.


  This paper proposes novel joint channel estimation and beamforming approach
for multicell wideband massive multiple input multiple output (MIMO) systems.
Using our channel estimation and beamforming approach, we determine the number
of cells $N_c$ that can utilize the same time and frequency resource while
mitigating the effect of pilot contamination. The proposed approach exploits
the multipath characteristics of wideband channels. Specifically, when the
channel has $L$ multipath taps, it is shown that $N_c\leq L$ cells can reliably
estimate the channels of their user equipments (UEs) and perform beamforming
while mitigating the effect of pilot contamination. For example, in a long term
evolution (LTE) channel environment having delay spread $T_d=4.69\mu$ second
and channel bandwidth $B=2.5$MHz, we have found that $L=18$ cells can use this
band. In practice, $T_d$ is constant for a particular environment and carrier
frequency, and hence $L$ increases as the bandwidth increases. The proposed
channel estimation and beamforming design is linear, simple to implement and
significantly outperforms the existing designs, and is validated by extensive
simulations.


  We examine whether future, nearly all-sky galaxy redshift surveys, in
combination with CMB priors, will be able to detect the signature of the cosmic
neutrino background and determine the absolute neutrino mass scale. We also
consider what constraints can be imposed on the effective number of neutrino
species. In particular we consider two spectroscopic strategies in the near-IR,
the so-called "slitless" and "multi-slit" approaches, whose examples are given
by future space-based galaxy surveys, as EUCLID for the slitless case, or
SPACE, JEDI, and possibly WFIRST in the future, for the multi-slit case. We
find that, in combination with Planck, these galaxy probes will be able to
detect at better than 3--sigma level and measure the mass of cosmic neutrinos:
a) in a cosmology-independent way, if the sum of neutrino masses is above 0.1
eV; b) assuming spatial flatness and that dark energy is a cosmological
constant, otherwise. We find that the sensitivity of such surveys is well
suited to span the entire range of neutrino masses allowed by neutrino
oscillation experiments, and to yield a clear detection of non-zero neutrino
mass. The detection of the cosmic relic neutrino background with cosmological
experiments will be a spectacular confirmation of our model for the early
Universe and a window into one of the oldest relic components of our Universe.


  We report observations of discrete charge states of a coherent dielectric
two-level system (TLS) that is strongly coupled to an offset-charge-sensitive
superconducting transmon qubit. We measure an offset charge of 0.072$e$
associated with the two TLS eigenstates, which have a transition frequency of
2.9 GHz and a relaxation time exceeding 3 ms. Combining measurements in the
strong dispersive and resonant regime, we quantify both transverse and
longitudinal couplings of the TLS-qubit interaction. We further perform joint
tracking of TLS transitions and quasiparticle tunneling dynamics but find no
intrinsic correlations. This study demonstrates microwave-frequency TLS as a
source of low-frequency charge noise.


  We determine the detailed thermodynamic behavior of vortices in the O(2)
scalar model in 2D and of global monopoles in the O(3) model in 3D. We
construct new numerical techniques, based on cluster decomposition algorithms,
to analyze the point defect configurations. We find that these criteria produce
results for the Kosterlitz-Thouless temperature in agreement with a topological
transition between a polarizable insulator and a conductor, at which free
topological charges appear in the system. For global monopoles we find no pair
unbinding transition. Instead a transition to a dense state where pairs are no
longer distinguishable occurs at T<Tc, without leading to long range disorder.
We produce both extensive numerical evidence of this behavior as well as a
semi-analytic treatment of the partition function for defects. General
expectations for N=D>3 are drawn, based on the observed behavior.


  We analyze the superconducting state and c-axis charge dynamics of cuprates
at optimal doping using a charge ordered bilayer superlattice model in which
pairing is supported by inter-layer Coulomb energy gain (potential energy
driven superconductivity).The 2D pair-condensate can be characterized by a
charge ordered state with a "checkerboard" like pattern seen by scanning
tunneling microscopy.The pair condensation might lead to the sharp decrease of
the normal state c-axis anisotropy of the hole content and hence to the
decrease of inter-layer dielectric screening.The decrease of the c-axis
dielectric screening can be the primary source of the condensation energy below
$T_c$. We find that a net gain in the electrostatic energy occurs along the
c-axis, which is proportional to the measured condensation energy ($U_0$) and
with $T_c$:$E_c^{3D} \approx 2 (\xi_{ab}/a_0+1)^2 U_0 \approx k_B T_c$ and is
due to inter-layer charge complementarity (charge asymmetry of the boson
condensate) where $\xi_{ab}$ is the coherence length of the condensate and $a_0
\approx 3.9 \AA$ is the in-planelattice constant.


