  We present the first data release of the James Clerk Maxwell Telescope (JCMT)
Plane Survey (JPS), the JPS Public Release 1 (JPSPR1). JPS is an 850-um
continuum survey of six fields in the northern inner Galactic Plane in a
longitude range of l=7-63, made with the Sub-millimetre Common-User Bolometer
Array 2 (SCUBA-2). This first data release consists of emission maps of the six
JPS regions with an average pixel-to-pixel noise of 7.19 mJy beam^-1, when
smoothed over the beam, and a compact-source catalogue containing 7,813
sources. The 95 per cent completeness limits of the catalogue are estimated at
0.04 Jy beam^-1 and 0.3 Jy for the peak and integrated flux densities,
respectively. The emission contained in the compact-source catalogue is 42 +- 5
per cent of the total and, apart from the large-scale (greater than 8')
emission, there is excellent correspondence with features in the 500-um
Herschel maps. We find that, with two-dimensional matching, 98 +- 2 per cent of
sources within the fields centred at l=20, 30, 40 and 50 are associated with
molecular clouds, with 91 +- 3 per cent of the l=30 and 40 sources associated
with dense molecular clumps. Matching the JPS catalogue to Herschel 70-um
sources, we find that 38 +- 1 per cent of sources show evidence of ongoing star
formation. The images and catalogue will be a valuable resource for studies of
star formation in the Galaxy and the role of environment and spiral arms in the
star formation process.


  Deep Chandra observations of the Hydra A Cluster reveal a feature in the
X-ray surface brightness that surrounds the 330 MHz radio lobes of the AGN at
the cluster center. Surface brightness profiles of this feature and its close
association with the radio lobes argue strongly that it is a shock front driven
by the expanding radio lobes. The Chandra image also reveals other new
structure on smaller scales that is associated with the radio source, including
a large cavity and filament. The shock front extends 200 - 300 kpc from the AGN
at the cluster center and its strength varies along the front, with Mach
numbers in the range ~ 1.2 - 1.4. It is stronger where it is more distant from
the cluster center, as expected for a shock driven by expanding radio lobes.
Simple modeling gives an age for the shock front ~ 1.4\times10^8 y and a total
energy driving it of ~ 10^{61} erg. The mean mechanical power driving the shock
is comparable to quasar luminosities, well in excess of that needed to regulate
the cooling core in Hydra A. This suggests that the feedback regulating cooling
cores is inefficient, in that the bulk of the energy is deposited beyond the
cooling core. In that case, a significant part of cluster "preheating" is a
byproduct of the regulation of cooling cores.


  In a recent publication, we introduce the lift force as a common source to
accretion disk tilt that is likely relevant to accretion disk systems. Lift is
generated by slightly different supersonic gas stream speeds flowing over and
under the disk at the bright spot. In this conference proceeding, we focus on
whether the average white dwarf has enough mass to prevent a disk tilt in
non-magnetic Cataclysmic Variables (CVs) with accretion disks. Assuming a white
dwarf mass of 0.6M$_{\odot}$ and a disk mass of 10$^{-11}$M$_{\odot}$, we vary
the secondary mass to establish theoretical minimum mass transfer rates needed
to induce and maintain a disk tilt of four degrees around the line of nodes.
For mass ratios in the range \( (0.13 \le q=M_{2}M^{-1} \le 0.45) \), we
confirm that the secondary mass does not contribute significantly to disk tilt.
We also confirm that the average white dwarf does not have enough mass to
prevent a disk tilt. We find that disk tilt may be likely in low mass transfer
rate systems such as CV SU UMa's.


  Emerging reconfigurable datacenters allow to dynamically adjust the network
topology in a demand-aware manner. These datacenters rely on optical switches
which can be reconfigured to provide direct connectivity between racks, in the
form of edge-disjoint matchings. While state-of-the-art optical switches in
principle support microsecond reconfigurations, the demand-aware topology
optimization constitutes a bottleneck.
  This paper proposes a dynamic algorithms approach to improve the performance
of reconfigurable datacenter networks, by supporting faster reactions to
changes in the traffic demand. This approach leverages the temporal locality of
traffic patterns in order to update the interconnecting matchings
incrementally, rather than recomputing them from scratch. In particular, we
present six (batch-)dynamic algorithms and compare them to static ones. We
conduct an extensive empirical evaluation on 176 synthetic and 39 real-world
traces, and find that dynamic algorithms can both significantly improve the
running time and reduce the number of changes to the configuration, especially
in networks with high temporal locality, while retaining matching weight.


  In a recent paper, Brendle showed the uniqueness of the Bryant soliton among
3-dimensional $\kappa$-solutions. In this paper, we present an alternative
proof for this fact and show that compact $\kappa$-solutions are rotational
symmetric. Our proof arose from independent work relating to our Strong
Stability Theorem for singular Ricci flows.


  Inspired by the living bridges formed by ants, swarm robots have been
developed to self-assemble bridges to span gaps and self-disassemble them.
Self-disassembly of bridges (SDB) may increase the transport efficiency of
swarm robots by increasing the number of moving robots, and also may decrease
the efficiency by causing gaps to reappear. Our aim is to elucidate the
influence of SDB on the collective flow characteristics of swarm robots in a
single-lane and periodic system with a gap. In the system, robots span and
cross the gap by self-assembling a single-layer bridge. We consider two
scenarios in which SDB is prevented (prevent-scenario) or allowed
(allow-scenario). We represent the horizontal movement of robots with a typical
car-following model, and simply model the actions of robots for self-assembling
and self-disassembling bridges. Numerical simulations have revealed the
following results. Flow-density diagrams in both the scenarios shift to the
higher-density region as the gap length increases. When density is low,
allow-scenario exhibits the steady state of repeated self-assembly and
self-disassembly of bridges. If density is extremely low, flow in this state is
greater than flow in prevent-scenario owing to the increase in the number of
robots moving horizontally. Otherwise, flow in this state is smaller than flow
in prevent-scenario. Besides, flow in this state increases monotonically with
respect to the velocity of robots in joining and leaving bridges. Thus, SDB is
recommended for only extremely low-density conditions in periodic systems.
Moreover, we have found hysteresis under the absence of periodic boundary
conditions (in an open system). This study contributes to the development of
the collective dynamics of self-driven particles that self-assemble structures,
and stirs the dynamics with other self-assembled structures, such as ramps,
chains, and towers.


  These notes are the outgrowth of a series of lectures given at MSRI in
January 1995 at the beginning of the special semester in complex dynamics and
hyperbolic geometry. In these notes, the primary aim is to motivate the study
of complex dynamics in two variables and to introduce the major ideas in the
field. Hence the treatment of the subject is mostly expository.


  We have investigated the field-angle behaviors of magnetic excitations under
an in-plane magnetic field for proximate Kitaev systems. By employing the exact
diagonalization method in conjunction with the linear spin wave theory, we have
demonstrated that the magnetic excitation gap in the polarized phase is
determined by the magnon excitation at $M$ points and has a strong anisotropy
with respect to the field direction in the vicinity of the critical field
limit. The specific heat from this magnon excitation bears qualitatively the
same anisotropic behaviors as expected one for the non-Abelian spin liquid
phase in the Kitaev model and experimentally observed one of the intermediate
phases in $\alpha$-RuCl$_3$.


  Dibenzo[\emph{hi,st}]ovalene (DBOV) - a quasi-zero-dimensional `nanographene'
- displays strong, narrow, and well-defined optical-absorption transitions at
room temperature. On placing a DBOV-doped polymer film into an optical
microcavity, we demonstrate strong coupling of the \textbf{0 $\rightarrow$ 0'}
electronic and \textbf{0 $\rightarrow$ 1'} vibrational transitions to a
confined cavity mode, with coupling energies of 104 meV and 40 meV,
respectively. Photoluminescence measurements indicate that the polariton
population is distributed between the lower and middle polariton branches at
energies approximately coincident with the emission of the DBOV, indicating
polariton population via an optical pumping mechanism.


  This 'research preview' paper introduces an adaptive path planning framework
for robotic mission execution in assistive-care applications. The framework
provides a graph-based environment modelling approach, with dynamic path
finding performed using Dijkstra's algorithm. A predictive module that uses
probabilistic model checking is applied to estimate the human's movement
through the environment, allowing run-time re-planning of the robot's path. We
illustrate the use of the framework for a simulated assistive-care case study
in which a mobile robot navigates through the environment and monitors an end
user with mild physical or cognitive impairments.


  Modelling the temperature of Electric Vehicle (EV) batteries is a fundamental
task of EV manufacturing. Extreme temperatures in the battery packs can affect
their longevity and power output. Although theoretical models exist for
describing heat transfer in battery packs, they are computationally expensive
to simulate. Furthermore, it is difficult to acquire data measurements from
within the battery cell. In this work, we propose a data-driven surrogate model
(LiFe-net) that uses readily accessible driving diagnostics for battery
temperature estimation to overcome these limitations. This model incorporates
Neural Operators with a traditional numerical integration scheme to estimate
the temperature evolution. Moreover, we propose two further variations of the
baseline model: LiFe-net trained with a regulariser and LiFe-net trained with
time stability loss. We compared these models in terms of generalization error
on test data. The results showed that LiFe-net trained with time stability loss
outperforms the other two models and can estimate the temperature evolution on
unseen data with a relative error of 2.77 % on average.


  We present an observational survey program using planetary nebulae, globular
clusters, and X-ray emission to probe the halos of early-type galaxies. We
review evidence for scanty dark matter halos around ordinary elliptical
galaxies, and discuss the possible implications. We also present measurements
of rotation in the halos.


  Proton-proton collisions at sqrt{s} = 7 TeV and heavy ion collisions at
sqrt{s_NN} = 2.76 TeV were produced by the LHC and recorded using the ATLAS
experiment's trigger system in 2010. The LHC is designed with a maximum bunch
crossing rate of 40 MHz and the ATLAS trigger system is designed to record
approximately 200 of these per second. The trigger system selects events by
rapidly identifying signatures of muon, electron, photon, tau lepton, jet, and
B meson candidates, as well as using global event signatures, such as missing
transverse energy. An overview of the ATLAS trigger system, the evolution of
the system during 2010 and the performance of the trigger system components and
selections based on the 2010 collision data are shown. A brief outline of plans
for the trigger system in 2011 is presented


  This paper introduces RankMatch, an innovative approach for Semi-Supervised
Label Distribution Learning (SSLDL). Addressing the challenge of limited
labeled data, RankMatch effectively utilizes a small number of labeled examples
in conjunction with a larger quantity of unlabeled data, reducing the need for
extensive manual labeling in Deep Neural Network (DNN) applications.
Specifically, RankMatch introduces an ensemble learning-inspired averaging
strategy that creates a pseudo-label distribution from multiple weakly
augmented images. This not only stabilizes predictions but also enhances the
model's robustness. Beyond this, RankMatch integrates a pairwise relevance
ranking (PRR) loss, capturing the complex inter-label correlations and ensuring
that the predicted label distributions align with the ground truth.
  We establish a theoretical generalization bound for RankMatch, and through
extensive experiments, demonstrate its superiority in performance against
existing SSLDL methods.


