  A few results that indicate the presence of temperature variations in gaseous
nebulae are reviewed. The evidence is based on: a) temperatures derived from
different methods, and b) on comparisons of abundances predicted by models of
galactic chemical evolution with abundances derived from observations.


  We show that, when graphene is only subject to strain, the spin connection
gauge field that arises plays no measurable role, but when intrinsic curvature
is present and strain is small, spin connection dictates most the physics. We
do so by showing that the Weyl field associated with strain is a pure gauge
field and no constraint on the $(2+1)$-dimensional spacetime appears. On the
other hand, for constant intrinsic curvature that also gives a pure-gauge Weyl
field, we find a classical manifestation of a quantum Weyl anomaly, descending
from a constrained spacetime. We are in the position to do this because we find
the equations that the conformal factor in $(2+1)$-dimensions has to satisfy,
that is a nontrivial generalization to $(2+1)$-dimensions of the classic
Liouville equation of differential geometry of surfaces. Finally, we comment on
the peculiarities of the only gauge field that can describe strain, that is the
well known {\it pseudogauge field} $A_1 \sim u_{11} - u_{22}$ and $A_2 \sim
u_{12}$, and conclude by offering some scenarios of fundamental physics that
this peculiar field could help to realize.


  In this work, we have investigated a 2D model of band-to-band tunneling based
on 2-band model and implemented it using 2D NEGF formalism. Being 2D in nature,
this model better addresses the variation in the directionality of the
tunneling process occurring in most practical TFET device structures. It also
works as a compromise between semi-classical and multiband quantum simulation
of TFETs. In this work, we have presented a sound step by step mathematical
development of the numerical model. We have also discussed how this model can
be implemented in simulators and pointed out a few optimizations that can be
made to reduce complexity and to save time. Finally, we have performed
elaborate simulations for a practical TFET design and compared the results with
commercially available TCAD simulations, to point out the limitations of the
simplistic models that are frequently used, and how our model overcomes these
limitations.


  We investigate the properties of both the prompt and X-ray afterglows of
gamma-ray bursts (GRBs) in the burst frame with a sample of 33 Swift GRBs.
Assuming that the steep decay segment in the canonical X-ray afterglow
lightcurves is due to the curvature effect, we fit the lightcurves with a
broken power-law to derive the zero time of the last emission epoch of the
prompt emission (t1) and the beginning as well as the end time of the shallow
decay segment (t2 and t3).We show that both the isotropic peak gamma-ray
luminosity and gamma-ray energy are correlated with the isotropic X-ray energy
of the shallow decay phase and the isotropic X-ray luminosity at t2. We infer
the properties of the progenitor stars based on a model proposed by Kumar et
al. who suggested that both the prompt gamma-rays and the X-ray afterglows are
due to the accretions of different layers of materials of the GRB progenitor
star by a central black hole (BH). We find that most of the derived masses of
the core layers are 0.1-5 solar mass with a radius of 10^8-10^10 cm. The
rotation parameter is correlated with the burst duration, being consistent with
the expectation of collapsar models. The estimated radii and the masses of the
fall-back materials for the envelope layers are 10^10-10^12 cm and 10^-3~1
solar mass, respectively. The average accretion rates in the shallow decay
phase are correlated with those in the prompt gamma-ray phase, but they are
much lower. The derived radii of the envelope are smaller than the photospheric
radii of Wolf-Rayet (WR) stars. It is interesting that the assembled mass
density profile for the bursts in our sample is also well consistent with the
simulation for a pre-supernova star with 25 solar mass.


  The CLAS collaboration has recently completed first measurements of the
double polarization observables C_x and C_z for the reactions K^+Lambda and
K^+Sigma^o photoproduction. C_x and C_z are the beam-recoil polarization
asymmetries measuring the polarization transfer from incoming circularly
polarized photons to the outgoing hyperons along two directions in the
reaction's production plane. The Lambda is found to be nearly maximally
polarized along the direction of the incident photon's polarization for
forward-going kaons. Polarization transfer to the Sigma^o is different from the
Lambda case.


  The advent of deep learning has led to a significant gain in machine
translation. However, most of the studies required a large parallel dataset
which is scarce and expensive to construct and even unavailable for some
languages. This paper presents a simple yet effective method to tackle this
problem for low-resource languages by augmenting high-quality sentence pairs
and training NMT models in a semi-supervised manner. Specifically, our approach
combines the cross-entropy loss for supervised learning with KL Divergence for
unsupervised fashion given pseudo and augmented target sentences derived from
the model. We also introduce a SentenceBERT-based filter to enhance the quality
of augmenting data by retaining semantically similar sentence pairs.
Experimental results show that our approach significantly improves NMT
baselines, especially on low-resource datasets with 0.46--2.03 BLEU scores. We
also demonstrate that using unsupervised training for augmented data is more
efficient than reusing the ground-truth target sentences for supervised
learning.


  Let $X$ be a fuzzy set--valued random variable (\frv{}), and $\huku{X}$ the
family of all fuzzy sets $B$ for which the Hukuhara difference $X\HukuDiff B$
exists $\mathbb{P}$--almost surely. In this paper, we prove that $X$ can be
decomposed as $X(\omega)=C\Mink Y(\omega)$ for $\mathbb{P}$--almost every
$\omega\in\Omega$, $C$ is the unique deterministic fuzzy set that minimizes
$\mathbb{E}[d_2(X,B)^2]$ as $B$ is varying in $\huku{X}$, and $Y$ is a centered
\frv{} (i.e. its generalized Steiner point is the origin). This decomposition
allows us to characterize all \frv{} translation (i.e. $X(\omega) = M \Mink
\indicator{\xi(\omega)}$ for some deterministic fuzzy convex set $M$ and some
random element in $\Banach$). In particular, $X$ is an \frv{} translation if
and only if the Aumann expectation $\mathbb{E}X$ is equal to $C$ up to a
translation.
  Examples, such as the Gaussian case, are provided.


  How do people form expectations about future prices in financial markets? One
of the dominant learning rules that explains the forecasting behavior is the
Adaptive Expectation Rule (ADA), which suggests that people adjust their
predictions by adapting to the most recent prediction error at a constant
weight. However, this rule also implies that they will continually learn and
adapt until the prediction error is zero, which contradicts recent experimental
evidence showing that people usually stop learning long before reaching zero
prediction error. A more recent learning rule, Reference Model Based Learning
(RMBL), extends and generalizes ADA, hypothesizing that: i) People apply ADA
but dynamically adjust the adaptive coefficient with regards to the
auto-correlation of the prediction error in the most recent two periods; ii)
Meanwhile, they also utilize a satisficing rule so that people would only
adjust their adaptive coefficient when the prediction error is higher than
their anticipation. This paper utilizes a rich set of experimental data with
observations of 41,490 predictions from 801 subjects from the
Learning-to-Forecast Experiments (LtFEs), i.e., the experiment that has been
used to study expectation formation. Our results concludes that RMBL fits
better than ADA in all the experiments.


  The problem of data exchange between multiple nodes with storage and
communication capabilities models several current multi-user communication
problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in
such problems is to design communication schemes which accomplish the desired
data exchange between the nodes with the optimal (minimum) amount of
communication load. In this work, we present a converse to such a general data
exchange problem. The expression of the converse depends only on the number of
bits to be moved between different subsets of nodes, and does not assume
anything further specific about the parameters in the problem. Specific problem
formulations, such as those in Coded Caching, Coded Data Shuffling, Coded
Distributed Computing, can be seen as instances of this generic data exchange
problem. Applying our generic converse, we are able to efficiently recover
known important converses in these formulations. Further, for a generic coded
caching problem with heterogeneous cache sizes at the clients with or without a
central server, we obtain a new general converse, which subsumes some existing
results. Finally we relate a `centralized' version of our bound to the known
generalized independence number bound in index coding, and discuss our bound's
tightness in this context.


  We investigate the onset and evolution of under the simultaneous application
of pressure and magnetic field of distinct itinerant N\'eel states using the
underscreened Anderson Lattice Model (UALM) which has been proposed to describe
$5f$-electron systems. The model is composed by two narrow $f$-bands (of either
$\alpha$ or $\beta$ character) that hybridize with a wide $d$-band and local
$5f$-electron interactions. We consider both cubic and tetragonal lattices. The
N\'eel order parameters $\phi^{\beta}$ and $\phi^{\alpha}$ are assumed to be
fixed by an Ising anisotropy. The applied magnetic field $h_z$ is parallel to
the anisotropy axis. It has been assumed that the variation of the band width
$W$ is sensitive to pressure. In the absence of a magnetic field, the increase
of $W$ takes the system from the phase AF$_1$ to another phase AF$_2$. The
phase AF$_1$ occurs when $\phi^{\beta}>\phi^{\alpha}>0$ while in the AF$_2$
phase the gaps satisfy $\phi^{\alpha}>\phi^{\beta}>0$. In the presence of a
magnetic field $h_z$, the phase AF$_2$ is quickly suppressed and reappears
again at intermediate values of the magnetic field while it is predominant at
higher magnetic fields. The analysis of the partial density of states close to
the phase transition between the phases AF$_1$ and AF$_2$, allows a better
understanding the mechanism responsible whereby the transition is induced by an
increase in the magnetic field. As a important general result, we found that
the magnetic field $h_z$ favours the phase AF$_2$ while the phase AF$_1$ is
suppressed. For the tetragonal lattice, the phase AF$_2$ is even more favored
when $h_z$ and $c/a$ increases concomitantly, where $c$ and $a$ are the lattice
parameters.


  In this study, a thenoyltriflouroacetone ligand (TTA) with a Tb3+ MOF was
synthesized (Tb=10 and 50% mol) and its structural and luminescent properties
were analyzed. The metalorganic compound was generated in a simple one-pot
reaction from terbium nitrate and 2-thenoyltrifluoroacetone precursors at room
temperature. By means of FTIR, it was confirmed the presence of carbon groups,
which made possible the terbium ion chelation, and also the Tb-O bonds
vibration modes. HNMR results confirm that the complex with 10% mol of Tb3+
contains three coordinates molecules of TTA and two waters molecules. The
powders exhibit rod-like morphology with size about 170 nm of diameter and a
length about 2 {\mu}m, the rod-like nature of powders was confirmed by SEM and
TEM analyses. By XRD it was concluded that at higher terbium concentration
(TTA-50Tb sample) higher the crystallite size and crystallinity, in fact the
TTA-10Tb sample shows a partial-amorphous nature. By photoluminescence
analyses, the 5D4-7FJ (J=3, 4, 5 and 6) emissions were recorded for both
synthesized samples ({\lambda}exc=376 nm). Furthermore, it was observed that
the emission intensity was enhanced in a factor of 3.5 for the TTA-50Tb. The
energy transfer efficiency from TTA to Tb3+ (antenna effect) was 0.984 for
TTA-10Tb and 0.993 for TTA-50Tb. Decay time analyses indicate effective
lifetime of 1.45 and 1.60ms for the samples doped at 10 and 50%, respectively,
indicating that the forbidden transition rules are stronger at higher
crystallinity. The integrated intensities of the 5D4 - 7F5 (green at 541 nm)
and 5D4 - 7F6 (blue at 486 nm) emissions and their intensity ratios IG/IB upon
376 nm excitation have been evaluated for TTA-10Tb andTTA-50Tb samples.