  For reactor antineutrino experiments, a thorough understanding of the fuel
composition and isotopic evolution is of paramount importance for the
extraction of $\theta_{13}$. To accomplish these goals, we employ the
deterministic lattice code DRAGON, and analyze the instantaneous antineutrino
rate from the San Onofre Nuclear Generating Station (SONGS) Unit 2 reactor in
California. DRAGON's ability to predict the rate for two consecutive fuel
cycles is examined.


  We develop the notion of $g$-angle between two subspaces of a normed space.
In particular, we discuss the $g$-angle between a $1$-dimensional subspace and
a $t$-dimensional subspace for $t\geq 1$ and the $g$-angle between a
$2$-dimensional subspace and a $t$-dimensional subspace for $t\geq 2$.
Moreover, we present an explicit formula for the $g$-angle between two
subspaces of $\ell^{p}$ spaces.


  The strength of the total magnetic field in our Milky Way from radio Zeeman
and synchrotron measurements is about 6 muG near the Sun and several mG in
dense clouds, pulsar wind nebulae, and filaments near the Galactic Center.
Diffuse polarized radio emission and Faraday rotation of the polarized emission
from pulsars and background sources show many small-scale magnetic features,
but the overall field structure in our Galaxy is still under debate. -- Radio
synchrotron observations of nearby galaxies reveal dynamically important
magnetic fields of 10-30 muG total strength in the spiral arms. Fields with
random orientations are concentrated in spiral arms, while ordered fields
(observed in radio polarization) are strongest in interarm regions and follow
the orientation of the adjacent gas spiral arms. Faraday rotation of the
diffuse polarized radio emission from the disks of spiral galaxies sometimes
reveals large-scale patterns which are signatures of coherent fields generated
by dynamos, but in most galaxies the field structure is more complicated. --
Strong magnetic fields are also observed in radio halos around edge-on
galaxies, out to large distances from the plane. The synchrotron scaleheight of
radio halos allows to measure the mean outflow velocity of the cosmic-ray
electrons. The ordered halo fields mostly form an X-shaped pattern, but no
large-scale pattern is seen in the Faraday rotation data. Diffuse polarized
radio emission in the outer disks and halos is an excellent tracer of galaxy
interactions and ram pressure by the intergalactic medium. -- Intracluster gas
can also be significantly magnetized and highly polarized due to shocks or
cluster mergers.


  Order diagrams are an important tool to visualize the complex structure of
ordered sets. Favorable drawings of order diagrams, i.e., easily readable for
humans, are hard to come by, even for small ordered sets. Many attempts were
made to transfer classical graph drawing approaches to order diagrams. Although
these methods produce satisfying results for some ordered sets, they
unfortunately perform poorly in general. In this work we present the novel
algorithm DimDraw to draw order diagrams. This algorithm is based on a relation
between the dimension of an ordered set and the bipartiteness of a
corresponding graph.


  Battery management system plays an important role for modern battery-powered
application such as Electric vehicles, portable electronic equipment and
storage for renewable energy sources. It also increases the life-cycle of the
battery, battery state and efficiency. Monitoring the state of charge of the
battery is a crucial factor for battery management system. This paper deals
with monitoring the state of charge of the battery along with temperature,
current for Solar panel fitted with battery for residential application.
Microcontroller is used for controlling purpose, analog sensors are used for
sensing the parameters of voltage, current. The information of the battery is
given with tabular form and shown in photograph. Battery parameters are
displayed with the LCD screen.


  We describe novel composite pulse sequences which act as general rotors and
thus are suitable for nuclear magnetic resonance (NMR) quantum computation. The
Resonance Offset Tailoring To Enhance Nutations (ROTTEN) approach permits
perfect compensation of off-resonance errors at two selected frequencies placed
symmetrically around the frequency of the RF source.


  We present a detailed analysis of the beam energy dependence of the
mechanisms for the generation of directed and elliptic flows in Au+Au
collisions focusing on the role of hadronic rescattering and spectator
shadowing within a microscopic transport model JAM with different equation of
state. A systematic study of the beam energy dependence is performed for Au+Au
collisions at $\sqrt{s_{NN}} =2.3 - 62.4$ GeV. The transition of the dynamical
origin of the directed flow is observed. We find that the initial Glauber type
nucleon-nucleon collisions generate negative $v_1$ for nucleons at midrapidity
due to the presence of spectator matter, and this negative nucleon $v_1$ is
turned to be positive by the meson-baryon interactions at the beam energy
region of $\sqrt{s_{NN}} < 30$ GeV. In contrast, above 30 GeV there is no
spectator shadowing at midrapidity, and initial nucleon-nucleon collisions do
not generate directed flow, but subsequent rescatterings among produced
particles generate negative $v_1$ for nucleons. It is demonstrated that
negative pion-directed flows are mostly generated by the interaction with the
spectator matter. It is also shown that the squeeze-out effect is largely
suppressed in the case of softening, which leads to the enhancement of elliptic
flow around $\sqrt{s_{NN}}=5-7$ GeV. The elliptic flow at midrapidity above 10
GeV is not influenced by the squeeze-out due to spectator matter, while its
effect is seen at the forward rapidity range of $y/y_\mathrm{c.m.}>0.5$, which
decreases as beam energy increases.