  In this paper, we present the analysis of stability for a piezoelectric beam
subject to a thermal law (Coleman-Gurtin or Gurtin-Pipkin thermal law) adding
some viscous damping mechanism to the electric field in $x-$direction and
$z-$direction, and we discuss several cases. Then, there is no need to control
the electrical field components in $x$-direction and $z-$ direction to
establish an exponential decay of solutions when the beam is subjected to a
Coleman-Gurtin law, otherwise a polynomial stability is established with
Gurtin-Pipkin thermal law in case when the electrical field components are
damped.


  Parking lots and autonomous warehouses for accommodating many vehicles/robots
adopt designs in which the underlying graphs are \emph{well-connected} to
simplify planning and reduce congestion. In this study, we formulate and delve
into the \emph{largest well-connected set} (LWCS) problem and explore its
applications in layout design for multi-robot path planning. Roughly speaking,
a well-connected set over a connected graph is a set of vertices such that
there is a path on the graph connecting any pair of vertices in the set without
passing through any additional vertices of the set. Identifying an LWCS has
many potential high-utility applications, e.g., for determining parking garage
layout and capacity, as prioritized planning can be shown to be complete when
start/goal configurations belong to an LWCS. In this work, we establish that
computing an LWCS is NP-complete. We further develop optimal and near-optimal
LWCS algorithms, with the near-optimal algorithm targeting large maps. A
complete prioritized planning method is given for planning paths for multiple
robots residing on an LWCS.


  The conservation of the vector current and the axial anomaly responsible for
the $\pi_0 \to \gamma \gamma$ decay amplitude are obtained in leading order
within the Taylor-Lagrange formulation of fields considered as operator-valued
distributions. As for gauge theories, where this formulation eliminates all
divergences and preserves gauge-symmetry, it is shown that the different
contributions can be evaluated directly in $4$-dimensional space-time, with no
restrictions whatsoever on the four-momentum of the internal loop, and without
the need to introduce any additional non physical degrees of freedom like
Pauli-Villars fields. We comment on the similar contributions responsible for
the decay of the Higgs boson into two photons.


  In a previous paper [S.Q. Wu, Phys. Rev. D 83, 121502(R) (2011)], a new kind
of metric ansatz was found to fairly describe all already known black hole
solutions in the ungauged Kaluza-Klein (KK) supergravity theories. That metric
ansatz somewhat resembles to the famous Kerr-Schild (KS) form, but it is
different from the KS one in two distinct aspects. That is, apart from a global
conformal factor, the metric ansatz can be written as a vacuum background
spacetime plus a "perturbation" modification term, the latter of which is
associated with a timelike geodesic vector field rather than a null geodesic
congruence in the usual KS ansatz. In this paper, we shall study this novel
metric ansatz in detail, aiming at achieving some inspiration as to the
construction of rotating charged AdS black holes with multiple charges in other
gauged supergravity theories. In order to investigate the metric properties of
the general KK-AdS solutions, in this paper we devise a new effective method,
dubbed the background metric expansion method, which can be thought of as a
generalization of the perturbation expansion method, to deal with the
Lagrangian and all equations of motion. In addition to two previously known
conditions, namely the timelike and geodesic properties of the vector, we get
the three additional constraints via contracting the Maxwell and Einstein
equations once or twice with this timelike geodesic vector. In particular, we
find that these are a simple set of sufficient conditions to determine the
vector and the dilaton scalar around the background metric, which is helpful in
obtaining new exact solutions. With these five simplified equations in hand, we
rederive the general rotating charged KK-(A)dS black hole solutions with
spherical horizon topology and obtain new solutions with planar topology in all
dimensions.


  We provide a new, much simplified and straightforward proof to a result of
Pavlov [2011] regarding the revenue maximizing mechanism for selling two goods
with uniformly i.i.d. valuations over intervals $[c,c+1]$, to an additive
buyer. This is done by explicitly defining optimal dual solutions to a relaxed
version of the problem, where the convexity requirement for the bidder's
utility has been dropped. Their optimality comes directly from their structure,
through the use of exact complementarity. For $c=0$ and $c\geq 0.092$ it turns
out that the corresponding optimal primal solution is a feasible selling
mechanism, thus the initial relaxation comes without a loss, and revenue
maximality follows. However, for $0<c<0.092$ that's not the case, providing the
first clear example where relaxing convexity provably does not come for free,
even in a two-item regularly i.i.d. setting.


  Current adversarial adaptation methods attempt to align the cross-domain
features, whereas two challenges remain unsolved: 1) the conditional
distribution mismatch and 2) the bias of the decision boundary towards the
source domain. To solve these challenges, we propose a novel framework for
semi-supervised domain adaptation by unifying the learning of opposite
structures (UODA). UODA consists of a generator and two classifiers (i.e., the
source-scattering classifier and the target-clustering classifier), which are
trained for contradictory purposes. The target-clustering classifier attempts
to cluster the target features to improve intra-class density and enlarge
inter-class divergence. Meanwhile, the source-scattering classifier is designed
to scatter the source features to enhance the decision boundary's smoothness.
Through the alternation of source-feature expansion and target-feature
clustering procedures, the target features are well-enclosed within the dilated
boundary of the corresponding source features. This strategy can make the
cross-domain features to be precisely aligned against the source bias
simultaneously. Moreover, to overcome the model collapse through training, we
progressively update the measurement of feature's distance and their
representation via an adversarial training paradigm. Extensive experiments on
the benchmarks of DomainNet and Office-home datasets demonstrate the
superiority of our approach over the state-of-the-art methods.


  Many name tagging approaches use local contextual information with much
success, but fail when the local context is ambiguous or limited. We present a
new framework to improve name tagging by utilizing local, document-level, and
corpus-level contextual information. We retrieve document-level context from
other sentences within the same document and corpus-level context from
sentences in other topically related documents. We propose a model that learns
to incorporate document-level and corpus-level contextual information alongside
local contextual information via global attentions, which dynamically weight
their respective contextual information, and gating mechanisms, which determine
the influence of this information. Extensive experiments on benchmark datasets
show the effectiveness of our approach, which achieves state-of-the-art results
for Dutch, German, and Spanish on the CoNLL-2002 and CoNLL-2003 datasets.


  This paper presents experimental results of the implementation of network
steganography method called RSTEG (Retransmission Steganography). The main idea
of RSTEG is to not acknowledge a successfully received packet to intentionally
invoke retransmission. The retransmitted packet carries a steganogram instead
of user data in the payload field. RSTEG can be applied to many network
protocols that utilize retransmissions. We present experimental results for
RSTEG applied to TCP (Transmission Control Protocol) as TCP is the most popular
network protocol which ensures reliable data transfer. The main aim of the
performed experiments was to estimate RSTEG steganographic bandwidth and
detectability by observing its influence on the network retransmission level.


  This paper presents a twist to the generation of binary random sequences by
starting with decimal sequences. Rather than representing the prime reciprocal
sequence directly in base 2, we first right the prime reciprocal in base 10 and
then convert it into the binary form. The autocorrelation and cross-correlation
properties of these binary random (BRD) sequences are discussed.


  We construct an edge-colored digraph analogous to Mathon's construction for
undirected graphs. We show that this graph is connected to the $k$-th power
Paley digraphs and we use this connection to produce improved lower bounds for
multicolor directed Ramsey numbers.


  Robust, simple, and flexible quantum key distribution (QKD) is vital for
realising practical applications of this technique. Contrary to typical
phase-coded QKD schemes, the plug-and-play QKD design requires only one
arm-length-insensitive interferometer without active feedback, in which the
noise is automatically compensated by the two-way structure. However, there are
certain possible loopholes in the typical plug-and-play designs, which require
consideration and strict monitoring. This study proposes a loophole-free
plug-and-play QKD scheme with two-way protocol and presents an experimental
demonstration of said scheme. The security is analysed under a collective
attack scenario assisted by the decoy state method. The scheme was implemented
in a 50.4 km commercial fibre without active feedback. The system showed highly
robust performance with an ultra-low error rate and maintained an ultra-high
visibility of 0.9947+/-0.0002 through significant environmental changes over 24
hours.


  The efficient modulation and control of ultrafast signals on-chip is of
central importance in terahertz (THz) communications and a promising route
toward sub-diffraction limit THz spectroscopy. Two-dimensional (2D) materials
may provide a platform for these endeavors. We explore this potential,
integrating high-quality graphene p-n junctions within two types of planar
transmission line circuits to modulate and emit picosecond pulses. In a
coplanar stripline geometry, we demonstrate electrical modulation of THz signal
transmission by 95%. In a Goubau waveguide geometry, we achieve complete
gate-tunable control over THz emission from a photoexcited graphene junction.
These studies inform the development of on-chip signal manipulation and
highlight prospects for 2D materials in THz applications.


  The Space Telescope Imaging Spectrograph (STIS) has measured the flux for
Sirius from 0.17--1.01~$\mu$m on the \emph{HST} White Dwarf scale. Because of
the cool debris disk around Vega, Sirius is commonly recommended as the primary
IR flux standard. The measured STIS flux agrees well with predictions of a
special Kurucz model atmosphere, adding confidence to the modeled IR flux
predictions. The IR flux agrees to 2--3% with respect to the standard template
of Cohen and to 2% with the MSX absolute flux measurements in the mid-IR. A
weighted average of the independent visible and mid-IR absolute flux measures
implies that the monochromatic flux at 5557.5~\AA\ (5556~\AA\ in air) for
Sirius and Vega, respectively, is $1.35\times10^{-8}$ and
$3.44\times10^{-9}$~erg cm$^{-2}$ s$^{-1}$ \AA$^{-1}$ with formal uncertainties
of 0.5%. Contrary to previously published conclusions, the Hipparcos photometry
offers no support for the variability of Vega. Pulse pileup severely affects
the Hp photometry for the brightest stars.