  Conjecturally, there are only finitely many Heegaard Floer L-space knots in
$S^3$ of a given genus. We examine this conjecture for twist families of knots
$\{K_n\}$ obtained by twisting a knot $K$ in $S^3$ along an unknot $c$ in terms
of the linking number $\omega$ between $K$ and $c$. We establish the conjecture
in case of $|\omega| \neq 1$, prove that $\{K_n\}$ contains at most three
L-space knots if $\omega = 0$, and address the case where $|\omega| = 1$ under
an additional hypothesis about Seifert surgeries. To that end, we characterize
a twisting circle $c$ for which $\{ (K_n, r_n) \}$ contains at least ten
Seifert surgeries. We also pose a few questions about the nature of twist
families of L-space knots, their expressions as closures of positive (or
negative) braids, and their wrapping about the twisting circle.


  We report the results of a search for the decay B0 -> K*+ K*- with a sample
of 454 +/- 5 million BBbar pairs collected with the BaBar detector at the
PEP-II asymmetric-energy e+e- collider at the Stanford Linear Accelerator
Center. We obtain an upper limit at the 90% confidence level on the branching
fraction for Br(B0 -> K*+ K*-) < 2.0 x 10^{-6}, assuming the decay is fully
longitudinally polarized.


  We suggest that the analysis of incomplete contracting developed by law and
economics researchers can provide a useful framework for understanding the AI
alignment problem and help to generate a systematic approach to finding
solutions. We first provide an overview of the incomplete contracting
literature and explore parallels between this work and the problem of AI
alignment. As we emphasize, misalignment between principal and agent is a core
focus of economic analysis. We highlight some technical results from the
economics literature on incomplete contracts that may provide insights for AI
alignment researchers. Our core contribution, however, is to bring to bear an
insight that economists have been urged to absorb from legal scholars and other
behavioral scientists: the fact that human contracting is supported by
substantial amounts of external structure, such as generally available
institutions (culture, law) that can supply implied terms to fill the gaps in
incomplete contracts. We propose a research agenda for AI alignment work that
focuses on the problem of how to build AI that can replicate the human
cognitive processes that connect individual incomplete contracts with this
supporting external structure.


  We have investigated the phase transition and disoriented chiral condensate
domain formation in linear sigma model. Solving the Langevin equation for the
linear $\sigma$ model, we have shown that for zero mass pions the fields
undergo phase transition above a certain temperature ($T_c$). For finite mass
pions, there is no phase transition. It was also shown that when the fields,
thermalised at temperature above $T_c$ are cooled down sufficiently rapidly,
disoriented chiral condensate domains are formed quite late in the evolution,
if the pions are assumed to be mass less. For massive pions, no large DCC
domain is formed.


  We present some results and remarks based on a combinatorial approach of the
evaluation of the nuclear level density. First, we show that it is possible to
extract some reliable information from the output of the program whose rough
data present a strong statistical fluctuation from bin to bin. This includes
smoothing and evaluation of the desired quantities. After some comments about
the spin and parity distributions, we consider the question of the
non-equipartition of parities, mainly, at low energies. Finally, we present a
simple model to include and test this effect in the computation of
thermonuclear reaction rates.


  This paper introduces a new type of grammar learning algorithm, inspired by
string edit distance (Wagner and Fischer, 1974). The algorithm takes a corpus
of flat sentences as input and returns a corpus of labelled, bracketed
sentences. The method works on pairs of unstructured sentences that have one or
more words in common. When two sentences are divided into parts that are the
same in both sentences and parts that are different, this information is used
to find parts that are interchangeable. These parts are taken as possible
constituents of the same type. After this alignment learning step, the
selection learning step selects the most probable constituents from all
possible constituents.
  This method was used to bootstrap structure on the ATIS corpus (Marcus et
al., 1993) and on the OVIS (Openbaar Vervoer Informatie Systeem (OVIS) stands
for Public Transport Information System.) corpus (Bonnema et al., 1997). While
the results are encouraging (we obtained up to 89.25 % non-crossing brackets
precision), this paper will point out some of the shortcomings of our approach
and will suggest possible solutions.


  When an electron probes another elementary particle Q, the wave function of
the electron can be separated into two independent parts, the first part
represents the electronic motion, the second part represents the
electromagnetic field mode around the particle Q. In analogy with optical modes
$TEM_{nlm}$ for a laser resonator, when the electromagnetic field around the
particle Q forms into a mode, the quantum numbers of the mode satisfy the
Gell-Mann-Nishijima formula, these quantum numbers are recognized as the charge
number, baryon number and strangeness number. The modes are used as a visual
model to understand the abstract baryon number and strangeness number of
hadrons.


  Contextualized representations based on neural language models have furthered
the state of the art in various NLP tasks. Despite its great success, the
nature of such representations remains a mystery. In this paper, we present an
empirical property of these representations -- "average" approximates "first
principal component". Specifically, experiments show that the average of these
representations shares almost the same direction as the first principal
component of the matrix whose columns are these representations. We believe
this explains why the average representation is always a simple yet strong
baseline. Our further examinations show that this property also holds in more
challenging scenarios, for example, when the representations are from a model
right after its random initialization. Therefore, we conjecture that this
property is intrinsic to the distribution of representations and not
necessarily related to the input structure. We realize that these
representations empirically follow a normal distribution for each dimension,
and by assuming this is true, we demonstrate that the empirical property can be
in fact derived mathematically.


  We calculate the motivic stable homotopy groups of the two-complete sphere
spectrum after inverting multiplication by the Hopf map eta over fields of
cohomological dimension at most 2 with characteristic different from 2 (this
includes the p-adic fields and the finite fields of odd characteristic) and the
field of rational numbers; the ring structure is also determined.


  Using combined data from the Relativistic Heavy Ion and Large Hadron
Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma
(QGP) at temperatures of ${\sim\,}150{-}350$ MeV. We use Bayesian inference to
translate experimental and theoretical uncertainties into probabilistic
constraints for the viscosities. With Bayesian Model Averaging we account for
the irreducible model ambiguities in the transition from a fluid description of
the QGP to hadronic transport in the final evolution stage, providing the most
reliable phenomenological constraints to date on the QGP viscosities.


  We investigate a hypothesis that energy accumulation and the subsequent
release in NaI(Tl) may lead to pulse-like events in the few-keV energy regime,
a phenomenon suggested by the crystal manufacturing company Saint-Gobain, who
provided the crystals for DAMA-LIBRA. While we observed delayed long-lasting
light emission in a 3" NaI(Tl) crystal after exposing it to UV light, the
delayed light consists primarily of single photons that are uncorrelated with
each other. We also observe delayed light emission in NaI(Tl) following gamma
radiation and large ionization events like cosmic-ray muons. We found that
irradiating the crystal with red light after UV exposure significantly
suppressed delayed photon emissions.


  We present a new determination of the intergalactic CIV mass density at 4.3 <
z < 6.3. Our constraints are derived from high signal-to-noise spectra of seven
quasars at z > 5.8 obtained with the newly commissioned FIRE spectrograph on
the Magellan Baade telescope, coupled with six observations of northern objects
taken from the literature. We confirm the presence of a downturn in the CIV
abundance at <z>=5.66 by a factor of 4.1 relative to its value at <z>=4.96, as
measured in the same sightlines. In the FIRE sample, a strong system previously
reported in the literature as CIV at z=5.82 is re-identified as MgII at z=2.78,
leading to a substantial downward revision in $\Omega_{CIV}$ for these prior
studies. Additionally we confirm the presence of at least two systems with
low-ionization CII, SiII, and OI absorption but relatively weak signal from
CIV. The latter systems systems may be of interest if the downward trend in
$\Omega_{CIV}$ at high redshift is driven in part by ionization effects.


  Exact structured inference with neural network scoring functions is
computationally challenging but several methods have been proposed for
approximating inference. One approach is to perform gradient descent with
respect to the output structure directly (Belanger and McCallum, 2016). Another
approach, proposed recently, is to train a neural network (an "inference
network") to perform inference (Tu and Gimpel, 2018). In this paper, we compare
these two families of inference methods on three sequence labeling datasets. We
choose sequence labeling because it permits us to use exact inference as a
benchmark in terms of speed, accuracy, and search error. Across datasets, we
demonstrate that inference networks achieve a better speed/accuracy/search
error trade-off than gradient descent, while also being faster than exact
inference at similar accuracy levels. We find further benefit by combining
inference networks and gradient descent, using the former to provide a warm
start for the latter.


  The multi-UAV network is promising to extend conventional networks by
providing broader coverage and better reliability. Nevertheless, the broadcast
nature of wireless signals and the broader coverage expose multi-UAV
communications to the threats of passive eavesdroppers. Recent studies mainly
focus on securing a single legitimate link, or communications between a UAV and
multiple ground users in one/two-UAV-aided networks, while the physical layer
secrecy analysis for hierarchical multi-UAV networks is underexplored. In this
paper, we investigate a general two-tier UAV network consisting of multiple UAV
transmitters (UTs) and multiple UAV receivers (URs) in the presence of multiple
UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at
the physical layer, we design a two-stage framework consisting of a UT-UR
association stage and a cooperative transmission stage. Specifically, we
formulate the secure transmission problem into a many-to-one matching game
followed by an overlapping coalition formation (OCF) game, taking into account
the limited capabilities and the throughput requirements of URs, as well as the
transmission power constraints of UTs. A matching algorithm and an OCF
algorithm are proposed to solve these two sequential games whose convergences
and stabilities are guaranteed theoretically. Simulation results show the
superiority of our algorithms and the effectiveness of our two-stage game
framework in the terms of secrecy performance.