  In the previous paper of Alicki et.al. a model of a solar cell has been
proposed in which the non-periodic source of energy - photon flux - drives the
collective periodic motion of electrons in a form of plasma oscillations.
Subsequently, plasma oscillations are rectified by the p-n junction diode into
the direct current (work). This approach makes a solar cell similar to standard
macroscopic heat motors or turbines which always contain two heat baths, the
working medium and the periodically moving piston or rotor. Here, a very
similar model is proposed in order to describe the operation principles of
thermoelectric generators based either on bimetallic or semiconductor p-n
junctions. Again plasma oscillation corresponds to a piston and sunlight is
replaced by a hot bath. The mathematical formalism is based on the Markovian
master equations which can be derived in a rigorous way from the underlying
Hamiltonian models and are consistent with the laws of thermodynamics.


  Supersymmetric (SUSY) theories are often thought to give large branching
ratios for $b \rightarrow s \gamma$ from charged Higgs loops. We show that in
many cases chargino loop contributions can cancel those of the Higgs, and SUSY
can give $B(b \rightarrow s \gamma)$ at or below the \SM\ prediction. We show
this occurs because the large stop mass splittings usually found in SUSY break
a GIM mechanism suppression. These effects are strongly enhanced by large
$\tan\beta$, so that $B(b \rightarrow s \gamma)$ is very sensitive to the value
of $\tan\beta$, contrary to what has been claimed. We also note that the
supergravity relation $B_0 = A_0-1$ is somewhat disfavored over the general
case.


  Let $\mathcal{W}$ be a complete local commutative Noetherian ring with
residue field $k$ of positive characteristic $p$. We study the inverse problem
for the versal deformation rings $R_{\mathcal{W}}(\Gamma,V)$ relative to
$\mathcal{W}$ of finite dimensional representations $V$ of a profinite group
$\Gamma$ over $k$. We show that for all $p$ and $n \ge 1$, the ring
$\mathcal{W}[[t]]/(p^n t,t^2)$ arises as a universal deformation ring. This
ring is not a complete intersection if $p^n\mathcal{W}\neq\{0\}$, so we obtain
an answer to a question of M. Flach in all characteristics. We also study the
`inverse inverse problem' for the ring $\mathcal{W}[[t]]/(p^n t,t^2)$; this is
to determine all pairs $(\Gamma, V)$ such that $R_{\mathcal{W}}(\Gamma,V)$ is
isomorphic to this ring.


  The theory of gamma-ray emission from e$^{\pm}$ jets and the implications for
jet formation, dynamics and structure are reviewed. In particular, possible
carriers of the jet's thrust on small scales, the transition from
electromagnetic to particle dominance in Poynting flux jets, formation of pair
cascades, synchrotron emission by cascading pairs, and formation of shocks due
to unsteadiness in the jet parameters are considered, with emphasis on the
observational consequences. Some recent progress in modeling transient emission
from blazars is also briefly discussed.


  In this paper we investigate Uludag's method for constructing new curves
whose fundamental groups are central extensions of the fundamental group of the
original curve by finite cyclic groups.
  In the first part, we give some generalizations to his method in order to get
new families of curves with controlled fundamental groups. In the second part,
we discuss some properties of groups which are preserved by these methods.
Afterwards, we describe precisely the families of curves which can be obtained
by applying the generalized methods to several types of plane curves. We also
give an application of the general methods for constructing new Zariski pairs.


  The main goal of this paper is applying the cut-off projection for solving
one-dimensional backward heat conduction problem in a two-slab system with a
perfect contact. In a constructive manner, we commence by demonstrating the
Fourier-based solution that contains the drastic growth due to the
high-frequency nature of the Fourier series. Such instability leads to the need
of studying the projection method where the cut-off approach is derived
consistently. In the theoretical framework, the first two objectives are to
construct the regularized problem and prove its stability for each noise level.
Our second interest is estimating the error in $L^2$-norm. Another
supplementary objective is computing the eigen-elements. All in all, this paper
can be considered as a preliminary attempt to solve the heating/cooling of a
two-slab composite system backward in time. Several numerical tests are
provided to corroborate the qualitative analysis.