  A wireless packet network is considered in which each user transmits a stream
of packets to its destination. The transmit power of each user interferes with
the transmission of all other users. A convex cost function of the completion
times of the user packets is minimized by optimally allocating the users'
transmission power subject to their respective power constraints. At all ranges
of SINR, completion time minimization can be formulated as a convex
optimization problem and hence can be efficiently solved. In particular,
although the feasible rate region of the wireless network is non-convex, its
corresponding completion time region is shown to be convex. When channel
knowledge is imperfect, robust power control is considered based on the channel
fading distribution subject to outage probability constraints. The problem is
shown to be convex when the fading distribution is log-concave in exponentiated
channel power gains; e.g., when each user is under independent Rayleigh,
Nakagami, or log-normal fading. Applying the optimization frameworks in a
wireless cellular network, the average completion time is significantly reduced
as compared to full power transmission.


  We study the applicability of the time-dependent variational principle in
matrix product state manifolds for the long time description of quantum
interacting systems. By studying integrable and nonintegrable systems for which
the long time dynamics are known we demonstrate that convergence of long time
observables is subtle and needs to be examined carefully. Remarkably, for the
disordered nonintegrable system we consider the long time dynamics are in good
agreement with the rigorously obtained short time behavior and with previous
obtained numerically exact results, suggesting that at least in this case the
apparent convergence of this approach is reliable. Our study indicates that
while great care must be exercised in establishing the convergence of the
method, it may still be asymptotically accurate for a class of disordered
nonintegrable quantum systems.


  We investigate phase transitions in the two-dimensional dipolar Heisenberg
model with uniaxial anisotropy with a specific ratio between the exchange and
dipolar constants, $\delta=1$. We obtain the $\eta$--$T$ (anisotropy vs.
temperature) phase diagrams for typical values of magnetic field by a Monte
Carlo method with an $O(N)$ algorithm. We find that at lower fields, the
$\eta$--$T$ phase diagram consists of the planar ferromagnetic (F),
(perpendicular) stripe-ordered (SO), and paramagnetic (P) phases, and is
characterized by the triple point. In the SO phase realized at larger $\eta$
and smaller $T$, the SO pattern changes depending on the field. On the other
hand, we find that at higher fields, the SO phase does not exist, while the
planer F phase robustly remains. We study the properties of the phase
boundaries by employing finite-size-scaling analyses. We find that the slope of
the spin-reorientation-transition line is positive with and without field,
i.e., $\frac{d\eta}{dT}>0$, which implies that the planar F phase changes to
the SO phase with lowering temperature. In the phase diagrams we observe a
characteristic shape of the P--planer F phase-transition line, whose maximum
point of $\eta$ is located at an intermediate temperature. This structure leads
to the temperature-induced reentrant transition associated with P and planar F
phases, which appears in successive phase transitions with lowering
temperature: P $\rightarrow$ planar F $\rightarrow$ P $\rightarrow$ SO phase at
lower fields and P $\rightarrow$ planar F $\rightarrow$ P phases at higher
fields.


  We construct parametrizations of light-cone distribution amplitudes (LCDAs)
for B-mesons and Lambda_b-baryons that obey various theoretical constraints,
and which are simple to use in factorization theorems relevant for
phenomenological applications in heavy-flavour physics. In particular, we find
the eigenfunctions of the Lange-Neubert renormalization kernel, which allow for
a systematic implementation of renormalization-group evolution effects for both
B-meson and \Lambda_b-baryon decays. We also present a new strategy to
construct LCDA models from momentum-space projectors, which can be used to
implement Wandzura-Wilczek--like relations, and which allow for a comparison
with theoretical approaches that go beyond the collinear limit for the
light-quark momenta in energetic heavy-hadron decays.


  The impact of Artificial Intelligence does not depend only on fundamental
research and technological developments, but for a large part on how these
systems are introduced into society and used in everyday situations. Even
though AI is traditionally associated with rational decision making,
understanding and shaping the societal impact of AI in all its facets requires
a relational perspective. A rational approach to AI, where computational
algorithms drive decision making independent of human intervention, insights
and emotions, has shown to result in bias and exclusion, laying bare societal
vulnerabilities and insecurities. A relational approach, that focus on the
relational nature of things, is needed to deal with the ethical, legal,
societal, cultural, and environmental implications of AI. A relational approach
to AI recognises that objective and rational reasoning cannot does not always
result in the 'right' way to proceed because what is 'right' depends on the
dynamics of the situation in which the decision is taken, and that rather than
solving ethical problems the focus of design and use of AI must be on asking
the ethical question. In this position paper, I start with a general discussion
of current conceptualisations of AI followed by an overview of existing
approaches to governance and responsible development and use of AI. Then, I
reflect over what should be the bases of a social paradigm for AI and how this
should be embedded in relational, feminist and non-Western philosophies, in
particular the Ubuntu philosophy.


  We present the analysis of two outflows (S1 at --5500 km s$^{-1}$ and S2 at
--9700 km s$^{-1}$) seen in recent HST/COS observations of quasar SDSS
J0755+2306 (z = 0.854). The outflows are detected as absorption troughs from
both high-ionization species, including N III, O III, and S IV, and very
high-ionization species, including Ar VIII, Ne VIII, and Na IX. The derived
photoionization solutions show that each outflow requires a two
ionization-phase solution. For S1, troughs from S IV* and S IV allow us to
derive an electron number density, $n_{e}$ = 1.8$\times$10$^4$ cm$^{-3}$, and
its distance from the central source of $R$ = 270 pc. For S2, troughs from O
III* and O III yield $n_{e}$ = 1.2$\times$10$^3$ cm$^{-3}$ and $R$ = 1600 pc.
The kinetic luminosity of S2 is $>$ 12% of the Eddington luminosity for the
quasar and therefore can provide strong AGN feedback effects. Comparison of
absorption troughs from O III and O VI in both outflow systems supports the
idea that for a given element, higher ionization ions have larger covering
fractions than lower ionization ones.


  We extend the promising heterotic string searches for MSSM-like models to Z_8
orbifolds. There exist five inequivalent Z_8 toroidal orbifolds distinguished
by two types of twists that act on five different torus lattices; one of which
cannot be represented as a Lie-algebra root lattice. Contrary to previous
investigations, we study the consequences of the different underlying orbifold
torus lattices. Therefore, rather than focussing on one particular geometry, we
perform systematic model searches on these five Z_8 orbifolds simultaneously,
taking all possible inequivalent SU(5) and SO(10) gauge shifts as our starting
point. We present cumulative Tables and Figures comparing the chiral SM and
vector-like exotic spectra on these geometries.


  In this work we analyze the $Spin(V)$-structure of the secant variety of
lines $\sigma_{2}(\mathbb{S})$ to a Spinor variety $\mathbb{S}$ minimally
embedded in its spin representation. In particular, we determine the poset of
the $Spin(V)$-orbits and their dimensions. We use it for solving the problems
of identifiability and tangential-identifiability in $\sigma_2(\mathbb S)$, and
for determining the second Terracini locus of $\mathbb{S}$. Finally, we show
that the singular locus $Sing(\sigma_{2}(\mathbb{S}))$ contains the two
$Spin(V)$-orbits of lowest dimensions and it lies in the tangential variety
$\tau(\mathbb{S})$: we also conjecture what it set-theoretically is.


  NbSe$_3$ and monoclinic-TaS$_3$ ($m$-TaS$_3$) are quasi-1D metals containing
three different types of chains and undergoing two different charge density
wave (CDW) Peierls transitions at T$_{P_1}$ and T$_{P_2}$. The nature of these
transitions is discussed on the basis of first-principles DFT calculation of
their electron-hole Lindhard response function. As a result of stronger
inter-chain interactions the Fermi surface (FS) and Lindhard function of
NbSe$_3$ are considerably more complex than those for $m$-TaS$_3$; however a
common scenario can be put forward to rationalize the results. The intra-chain
inter-band nesting processes dominate the strongest response for both type I
and type III chains of the two compounds. Two well-defined maxima of the
Lindhard response for NbSe$_3$ are found with the (0$a$*, 0$c$*) and (1/2$a$*,
1/2$c$*) transverse components at T$_{P_1}$ and T$_{P_2}$, respectively,
whereas the second maximum is not observed for $m$-TaS$_3$ at T$_{P2}$.
Analysis of the different inter-chain coupling mechanisms leads to the
conclusion that FS nesting effects are only relevant to set the transverse $a$*
components in NbSe$_3$. For the transverse coupling along $c$* in NbSe$_3$ and
along both $a$* and $c$* for $m$-TaS$_3$, one must take into account the
strongest inter-chain Coulomb coupling mechanism. Phonon spectrum calculations
show the formation of a giant 2$k_F$ Kohn anomaly in $m$-TaS$_3$. All these
results support the weak coupling scenario for the Peierls transition of
transition metal trichalcogenides.


  Lobe dynamics and escape from a potential well are general frameworks
introduced to study phase space transport in chaotic dynamical systems. While
the former approach studies how regions of phase space are transported by
reducing the flow to a two-dimensional map, the latter approach studies the
phase space structures that lead to critical events by crossing periodic orbit
around saddles. Both of these frameworks require computation with curves
represented by millions of points-computing intersection points between these
curves and area bounded by the segments of these curves-for quantifying the
transport and escape rate. We present a theory for computing these intersection
points and the area bounded between the segments of these curves based on a
classification of the intersection points using equivalence class. We also
present an alternate theory for curves with nontransverse intersections and a
method to increase the density of points on the curves for locating the
intersection points accurately.The numerical implementation of the theory
presented herein is available as an open source software called Lober. We used
this package to demonstrate the application of the theory to lobe dynamics that
arises in fluid mechanics, and rate of escape from a potential well that arises
in ship dynamics.