  Reinforcement learning (RL) for motion planning of multi-degree-of-freedom
robots still suffers from low efficiency in terms of slow training speed and
poor generalizability. In this paper, we propose a novel RL-based robot motion
planning framework that uses implicit behavior cloning (IBC) and dynamic
movement primitive (DMP) to improve the training speed and generalizability of
an off-policy RL agent. IBC utilizes human demonstration data to leverage the
training speed of RL, and DMP serves as a heuristic model that transfers motion
planning into a simpler planning space. To support this, we also create a human
demonstration dataset using a pick-and-place experiment that can be used for
similar studies. Comparison studies in simulation reveal the advantage of the
proposed method over the conventional RL agents with faster training speed and
higher scores. A real-robot experiment indicates the applicability of the
proposed method to a simple assembly task. Our work provides a novel
perspective on using motion primitives and human demonstration to leverage the
performance of RL for robot applications.


  This report introduces a solution to the Topic 1 Zero-shot Image Captioning
of 2024 NICE : New frontiers for zero-shot Image Captioning Evaluation. In
contrast to NICE 2023 datasets, this challenge involves new annotations by
humans with significant differences in caption style and content. Therefore, we
enhance image captions effectively through retrieval augmentation and caption
grading methods. At the data level, we utilize high-quality captions generated
by image caption models as training data to address the gap in text styles. At
the model level, we employ OFA (a large-scale visual-language pre-training
model based on handcrafted templates) to perform the image captioning task.
Subsequently, we propose caption-level strategy for the high-quality caption
data generated by the image caption models and integrate them with retrieval
augmentation strategy into the template to compel the model to generate higher
quality, more matching, and semantically enriched captions based on the
retrieval augmentation prompts. Our approach achieves a CIDEr score of 234.11.


  We theoretically study the propagation through a resonant absorbing medium of
a time-dependent perturbation modulating the amplitude of a continuous wave
(cw). Modeling the medium as a system of two-level atoms and linearizing the
Maxwell-Bloch equations for the perturbation, we establish an exact analytical
expression of the transfer function relating the Fourier transforms of the
incident and transmitted perturbations. It directly gives the gain and the
phase shift undergone in the medium by a harmonic modulation. For the case of a
pulse modulation, it enables us to determine the transmission time of the pulse
center-of-mass (group delay), evidencing the relative contributions of the
coherent and incoherent (population) relaxation. We show that the group delay
has a negative value (fast light) fixed by the coherent effects when the cw
intensity is small compared to the saturation intensity and becomes positive
(slow light) when this intensity increases, before attaining a maximum that
cannot exceed the population relaxation time. The analytical results are
completed by numerical determinations of the shape of the transmitted pulses in
the different regimes.


  Probabilistic cloning was first proposed by Duan and Guo. Then Pati
established a novel cloning machine (NCM) for copying superposition of multiple
clones simultaneously. In this paper, we deal with the novel cloning machine
with supplementary information (NCMSI). For the case of cloning two states, we
demonstrate that the optimal efficiency of the NCMSI in which the original
party and the supplementary party can perform quantum communication equals that
achieved by a two-step cloning protocol wherein classical communication is only
allowed between the original and the supplementary parties. From this
equivalence it follows that NCMSI may increase the success probabilities for
copying. Also, an upper bound on the unambiguous discrimination of two
nonorthogonal pure product states is derived. Our investigation generalizes and
completes the results in the literature.


  Applying the known Nyman--Beurling criterion, it is disproved the Riemann
hypothesis on zeros of $\zeta -$function.


  We provide a non-recursive description for the bounded admissible sets of
masks used by Deodhar's algorithm to calculate the Kazhdan--Lusztig polynomials
$P_{x,w}(q)$ of type $A$, in the case when $w$ is hexagon avoiding and
maximally clustered. This yields a combinatorial description of the
Kazhdan--Lusztig basis elements of the Hecke algebra associated to such
permutations $w$. The maximally-clustered hexagon-avoiding elements are
characterized by avoiding the seven classical permutation patterns $\{3421,
4312, 4321, 46718235, 46781235, 56718234, 56781234\}$. We also briefly discuss
the application of heaps to permutation pattern characterization.


  We have investigated the dielectric anomalies associated with spin ordering
transitions in the tetragonal spinel Mn$_3$O$_4$, using thermodynamic,
magnetic, and dielectric measurements. We find that two of the three magnetic
ordering transitions in Mn$_3$O$_4$ lead to decreases in the temperature
dependent dielectric constant at zero applied field. Applying a magnetic field
to the polycrystalline sample leaves these two dielectric anomalies practically
unchanged, but leads to an increase in the dielectric constant at the
intermediate spin-ordering transition. We discuss possible origins for this
magnetodielectric behavior in terms of spin-phonon coupling. Band structure
calculations suggest that in its ferrimagnetic state, Mn$_3$O$_4$ corresponds
to a semiconductor with no orbital degeneracy due to strong Jahn-Teller
distortion.


  In the model of synthetic differential geometry consisting of sheaves (with
respect to open covers) over the opposite category of the category of closed
finitely generated C-infinity rings, any morphism from S, the zeroes of the
"amazing right adjoint" of dx, to the real line R extends to a morphism from R
to R. This shows that the De Rham cohomology of the space S is the same as the
characteristic cohomology of the ideal generated by dx.


  We formulate computation offloading as a decentralized decision-making
problem with autonomous agents. We design an interaction mechanism that
incentivizes agents to align private and system goals by balancing between
competition and cooperation. The mechanism provably has Nash equilibria with
optimal resource allocation in the static case. For a dynamic environment, we
propose a novel multi-agent online learning algorithm that learns with partial,
delayed and noisy state information, and a reward signal that reduces
information need to a great extent. Empirical results confirm that through
learning, agents significantly improve both system and individual performance,
e.g., 40% offloading failure rate reduction, 32% communication overhead
reduction, up to 38% computation resource savings in low contention, 18%
utilization increase with reduced load variation in high contention, and
improvement in fairness. Results also confirm the algorithm's good convergence
and generalization property in significantly different environments.


  In this note, we show that if $T$ is a multilinear singular integral operator
associated with a kernel satisfies the so-called multilinear
$L^{r}$-H\"ormander condition, then $T$ can be dominated by multilinear sparse
operators.


  We study the Goos-H\"anchen shifts for Dirac fermions in graphene scattered
by a triangular double barrier potential. The massless Dirac-like equation was
used to describe the scattered fermions by such potential configuration. Our
results show that the GHL shifts is affected by the geometrical structure of
the double barrier. In particular the GHL shifts change sign at the
transmission zero energies and exhibit enhanced peaks at each bound state
associated with the double barrier when the incident angle is less than the
critical angle associated with the total reflection.


  We investigate the persistent spin currents in one- and two-dimensional
electron systems with spin-orbit interaction in thermodynamics equilibrium at
absolute zero temperature. The persistent spin current is the intrinsic one
which is connected with the Berry phases in the configuration spaces of an
electron system and winding numbers in the field configurations of electrons.
When the topological space of the configuration of a system has the nontrivial
first homotopy groups, the persistent spin currents in the system could be
nonzero and not easily destroyed by impurity scattering in ballistic limit. The
non-vanishing background spin currents in infinite two-dimensional electron
system found by Rashba could be realized by the transport persistent spin
currents in a finite torus electron system with spin-orbit interaction. In this
sense, we meet the challenge proposed by Rashba.


  Hierarchical cooperation has recently been shown to achieve better throughput
scaling than classical multihop schemes under certain assumptions on the
channel model in static wireless networks. However, the end-to-end delay of
this scheme turns out to be significantly larger than those of multihop
schemes. A modification of the scheme is proposed here that achieves a
throughput-delay trade-off $D(n)=(\log n)^2 T(n)$ for T(n) between
$\Theta(\sqrt{n}/\log n)$ and $\Theta(n/\log n)$, where D(n) and T(n) are
respectively the average delay per bit and the aggregate throughput in a
network of n nodes. This trade-off complements the previous results of El Gamal
et al., which show that the throughput-delay trade-off for multihop schemes is
given by D(n)=T(n) where T(n) lies between $\Theta(1)$ and $\Theta(\sqrt{n})$.
  Meanwhile, the present paper considers the network multiple-access problem,
which may be of interest in its own right.


  Dental biofilm is the deposition of microbial material over a tooth
substratum. Several methods have recently been reported in the literature for
biofilm quantification; however, at best they provide a barely automated
solution requiring significant input needed from the human expert. On the
contrary, state-of-the-art automatic biofilm methods fail to make their way
into clinical practice because of the lack of effective mechanism to
incorporate human input to handle praxis or misclassified regions. Manual
delineation, the current gold standard, is time consuming and subject to expert
bias. In this paper, we introduce a new semi-automated software tool,
BiofilmQuant, for dental biofilm quantification in quantitative light-induced
fluorescence (QLF) images. The software uses a robust statistical modeling
approach to automatically segment the QLF image into three classes (background,
biofilm, and tooth substratum) based on the training data. This initial
segmentation has shown a high degree of consistency and precision on more than
200 test QLF dental scans. Further, the proposed software provides the
clinicians full control to fix any misclassified areas using a single click. In
addition, BiofilmQuant also provides a complete solution for the longitudinal
quantitative analysis of biofilm of the full set of teeth, providing greater
ease of usability.


  We use UBVI,Ha images of the Whirlpool galaxy, M51, taken with the ACS and
WFPC2 cameras on the Hubble Space Telescope (HST) to select star clusters, and
to estimate their masses and ages by comparing their observed colors with
predictions from population synthesis models. We construct the mass function of
intermediate age (1-4x10^8 yr) clusters, and find that it is well described by
a power law, psi(M) propto M^beta, with beta=-2.1 +/- 0.2, for clusters more
massive than approximately 6x10^3 Msun. This extends the mass function of
intermediate age clusters in M51 to masses lower by nearly a factor of five
over previous determinations. The mass function does not show evidence for
curvature at either the high or low mass end. This shape indicates that there
is no evidence for the earlier disruption of lower mass clusters compared with
their higher mass counterparts (i.e., no mass-dependent disruption) over the
observed range of masses and ages, or for a physical upper mass limit Mc with
which clusters in M51 can form. These conclusions differ from previous
suggestions based on poorer-quality HST observations. We discuss their
implications for the formation and disruption of the clusters. Ages of clusters
in two "feathers," stellar features extending from the outer portion of a
spiral arm, show that the feather with a larger pitch angle formed earlier, and
over a longer period, than the other.