  Compared to standard tomographic reconstruction, iterative approaches offer
the possibility to account for extraneous experimental influences, which allows
for a suppression of related artifacts. However, the inclusion of corresponding
parameters in the iterative forward model typically leads to longer computation
times. Here, we demonstrate experimentally for phase sensitive X-ray imaging
based on the edge illumination principle that inadequately sampled illumination
curves result in ring artifacts in tomographic reconstructions. We take
advantage of appropriately sampled illumination curves instead, which enables
us to eliminate the corresponding parameter from the forward model and
substantially increase computational speed. In addition, we demonstrate a 30\%
improvement in spatial resolution of the iterative approach compared with the
standard non-iterative single shot approach. Further, we report on several
significant improvements in our numerical implementation of the iterative
approach, which we make available online with this publication. Finally, we
show that the combination of both experimental and algorithmic advancement lead
to a total speed increase by one order of magnitude and an improved contrast to
noise ratio in the reconstructions.


  In this letter, we present a spectacular eruptive flare (X8.2) associated
with a coronal mass ejection (CME) on 2017 September 10 at the west limb of the
Sun. A flux rope eruption is followed by the inflow, the formation of a current
sheet and a cusp structure, which were simultaneously observed during the
occurrence of this flare. The hierarchical layers of the cusp-shaped structure
are well observed in 131 \AA\ observation. The scenario that can be created
from these observations is very consistent with the predictions of some
eruptive models. Except for the characteristics mentioned above in the process
of the flare predicted by classical eruption models, the current sheet
separating into several small current sheets is also observed at the final
stage of the flux rope eruption. The quantitative calculation of the velocities
and accelerations of the inflow, hot cusp structure, and post-flare loops is
presented. The width of the current sheet is estimated to be about 3 $\times$
$10^{3}$ km. These observations are very useful to understand the process of
solar eruptions.


  The close-packed AB$_2$ structures called Laves phases constitute the largest
group of intermetallic compounds. In this paper we computationally investigated
the pseudo-binary Laves phase system Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$
spanning between the YFe$_2$, YCo$_2$, GdFe$_2$, and GdCo$_2$ vertices. While
the vast majority of the Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$ phase diagram is
the ferrimagnetic phase, YCo$_2$ along with a narrow range of concentrations
around it is the paramagnetic phase. We presented results obtained by Monte
Carlo simulations of the Heisenberg model with parameters derived from
first-principles calculations. For calculations, we used the Uppsala atomistic
spin dynamics (UppASD) code together with the spin-polarized relativistic
Korringa-Kohn-Rostoker (SPR-KKR) code. From first principles we calculated the
magnetic moments and exchange integrals for the considered pseudo-binary
system, together with spin-polarized densities of states for boundary
compositions. Furthermore, we showed how the compensation point with the
effective zero total moment depends on the concentration in the considered
ferrimagnetic phases. However, the main result of our study was the
determination of the Curie temperature dependence for the system
Y$_{1-x}$Gd$_x$(Fe$_{1-y}$Co$_y$)$_2$. Except for the paramagnetic region
around YCo$_2$, the predicted temperatures were in good qualitative and
quantitative agreement with experimental results, which confirmed the ability
of the method to predict magnetic transition temperatures for systems
containing up to three different magnetic elements (Fe, Co, and Gd)
simultaneously. For the Y(Fe$_{1-y}$Co$_y$)$_2$ and Gd(Fe$_{1-y}$Co$_y$)$_2$
systems our calculations matched the experimentally-confirmed
Slater-Pauling-like behavior of T$_C$ dependence on the Co concentration.


  In graph theoretical models of the spread of disease through populations, the
spread of opinion through social networks, and the spread of faults through
distributed computer networks, vertices are in two states, either black or
white, and these states are dynamically updated at discrete time steps
according to the rules of the particular conversion process used in the model.
This paper considers the irreversible k-threshold and majority conversion
processes. In an irreversible k-threshold (resp., majority) conversion process,
a vertex is permanently colored black in a certain time period if at least k
(resp., at least half) of its neighbors were black in the previous time period.
A k-conversion set (resp., dynamic monopoly) is a set of vertices which, if
initially colored black, will result in all vertices eventually being colored
black under a k-threshold (resp., majority) conversion process. We answer
several open problems by presenting bounds and some exact values of the minimum
number of vertices in k-conversion sets and dynamic monopolies of complete
multipartite graphs, as well as of Cartesian and tensor products of two graphs.


  We investigated the formation of the Mg II h-k doublet in a weakly magnetized
atmosphere (20-100 G) using a newly developed numerical code for polarized RT
in a plane-parallel geometry, which implements a recent formulation of
partially coherent scattering by polarized multi-term atoms in arbitrary
magnetic field regimes. Our results confirm the importance of partial
redistribution effects in the formation of the Mg II h and k lines, as pointed
out by previous work in the non-magnetic case. We show that the presence of a
magnetic field can produce measurable modifications of the broadband linear
polarization even for relatively small field strengths (~10 G), while the
circular polarization remains well represented by the classical magnetograph
formula. Both these results open an important new window for the weak-field
diagnostics of the upper solar atmosphere.