  Magnetic nanoparticles (MNPs) have been extensively used as tiny heating
sources in magnetic hyperthermia therapy, contrast agents in magnetic resonance
imaging (MRI), tracers in magnetic particle imaging (MPI), carriers for
drug/gene delivery, etc. There have emerged many magnetic
nanoparticle/microbeads suppliers since the last decade, such as Ocean
NanoTech, Nanoprobes, US Research Nanomaterials, Miltenyi Biotec, micromod
Partikeltechnologie GmbH, and nanoComposix, etc. In this paper, we report the
physical and magnetic characterizations on iron oxide nanoparticle products
from Ocean NanoTech. Standard characterization tools such as Vibrating-Sample
Magnetometer (VSM), X-Ray Diffraction (XRD), Dynamic Light Scattering (DLS),
Transmission Electron Microscopy (TEM), and Zeta Potential Analyzer are used to
provide magnetic nanoparticle customers and researchers with an overview of
these iron oxide nanoparticle products. In addition, the dynamic magnetic
responses of these iron oxide nanoparticles in aqueous solutions are
investigated under low and high frequency alternating magnetic fields, giving a
standardized operating procedure for characterizing the MNPs from Ocean
NanoTech, thereby yielding the best of magnetic nanoparticles for different
applications.


  The direct numerical simulation (DNS) of compressible isotropic turbulence up
to the supersonic regime $Ma_{t} = 1.2$ has been investigated by high-order
gas-kinetic scheme (HGKS) [{\it{Computers}} \& {\it{Fluids, 192, 2019}}]. In
this study, the coarse-grained analysis of subgrid-scale (SGS) turbulent
kinetic energy $K_{sgs}$ budget is fully analyzed for constructing one-equation
SGS model in the compressible large eddy simulation (LES). The DNS on a much
higher turbulent Mach number up to $Ma_{t} = 2.0$ has been obtained by HGKS,
which confirms the super robustness of HGKS. Then, the exact compressible SGS
turbulent kinetic energy $K_{sgs}$ transport equation is derived with density
weighted filtering process. Based on the compressible $K_{sgs}$ transport
equation, the coarse-grained processes are implemented on three sets of
unresolved grids with the Box filter. The coarse-grained analysis of
compressible $K_{sgs}$ budgets shows that all unresolved source terms are
dominant terms in current system. Especially, the magnitude of SGS
pressure-dilation term is in the order of SGS solenoidal dissipation term
within the initial acoustic time scale. Therefore, it can be concluded that the
SGS pressure-dilation term cannot be neglected as the previous work. The
delicate coarse-grained analysis of SGS diffusion terms in compressible
$K_{sgs}$ equation confirms that both the fluctuation velocity triple
correlation term and the pressure-velocity correlation term are dominant terms.
Current coarse-grained analysis gives an indication of the order of magnitude
of all SGS terms in compressible $K_{sgs}$ budget, which provides a solid basis
for compressible LES modeling in high Mach number turbulent flow.


  Recently introduced instruction-paradigm empowers non-expert users to
leverage NLP resources by defining a new task in natural language.
Instruction-tuned models have significantly outperformed multitask learning
models (without instruction); however they are far from state-of-the-art
task-specific models. Conventional approaches to improve model performance via
creating datasets with large number of task instances or architectural changes
in the model may not be feasible for non-expert users. However, they can write
alternate instructions to represent an instruction task. Is
Instruction-augmentation helpful? We augment a subset of tasks in the expanded
version of NATURAL INSTRUCTIONS with additional instructions and find that it
significantly improves model performance (up to 35%), especially in the
low-data regime. Our results indicate that an additional instruction can be
equivalent to ~200 data samples on average across tasks.


  This paper studies the economics of carbon-neutral synthetic fuel production
from renewable electricity in remote areas where high-quality renewable
resources are abundant. To this end, a graph-based optimisation modelling
framework directly applicable to the strategic planning of remote renewable
energy supply chains is proposed. More precisely, a hypergraph abstraction of
planning problems is introduced, wherein nodes can be viewed as optimisation
subproblems with their own parameters, variables, constraints and local
objective. Nodes typically represent a subsystem such as a technology, a plant
or a process. Hyperedges, on the other hand, express the connectivity between
subsystems. The framework is leveraged to study the economics of carbon-neutral
synthetic methane production from solar and wind energy in North Africa and its
delivery to Northwestern European markets. The full supply chain is modelled in
an integrated fashion, which makes it possible to accurately capture the
interaction between various technologies on an hourly time scale. Results
suggest that the cost of synthetic methane production and delivery would be
slightly under 150 EUR/MWh (higher heating value) by 2030 for a system
supplying 10 TWh annually and relying on a combination of solar photovoltaic
and wind power plants, assuming a uniform weighted average cost of capital of
7%. A comprehensive sensitivity analysis is also carried out in order to assess
the impact of various techno-economic parameters and assumptions on synthetic
methane cost, including the availability of wind power plants, the investment
costs of electrolysis, methanation and direct air capture plants, their
operational flexibility, the energy consumption of direct air capture plants,
and financing costs.


  Two-color side-view probing of light-matter interaction from minute focal
volume of a tightly focused fs-laser pump pulse reveals charge dynamics with
high 0.9 micrometers optical resolution and approximately ~20 fs temporal
resolution in coincidence between pump and probe pulses. Use of two colors is
advantageous for probing optically excited plasma regions with different
density. Holographical digital focusing and spatial filtering were implemented
to obtain the same resolution images for subsequent Fourier analysis. Fast
electron removal with time constant ~150 fs was resolved and is consistent with
self-trapping. Potential applications of an optical control over a
light-induced defect placement with deep-sub-wavelength resolution is
discussed.


  In this paper we propose a protocol to suppress double-layer forces between
two microspheres immersed in a dielectric medium, being one microsphere
metallic at a controlled potential {\psi}M and the other a charged one either
metallic or dielectric. The approach is valid for a wide range of distances
between them. We show that, for a given distance between the two microspheres,
the double-layer force can be totally suppressed by simply tuning {\psi}M up to
values dictated by the linearized Poisson-Boltzmann equation. Our key finding
is that such values can be substantially different from the ones predicted by
the commonly used proximity force approximation (PFA), also known as Derjaguin
approximation, even in situations where the latter is expected to be accurate.
The proposed procedure can be used to suppress the double-layer interaction in
force spectroscopy experiments, thus paving the way for measurements of other
surface interactions, such as Casimir dispersion forces.


  We study the compressibility of enumerations, and its role in the relative
Kolmogorov complexity of computably enumerable sets, with respect to density.
With respect to a strong and a weak form of compression, we examine the gain:
the amount of auxiliary information embedded in the compressed enumeration.
Strong compression and weak gainless compression is shown for any computably
enumerable set, and a positional game is studied toward understanding strong
gainless compression.


  The capability of multi-input field-to-field regression, i.e. mapping the
initial field and applied conditions to the evolved field, is appealing,
enabling ultra-fast physics-free simulation of various field evolvements across
many disciplines. We hereby propose a y-shaped multi-input deep convolutional
network, yNet, which can effectively account for combined effects of multiple
mixed inputs on output field. The proposed yNet is applied to the simulation of
porosity evolution in selective lasering sintering (SLS). Upon testing, yNet
can simulate nearly identical porosity evolution and development to the
physics-based model, with a 99.13% morphological similarity for various SLS
conditions. We then effortlessly boost the porosity simulation capability to
the realistic, full-component level. yNet is generally applicable to simulating
various structural/morphological evolutions and other condition-concerned,
continuous field evolvements even with spatially and/or temporally non-uniform
evolving kinetics. Once trained, the light-weight yNet can be distributed
easily and ran with limited computational resource while reducing computation
time to a split second. It thus may have a transformative impact by
democratizing the capability of ultra-fast and extreme-scale simulation of
various field evolvements.


  Human-in-the-loop optimization utilizes human expertise to guide machine
optimizers iteratively and search for an optimal solution in a solution space.
While prior empirical studies mainly investigated novices, we analyzed the
impact of the levels of expertise on the outcome quality and corresponding
subjective satisfaction. We conducted a study (N=60) in text, photo, and 3D
mesh optimization contexts. We found that novices can achieve an expert level
of quality performance, but participants with higher expertise led to more
optimization iteration with more explicit preference while keeping satisfaction
low. In contrast, novices were more easily satisfied and terminated faster.
Therefore, we identified that experts seek more diverse outcomes while the
machine reaches optimal results, and the observed behavior can be used as a
performance indicator for human-in-the-loop system designers to improve
underlying models. We inform future research to be cautious about the impact of
user expertise when designing human-in-the-loop systems.


  Physics-informed neural network (PINN) has been successfully applied in
solving a variety of nonlinear non-convex forward and inverse problems.
However, the training is challenging because of the non-convex loss functions
and the multiple optima in the Bayesian inverse problem. In this work, we
propose a multi-variance replica exchange stochastic gradient Langevin
diffusion method to tackle the challenge of the multiple local optima in the
optimization and the challenge of the multiple modal posterior distribution in
the inverse problem. Replica exchange methods are capable of escaping from the
local traps and accelerating the convergence. However, it may not be efficient
to solve mathematical inversion problems by using the vanilla replica method
directly since the method doubles the computational cost in evaluating the
forward solvers (likelihood functions) in the two chains. To address this
issue, we propose to make different assumptions on the energy function
estimation and this facilities one to use solvers of different fidelities in
the likelihood function evaluation. We give an unbiased estimate of the
swapping rate and give an estimation of the discretization error of the scheme.
To verify our idea, we design and solve four inverse problems which have
multiple modes. The proposed method is also employed to train the Bayesian PINN
to solve the forward and inverse problems; faster and more accurate convergence
has been observed when compared to the stochastic gradient Langevin diffusion
(SGLD) method and vanila replica exchange methods.


  Protein pattern formation is essential for the spatial organization of many
intracellular processes like cell division, flagellum positioning, and
chemotaxis. A prominent example of intracellular patterns are the oscillatory
pole-to-pole oscillations of Min proteins in \textit{E. coli} whose biological
function is to ensure precise cell division. Cell polarization, a prerequisite
for processes such as stem cell differentiation and cell polarity in yeast, is
also mediated by a diffusion-reaction process. More generally, these functional
modules of cells serve as model systems for self-organization, one of the core
principles of life. Under which conditions spatio-temporal patterns emerge, and
how these patterns are regulated by biochemical and geometrical factors are
major aspects of current research. Here we review recent theoretical and
experimental advances in the field of intracellular pattern formation, focusing
on general design principles and fundamental physical mechanisms.