  Scrambling of quantum information is the process by which information
initially stored in the local degrees of freedom of a quantum many-body system
spreads over its many-body degrees of freedom, becoming inaccessible to local
probes and thus apparently lost. Scrambling and entanglement are key concepts
reconciling seemingly unrelated behaviors including thermalization of isolated
quantum systems and information loss in black holes, and have revolutionized
our understanding of non-equilibrium phenomena. Here, we demonstrate that a
family of fidelity out-of-time-order correlators (FOTOCs), recently measured in
a trapped-ion quantum simulator via time reversal of the many-body dynamics
followed by a fidelity measurement, can serve as a unifying diagnostic tool
that elucidates the intrinsic connection between fast scrambling, volume law
entanglement, ergodicity, quantum chaos, and the associated butterfly effect in
the semiclassical dynamics of the system. We demonstrate the utility of FOTOCs
by computing them in the Dicke model, an iconic model in quantum optics,
recently implemented in atomic and trapped-ion setups. This model describes the
coupling of a large spin to an oscillator and features rich behaviors,
including a quantum phase transition and chaos. Here, we show that FOTOCs
provide a direct measure of the spin-phonon Renyi entropy and quantum
thermalization. Moreover, we connect the FOTOCs to the variance of simple
operators, allowing us to observe fast scrambling in the parameter regime where
the system's classical trajectories are chaotic, and to explicitly relate the
quantum and classical Lyapunov exponents in a truly quantum many-body system.
Our results open a path for the experimental use of FOTOCs to quantify fast
scrambling, determine bounds on quantum information processing and to identify
possible candidates of black hole analogs in controllable quantum systems.


  All the hermitian representations of the ``symmetric" $q$-oscillator are
obtained by means of expansions. The same technique is applied to characterize
in a systematic way the $k$-order boson realizations of the $q$-oscillator and
$su(1,1)_q$. The special role played by the quadratic realizations of
$su(1,1)_q$ in terms of boson and $q$-boson operators is analysed and
clarified.


  The extraction of elastic form factors for mesons in the context of the
contact interaction model is revisited in this manuscript. The dressed masses
of quarks and mesons are determined through the gap and Bethe-Salpeter
equations. The generic elastic scattering process $M\gamma \to M$ is studied
for the meson $M$ formed of two differently flavored quarks. The charge radii
of scalar, pseudoscalar, vector and axial-vector mesons are also extracted by
virtue of explicit calculation of the meson elastic form factors.


  In this article we prove that locally Griffiths' horizontal distribution on
the period domain is given by a generalized version of the familiar contact
differential system. As a consequence of this description we obtain strong
local rigidity properties of maximal dimensional variations of Hodge structure.
For example, we prove that if the weight is odd then there is a unique germ of
maximal dimensional variation of Hodge stucture though every point of the
period domain. Similar results hold if the weight is even with the exception of
one case.


  We study the average long-time behavior of the binary opinions of a social
group with peer-to-peer interactions under the influence of an external bias
and a persuadable leader, a strongly-biased agent with a dynamic opinion with
the intention of spreading it across the system. We use a generalized,
fully-connected Ising model, with each spin representing the binary opinion of
an agent at a given time and a single, super spin representing the opinion of
the leader. External fields and interaction constants model the opinion bias
and peer-to-peer interactions, respectively, while the temperature $T$ models
an idealized social climate, representing an authoritarian regime if $T$ is low
or a liberal one if $T$ is high. We derive a mean-field solution for the
average magnetization $m$, the "social mood", and investigate how $m$ and the
super spin magnetization vary as a function of $T$. We find that, depending on
the initial conditions, due to the presence of metastable states, the sign of
the average magnetization depends on the temperature. Finally, we verify that
this effect is also present even if we consider only nearest-neighbor
interactions within the social group.


  We report new results on complex-temperature properties of Ising models.
These include studies of the $s=1/2$ model on triangular, honeycomb, kagom\'e,
$3 \cdot 12^2$, and $4 \cdot 8^2$ lattices. We elucidate the complex--$T$ phase
diagrams of the higher-spin 2D Ising models, using calculations of partition
function zeros. Finally, we investigate the 2D Ising model in an external
magnetic field, mapping the complex--$T$ phase diagram and exploring various
singularities therein. For the case $\beta H=i\pi/2$, we give exact results on
the phase diagram and obtain susceptibility exponents $\gamma'$ at various
singularities from low-temperature series analyses.


  The present work reports results of the electron paramagnetic resonance
(EPR), optical absorption, radio- and photoluminescence (RL and PL) complex
study of the Lu2Si2O7:Pr and Lu2Si2O7:Ce pyrosilicate crystals. In both
crystals, the EPR spectra demonstrate the presence of characteristic signals
originating from the Yb3+, Er3+, Nd3+, Dy3+, Gd3+ and V3+ ions existing in the
material as uncontrolled impurities. The corresponding spectra (except for
Gd3+) have been analysed in detail and g- and hyperfine tensors are determined
for all these ions in the lutetium pyrosilicate for the first time. Optical
absorption, RL and PL measurements in the Lu2Si2O7:Pr crystal demonstrated
presence of only characteristic Pr3+ transitions. In addition, the Pr3+- Pr3+
energy transfer was observed and confirmed experimentally.


  We develop the information-theoretical concepts required to study the
statistical dependencies among three variables. Some of such dependencies are
pure triple interactions, in the sense that they cannot be explained in terms
of a combination of pairwise correlations. We derive bounds for triple
dependencies, and characterize the shape of the joint probability distribution
of three binary variables with high triple interaction. The analysis also
allows us to quantify the amount of redundancy in the mutual information
between pairs of variables, and to assess whether the information between two
variables is or is not mediated by a third variable. These concepts are applied
to the analysis of written texts. We find that the probability that a given
word is found in a particular location within the text is not only modulated by
the presence or absence of other nearby words, but also, on the presence or
absence of nearby pairs of words. We identify the words enclosing the key
semantic concepts of the text, the triplets of words with high pairwise and
triple interactions, and the words that mediate the pairwise interactions
between other words.


  The weighted spanning tree enumerator of a graph $G$ with weighted edges is
the sum of the products of edge weights over all the spanning trees in $G$. In
the special case that all of the edge weights equal $1$, the weighted spanning
tree enumerator counts the number of spanning trees in $G$. The Weighted
Matrix-Tree Theorem asserts that the weighted spanning tree enumerator can be
calculated from the determinant of a reduced weighted Laplacian matrix of $G$.
That determinant, however, is not always easy to compute. In this paper, we
show how two well-known results from linear algebra, the Matrix Determinant
Lemma and the method of Schur complements, can be used to elegantly compute the
weighted spanning tree enumerator for several families of graphs.


  We establish new universal equations for higher genus Gromov-Witten
invariants of target manifolds, by studying both the Chern character and Chern
classes of the Hodge bundle on the moduli space of curves. As a consequence, we
find new push-forward relations on the moduli space of stable curves.


  We propose a compact analytic expression for the polylogarithmic part of the
n-point two-loop all-plus helicity amplitude in gauge theory.


  An integral relation is derived from the Fokker-Planck equation which
connects the steady-state probability currents with the dynamics of relaxation
on short timescales in the limit of small perturbation fields. As a consequence
of this integral relation, a lower bound on the steady-state entropy production
is obtained. For the particular case of an ensemble of random perturbation
fields of weak spatial gradient, a simpler bound is derived from the integral
relation which provides a feasible method to estimate entropy production from
relaxation experiments.


  Pre-trained models have achieved success in Chinese Short Text Matching (STM)
tasks, but they often rely on superficial clues, leading to a lack of robust
predictions. To address this issue, it is crucial to analyze and mitigate the
influence of superficial clues on STM models. Our study aims to investigate
their over-reliance on the edit distance feature, commonly used to measure the
semantic similarity of Chinese text pairs, which can be considered a
superficial clue. To mitigate STM models' over-reliance on superficial clues,
we propose a novel resampling training strategy called Gradually Learn Samples
Containing Superficial Clue (GLS-CSC). Through comprehensive evaluations of
In-Domain (I.D.), Robustness (Rob.), and Out-Of-Domain (O.O.D.) test sets, we
demonstrate that GLS-CSC outperforms existing methods in terms of enhancing the
robustness and generalization of Chinese STM models. Moreover, we conduct a
detailed analysis of existing methods and reveal their commonality.


  Pure spin currents are measured in micron-wide channels of GaAs
two-dimensional electron gas (2DEG). Spins are injected and detected using
quantum point contacts, which become spin polarized at high magnetic field.
High sensitivity to the spin signal is achieved in a nonlocal measurement
geometry, which dramatically reduces spurious signals associated with charge
currents. Measured spin relaxation lengths range from 30 to 50 microns, much
longer than has been reported in GaAs 2DEG's. The technique developed here
provides a flexible tool for the study of spin polarization and spin dynamics
in mesoscopic structures defined in 2D semiconductor systems.


  In many phenomena, data are collected on a large scale and of different
frequencies. In this context, functional data analysis (FDA) has become an
important statistical methodology for analyzing and modeling such data. The
approach of FDA is to assume that data are continuous functions and that each
continuous function is considered as a single observation. Thus, FDA deals with
large-scale and complex data. However, visualization and exploratory data
analysis, which is very important in practice, can be challenging due to the
complexity of the continuous functions. Here we propose some nonparametric
tools for functional data observed over time (functional time series). For
that, we propose to use the concept of record. We study the properties of the
trajectory of the number of record curves under different scenarios. Also, we
propose a unit root test based on the number of records. The trajectory of the
number of records over time and the unit root test can be used as visualization
and exploratory data analysis. We illustrate the advantages of our proposal
through a Monte Carlo simulation study. We also illustrate our method on two
different datasets: Annual mortality rates in France and daily wind speed
curves at Yanbu, Saudi Arabia. Overall, we can identify the type of functional
time series being studied based on the number of record curves observed.