  The evolution of antimicrobial resistance can be strongly affected by
variations of antimicrobial concentration. Here, we study the impact of
periodic alternations of absence and presence of antimicrobial on resistance
evolution in a microbial population, using a stochastic model that includes
variations of both population composition and size, and fully incorporates
stochastic population extinctions. We show that fast alternations of presence
and absence of antimicrobial are inefficient to eradicate the microbial
population and strongly favor the establishment of resistance, unless the
antimicrobial increases enough the death rate. We further demonstrate that if
the period of alternations is longer than a threshold value, the microbial
population goes extinct upon the first addition of antimicrobial, if it is not
rescued by resistance. We express the probability that the population is
eradicated upon the first addition of antimicrobial, assuming rare mutations.
Rescue by resistance can happen either if resistant mutants preexist, or if
they appear after antimicrobial is added to the environment. Importantly, the
latter case is fully prevented by perfect biostatic antimicrobials that
completely stop division of sensitive microorganisms. By contrast, we show that
the parameter regime where treatment is efficient is larger for biocidal drugs
than for biostatic drugs. This sheds light on the respective merits of
different antimicrobial modes of action.


  Let $\nu$ and $\mu$ be probability distributions on $\mathbb{R}^n$, and
$\nu_s,\mu_s$ be their evolution under the heat flow, that is, the probability
distributions resulting from convolving their density with the density of an
isotropic Gaussian random vector with variance $s$ in each entry. This paper
studies the rate of decay of $s\mapsto D(\nu_s\|\mu_s)$ for various
divergences, including the $\chi^2$ and Kullback-Leibler (KL) divergences. We
prove upper and lower bounds on the strong data-processing inequality (SDPI)
coefficients corresponding to the source $\mu$ and the Gaussian channel. We
also prove generalizations of de Brujin's identity, and Costa's result on the
concavity in $s$ of the differential entropy of $\nu_s$. As a byproduct of our
analysis, we obtain new lower bounds on the mutual information between $X$ and
$Y=X+\sqrt{s} Z$, where $Z$ is a standard Gaussian vector in $\mathbb{R}^n$,
independent of $X$, and on the minimum mean-square error (MMSE) in estimating
$X$ from $Y$, in terms of the Poincar\'e constant of $X$.


  Characterizing inhomogeneous temporal patterns in natural and social
phenomena is important to understand underlying mechanisms behind such complex
systems, hence even to predict and control them. Temporal inhomogeneities in
event sequences have been described in terms of bursts that are rapidly
occurring events in short time periods alternating with long inactive periods.
The bursts can be quantified by a simple measure, called burstiness parameter,
which was introduced by Goh and Barab\'asi [EPL \textbf{81}, 48002 (2008)]. The
burstiness parameter has been widely used due to its simplicity, which however
turns out to be strongly affected by the finite number of events in the time
series. As the finite-size effects on burstiness parameter have been largely
ignored, we analytically investigate the finite-size effects of the burstiness
parameter. Then we suggest an alternative definition of burstiness that is free
from finite-size effects and yet simple. Using our alternative burstiness
measure, one can distinguish the finite-size effects from the intrinsic bursty
properties in the time series. We also demonstrate the advantages of our
burstiness measure by analyzing empirical datasets.


  One might raise a question if the gravitational scalar field (dilaton)
mediates a finite-range force between local objects still behaving globally as
being massless to implement the scenario of a decaying cosmological constant.
We offer a non-negative reply by a detailed analysis of the field-theoretical
quantization procedure in relation to the observationally required suppression
of the vacuum-energy.


  Despite the indispensable role of X-ray computed tomography (CT) in
diagnostic medicine field, the associated ionizing radiation is still a major
concern considering that it may cause genetic and cancerous diseases.
Decreasing the exposure can reduce the dose and hence the radiation-related
risk, but will also induce higher quantum noise. Supervised deep learning can
be used to train a neural network to denoise the low-dose CT (LDCT). However,
its success requires massive pixel-wise paired LDCT and normal-dose CT (NDCT)
images, which are rarely available in real practice. To alleviate this problem,
in this paper, a shift-invariant property based neural network was devised to
learn the inherent pixel correlations and also the noise distribution by only
using the LDCT images, shaping into our probabilistic self-learning framework.
Experimental results demonstrated that the proposed method outperformed the
competitors, producing an enhanced LDCT image that has similar image style as
the routine NDCT which is highly-preferable in clinic practice.