  The kaon sector is characterised by several processes which are under active
investigation across different experiments. In this work, we present the global
picture that emerges from a study of the different decay modes. We begin by
revisiting the theoretical component of these decays and providing up-to-date
predictions of the Standard Model as well as the corresponding uncertainties.
Several new features emerge, in particular for $K_{S,L}\to \mu\bar\mu$, and are
presented in considerable detail. This offers an ideal platform for extracting
the parameter space supported by the existing data. Motivated by possible
lepton flavour universality violation in $B$ decays, we investigate such Beyond
the Standard Model effects also in rare kaon decays. Without loss of
generality, our primary analyses correspond to the paradigm where the Wilson
coefficients for operators involving tau leptons are chosen to be equal to that
involving the muon, i.e. $\delta C^\tau=\delta C^\mu$. We conclude by
presenting the possible picture that can be achieved towards the end of the run
of data accumulation in the planned experiments. This includes assumptions on
possible sensitivity goals that the experiments can aim to achieve, in order to
extract the kind of physics highlighted in this paper.


  We study distributed algorithms for seeking a Nash equilibrium in a class of
non-cooperative convex games with strongly monotone mappings. Each player has
access to her own smooth local cost function and can communicate to her
neighbors in some undirected graph. To deal with fast distributed learning of
Nash equilibria under such settings, we introduce a so called augmented game
mapping and provide conditions under which this mapping is strongly monotone.
We consider a distributed gradient play algorithm for determining a Nash
equilibrium (GRANE). The algorithm involves every player performing a gradient
step to minimize her own cost function while sharing and retrieving information
locally among her neighbors in the network. Using the reformulation of the Nash
equilibrium problem based on the strong monotone augmented game mapping, we
prove the convergence of this algorithm to a Nash equilibrium with a geometric
rate. Further, we introduce the Nesterov type acceleration for the gradient
play algorithm. We demonstrate that, similarly to the accelerated algorithms in
centralized optimization and variational inequality problems, our accelerated
algorithm outperforms GRANE in the convergence rate. Moreover, to relax
assumptions required to guarantee the strongly monotone augmented mapping, we
analyze the restricted strongly monotone property of this mapping and prove
geometric convergence of the distributed gradient play under milder
assumptions.


  We investigate large set axioms defined in terms of elementary embeddings
over constructive set theories, focusing on $\mathsf{IKP}$ and $\mathsf{CZF}$.
Most previously studied large set axioms, notably the constructive analogues of
large cardinals below $0^\sharp$, have proof-theoretic strength weaker than
full Second-order Arithmetic. On the other hand, the situation is dramatically
different for those defined via elementary embeddings. We show that by adding
to $\mathsf{IKP}$ the basic properties of an elementary embedding $j\colon V\to
M$ for $\Delta_0$-formulas, which we will denote by
$\mathsf{\Delta_0\text{-}BTEE}_M$, we obtain the consistency of $\mathsf{ZFC}$
and more. We will also see that the consistency strength of a Reinhardt set
exceeds that of $\mathsf{ZF+WA}$. Furthermore, we will define super Reinhardt
sets and $\mathsf{TR}$, which is a constructive analogue of $V$ being totally
Reinhardt, and prove that their proof-theoretic strength exceeds that of
$\mathsf{ZF}$ with choiceless large cardinals.


  PSR B0656+14 is a middle-aged pulsar with a characteristic age $\tau_c=110$
kyr and spin-down power $\dot{E}= 3.8\times 10^{34}$ erg s$^{-1}$. Using
Chandra data, we searched for a pulsar wind nebula (PWN) and found evidence of
extended emission in a 3.5-15 arcsec annulus around the pulsar, with a
luminosity $L_{\rm 0.5-8\,keV}^{\rm ext} \sim 8\times 10^{28}$ erg s$^{-1}$ (at
the distance of 288 pc), which is a fraction of $\sim 0.05$ of the non-thermal
pulsar luminosity. If the extended emission is mostly due to a PWN, its X-ray
efficiency, $\eta_{\rm pwn} = L_{\rm 0.5-8\,keV}^{\rm ext}/\dot{E} \sim 2\times
10^{-6}$, is lower than those of most other known PWNe but similar to that of
the middle-aged Geminga pulsar. The small radial extent and nearly round shape
of the putative PWN can be explained if the pulsar is receding (or approaching)
in the direction close to the line of sight. The very soft spectrum of the
extended emission ($\Gamma\sim 8$), much softer than those of typical PWNe,
could be explained by a contribution from a faint dust scattering halo, which
may dominate in the outer part of the extended emission.


  We present a suite of semi-analytic disk-bulge-halo models for the Andromeda
galaxy (M31) which satisfy three fundamental conditions: (1) internal
self-consistency; (2) consistency with observational data; and (3) stability of
the disk against the formation of a central bar. The models are chosen from a
set first constructed by Kuijken and Dubinski. We develop an algorithm to
search the parameter space for this set in order to best match observations of
the M31 rotation curve, inner velocity dispersion profile, and surface
brightness profile. Models are obtained for a large range of bulge and disk
masses; we find that the disk mass must be of order 8 * 10^10 M_sun and that
the preferred value for the bulge mass is 2.5 * 10^10 M_sun. N-body simulations
are carried out to test the stability of our models against the formation of a
bar within the disk. We also calculate the baryon fraction and halo
concentration parameter for a subset of our models and show that the results
are consistent with the predictions from cosmological theories of structure
formation. In addition, we describe how gravitational microlensing surveys and
dynamical studies of globular clusters and satellites can further constrain the
models.


  Boundedness properties of operators associated with non-degenerate symmetric
$\alpha$-stable, $\alpha \in (1,2)$, probability measures on $\mathbb{R}^d$ are
investigated on appropriate, Euclidean or otherwise, $L^p$-spaces, $p \in
(1,+\infty)$. Our approach is based on first obtaining Bismut-type formulae
which lead to useful representations for various operators. In the Euclidean
setting, the method of transference and one-dimensional multiplier theory
combined with fine properties of stable distributions provide dimension-free
estimates for the fractional Laplacian. In the non-Euclidean setting, we obtain
boundedness results for the non-singular cases as well as dimension-free
estimates when the reference measure is the rotationally invariant
$\alpha$-stable probability measure.


  The exploration of the strong-interaction matter under extreme conditions is
one of the main goals of relativistic heavy-ion collisions. We provide some of
the main results on the novel properties of quark-gluon plasma, with particular
focus given to the strong collectivity and the color opaqueness exhibited by
such hot and dense matter produced in high-energy nuclear collisions at RHIC
and the LHC.


  Liquid crystals formed by acute-angle bent-core (ABC) molecules with a 1,7
naphthalene central core show an intriguing phase behavior with the nematic
phase accompanied by poorly understood additional phases. In this work, we
characterize the physical properties of an ABC material, such as birefringence,
dielectric permittivities, elastic constants, and surface alignment, and
present X-ray diffraction and transmission electron microscopy studies of their
ordering. The ABC molecular shape resembling the letter $\lambda$ yields a very
small splay elastic constant in the uniaxial nematic phase and results in the
formation of a tetragonal positionally ordered columnar phase consisting of
molecular columns with a uniform uniaxial director that can be bent but not
splayed.


  Camera orientations (i.e., rotation and zoom) govern the content that a
camera captures in a given scene, which in turn heavily influences the accuracy
of live video analytics pipelines. However, existing analytics approaches leave
this crucial adaptation knob untouched, instead opting to only alter the way
that captured images from fixed orientations are encoded, streamed, and
analyzed. We present MadEye, a camera-server system that automatically and
continually adapts orientations to maximize accuracy for the workload and
resource constraints at hand. To realize this using commodity pan-tilt-zoom
(PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the
massive space of orientations to identify a fruitful subset at each time, and
(2) a novel knowledge distillation strategy to efficiently (with only camera
resources) select the ones that maximize workload accuracy. Experiments on
diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same
resource usage, or achieves the same accuracy with 2-3.7x lower resource costs.


  Context: The $\chi^{1}$ Fornacis cluster (Alessi 13) is one of a few open
clusters of its age and distance in the Solar neighbourhood that ought to
benefit from more attention as it can serve as a cornerstone for numerous
future studies related to star and planet formation.
  Aims: We take advantage of the early installment of the third data release of
the Gaia space mission in combination with archival data and our own
observations, to expand the census of cluster members and revisit some
properties of the cluster.
  Methods: We applied a probabilistic method to infer membership probabilities
over a field of more than 1000 deg${^2}$ to select the most likely cluster
members and derive the distances, spatial velocities, and physical properties
of the stars in this sample.
  Results: We identify 164 high-probability cluster members (including 61 new
members) covering the magnitude range from 5.1 to 19.6 mag in the G-band. Our
sample of cluster members is complete down to 0.04 M$_{\odot}$. We derive the
distance of $108.4\pm0.3$ pc from Bayesian inference and confirm that the
cluster is comoving with the Tucana-Horologium, Columba, and Carina young
stellar associations. We investigate the kinematics of the cluster from a
subsample of stars with measured radial velocities and we do not detect any
significant expansion or rotation effects in the cluster. Our results suggest
that the cluster is somewhat younger (about 30 Myr) than previously thought.
Based on spectroscopic observations, we argue that the cluster is
mass-segregated and that the distribution of spectral types shows little
variation compared to other young stellar groups.
  Conclusions: In this study, we deliver the most complete census of cluster
members that can be done with Gaia data alone and we use this new sample to
provide an updated picture on the 6D structure of the cluster.


  The notion of a symmetric extension extends the usual notion of forcing by
identifying a particular class of names which forms an intermediate model of ZF
between the ground model and the generic extension, and often the axiom of
choice fails in these models. Symmetric extensions are generally used to prove
choiceless consistency results. We develop a framework for iterating symmetric
extensions in order to construct new models of ZF. We show how to obtain some
well-known and lesser-known results using this framework. Specifically, we
discuss Kinna--Wagner principles and obtain some results related to their
failure.