  The task of causal representation learning aims to uncover latent
higher-level causal representations that affect lower-level observations.
Identifying true latent causal representations from observed data, while
allowing instantaneous causal relations among latent variables, remains a
challenge, however. To this end, we start from the analysis of three intrinsic
properties in identifying latent space from observations: transitivity,
permutation indeterminacy, and scaling indeterminacy. We find that transitivity
acts as a key role in impeding the identifiability of latent causal
representations. To address the unidentifiable issue due to transitivity, we
introduce a novel identifiability condition where the underlying latent causal
model satisfies a linear-Gaussian model, in which the causal coefficients and
the distribution of Gaussian noise are modulated by an additional observed
variable. Under some mild assumptions, we can show that the latent causal
representations can be identified up to trivial permutation and scaling.
Furthermore, based on this theoretical result, we propose a novel method,
termed Structural caUsAl Variational autoEncoder, which directly learns latent
causal representations and causal relationships among them, together with the
mapping from the latent causal variables to the observed ones. We show that the
proposed method learns the true parameters asymptotically. Experimental results
on synthetic and real data demonstrate the identifiability and consistency
results and the efficacy of the proposed method in learning latent causal
representations.


  We show that if a cusped hyperbolic manifold is Platonic, i.e., can be
decomposed into isometric Platonic solids, it can also be decomposed into
geodesic ideal tetrahedra.


  In a previous paper, we have developed a general theory of thermodynamic
limits. We apply it here to three different Coulomb quantum systems, for which
we prove the convergence of the free energy per unit volume.
  The first system is the crystal for which the nuclei are classical particles
arranged periodically in space and only the electrons are quantum particles. We
recover and generalize a previous result of Fefferman. In the second example,
both the nuclei and the electrons are quantum particles, submitted to a
periodic magnetic field. We thereby extend a seminal result of Lieb and
Lebowitz. Finally, in our last example we take again classical nuclei but
optimize their position. To our knowledge such a system was never treated
before.
  The verification of the assumptions introduced in the previous paper uses
several tools which have been introduced before in the study of large quantum
systems. In particular, an electrostatic inequality of Graf and Schenker is one
main ingredient of our new approach.


  We describe features of the X-ray: Generate and Analyse (XGA) open-source
software package that have been developed to facilitate automated hydrostatic
mass ($M_{\rm hydro}$) measurements from XMM X-ray observations of clusters of
galaxies. This includes describing how XGA measures global, and radial, X-ray
properties of galaxy clusters. We then demonstrate the reliability of XGA by
comparing simple X-ray properties, namely the X-ray temperature and gas mass,
with published values presented by the XMM Cluster Survey (XCS), the Ultimate
XMM eXtragaLactic survey project (XXL), and the Local Cluster Substructure
Survey (LoCuSS). XGA measured values for temperature are, on average, within 1%
of the values reported in the literature for each sample. XGA gas masses for
XXL clusters are shown to be ${\sim}$10% lower than previous measurements
(though the difference is only significant at the $\sim$1.8$\sigma$ level),
LoCuSS $R_{2500}$ and $R_{500}$ gas mass re-measurements are 3% and 7% lower
respectively (representing a 1.5$\sigma$ and 3.5$\sigma$ difference).
Like-for-like comparisons of hydrostatic mass are made to LoCuSS results, which
show that our measurements are $10{\pm}3%$ ($19{\pm}7%$) higher for $R_{2500}$
($R_{500}$). The comparison between $R_{500}$ masses shows significant scatter.
Finally, we present new $M_{\rm hydro}$ measurements for 104 clusters from the
SDSS DR8 redMaPPer XCS sample (SDSSRM-XCS). Our SDSSRM-XCS hydrostatic mass
measurements are in good agreement with multiple literature estimates, and
represent one of the largest samples of consistently measured hydrostatic
masses. We have demonstrated that XGA is a powerful tool for X-ray analysis of
clusters; it will render complex-to-measure X-ray properties accessible to
non-specialists.


  We construct an ensemble distribution to describe steady immiscible two-phase
flow of two incompressible fluids in a porous medium. The system is found to be
ergodic. The distribution is used to compute macroscopic flow parameters. In
particular, we find an expression for the overall mobility of the system from
the ensemble distribution. The entropy production at the scale of the porous
medium is shown to give the expected product of the average flow and its
driving force, obtained from a black-box description. We test numerically some
of the central theoretical results.


  Let $T$ be a bijective map on $\mathbb{R}^n$ such that both $T$ and $T^{-1}$
are Borel measurable. For any $\btheta \in \mathbb{R}^n$ and any real $n \times
n$ positive definite matrix $\Sigma,$ let $N (\btheta, \Sigma)$ denote the
$n$-variate normal (gaussian) probability measure on $\mathbb{R}^n$ with mean
vector $\btheta$ and covariance matrix $\Sigma.$ Here we prove the following
two results: (1) Suppose $N(\btheta_j, I)T^{-1}$ is gaussian for $0 \leq j \leq
n$ where $I$ is the identity matrix and $\{\btheta_j - \btheta_0, 1 \leq j \leq
n \}$ is a basis for $\mathbb{R}^n.$ Then $T$ is an affine linear
transformation; (2) Let $\Sigma_j = I + \epsilon_j \mathbf{u}_j
\mathbf{u}_j^{\prime},$ $1 \leq j \leq n$ where $\epsilon_j > -1$ for every $j$
and ${\mathbf{u}_j, 1 \leq j \leq n}$ is a basis of unit vectors in
$\mathbb{R}^n$ with $\mathbf{u}_j^{\prime}$ denoting the transpose of the
column vector $\mathbf{u}_j.$ Suppose $N(\mathbf{0}, I)T^{-1}$ and $N
(\mathbf{0}, \Sigma_j)T^{-1},$ $1 \leq j \leq n$ are gaussian. Then
$T(\mathbf{x}) = \sum\limits_{\mathbf{s}} 1_{E_{\mathbf{s}}} V \mathbf{s} U
\mathbf{x}$ a.e. $\mathbf{x}$ where $\mathbf{s}$ runs over the set of $2^n$
diagonal matrices of order $n$ with diagonal entries $\pm 1,$ $U,\, V$ are $n
\times n$ orthogonal matrices and $\{E_{\mathbf{s}}\}$ is a collection of $2^n$
Borel subsets of $\mathbb{R}^n$ such that $\{E_{\mathbf{s}}\}$ and $\{V
\mathbf{s} U (E_{\mathbf{s}})\}$ are partitions of $\mathbb{R}^n$ modulo
Lebesgue-null sets and for every $j,$ $V \mathbf{s} U \Sigma_j (V \mathbf{s}
U)^{-1}$ is independent of all $\mathbf{s}$ for which the Lebesgue measure of
$E_{\mathbf{s}}$ is positive. The converse of this result also holds.
\vskip0.1in Our results constitute a sharpening of the results of S. Nabeya and
T. Kariya


  The entanglement spectrum describing quantum correlations in many-body
systems has been recently recognized as a key tool to characterize different
quantum phases, including topological ones. Here we derive its analytically
scaling properties in the vicinity of some integrable quantum phase transitions
and extend our studies also to non integrable quantum phase transitions in one
dimensional spin models numerically. Our analysis shows that, in all studied
cases, the scaling of the difference between the two largest non degenerate
Schmidt eigenvalues yields with good accuracy critical points and mass scaling
exponents.


  We study bound states in a model with scalar nucleons interacting via an
exchanged scalar meson using the Hamiltonian formalism on the light front. In
this approach manifest rotational invariance is broken when the Fock space is
truncated. By considering an effective Hamiltonian that takes into account two
meson exchanges, we find that this breaking of rotational invariance is
decreased from that which occurs when only one meson exchange is included. The
best improvement occurs when the states are weakly bound.


  In this paper we implement the holonomic gradient method to exactly compute
the normalising constant of Bingham distributions. This idea is originally
applied for general Fisher-Bingham distributions in Nakayama et al. (2011). In
this paper we explicitly apply this algorithm to show the exact calculation of
the normalising constant; derive explicitly the Pfaffian system for this
parametric case; implement the general approach for the maximum likelihood
solution search and finally adjust the method for degenerate cases, namely when
the parameter values have multiplicities.


  In this paper, we show that the depth of an isolated log canonical center is
determined by the cohomology of the -1 discrepancy diviors over it. A similar
result also holds for normal isolated Du Bois singularities.


  We discuss the infrared structure of processes with massive quarks in the
initial state. It is well known that, starting from next-to-next-to-leading
order in perturbative QCD, such processes exhibit a violation of the
Bloch-Nordsieck theorem, in that the sum of real and virtual contributions to
partonic cross sections contains uncanceled infrared singularities. The main
purpose of this paper is to present a simple physical argument that elucidates
the origin of these singularities and simplifies the derivation of
infrared-singular contributions to heavy-quark initiated cross sections.


  In this paper, the structures to a family of biorthogonal polynomials that
approximate to the Hermite and Generalized Laguerre polynomials are discussed
respectively. Therefore, the asymptotic relation between several orthogonal
polynomials and combinatorial polynomials are derived from the systems, which
in turn verify the Askey scheme of hypergeometric orthogonal polynomials. As
the applications of these properties, the asymptotic representations of the
generalized Buchholz, Laguerre, Ultraspherical(Gegenbauer), Bernoulli, Euler,
Meixner and Meixner-Pllaczekare polynomials are derived from the theorems
directly. The relationship between Bernoulli and Euler polynomials are shown as
a special case of the characterization theorem of the Appell sequence generated
by $\alpha$ scaling functions.


  In the framework of special Kahler geometry we consider the
supergravity-matter system which emerges on a K3-fibered Calabi-Yau manifold.
By applying the rigid limit procedure in the vicinity of a conifold singularity
we compute the Kahler potential of the scalars and the kinetic matrix of the
vectors to first order in the gravitational coupling.


  We propose an out-of-equilibrium impurity model for the dynamical mean-field
description of the Hubbard model driven by a finite electric field. The
out-of-equilibrium impurity environment is represented by a collection of
equilibrium reservoirs at different chemical potentials. We discuss the
validity of the impurity model and propose a non-perturbative method, based on
a quantum Monte Carlo solver, which provides the steady-state solutions of the
impurity and original lattice problems. We discuss the relevance of this
approach to other non-equilibrium steady-state contexts.


  We perform detailed close-coupling calculations for the rubidium isotopes
85Rb and 87Rb to ascertain the nature of their spinor Bose-Einstein
condensates. These calculations predict that the spinor condensate for the
spin-1 boson 87Rb has a ferromagnetic nature. The spinor condensates for the
spin-2 bosons 85Rb and 87Rb, however, are both predicted to be polar. The
nature of a spin-1 condensate hinges critically on the sign of the difference
between the s-wave scattering lengths for total spin 0 and 2 while the nature
of a spin-2 condensate depends on the values of the differences between s-wave
scattering lengths for the total spin 0, 2 and 4. These scattering lengths were
extracted previously and found to have overlapping uncertainties for all three
cases, thus leaving the nature of the spinor condensates ambiguous. The present
study exploits a refined uncertainty analysis of the scattering lengths based
on recently improved result from experimental work by Roberts et al., which
permits us to extract an unambiguous result for the nature of the ground state
spinor condensates.