  We investigate the validity of two resampling techniques when carrying out
inference on the underlying unknown copula using a recently proposed class of
smooth, possibly data-adaptive nonparametric estimators that contains empirical
Bernstein copulas (and thus the empirical beta copula). Following
\cite{KirSegTsu21}, the first resampling technique is based on drawing samples
from the smooth estimator and can only can be used in the case of independent
observations. The second technique is a smooth extension of the so-called
sequential dependent multiplier bootstrap and can thus be used in a time series
setting and, possibly, for change-point analysis. The two studied resampling
schemes are applied to confidence interval construction and the offline
detection of changes in the cross-sectional dependence of multivariate time
series, respectively. Monte Carlo experiments confirm the possible advantages
of such smooth inference procedures over their non-smooth counterparts. A
by-product of this work is the study of the weak consistency and finite-sample
performance of two classes of smooth estimators of the first-order partial
derivatives of a copula which can have applications in mean and quantile
regression.


  This paper presents the first implementation of autonomous robotic
auscultation of heart and lung sounds. To select auscultation locations that
generate high-quality sounds, a Bayesian Optimization (BO) formulation
leverages visual anatomical cues to predict where high-quality sounds might be
located, while using auditory feedback to adapt to patient-specific anatomical
qualities. Sound quality is estimated online using machine learning models
trained on a database of heart and lung stethoscope recordings. Experiments on
4 human subjects show that our system autonomously captures heart and lung
sounds of similar quality compared to tele-operation by a human trained in
clinical auscultation. Surprisingly, one of the subjects exhibited a previously
unknown cardiac pathology that was first identified using our robot, which
demonstrates the potential utility of autonomous robotic auscultation for
health screening.


  Although pre-training on a large amount of data is beneficial for robot
learning, current paradigms only perform large-scale pretraining for visual
representations, whereas representations for other modalities are trained from
scratch. In contrast to the abundance of visual data, it is unclear what
relevant internet-scale data may be used for pretraining other modalities such
as tactile sensing. Such pretraining becomes increasingly crucial in the
low-data regimes common in robotics applications. In this paper, we address
this gap by using contact microphones as an alternative tactile sensor. Our key
insight is that contact microphones capture inherently audio-based information,
allowing us to leverage large-scale audio-visual pretraining to obtain
representations that boost the performance of robotic manipulation. To the best
of our knowledge, our method is the first approach leveraging large-scale
multisensory pre-training for robotic manipulation. For supplementary
information including videos of real robot experiments, please see
https://sites.google.com/view/hearing-touch.


  We investigate cohomological gauge theories in noncommutative R^{2D}. We show
that vacuum expectation values of the theories do not depend on noncommutative
parameters, and the large noncommutative parameter limit is equivalent to the
dimensional reduction. As a result of these facts, we show that a partition
function of a cohomological theory defined in noncommutative R^{2D} and a
partition function of a cohomological field theory in R^{2D+2} are equivalent
if they are connected through dimensional reduction. Therefore, we find several
partition functions of supersymmetric gauge theories in various dimensions are
equivalent. Using this technique, we determine the partition function of the
N=4 U(1) gauge theory in noncommutative R^4, where its action does not include
a topological term. The result is common among (8-dim, N=2), (6-dim, N=2),
(2-dim, N=8) and the IKKT matrix model given by their dimensional reduction to
0-dim.


  Massive stars are always the focus of astronomical research and a significant
part of them (10--20%) moves in space at a high (supersonic) velocity. This
paper presents the results of a study of the $\alpha$ Crucis system, located at
$\sim$114 pc distance from the Sun, with an observed bow shock around it. We
used data and images from the Gaia and WISE space telescopes. The coordinates,
distance, and proper motion of the $\alpha$ Crucis system were used to
determine its space velocity. We managed to find a stellar cluster to which the
$\alpha$ Crucis system belongs, that is, it has not been ejected from its
parent cluster, but is moving in space together with other members of the
cluster. The $\alpha$ Crucis system has a velocity of $\sim$1.3 km/s relative
to the star cluster. The geometric parameters of the bow shock are compatible
with other known bow shocks. The bow shock is unaligned, i.e., most likely
interstellar medium large-scale motions are responsible for the resulting bow
shock, which is further evidence that the $\alpha$ Crucis system is not runaway
in nature.


  We derive an equivalence between the (2,0) superconformal M5-brane field
theory dimensionally reduced on a squashed three-sphere, and Chern-Simons
theory with complex gauge group. In the reduction, the massless fermions obtain
an action which is second order in derivatives and are reinterpreted as ghosts
for gauge fixing the emergent non-compact gauge symmetry. A squashing parameter
in the geometry controls the imaginary part of the complex Chern-Simons level.


  We propose a dynamic mechanism of a molecular machine for energy conversion,
by considering a simple model describing the dynamics of two components, the
head and the chain. After injection of energy to the head region, the energy is
stored at one part for some time, and is used step by step, allowing the head
to move directionally along the chain, irrespectively of the direction of the
input, under a fluctuating environment. Our system can adjust the timing with
which the head crosses the energy barrier by taking advantage of internal
dynamics and the flexibility of components. The mechanism is shown to be robust
and efficient. Some suggestions are given for molecular machines.