  We present new maps of emission-line flux distributions and kinematics in
both ionized (traced by HI and [FeII] lines) and molecular (H2) gas of the
inner 0.7x0.7kpc2 of the galaxy NGC4303, with a spatial resolution 40-80pc and
velocity resolution 90-150 km/s obtained from near-IR integral field
specroscopy using the VLT instrument SINFONI. The most promiment feature is a
200-250pc ring of circum-nuclear star-forming regions. The emission from
ionized and molecular gas shows distinct flux distributions: while the
strongest HI and [FeII] emission comes from regions in the west side of the
ring (ages~4Myr), the H2 emission is strongest at the nucleus and in the east
side of the ring (ages>10Myr). We find that regions of enhanced hot H2 emission
are anti-correlated with those of enhanced [FeII] and HI emission, which can be
attributed to post starburst regions that do not have ionizing photons anymore
but still are hot enough (~2000K) to excite the H2 molecule. The line ratios
are consistent with the presence of an AGN at the nucleus.
  The youngest regions have stellar masses in the range 0.3-1.5E5 MSun and
ionized and hot molecular gas masses of ~0.25-1.2E4 Msun and 2.5-5 Msun,
respectively. The stellar and gas velocity fields show a rotation pattern, with
the gas presenting larger velocity amplitudes than the stars, with a deviation
observed for the H2 along the nuclear bar, where increased velocity dispersion
is also observed, possibly associated with non circular motions along the bar.
The stars in the ring show smaller velocity dispersion than the surroundings,
that can be attributed to a cooler dynamics due to their recent formation from
cool gas.


  We consider the population of critical points generated from the critical
point of the master function with no variables, which is associated with the
trivial representation of the twisted affine Lie algebra $A^{(2)}_{2n}$. The
population is naturally partitioned into an infinite collection of complex
cells $\mathbb{C}^m$, where $m$ are some positive integers. For each cell we
define an injective rational map $\mathbb{C}^m \to M(A^{(2)}_{2n})$ of the cell
to the space $M(A^{(2)}_{2n})$ of Miura opers of type $A^{(2)}_{2n}$. We show
that the image of the map is invariant with respect to all mKdV flows on
$M(A^{(2)}_{2n})$ and the image is point-wise fixed by all mKdV flows
$\frac\partial{\partial t_r}$ with index $r$ greater than $4m$.


  When material parameters are fixed, optical responses of nanoresonators are
dictated by their shapes and dimensions. Therefore, both designing
nanoresonators and understanding their underlying physics would benefit from a
theory that predicts the evolutions of resonance modes of open systems---the
so-called quasinormal modes (QNMs)---as the nanoresonator shape changes. QNM
perturbation theories (PTs) are one ideal choice. However, existing theories
developed for material changes are unable to provide accurate perturbation
corrections for shape deformations. By introducing a novel extrapolation
technique, we develop a rigorous QNM PT that faithfully represents the
electromagnetic fields in perturbed domain. Numerical tests performed on the
eigenfrequencies, eigenmodes and optical responses of deformed nanoresonators
evidence the predictive force of the present PT, even for large deformations.
This opens new avenues for inverse design, as we exemplify by designing
super-cavity modes and exceptional points with remarkable ease and physical
insight.


  Fluid flow around a random distribution of stationary spherical particles is
a problem of substantial importance in the study of dispersed multiphase flows.
In this paper we present a machine learning methodology using Generative
Adversarial Network framework and Convolutional Neural Network architecture to
recreate particle-resolved fluid flow around a random distribution of
monodispersed particles. The model was applied to various Reynolds number and
particle volume fraction combinations spanning over a range of [2.69, 172.96]
and [0.11, 0.45] respectively. Test performance of the model for the studied
cases is very promising.


  The majority of estimates of the social cost of carbon use preference
parameters calibrated to data for North America and Europe. We here use
representative data for attitudes to time and risk across the world. The social
cost of carbon is substantially higher in the global north than in the south.
The difference is more pronounced if we count people rather than countries.


  In recent years, multimodal natural language processing, aimed at learning
from diverse data types, has garnered significant attention. However, there
needs to be more clarity when it comes to analysing multimodal tasks in
multi-lingual contexts. While prior studies on sentiment analysis of tweets
have predominantly focused on the English language, this paper addresses this
gap by transforming an existing textual Twitter sentiment dataset into a
multimodal format through a straightforward curation process. Our work opens up
new avenues for sentiment-related research within the research community.
Additionally, we conduct baseline experiments utilising this augmented dataset
and report the findings. Notably, our evaluations reveal that when comparing
unimodal and multimodal configurations, using a sentiment-tuned large language
model as a text encoder performs exceptionally well.


  This paper proposes a novel framework of aggregated intersection bounds,
where the target parameter is obtained by averaging the minimum (or maximum) of
a collection of regression functions over the covariate space. Examples of such
quantities include the lower and upper bounds on distributional effects
(Fr\'echet-Hoeffding, Makarov) as well as the optimal welfare in statistical
treatment choice problems. The proposed estimator -- the envelope score
estimator -- is shown to have an oracle property, where the oracle knows the
identity of the minimizer for each covariate value. Next, the result is
extended to the aggregated minimax values of a collection of regression
functions, covering optimal distributional welfare in worst-case and best-case,
respectively. This proposed estimator -- the envelope saddle value estimator --
is shown to have an oracle property, where the oracle knows the identity of the
saddle point.


  Kagome spin ice is one of the canonical examples of highly frustrated
magnets. The effective magnetic degrees of freedom in kagome spin ice are Ising
spins residing on a two-dimensional network of corner-sharing triangles. Due to
strong geometrical frustration, nearest-neighbor antiferromagnetic interactions
on the kagome lattice give rise to a macroscopic number of degenerate classical
ground states characterized by ice rules. Elementary excitations at low
temperatures are defect-triangles that violate the ice rules and carry an
additional net magnetic charge relative to the background. We perform
large-scale Glauber dynamics simulations to study the nonequilibrium dynamics
of kagome ice under slow cooling. We show that the density of residual charge
defects exhibits a power law dependence on the quench rate for the class of
algebraic cooling protocols. The numerical results are well captured by the
rate equation for the charge defects based on the reaction kinetics theory. As
the relaxation time of the kagome ice phase remains finite, there is no
dynamical freezing as in the Kibble-Zurek scenario. Instead, we show that the
power-law behavior originates from a thermal excitation that decay
algebraically with time at the late stage of the cooling schedule. Similarities
and differences in quench dynamics of other spin ice systems are also
discussed.


  Humor is an inherently social phenomenon, with humorous utterances shaped by
what is socially and culturally accepted. Understanding humor is an important
NLP challenge, with many applications to human-computer interactions. In this
work we explore humor in the context of Cards Against Humanity -- a party game
where players complete fill-in-the-blank statements using cards that can be
offensive or politically incorrect. We introduce a novel dataset of 300,000
online games of Cards Against Humanity, including 785K unique jokes, analyze it
and provide insights. We trained machine learning models to predict the winning
joke per game, achieving performance twice as good (20\%) as random, even
without any user information. On the more difficult task of judging novel
cards, we see the models' ability to generalize is moderate. Interestingly, we
find that our models are primarily focused on punchline card, with the context
having little impact. Analyzing feature importance, we observe that short,
crude, juvenile punchlines tend to win.


  The third order correlation function of the scalar field advected by a
Gaussian random velocity, with a spatial scaling exponent $2 - \epsilon$, and
in the presence of a mean gradient, is calculated perturbatively in $\epsilon
<< 1$. This expansion corresponds to the regime close to Batchelor's advection
by linear diffeomorphisms. The scaling exponent is found to be equal to 1 in
dimensions 2 and 3, up to corrections smaller than $ { \cal O } ( \epsilon ) $,
implying an anomalous scaling of the third order correlation function and the
persistence of small scale anisotropy.


  The generation of high-quality images has become widely accessible and is a
rapidly evolving process. As a result, anyone can generate images that are
indistinguishable from real ones. This leads to a wide range of applications,
including malicious usage with deceptive intentions. Despite advances in
detection techniques for generated images, a robust detection method still
eludes us. Furthermore, model personalization techniques might affect the
detection capabilities of existing methods. In this work, we utilize the
architectural properties of convolutional neural networks (CNNs) to develop a
new detection method. Our method can detect images from a known generative
model and enable us to establish relationships between fine-tuned generative
models. We tested the method on images produced by both Generative Adversarial
Networks (GANs) and recent large text-to-image models (LTIMs) that rely on
Diffusion Models. Our approach outperforms others trained under identical
conditions and achieves comparable performance to state-of-the-art pre-trained
detection methods on images generated by Stable Diffusion and MidJourney, with
significantly fewer required train samples.


  We present a method of implementing GraphQL live queries at the database
level. Our DynamoDB simulation in Go mimics a distributed key-value store and
implements live queries to expose possible pitfalls. Two key components for
implementing live queries are storing fields selected in a live query and
determining which object fields have been updated in each database write. A
stream(key, fields) request to the system contains fields to include in the
live query stream and on subsequent put(key, object) operations, the database
asynchronously determines which fields were updated and pushes a new query view
to the stream if those fields overlap with the stream() request. Following a
discussion of our implementation, we explore motivations for using live queries
such as simplifying software communication, minimizing data transfer, and
enabling real-time data and describe an architecture for building software with
GraphQL and live queries.


  The Paper actually concerns a toy model, not physical Casimir cavities made
of conducting plates, but the results are taken implicitly to apply in general.
We question on general physical grounds one basic assumption and the results of
a renormalization procedure. Then, for physical systems, i) considering
condensed matter theory/experiments, we find strong evidence against the
conclusive claims concerning a putative and dominating surface energy present
individually on the plates, and ii) we propose two experiments with physical
Casimir cavities to show in detail that the results of the renormalization in
this case look somewhat paradoxical. In any case the proposed experiments
appear to be feasible and thus it could be tested if the putative self-energies
of the plates are indeed there in a physical Casimir cavity, or if the toy
model of the Paper has by contrast no connection with physical reality. However
at the moment the authors are not legitimate to issue as conclusive claims
statements like- refute the claim sometimes attributed to Feynman that virtual
photons do not gravitate -


  We develop a framework for solving the stationary, incompressible Stokes
equations in an axisymmetric domain. By means of Fourier expansion with respect
to the angular variable, the three-dimensional Stokes problem is reduced to an
equivalent, countable family of decoupled two-dimensional problems. By using
decomposition of three-dimensional Sobolev norms we derive natural variational
spaces for the two-dimensional problems, and show that the variational
formulations are well-posed. We analyze the error due to Fourier truncation and
conclude that, for data that are sufficiently regular, it suffices to solve a
small number of two-dimensional problems.