  This paper considers two sets of formulas related to a Spin Coherence Time
(SCT) case with only vertical oscillations in a purely electric ring, the first
derived by the author for the field index m > 0 and the second by Ivan Koop for
the field index m = 0. I argue that a continuous transition can exist from one
set to the other (contrary to appearances), and assume that a necessary
condition for the transition is that both sets of formulas follow from the same
equation. I demonstrate that they do follow when one takes into account that
the first set of formulas holds only for times much larger than the period of
the vertical oscillations. This demonstration confirms the correctness of the
formulas and the equation.


  We give a short review about the hydrodynamic model and its application to
the elliptic flow phenomena and the pion interferometry in relativistic heavy
ion collisions.


  Let X be a smooth projective surface over C and let L be a line bundle on X
generated by its global sections. Let f:X-->P^r be the morphism associated to L
and let T be the tangent bundle of P^r; we investigate the \mu-stability of f*T
with respect to L when X is either a regular surface with p_g=0, a K3 surface
or an abelian surface. In particular, we show that it is \mu-stable when X is
K3 and L is ample and when X is abelian and L^2>13.


  Integrating renewable energy into the power grid while balancing supply and
demand is a complex issue, given its intermittent nature. Demand side
management (DSM) offers solutions to this challenge. We propose a new method
for DSM, in particular the problem of controlling a large population of
electrical devices to follow a desired consumption signal. We model it as a
finite horizon Markovian mean field control problem. We develop a new
algorithm, MD-MFC, which provides theoretical guarantees for convex and
Lipschitz objective functions. What distinguishes MD-MFC from the existing load
control literature is its effectiveness in directly solving the target tracking
problem without resorting to regularization techniques on the main problem. A
non-standard Bregman divergence on a mirror descent scheme allows dynamic
programming to be used to obtain simple closed-form solutions. In addition, we
show that general mean-field game algorithms can be applied to this problem,
which expands the possibilities for addressing load control problems. We
illustrate our claims with experiments on a realistic data set.


  We consider dynamic random walks where the nearest neighbour jump rates are
determined by an underlying supercritical contact process in equilibrium. This
has previously been studied by den Hollander and dos Santos and den Hollander,
dos Santos, Sidoravicius. We show the CLT for such a random walk, valid for all
supercritical infection rates for the contact process environment.


  A scheme for measurement of microwave (MW) electric field is proposed via
multi-photon coherence in Rydberg atoms. It is based on the three-photon
electromagnetically induced absorption (TPEIA) spectrum. In this process, the
multi-photon produces a narrow absorption peak, which has a larger magnitude
than the electromagnetically induced transparency (EIT) peak under the same
conditions. The TPEIA peak is sensitive to MW fields, and can be used to
measure MW electric field strength. It is interesting to find that the
magnitude of TPEIA peaks shows a linear relationship with the MW field
strength. The simulation results show that the minimum detectable strength of
the MW fields is about 1/10 that based on an common EIT effect, and the probe
sensitivity is improved by about 4 times. Furthermore, the MW sensing based on
three-photon coherence shows a broad tunability, and the scheme may be useful
for designing novel MW sensing devices.


  Applying Dixon's general equations of motion for extended bodies, we compute
the Papapetrou's equations for an extended test body on static and isotropic
metrics. We incorporate the force and the torque terms which involve multipole
moments, beyond dipole moment, from the energy-momentum tensor. We obtain the
vector form equations for both Corinaldesi-Papapetrou and Tulczyjew-Dixon spin
supplementary conditions. An expanded effective mass, including interactions
between the structure of the body and the gravitational field, is also found.


  Nontrivial $p$-polygonal equalities impose certain conditions on the geometry
of a metric space $(X,d)$ and so it is of interest to be able to identify the
values of $p \in [0,\infty)$ for which such equalities exist. Following work of
Li and Weston, Kelleher, Miller, Osborn and Weston established that if a metric
space $(X,d)$ is of $p$-negative type, then $(X,d)$ admits no nontrivial
$p$-polygonal equalities if and only if it is of strict $p$-negative type. In
this note we remove the underlying premise of $p$-negative type from this
theorem. As an application we show that the set of all $p$ for which a finite
metric space $(X,d)$ admits a nontrivial $p$-polygonal equality is always a
closed interval of the form $[\wp, \infty)$, where $\wp > 0$, or the empty set.
It follows that for each $q \not= 2$, the Schatten $q$-class $\mathcal{C}_{q}$
admits a nontrivial $p$-polygonal equality for each $p > 0$. Other spaces with
this same property include $C[0, 1]$ and $\ell_{q}^{(3)}$ for all $q > 2$.


  Nature's many complex systems--physical, biological, and cultural--are
islands of low-entropy order within increasingly disordered seas of
surrounding, high-entropy chaos. Energy is a principal facilitator of the
rising complexity of all such systems in the expanding Universe, including
galaxies, stars, planets, life, society, and machines. A large amount of
empirical evidence--relating neither entropy nor information, rather
energy--suggests that an underlying simplicity guides the emergence and growth
of complexity among many known, highly varied systems in the
14-billion-year-old Universe, from big bang to humankind. Energy flows are as
centrally important to life and society as they are to stars and galaxies. In
particular, the quantity energy rate density--the rate of energy flow per unit
mass--can be used to explicate in a consistent, uniform, and unifying way a
huge collection of diverse complex systems observed throughout Nature.
Operationally, those systems able to utilize optimal amounts of energy tend to
survive and those that cannot are non-randomly eliminated.


  Three models of growing random networks with fitness dependent growth rates
are analysed using the rate equations for the distribution of their
connectivities. In the first model (A), a network is built by connecting
incoming nodes to nodes of connectivity $k$ and random additive fitness $\eta$,
with rate $(k-1)+ \eta $. For $\eta >0$ we find the connectivity distribution
is power law with exponent $\gamma=<\eta>+2$. In the second model (B), the
network is built by connecting nodes to nodes of connectivity $k$, random
additive fitness $\eta$ and random multiplicative fitness $\zeta$ with rate
$\zeta(k-1)+\eta$. This model also has a power law connectivity distribution,
but with an exponent which depends on the multiplicative fitness at each node.
In the third model (C), a directed graph is considered and is built by the
addition of nodes and the creation of links. A node with fitness $(\alpha,
\beta)$, $i$ incoming links and $j$ outgoing links gains a new incoming link
with rate $\alpha(i+1)$, and a new outgoing link with rate $\beta(j+1)$. The
distributions of the number of incoming and outgoing links both scale as power
laws, with inverse logarithmic corrections.


  A drop rebounding from a hydrophobic and chemically heterogeneous surface is
investigated using the multiphase lattice Boltzmann method. The behaviors of
drop rebounding are dependent on the degrees of the hydrophobicity and
heterogeneity of the surface. When the surface is homogeneous, the drop
rebounds vertically and the height is getting higher and higher with increases
of the surface hydrophobicity. When the surface consists of two different
hydrophobic surfaces, the drop rebounds laterally towards the low hydrophobic
side. The asymmetrical rebounding is because the unbalanced Young's force
exerted on the contact line by the high hydrophobic side is greater than that
by the low hydrophobic surface. A set of contours of momentum distribution
illustrate the dynamic process of drop spreading, shrinking and rebounding.
This work promotes the understanding of the rebound mechanism of a drop
impacting the surface and also provides a guiding strategy for precisely
controlling the lateral behavior of rebounding drops by hydrophobic degrees and
heterogeneous surfaces.


  We report on a versatile method to compensate the linear attenuation in a
medium, independently of its microscopic origin. The method exploits
diffraction-limited Bessel beams and tailored on-axis intensity profiles which
are generated using a phase-only spatial light modulator. This technique for
compensating one of the most fundamental limiting processes in linear optics is
shown to be efficient for a wide range of experimental conditions (modifying
the refractive index and the attenuation coefficient). Finally, we explain how
this method can be advantageously exploited in applications ranging from
bio-imaging light sheet microscopy to quantum memories for future quantum
communication networks.


  Calculating the most efficient schedule of work in a neural network compiler
is a difficult task. There are many parameters to be accounted for that can
positively or adversely affect that schedule depending on their configuration -
How work is shared between distributed targets, the subdivision of tensors to
fit in memory, toggling the enablement of optimizations, etc. Traditionally,
neural network compilers determine how to set these values by building a graph
of choices and choosing the path with minimal 'cost'. These choices and their
corresponding costs are usually determined by an algorithm crafted by engineers
with a deep knowledge of the target platform. However, when the amount of
options available to a compiler is large, it is very difficult to ensure that
these models consistently produce an optimal schedule for all scenarios, whilst
still completing compilation in an acceptable timeframe. This paper presents
'VPUNN' - a neural network-based cost model trained on low-level task profiling
that consistently outperforms the state-of-the-art cost modeling in Intel's
line of VPU processors.


  In this paper, we will first give a derivation of the multipole expansion
(ME) and local expansion (LE) for the far field from sources in general 2-D
layered media and the multipole-to-local translation (M2L) operator by using
the generating function for Bessel functions. Then, we present a rigorous proof
of the exponential convergence of the ME, LE, and M2L for 2-D Helmholtz
equations in layered media. It is shown that the convergence of ME, LE, and M2L
for the reaction field component of the Green's function depends on a polarized
distance between the target and a polarized image of the source.


  We investigate the integrability of the Schwinger-Dyson equations in $c = 1 -
\frac{6}{m(m+1)}$ string field theory which were proposed by Ikehara et al as
the continuum limit of the Schwinger-Dyson equations of the matrix chain model.
We show the continuum Schwinger-Dyson equations generate a closed algebra. This
algebra contains Virasoro algebra but does not coincide with $W_{\infty}$
algebra. We include in the Schwinger-Dyson equations a new process of removing
from the loop boundaries the operator ${\cal H}(\sigma)$ which locally changes
the spin configuration. We also derive the string field Hamiltonian from the
continuum Schwinger-Dyson equations. Its form is universal for all $c = 1 -
\frac{6}{m(m+1)}$ string theories.