  Since fake news poses a serious threat to society and individuals, numerous
studies have been brought by considering text, propagation and user profiles.
Due to the data collection problem, these methods based on propagation and user
profiles are less applicable in the early stages. A good alternative method is
to detect news based on text as soon as they are released, and a lot of
text-based methods were proposed, which usually utilized words, sentences or
paragraphs as basic units. But, word is a too fine-grained unit to express
coherent information well, sentence or paragraph is too coarse to show specific
information. Which granularity is better and how to utilize it to enhance text
representation for fake news detection are two key problems. In this paper, we
introduce Elementary Discourse Unit (EDU) whose granularity is between word and
sentence, and propose a multi-EDU-structure awareness model to improve text
representation for fake news detection, namely EDU4FD. For the
multi-EDU-structure awareness, we build the sequence-based EDU representations
and the graph-based EDU representations. The former is gotten by modeling the
coherence between consecutive EDUs with TextCNN that reflect the semantic
coherence. For the latter, we first extract rhetorical relations to build the
EDU dependency graph, which can show the global narrative logic and help
deliver the main idea truthfully. Then a Relation Graph Attention Network
(RGAT) is set to get the graph-based EDU representation. Finally, the two EDU
representations are incorporated as the enhanced text representation for fake
news detection, using a gated recursive unit combined with a global attention
mechanism. Experiments on four cross-source fake news datasets show that our
model outperforms the state-of-the-art text-based methods.


  We investigate the effects of a repulsive three-body interaction on the Mott
transition of the repulsively interacting three-component fermionic atoms in
optical lattices by means of the self-energy functional approach. We find that
the three-body repulsion hardly affects the qualitative features of the Mott
transition, because the three-body repulsion does not compete with the two-body
repulsions. When the three-body repulsion is extremely strong, the triple
occupancy vanishes in the Fermi liquid state. This situation is equivalent to
that caused by strong three-body losses. Our results imply that three-body
losses have little influence on the Mott transitions in the repulsively
interacting three-component fermionic atoms in optical lattices.


  In this study we investigate the potential for using synthetic aperture radar
(SAR) data to provide high resolution defoliation and regrowth mapping of trees
in the tundra-forest ecotone. Using aerial photographs, four areas with live
forest and four areas with dead trees were identified. Quad-polarimetric SAR
data from RADARSAT-2 was collected from the same area, and the complex
multilook polarimetric covariance matrix was calculated using a novel extension
of guided nonlocal means speckle filtering. The nonlocal approach allows us to
preserve the high spatial resolution of single-look complex data, which is
essential for accurate mapping of the sparsely scattered trees in the study
area. Using a standard random forest classification algorithm, our filtering
results in over $99.7 \%$ classification accuracy, higher than traditional
speckle filtering methods, and on par with the classification accuracy based on
optical data.


  New fluctuation properties arise in problems where both spatial integration
and energy summation are necessary ingredients. The quintessential example is
given by the short-range approximation to the first order ground state
contribution of the residual Coulomb interaction. The dominant features come
from the region near the boundary where there is an interplay between Friedel
oscillations and fluctuations in the eigenstates. Quite naturally, the
fluctuation scale is significantly enhanced for Neumann boundary conditions as
compared to Dirichlet. Elements missing from random plane wave modeling of
chaotic eigenstates lead surprisingly to significant errors, which can be
corrected within a purely semiclassical approach.


  We solve a problem on filling by Levi-flat hypersurfaces for a class of
totally real 2-tori in a real 4-manifold with an almost complex structure tamed
by an exact symplectic form. As an application we obtain a simple proof of
Gromov's non-squeezing theorem in dimension 4 and new results on rigidity of
symplectic structures.


  Learning by children and animals occurs effortlessly and largely without
obvious supervision. Successes in automating supervised learning have not
translated to the more ambiguous realm of unsupervised learning where goals and
labels are not provided. Barlow (1961) suggested that the signal that brains
leverage for unsupervised learning is dependence, or redundancy, in the sensory
environment. Dependence can be characterized using the information-theoretic
multivariate mutual information measure called total correlation. The principle
of Total Cor-relation Ex-planation (CorEx) is to learn representations of data
that "explain" as much dependence in the data as possible. We review some
manifestations of this principle along with successes in unsupervised learning
problems across diverse domains including human behavior, biology, and
language.