  In this work we continue the investigation about the interplay between
hypergeometric functions and Fourier-Legendre ($\textrm{FL}$) series
expansions. In the section "Hypergeometric series related to $\pi,\pi^2$ and
the lemniscate constant", through the FL-expansion of $\left[x(1-x)\right]^\mu$
(with $\mu+1\in\frac{1}{4}\mathbb{N}$) we prove that all the hypergeometric
series $$
  \sum_{n\geq
0}\frac{(-1)^n(4n+1)}{p(n)}\left[\frac{1}{4^n}\binom{2n}{n}\right]^3,\quad
\sum_{n\geq 0}\frac{(4n+1)}{p(n)}\left[\frac{1}{4^n}\binom{2n}{n}\right]^4,$$
  $$\quad \sum_{n\geq
0}\frac{(4n+1)}{p(n)^2}\left[\frac{1}{4^n}\binom{2n}{n}\right]^4,\; \sum_{n\geq
0}\frac{1}{p(n)}\left[\frac{1}{4^n}\binom{2n}{n}\right]^3,\; \sum_{n\geq
0}\frac{1}{p(n)}\left[\frac{1}{4^n}\binom{2n}{n}\right]^2 $$ return rational
multiples of $\frac{1}{\pi},\frac{1}{\pi^2}$ or the lemniscate constant, as
soon as $p(x)$ is a polynomial fulfilling suitable symmetry constraints.
Additionally, by computing the FL-expansions of $\frac{\log x}{\sqrt{x}}$ and
related functions, we show that in many cases the hypergeometric
$\phantom{}_{p+1} F_{p}(\ldots , z)$ function evaluated at $z=\pm 1$ can be
converted into a combination of Euler sums. In particular we perform an
explicit evaluation of $$ \sum_{n\geq
0}\frac{1}{(2n+1)^2}\left[\frac{1}{4^n}\binom{2n}{n}\right]^2,\quad \sum_{n\geq
0}\frac{1}{(2n+1)^3}\left[\frac{1}{4^n}\binom{2n}{n}\right]^2. $$ In the
section "Twisted hypergeometric series" we show that the conversion of some
$\phantom{}_{p+1} F_{p}(\ldots,\pm 1)$ values into combinations of Euler sums,
driven by FL-expansions, applies equally well to some twisted hypergeometric
series, i.e. series of the form $\sum_{n\geq 0} a_n b_n$ where $a_n$ is a
Stirling number of the first kind and $\sum_{n\geq 0}b_n z^n = \phantom{}_{p+1}
F_{p}(\ldots;z)$.


  For every infinite graph $\Gamma$ we construct a non-Desarguesian projective
plane $P^*_{\Gamma}$ of the same size as $\Gamma$ such that $Aut(\Gamma) \cong
Aut(P^*_{\Gamma})$ and $\Gamma_1 \cong \Gamma_2$ iff $P^*_{\Gamma_1} \cong
P^*_{\Gamma_2}$. Furthermore, restricted to structures with domain $\omega$,
the map $\Gamma \mapsto P^*_{\Gamma}$ is Borel. On one side, this shows that
the class of countable non-Desarguesian projective planes is Borel complete,
and thus not admitting a Ulm type system of invariants. On the other side, we
rediscover the main result of [15] on the realizability of every group as the
group of collineations of some projective plane. Finally, we use classical
results of projective geometry to prove that the class of countable Pappian
projective planes is Borel complete.


  We use the geometric technique, developed by Weyman, to calculate the
resolution of orbit closures of representations of Dynkin quivers with every
vertex being source or sink. We use this resolution to derive the normality of
such orbit closures. As a consequence we obtain the normality of certain orbit
closures of type E.


  We present our analysis on micro-rheology of a bench-scale pulsed fluidized
bed, which represents a weakly confined system. Non-linear gas-particle and
particle-particle interactions resulting from pulsed flow are associated with
harmonic and sub-harmonic modes. While periodic structured bubble patterns are
observed at the meso-scale, particle-scale measurements reveal anomolous
diffusion in the driven granular medium. We use single-particle tracks to
analyze ergodicity and ageing properties at two pulsing frequencies having
remarkably different meso-scale features. The scaling of ensemble-averaged mean
squared displacement is not unique. The distribution of time-averaged mean
squared displacements is non-Gaussian, asymmetric and has a finite trivial
contribution from particles in crowded quasi-static surroundings. Results
indicate weak ergodicity breaking which along with ageing characterize the
non-stationary and out-of-equilibrium dynamics.


  Quantum key distribution enables secure communication based on the principles
of quantum mechanics. The distance in fiber-based quantum communication is
limited to about a hundred kilometers due to signal attenuation. Thus, quantum
repeaters are required to establish large-scale quantum networks. Ideal quantum
repeater nodes possess a quantum memory which is efficiently connected to
photons, the carrier of quantum information. Color centers in diamond and, in
particular, the negatively-charged silicon-vacancy centers are promising
candidates to establish such nodes. The major obstacle is an inefficient
connection between the color centers spin to the Gaussian optics of fiber
networks. Here, we present an efficient spin-photon interface. Individual
silicon-vacancy centers coupled to the mode of a hemispherical Fabry-P\'erot
microcavity show Purcell-factors larger than 1 when operated in a bath of
liquid Helium. We demonstrate coherent optical driving with a Rabi frequency of
$290\,\mathrm{MHz}$ and all-optical access to the electron spin in strong
magnetic fields of up to $3.2\,\mathrm{T}$. Spin initialization within
$67\,\mathrm{ns}$ with a fidelity of $80\,\%$ and a lifetime of
$350\,\mathrm{ns}$ are reached inside the cavity. The spin-photon interface is
passively stable, enabled by placing a color center containing nanodiamond in
the hemispherical Fabry-P\'erot mirror structure and by choosing short cavity
lengths. Therefore, our demonstration opens the way to realize quantum repeater
applications.


  We present an explicit $\U_h\bigl(gl(n,\C)\bigr)$-equivariant quantization on
coadjoint orbits of $GL(n,\C)$. It forms a two-parameter family quantizing the
Poisson pair of the reflection equation and Kirillov-Kostant-Souriau brackets.


  Neural gates compute functions based on weighted sums of the input variables.
The expressive power of neural gates (number of distinct functions it can
compute) depends on the weight sizes and, in general, large weights
(exponential in the number of inputs) are required. Studying the trade-offs
among the weight sizes, circuit size and depth is a well-studied topic both in
circuit complexity theory and the practice of neural computation. We propose a
new approach for studying these complexity trade-offs by considering a related
algebraic framework. Specifically, given a single linear equation with
arbitrary coefficients, we would like to express it using a system of linear
equations with smaller (even constant) coefficients. The techniques we
developed are based on Siegel's Lemma for the bounds, anti-concentration
inequalities for the existential results and extensions of Sylvester-type
Hadamard matrices for the constructions.
  We explicitly construct a constant weight, optimal size matrix to compute the
EQUALITY function (checking if two integers expressed in binary are equal).
Computing EQUALITY with a single linear equation requires exponentially large
weights. In addition, we prove the existence of the best-known weight size
(linear) matrices to compute the COMPARISON function (comparing between two
integers expressed in binary). In the context of the circuit complexity theory,
our results improve the upper bounds on the weight sizes for the best-known
circuit sizes for EQUALITY and COMPARISON.


  So far, a number of wave energy converters (WEC) have been proposed to
increase efficiency and economic feasibility. Particularly, tubular WEC with
permanent magnets and coil winding packs is mostly used to convert the wave
energy. Due to the demand for high magnetic flux density in WEC, research has
been conducted on high-temperature superconductors (HTS) WEC. In this paper,
the conceptual design of no-insulation (NI) HTS tubular WEC and its
optimization process are proposed. Using NI technology, it has become possible
to design WEC with high volumetric efficiency and cost-effectiveness.
Furthermore, the design is analyzed in the aspect of electromagnetism,
mechanical force, and cryogen. The performance of the proposed WEC is evaluated
as a response to various waveforms and their amplitudes. A rectifying circuit
of WEC connected in parallel with load resistance is used for the output power
study.


  This paper presents a method for performing Uncertainty Quantification in
high-dimensional uncertain spaces by combining arbitrary polynomial chaos with
a recently proposed scheme for sensitivity enhancement (1). Including available
sensitivity information offers a way to mitigate the curse of dimensionality in
Polynomial Chaos Expansions (PCEs). Coupling the sensitivity enhancement to
arbitrary Polynomial Chaos allows the formulation to be extended to a wide
range of stochastic processes, including multi-modal, fat-tailed, and truncated
probability distributions. In so doing, this work addresses two of the barriers
to widespread industrial application of PCEs. The method is demonstrated for a
number of synthetic test cases, including an uncertainty analysis of a Finite
Element structure, determined using Topology Optimisation, with 306 uncertain
inputs. We demonstrate that by exploiting sensitivity information, PCEs can
feasibly be applied to such problems and through the Sobol sensitivity indices,
can allow a designer to easily visualise the spatial distribution of the
contributions to uncertainty in the structure.


  It is well known that the energy density of a quantum state can be negative.
It has been shown that there are limits on this negative energy density which
are called the quantum inequalities. In this paper we will demonstrate an
example of a quantum state which violates the quantum inequalities.


  Radiation by elementary sources is a basic problem in wave physics. We show
that the time-domain energy flux radiated from electromagnetic and acoustic
subwalength sources exhibits remarkable features. In particular, a subtle
trade-off between source emission and absorption underlies the mechanism of
radiation. This behavior should be observed for any kind of classical waves,
thus having broad potential implications. We discuss the implication for
subwavelength focusing by time reversal with active sources.


  Let $\Gamma \subset \operatorname{PU}(1,n)$ be a lattice, and $S_\Gamma$ the
associated ball quotient. We prove that, if $S_\Gamma$ contains infinitely many
maximal totally geodesic subvarieties, then $\Gamma$ is arithmetic. We also
prove an Ax-Schanuel Conjecture for $S_\Gamma$, similar to the one recently
proven by Mok, Pila and Tsimerman. One of the main ingredients in the proofs is
to realise $S_\Gamma$ inside a period domain for polarised integral variations
of Hodge structures and interpret totally geodesic subvarieties as unlikely
intersections.


  By exploiting singular spatial modulations of the graphene conductivity, we
design a broadband, tunable THz absorber whose efficiency approaches the
theoretical upper bound for a wide absorption band with a fractional bandwidth
of 185\%. Strong field enhancement is exhibited by the modes of this extended
structure, which is able to excite a wealth of high order surface plasmons,
enabling deeply subwavelength focussing of incident THz radiation. Previous
studies have shown that the conductivity can be modulated at GHz frequencies,
which might lead to the development of efficient high speed broadband switching
by an atomically thin layer.