  We treat the change point problem in ergodic diffusion processes from
discrete observations. Tonaki et al. (2020) proposed adaptive tests for
detecting changes in the diffusion and drift parameters in ergodic diffusion
models. When any changes are detected by this method, the next question to be
considered is where the change point is. Therefore, we propose the method to
estimate the change point of the parameter for two cases: the case where there
is a change in the diffusion parameter, and the case where there is no change
in the diffusion parameter but a change in the drift parameter. Furthermore, we
present rates of convergence and distributional results of the change point
estimators. Some examples and simulation results are also given.


  In 1992, F. Holland conjectured a mixed arithmetic-mean, geometric-mean
inequality and it was proved by K. Kedlaya in 1994. In this note, we provide
more extended inequality: a mixed arithmetic-mean, geometric-mean,
harmonic-mean inequality.


  Estimating structures in "big data" and clustering them are among the most
fundamental problems in computer vision, pattern recognition, data mining, and
many other other research fields. Over the past few decades, many studies have
been conducted focusing on different aspects of these problems. One of the main
approaches that is explored in the literature to tackle the problems of size
and dimensionality is sampling subsets of the data in order to estimate the
characteristics of the whole population, e.g. estimating the underlying
clusters or structures in the data. In this paper, we propose a `one-time-grab'
sampling algorithm\cite{jaberi2015swift,jaberi2018sparse}. This method can be
used as the front end to any supervised or unsupervised clustering method.
Rather than focusing on the strategy of maximizing the probability of sampling
inliers, our goal is to minimize the number of samples needed to instantiate
all underlying model instances. More specifically, our goal is to answer the
following question: {\em `Given a very large population of points with $C$
embedded structures and gross outliers, what is the minimum number of points
$r$ to be selected randomly in one grab in order to make sure with probability
$P$ that at least $\varepsilon$ points are selected on each structure, where
$\varepsilon$ is the number of degrees of freedom of each structure.'}


  This paper presents a non-parametric classification technique for identifying
a candidate bi-allelic genetic marker set that best describes disease
susceptibility in gene-gene interaction studies. The developed technique
functions by creating a mapping between inferred haplotypes and case/control
status. The technique cycles through all possible marker combination models
generated from the available marker set where the best interaction model is
determined from prediction accuracy and two auxiliary criteria including
low-to-high order haplotype propagation capability and model parsimony. Since
variable-length haplotypes are created during the best model identification,
the developed technique is referred to as a variable-length haplotype
construction for gene-gene interaction (VarHAP) technique. VarHAP has been
benchmarked against a multifactor dimensionality reduction (MDR) program and a
haplotype interaction technique embedded in a FAMHAP program in various
two-locus interaction problems. The results reveal that VarHAP is suitable for
all interaction situations with the presence of weak and strong linkage
disequilibrium among genetic markers.


  The origin of a chemical reaction between two reactant atoms is associated to
the activation energy, with the assumption that, high-energy collisions between
these atoms, are the ones that overcome the activation energy. Here, we (i)
show that a stronger attractive van der Waals (vdW) and electron-ion Coulomb
interactions between two polarized atoms are responsible to initiate a chemical
reaction, either before or after the collision. We derive this stronger vdW
attraction formula exactly using the quasi one-dimensional Drude model within
the ionization energy theory and the energy-level spacing renormalization group
method. Along the way, we (ii) expose the precise physical mechanism
responsible for the existence of a stronger vdW interaction for both long and
short distances, and also show how to technically avoid the electron-electron
Coulomb repulsion between polarized electrons from these two reactant atoms.
Finally, we properly and correctly associate the existence of this stronger
attraction to Ramachandran's 'normal limits' (distance shorter than what is
allowed by the standard vdW bond) between chemically nonbonded atoms.


  The striking ability of unsupervised word translation has been demonstrated
with the help of word vectors / pretraining; however, they require large
amounts of data and usually fails if the data come from different domains. We
propose coocmap, a method that can use either high-dimensional co-occurrence
counts or their lower-dimensional approximations. Freed from the limits of low
dimensions, we show that relying on low-dimensional vectors and their
incidental properties miss out on better denoising methods and useful world
knowledge in high dimensions, thus stunting the potential of the data. Our
results show that unsupervised translation can be achieved more easily and
robustly than previously thought -- less than 80MB and minutes of CPU time is
required to achieve over 50\% accuracy for English to Finnish, Hungarian, and
Chinese translations when trained on similar data; even under domain mismatch,
we show coocmap still works fully unsupervised on English NewsCrawl to Chinese
Wikipedia and English Europarl to Spanish Wikipedia, among others. These
results challenge prevailing assumptions on the necessity and superiority of
low-dimensional vectors, and suggest that similarly processed co-occurrences
can outperform dense vectors on other tasks too.


  We consider the quantization of a scalar kappa-deformed field up to the point
of obtaining an expression for its vacuum energy. The expression is given by
the half sum of the field frequencies, as in the non-deformed case, but with
the frequencies obeying the kappa-deformed dispersion relation. We consider a
set of kappa-deformed Maxwell equations and show that for the purpose of
calculating the Casimir energy the Maxwell field, as in the non-deformed case,
behaves as a pair of scalar fields. Those results provide a foundation for
computing the Casimir energy starting from the the half sum of field
frequencies. A method of calculation starting from this expression is briefly
described.


  Since the Great Financial Crisis (GFC), the use of stress tests as a tool for
assessing the resilience of financial institutions to adverse financial and
economic developments has increased significantly. One key part in such
exercises is the translation of macroeconomic variables into default
probabilities for credit risk by using macrofinancial linkage models. A key
requirement for such models is that they should be able to properly detect
signals from a wide array of macroeconomic variables in combination with a
mostly short data sample. The aim of this paper is to compare a great number of
different regression models to find the best performing credit risk model. We
set up an estimation framework that allows us to systematically estimate and
evaluate a large set of models within the same environment. Our results
indicate that there are indeed better performing models than the current
state-of-the-art model. Moreover, our comparison sheds light on other potential
credit risk models, specifically highlighting the advantages of machine
learning models and forecast combinations.


  Two dimensional square lattice general model of the magnetic dot array is
introduced. In this model the intradot self-energy is predicted via the neural
network and interdot magnetostatic coupling is approximated by the collection
of several dipolar terms. The model has been applied to disk-shaped cluster
involving 193 ultrathin dots and 772 interaction centers. In this case among
the intradot magnetic structures retrieved by neural networks the important
role play single-vortex magnetization modes. Several aspects of the model have
been understood numerically by means of the simulated annealing method.


  The present paper is the first in a series that addresses the calculation of
the full one-loop corrections of dark matter (DM) annihilation cross-sections
in the low mass region of the inert doublet model (IDM). We first review the
renormalisation of the model both in a fully on-shell (OS) scheme and a mixed
scheme combining on-shell (for the masses) and a $\overline{{\rm MS}}$ approach
when the partial invisible width is closed and does not allow the use of a full
OS scheme. The scale dependence introduced by the mixed scheme is shown to be
tracked through an analysis of a parametrisation of the tree-level
cross-section and the $\beta$ constant of a specific coupling. We discuss how
to minimise the scale dependence. The theoretical uncertainty brought by the
scale dependence leads us to introduce a new criterion on the perturbativity of
the IDM. This criterion further delimits the allowed parameter space which we
investigate carefully by including a host of constraints, both theoretical and
experimental, including in particular, new data from the LHC. We come up with a
set of benchmark points that cover three different mechanisms for a viable
relic density of DM: {\it i)} a dominance of co-annihilation into a fermion
pair, { \it ii)} annihilation into 2 vector bosons of which one is off-shell
that requires the calculation of a $2 \to 3$ process at one-loop, {\it iii)}
annihilation that proceeds through the very narrow standard model Higgs
resonance. Since the $2 \to 3$ vector boson channel features in all three
channels and is essentially a build up on the simpler annihilation to OS vector
bosons, we study the latter in detail in the present paper. We confirm again
that the corrected cross-sections involve a parameter that represents
rescattering in the dark sector that a tree-level computation in not sensitive
to.


  The properties of the scalar and vector correlations in the hot and/or dense
hadronic matter close to chiral transition are discussed. Presuming that the
linear realization of chiral symmetry will become appropriate at least near the
critical point, we argue that the strength function in the $I=J=0$ channel will
soften near the critical point, and the sigma meson will accordingly become a
clearer resonance in hot and/or dense medium than in the free space. It is
shown that the steep rise of the baryon-number susceptibility $\chi_B$ around
the critical point seen in the lattice simulations may suggest that the
interactions between quarks in the vector channel become week near the critical
point; this implies that the peculiar behavior of the $\chi_B$ does not
necessarily imply a proliferation of baryons and antibaryons (Skyrmions) which
is though a natural scenario of chiral transition in the non-linear realization
of chiral symmetry.


  Let $S$ be a set of transpositions that generates the symmetric group $S_n$,
where $n \ge 3$. The transposition graph $T(S)$ is defined to be the graph with
vertex set $\{1,\ldots,n\}$ and with vertices $i$ and $j$ being adjacent in
$T(S)$ whenever $(i,j) \in S$. We prove that if the girth of the transposition
graph $T(S)$ is at least 5, then the automorphism group of the Cayley graph
$\Cay(S_n,S)$ is the semidirect product $R(S_n) \rtimes \Aut(S_n,S)$, where
$\Aut(S_n,S)$ is the set of automorphisms of $S_n$ that fixes $S$. This
strengthens a result of Feng on transposition graphs that are trees. We also
prove that if the transposition graph $T(S)$ is a 4-cycle, then the set of
automorphisms of the Cayley graph $\Cay(S_4,S)$ that fixes a vertex and each of
its neighbors is isomorphic to the Klein 4-group and hence is nontrivial. We
thus identify the existence of 4-cycles in the transposition graph as being an
important factor in causing a potentially larger automorphism group of the
Cayley graph.


  We study the automorphisms group action on a bounded domain in $\CC^n$ having
a boundary point that is exponentially flat. Such a domain typically has a
compact automorphism group. Our results enable us to generate many new
examples.


  We study melting in a two-dimensional system of classical particles with
Gaussian-core interactions in disordered environments. The pure system
validates the conventional two-step melting with a hexatic phase intervening
between the solid and the liquid. This picture is modified in the presence of
pinning impurities. A random distribution of pinning centers forces a
hexatic-like low temperature phase that transits into a liquid at a single
melting temperature $T^{\rm RP}_{\rm m}$. In contrast, pinning centers located
at randomly chosen sites of a perfect crystal anchors a solid at low
temperatures which undergoes a direct transition to the liquid at $T^{\rm
CP}_{\rm m}$. Thus, the two-step melting is lost in either cases of disorder.
We discuss the characteristics of melting depending on the nature of the
impurities.