  We present a machine learning analysis of five labelled galaxy catalogues
from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and
SersicCatUKIDSS catalogues containing morphological features, the
GaussFitSimple catalogue containing spectroscopic features, the MagPhys
catalogue including physical parameters for galaxies, and the Lambdar
catalogue, which contains photometric measurements. Extending work previously
presented at the ESANN 2018 conference - in an analysis based on Generalized
Relevance Matrix Learning Vector Quantization and Random Forests - we find that
neither the data from the individual catalogues nor a combined dataset based on
all 5 catalogues fully supports the visual-inspection-based galaxy
classification scheme employed to categorise the galaxies. In particular, only
one class, the Little Blue Spheroids, is consistently separable from the other
classes. To aid further insight into the nature of the employed visual-based
classification scheme with respect to physical and morphological features, we
present the galaxy parameters that are discriminative for the achieved class
distinctions.


  We describe a visualization tool that can be used to view the change in
meaning of words over time. The tool makes use of existing (static) word
embedding datasets together with a timestamped $n$-gram corpus to create {\em
temporal} word embeddings.


  We show that the measurement of the baryonic acoustic oscillations in large
high redshift galaxy surveys offers a precision route to the measurement of
dark energy. The cosmic microwave background provides the scale of the
oscillations as a standard ruler that can be measured in the clustering of
galaxies, thereby yielding the Hubble parameter and angular diameter distance
as a function of redshift. This, in turn, enables one to probe dark energy. We
use a Fisher matrix formalism to study the statistical errors for redshift
surveys up to z=3 and report errors on cosmography while marginalizing over a
large number of cosmological parameters including a time-dependent equation of
state. With redshifts surveys combined with cosmic microwave background
satellite data, we achieve errors of 0.037 on Omega_x, 0.10 on w(z=0.8), and
0.28 on dw(z)/dz for cosmological constant model. Models with less negative
w(z) permit tighter constraints. We test and discuss the dependence of
performance on redshift, survey conditions, and fiducial model. We find results
that are competitive with the performance of future supernovae Ia surveys. We
conclude that redshift surveys offer a promising independent route to the
measurement of dark energy.


  Pre-training on large-scale databases consisting of natural images and then
fine-tuning them to fit the application at hand, or transfer-learning, is a
popular strategy in computer vision. However, Kataoka et al., 2020 introduced a
technique to eliminate the need for natural images in supervised deep learning
by proposing a novel synthetic, formula-based method to generate 2D fractals as
training corpus. Using one synthetically generated fractal for each class, they
achieved transfer learning results comparable to models pre-trained on natural
images. In this project, we take inspiration from their work and build on this
idea -- using 3D procedural object renders. Since the image formation process
in the natural world is based on its 3D structure, we expect pre-training with
3D mesh renders to provide an implicit bias leading to better generalization
capabilities in a transfer learning setting and that invariances to 3D rotation
and illumination are easier to be learned based on 3D data. Similar to the
previous work, our training corpus will be fully synthetic and derived from
simple procedural strategies; we will go beyond classic data augmentation and
also vary illumination and pose which are controllable in our setting and study
their effect on transfer learning capabilities in context to prior work. In
addition, we will compare the 2D fractal and 3D procedural object networks to
human and non-human primate brain data to learn more about the 2D vs. 3D nature
of biological vision.


  We study the Liouville type problem for the stationary 3D Navier-Stokes
equations on $\Bbb R^3$. Specifically, we prove that if $v$ is a smooth
solution to (NS) satisfying $\omega={\rm curl}\,v \in L^q (\Bbb R^3) $ for some
$\frac32 \leq q< 3$, and $|v(x)|\to 0$ as $|x|\to +\infty$, then either $v=0$
on $\Bbb R^3$, or $\int_{\Bbb R^6} \Phi_+ dxdy=\int_{\Bbb R^6} \Phi_-
dxdy=+\infty$, where $\Phi(x,y) :=\frac{1}{4\pi}\frac{\omega
(x)\cdot(x-y)\times (v(y)\times \omega(y) )}{|x-y|^3} $, and $\Phi_\pm:=\max\{
0, \pm \Phi\}$. The proof uses crucially the structure of nonlinear term of the
equations.


  Whole-cell modelling is constrained by the laws of nature in general and the
laws of thermodynamics in particular. This paper shows how one prolific source
of information, stoichiometric models of biomolecular systems, can be
integrated with thermodynamic principles using the bond graph approach to
network thermodynamics.


  Positive definite operator-valued kernels generalize the well-known notion of
reproducing kernels, and are naturally adapted to multi-output learning
situations. This paper addresses the problem of learning a finite linear
combination of infinite-dimensional operator-valued kernels which are suitable
for extending functional data analysis methods to nonlinear contexts. We study
this problem in the case of kernel ridge regression for functional responses
with an lr-norm constraint on the combination coefficients. The resulting
optimization problem is more involved than those of multiple scalar-valued
kernel learning since operator-valued kernels pose more technical and
theoretical issues. We propose a multiple operator-valued kernel learning
algorithm based on solving a system of linear operator equations by using a
block coordinatedescent procedure. We experimentally validate our approach on a
functional regression task in the context of finger movement prediction in
brain-computer interfaces.