  This paper presents a numerical study of three-dimensional laminar mixed
convection within a liquid flowing on a horizontal channel heated uniformly
from below. The upper surface is free and assumed to be flat. The coupled
Navier-Stokes and energy equations are solved numerically by the finite volume
method taking into account the thermocapillary effects (Marangoni effect). When
the strength of the buoyancy, thermocapillary effects and forced convective
currents are comparable $(Ri\backsimeq O(1)$ and $Bd=Ra/Ma \backsimeq O(1))$,
the results show that the development of instabilities in the form of steady
longitudinal convective rolls is similar to those encountered in the
Poiseuille-Rayleigh-B\'enard flow. The number and spatial distribution of these
rolls along the channel depend on the flow conditions. The objective of this
work is to study the influence of parameters, such as the Reynolds, Rayleigh
and Biot numbers, on the flow patterns and heat transfer characteristics. The
effects of variations in the surface tension with temperature gradients
(Marangoni effect) are also considered.


  The transitional millisecond pulsar binary system PSR J1023+0038 re-entered
an accreting state in 2013 June, in which it bears many similarities to
low-mass X-ray binaries (LMXBs) in quiescence or near-quiescence. At a distance
of just 1.37 kpc, PSR J1023+0038 offers an unsurpassed ability to study
low-level accretion onto a highly-magnetized compact object. We have monitored
PSR J1023+0038 intensively using radio imaging with the Karl G. Jansky Very
Large Array, the European VLBI Network and LOFAR, seeing rapidly variable, flat
spectrum emission that persists over a period of 6 months. The flat spectrum
and variability are indicative of synchrotron emission originating in an
outflow from the system, most likely in the form of a compact, partially
self-absorbed jet, as is seen in LMXBs at higher accretion rates. The radio
brightness, however, greatly exceeds extrapolations made from observations of
more vigorously accreting neutron star LMXB systems. We postulate that PSR
J1023+0038 is undergoing radiatively inefficient 'propeller-mode' accretion,
with the jet carrying away a dominant fraction of the liberated accretion
luminosity. We confirm that the enhanced gamma-ray emission seen in PSR
J1023+0038 since it re-entered an accreting state has been maintained; the
increased gamma-ray emission in this state can also potentially be associated
with propeller-mode accretion. Similar accretion modes can be invoked to
explain the radio and X-ray properties of the other two known transitional
millisecond pulsar systems XSS J12270-4859 and PSR J1824-2452I (M28I),
suggesting that radiatively inefficient accretion may be an ubiquitous
phenomenon amongst (at least one class of) neutron star binaries at low
accretion rates.


  We consider reconstructing multi-channel images from measurements performed
by photon-counting and energy-discriminating detectors in the setting of
multi-spectral X-ray computed tomography (CT). Our aim is to exploit the strong
structural correlation that is known to exist between the channels of
multi-spectral CT images. To that end, we adopt the multi-channel Potts prior
to jointly reconstruct all channels. This prior produces piecewise constant
solutions with strongly correlated channels. In particular, edges are enforced
to have the same spatial position across channels which is a benefit over
TV-based methods. We consider the Potts prior in two frameworks: (a) in the
context of a variational Potts model, and (b) in a Potts-superiorization
approach that perturbs the iterates of a basic iterative least squares solver.
We identify an alternating direction method of multipliers (ADMM) approach as
well as a Potts-superiorized conjugate gradient method as particularly
suitable. In numerical experiments, we compare the Potts prior based approaches
to existing TV-type approaches on realistically simulated multi-spectral CT
data and obtain improved reconstruction for compound solid bodies.


  In this paper, we introduce the higher order generalization of Bernstein type
operators defined by (p,q)-integers. We establish some approximation results
for these new operators by using the modulus of continuity.


  The proliferation of diverse wireless services in 5G and beyond has led to
the emergence of network slicing technologies. Among these, admission control
plays a crucial role in achieving service-oriented optimization goals through
the selective acceptance of service requests. Although deep reinforcement
learning (DRL) forms the foundation in many admission control approaches thanks
to its effectiveness and flexibility, initial instability with excessive
convergence delay of DRL models hinders their deployment in real-world
networks. We propose a digital twin (DT) accelerated DRL solution to address
this issue. Specifically, we first formulate the admission decision-making
process as a semi-Markov decision process, which is subsequently simplified
into an equivalent discrete-time Markov decision process to facilitate the
implementation of DRL methods. A neural network-based DT is established with a
customized output layer for queuing systems, trained through supervised
learning, and then employed to assist the training phase of the DRL model.
Extensive simulations show that the DT-accelerated DRL improves resource
utilization by over 40% compared to the directly trained state-of-the-art
dueling deep Q-learning model. This improvement is achieved while preserving
the model's capability to optimize the long-term rewards of the admission
process.


  We provide an informal introduction to tensor field theories and to their
associated renormalization group. We focus more on the general motivations
coming from quantum gravity than on the technical details. In particular we
discuss how asymptotic freedom of such tensor field theories gives a concrete
example of a natural "quantum relativity" postulate: physics in the deep
ultraviolet regime becomes asymptotically more and more independent of any
particular choice of Hilbert basis in the space of states of the universe.


  We consider the problem of variable screening in ultra-high dimensional
generalized linear models (GLMs) of non-polynomial orders. Since the popular
SIS approach is extremely unstable in the presence of contamination and noise,
we discuss a new robust screening procedure based on the minimum density power
divergence estimator (MDPDE) of the marginal regression coefficients. Our
proposed screening procedure performs well under pure and contaminated data
scenarios. We provide a theoretical motivation for the use of marginal MDPDEs
for variable screening from both population as well as sample aspects; in
particular, we prove that the marginal MDPDEs are uniformly consistent leading
to the sure screening property of our proposed algorithm. Finally, we propose
an appropriate MDPDE based extension for robust conditional screening in GLMs
along with the derivation of its sure screening property. Our proposed methods
are illustrated through extensive numerical studies along with an interesting
real data application.


  Recently, it has been observed that a quantum field theory need not be
Hermitian to have a real, positive spectrum. What seems to be required is
symmetry under combined parity and time-reversal transformations. This idea is
extended to massless electrodynamics, in which the photon couples to the
axial-vector current with an imaginary coupling constant. The eigenvalue
condition necessary for the finiteness of the theory can now be solved; the
value for the charge appears to be stable order-by-order. Similarly, the
semiclassical Casimir model for the fine-structure constant yields a positive
value.


  We show that the v-sheaf local models of moduli spaces of $p$-adic shtukas
are unibranch. In particular, this proves that the scheme-theoretic local
models defined in our joint work with Ansch\"{u}tz and Richarz are always
normal with reduced special fiber, thereby establishing the remaining cases of
the geometric part of the Scholze$-$Weinstein conjecture when $p \leq 3$. Our
methods are general, topological, and simplify Zhu's proof of the coherence
conjecture. As a technical input, we generalize a comparison theorem of nearby
cycles of Huber to the v-sheaf setup.


  Topological Data Analysis (TDA), a relatively new field of data analysis, has
proved very useful in a variety of applications. The main persistence tool from
TDA is persistent homology in which data structure is examined at many scales.
Representations of persistent homology include persistence barcodes and
persistence diagrams, both of which are not straightforward to reconcile with
traditional machine learning algorithms as they are sets of intervals or
multisets. The problem of faithfully representing barcodes and persistent
diagrams has been pursued along two main avenues: kernel methods and
vectorizations. One vectorization is the Betti sequence, or Betti curve,
derived from the persistence barcode. While the Betti sequence has been used in
classification problems in various applications, to our knowledge, the
stability of the sequence has never before been discussed. In this paper we
show that the Betti sequence is unstable under the 1-Wasserstein metric with
regards to small perturbations in the barcode from which it is calculated. In
addition, we propose a novel stabilized version of the Betti sequence based on
the Gaussian smoothing seen in the Stable Persistence Bag of Words for
persistent homology. We then introduce the normalized cumulative Betti sequence
and provide numerical examples that support the main statement of the paper.


  The radial characteristics of plasma potential and density around an
insulating disc obstacle, placed inside a partially magnetized plasma flow
created in cylindrical chamber by hot cathode filament are presented. In the
absence of obstacle, centrally sharp minima in potential and maxima in plasma
density is observed; however when a macroscopic obstacle is introduced in
plasma flow, a clear radially off-centred minima in plasma potential is
observed having plasma density peaking near the edge of the obstacle. The depth
of potential around the obstacle depends on the axial magnetic field strength.
This off-centred radial potential profile in the plasma flow gives rise to
focusing of ions around the obstacle edge. Experimentally it is found that the
drift velocity of focused positive ions is directly depended on the magnetic
field strength and axial positive ion flow velocity. A phenomenological model
based on short-circuiting effect is applied to explain the plasma density and
potential in the wake region.


  We have performed large-scale density-matrix renormalization group studies of
the lightly doped Hubbard model on the honeycomb lattice on long three and
four-leg cylinders. We find that the ground state of the system upon lightly
doping is consistent with that of a superconducting state with coexisting
quasi-long-range superconducting and charge density wave orders. Both the
superconducting and charge density wave correlations decay as a power law at
long distances with corresponding exponents $K_{sc}<2$ and $K_c<2$. On the
contrary, the spin-spin and single-particle correlations decay exponentially,
although with relatively long correlation lengths.


  We propose a simple probabilistic model to explain the spatial structure of
the rent distribution of housing market in city of Sapporo. Here we modify the
mathematical model proposed by Gauvin et. al. Especially, we consider the
competition between two distances, namely, the distance between house and
center, and the distance between house and office. Computer simulations are
carried out to reveal the self-organized spatial structure appearing in the
rent distribution. We also compare the resulting distribution with empirical
rent distribution in Sapporo as an example of cities designated by ordinance.
We find that the lowest ranking agents (from the viewpoint of the lowest
`willing to pay') are swept away from relatively attractive regions and make
several their own `communities' at low offering price locations in the city.


  D0-branes are unstable in the presence of an R-R field strength background. A
fuzzy two-sphere is classically stable under such a background, this phenomenon
being called the Myers effect. We analyze this effect from the viewpoint of
tachyon condensation. It is explicitly shown that a fuzzy two-sphere is
realized by the condensation of tachyons which appear from strings connecting
different D0-branes. The formation of a fuzzy $CP^{2}$ is also investigated by
considering the SU(3) invariant R-R field strength background. We find that the
dynamics of the D-branes depends on the properties of the associated algebra.