  A positive muon is a spin-1/2 particle. Beams of muons with all their spins
polarized can be prepared and subsequently implanted in various types of
condensed matter. The subsequent precession and relaxation of their spins can
then be used to investigate a variety of static and dynamic effects in a sample
and hence to deduce properties concerning magnetism, superconductivity and
molecular dynamics. Though strictly a lepton, and behaving essentially like a
heavy electron, it is convenient to think of a muon as a light proton, and it
is often found with a captured electron in a hydrogen-like atom known as
muonium. This article outlines the principles of various experimental
techniques which involve implanted muons and describes some recent
applications. The use of muons in condensed matter physics has shed new light
on subjects as diverse as passivation in semiconductors, frustrated spin
systems, vortex lattice melting, and quantum diffusion of light particles.


  The soft-disk model previously developed and applied by Durian [D. J. Durian,
Phys. Rev. Lett. 75, 4780 (1995)] is brought to bear on problems of foam
rheology of longstanding and current interest, using two-dimensional systems.
The questions at issue include the origin of the Herschel-Bulkley relation,
normal stress effects (dilatancy), and localization in the presence of wall
drag. We show that even a model that incorporates only linear viscous effects
at the local level gives rise to nonlinear (power-law) dependence of the limit
stress on strain rate. With wall drag, shear localization is found. Its
nonexponential form and the variation of localization length with boundary
velocity are well described by a continuum model in the spirit of Janiaud et
al. [Phys. Rev. Lett. 97, 038302 (2006)]. Other results satisfactorily link
localization to model parameters, and hence tie together continuum and local
descriptions.


  Room-temperature Raman scattering has been measured in barium tungstate
(BaWO4) up to 16 GPa. We report the pressure dependence of all the Raman-active
first-order phonons of the tetragonal scheelite phase (BaWO4-I, space group
I41/a), which is stable at normal conditions. As pressure increases the Raman
spectrum undergoes significant changes around 6.9 GPa due to the onset of the
structural phase transition to the monoclinic BaWO4-II phase (space group
P21/n). This transition is only completed above 9.5 GPa. A further change in
the spectrum is observed at 7.5 GPa related to a scheelite-to-fergusonite
transition. The scheelite, BaWO4-II, and fergusonite phases coexist up to 9.0
GPa due to the sluggishness of the I?II phase transition. Further to the
experimental study, we have performed ab initio lattice dynamics calculations
that have greatly helped us in assigning and discussing the pressure behaviour
of the observed Raman modes of the three phases.


  In a dynamical system the first Lyapunov vector (LV) is associated with the
largest Lyapunov exponent and indicates ---at some point on the attractor---
the direction of maximal growth in tangent space. The LV corresponding to the
second largest Lyapunov exponent generally points at a different direction, but
tangencies between both vectors can in principle occur. Here we find that the
probability density function (PDF) of the angle \psi spanned by the first and
the second LVs should be expected approximately symmetric around \pi/4 and
peaked at 0 and \pi/2. Moreover, for small angles we uncover a scaling law for
the PDF Q of \psi_l=\ln\psi with the system size L: Q(\psi_l)=L^{-1/2} f(\psi_l
L^{-1/2}). We give a theoretical argument that justifies this scaling form and
also explains why it should be universal (irrespective of the system details)
for spatio-temporal chaos in one spatial dimension.


  This paper presents the analysis of optical integral field spectra for the HI
eXtreme (HIX) galaxy sample. HIX galaxies host at least 2.5 times more atomic
gas (HI) than expected from their optical R-band luminosity. Previous
examination of their star formation activity and HI kinematics suggested that
these galaxies stabilise their large HI discs (radii up to 94 kpc) against star
formation due to their higher than average baryonic specific angular momentum.
A comparison to semi-analytic models further showed that the elevated baryonic
specific angular momentum is inherited from the high spin of the dark matter
host. In this paper we now turn to the gas-phase metallicity as well as stellar
and ionised gas kinematics in HIX galaxies to gain insights into recent
accretion of metal-poor gas or recent mergers. We compared the stellar,
ionised, and atomic gas kinematics, and examine the variation in the gas-phase
metallicity throughout the stellar disc of HIX galaxies. We find no indication
for counter-rotation in any of the components, the central metallicities tend
to be lower than average, but as low as expected for galaxies of similar HI
mass. Metallicity gradients are comparable to other less HI-rich, local star
forming galaxies. We conclude that HIX galaxies show no conclusive evidence for
recent major accretion or merger events. Their overall lower metallicities are
likely due to being hosted by high spin halos, which slows down their evolution
and thus the enrichment of their interstellar medium.


  ATLAS and CMS observe deviations from the expected background in the diphoton
invariant mass searches of new resonances around 750 GeV. We show that a simple
realization in terms of a new pseudoscalar state can accommodate the
observations. The model leads to further footprints that can be soon observed.
The new state can be interpreted both as an axion or as a {highly natural}
composite state arising from minimal models of dynamical electroweak symmetry
breaking. We further show how to disentangle the two scenarios. Beyond the
possible explanation of the diphoton excess the results show that it is
possible to directly test and constrain composite dynamics via processes
stemming from its distinctive topological sector.


  We carry out an interacting shell-model study of binding energies and spectra
in the $sd$-shell nuclei to examine the effect of truncation of the shell-model
spaces. Starting with a Hamiltonian defined in a larger space and truncating to
the $sd$ shell, the binding energies are strongly affected by the truncation,
but the effect on the excitation energies is an order of magnitude smaller. We
then refit the matrix elements of the two-particle interaction to compensate
for the space truncation, and find that it is easy to capture 90% of the
binding energy shifts by refitting a few parameters. With the full parameter
space of the two-particle Hamiltonian, we find that both the binding energies
and the excitation energy can be fitted with remaining residual error about 5%
of the average error from the truncation. Numerically, the rms initial error
associated with our Hamiltonian is 3.4 MeV and the remaining residual error is
0.16 MeV. This is comparable to the empirical error found in $sd$-shell
interacting shell model fits to experimental data\cite{br06}.


  Light (anti-) nuclei are a powerful tool both in collider physics and
astrophysics. In searches for new and exotic physics, the expected small
astrophysical backgrounds at low energies make these antinuclei ideal probes
for, e.g., dark matter. At the same time, their composite structure and small
binding energies imply that they can be used in collider experiments to probe
the hadronisation process and two-particle correlations. For the proper
interpretation of such experimental studies, an improved theoretical
understanding of (anti-) nuclei production in specific kinematic regions and
detector setups is needed. In this work, we develop a coalescence framework for
(anti-) deuteron production which accounts for both the emission volume and
momentum correlations on an event-by-event basis. This framework goes beyond
the equal-time approximation, which has been commonly assumed in femtoscopy
experiments and (anti-) nucleus production models until now. Using PYTHIA~8 as
an event generator, we find that the equal-time approximation leads to an error
of O(10%) in low-energy processes like $\Upsilon$ decays, while the errors are
negligible at LHC energies. The framework introduced in this work paves the way
for tuning event generators to (anti-) nuclei measurements.


  According to the recent astronomical data, the most part of energy in the
Universe is in the 'dark' form, which is effectively described by Lambda-term
in Einstein equations. All arguments in favor of the dark energy were obtained
so far from the observational data related to very large (intergalactic)
scales. Is it possible to find a manifestation of the dark energy at much less
scales (e.g. inside the Solar system)?


  An electrocardiogram (ECG) is a time-series signal that is represented by
one-dimensional (1-D) data. Higher dimensional representation contains more
information that is accessible for feature extraction. Hidden variables such as
frequency relation and morphology of segment is not directly accessible in the
time domain. In this paper, 1-D time series data is converted into
multi-dimensional representation in the form of multichannel 2-D images.
Following that, deep learning was used to train a deep neural network based
classifier to detect arrhythmias. The results of simulation on testing database
demonstrate the effectiveness of the proposed methodology by showing an
outstanding classification performance compared to other existing methods and
hand-crafted annotations made by certified cardiologists.


  Pervasive technologies combined with powerful AI have been recently
introduced to enhance work productivity. Yet, some of these technologies are
judged to be invasive. To identify which ones, we should understand how
employees tend to judge these technologies. We considered 16 technologies that
track productivity, and conducted a study in which 131 crowd-workers judged
these scenarios. We found that a technology was judged to be right depending on
the following three aspects of increasing importance. That is, whether the
technology: 1) was currently supported by existing tools; 2) did not interfere
with work or was fit for purpose; and 3) did not cause any harm or did not
infringe on any individual rights. Ubicomp research currently focuses on how to
design better technologies by making them more accurate, or by increasingly
blending them into the background. It might be time to design better ubiquitous
technologies by unpacking AI ethics as well.


  Every country must dispose of old banknotes. At the Hong Kong Monetary
Authority visitor center, visitors can buy a paperweight souvenir full of
shredded banknotes. Even though the shredded banknotes are small, by using
computer vision, it is possible to reconstruct the whole banknote like a jigsaw
puzzle. Each paperweight souvenir costs \$100 HKD, and it is claimed to contain
shredded banknotes equivalent to 138 complete \$1000 HKD banknotes. In theory,
\$138,000 HKD can be recovered by using computer vision. This paper discusses
the technique of collecting shredded banknote pieces and applying a computer
vision program.


  In clinical practice, many diagnosis tasks rely on the identification of
cells in histopathology images. While supervised machine learning techniques
require labels, providing manual cell annotations is time-consuming due to the
large number of cells. In this paper, we propose a self-supervised framework
(VOLTA) for cell representation learning in histopathology images using a novel
technique that accounts for the cell's mutual relationship with its environment
for improved cell representations. We subjected our model to extensive
experiments on the data collected from multiple institutions around the world
comprising of over 700,000 cells, four cancer types, and cell types ranging
from three to six categories for each dataset. The results show that our model
outperforms the state-of-the-art models in cell representation learning. To
showcase the potential power of our proposed framework, we applied VOLTA to
ovarian and endometrial cancers with very small sample sizes (10-20 samples)
and demonstrated that our cell representations can be utilized to identify the
known histotypes of ovarian cancer and provide novel insights that link
histopathology and molecular subtypes of endometrial cancer. Unlike supervised
deep learning models that require large sample sizes for training, we provide a
framework that can empower new discoveries without any annotation data in
situations where sample sizes are limited.