  In car-body production the pre-formed sheet metal parts of the body are
assembled on fully-automated production lines. The body passes through multiple
stations in succession, and is processed according to the order requirements.
The timely completion of orders depends on the individual station-based
operations concluding within their scheduled cycle times. If an error occurs in
one station, it can have a knock-on effect, resulting in delays on the
downstream stations. To the best of our knowledge, there exist no methods for
automatically distinguishing between source and knock-on errors in this
setting, as well as establishing a causal relation between them. Utilizing
real-time information about conditions collected by a production data
acquisition system, we propose a novel vehicle manufacturing analysis system,
which uses deep learning to establish a link between source and knock-on
errors. We benchmark three sequence-to-sequence models, and introduce a novel
composite time-weighted action metric for evaluating models in this context. We
evaluate our framework on a real-world car production dataset recorded by
Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences
contain either a source or knock-on error. With respect to seq2seq model
training, we find that the Transformer demonstrates a better performance
compared to LSTM and GRU in this domain, in particular when the prediction
range with respect to the durations of future actions is increased.


  We consider structure-preserving methods for conservative systems, which
rigorously replicate the conservation property yielding better numerical
solutions. There, corresponding to the skew-symmetry of the differential
operator, that of difference operators is essential to the discrete
conservation law. Unfortunately, however, when we employ the standard central
difference operator, the simplest one, the numerical solutions often suffer
from undesirable spatial oscillations. In this letter, we propose a novel
"average-difference method," which is tougher against such oscillations, and
combine it with an existing conservative method. Theoretical and numerical
analysis in the linear case show the superiority of the proposed method.


  Wireless Sensor networks (WSN) is an emerging technology and have great
potential to be employed in critical situations like battlefields and
commercial applications such as building, traffic surveillance, habitat
monitoring and smart homes and many more scenarios. One of the major challenges
wireless sensor networks face today is security. While the deployment of sensor
nodes in an unattended environment makes the networks vulnerable to a variety
of potential attacks, the inherent power and memory limitations of sensor nodes
makes conventional security solutions unfeasible. The sensing technology
combined with processing power and wireless communication makes it profitable
for being exploited in great quantity in future. The wireless communication
technology also acquires various types of security threats. This paper
discusses a wide variety of attacks in WSN and their classification mechanisms
and different securities available to handle them including the challenges
faced.


  The photon-assisted tunneling (PAT) through a single wall carbon nanotube
quantum well (QW) under influence an external electromagnetic field for probing
of the Tomonaga Luttinger liquid (TLL) state is suggested. The elementary TLL
excitations inside the quantum well are density ($\rho_{\pm}$) and spin
($\sigma_{\pm} $) bosons. The bosons populate the quantized energy levels
$\epsilon^{\rho +}_n =\Delta n/ g$ and $\epsilon^{\rho -(\sigma \pm)}_n =
\Delta n$ where $\Delta = h v_F /L $ is the interlevel spacing, $n$ is an
integer number, $L$ is the tube length, $g$ is the TLL parameter. Since the
electromagnetic field acts on the $\rho_{+}$ bosons only while the neutral
$\rho_{-}$ and $\sigma_{\pm} $ bosons remain unaffected, the PAT spectroscopy
is able of identifying the $\rho_{+}$ levels in the QW setup. The spin
$\epsilon_n^{\sigma+} $ boson levels in the same QW are recognized from Zeeman
splitting when applying a d.c. magnetic field $H \neq 0$ field. Basic TLL
parameters are readily extracted from the differential conductivity curves.


  The radiation-dominated k=0 FRW cosmology emerges as the induced metric on a
codimension one hypersurface of constant extrinsic curvature in the
five-dimensional AdS-Schwarzschild solution. That we should get FRW cosmology
in this way is an expected result from AdS/CFT in light of recent comments
regarding the coupling of gravity to "boundary" conformal field theories. I
remark on how this calculation bears on the understanding of Randall and
Sundrum's "alternative to compactification." A generalization of the AdS/CFT
prescription for computing Green's functions is suggested, and it is shown how
gravity emerges from it with a strength G_4 = 2 G_5/L. Some numerical bounds
are set on the radius of curvature L of AdS_5. One of them comes from
estimating the rate of leakage of visible sector energy into the CFT. That rate
is connected via a unitarity relation to deviations from Newton's force law at
short distances. The best bound on L obtained in this paper comes from a match
to the parameters of string theory. It is L < 1 nm if the string scale is 1
GeV. Higher string scales imply a tighter bound on L.


  A 3-D inverse medium problem in the frequency domain is considered. Another
name for this problem is Coefficient Inverse Problem. The goal is to
reconstruct spatially distributed dielectric constants from scattering data.
Potential applications are in detection and identification of explosive-like
targets. A single incident plane wave and multiple frequencies are used. A new
numerical method is proposed. A theorem is proved, which claims that a small
neigborhood of the exact solution of that problem is reached by this method
without any advanced knowledge of that neighborhood. We call this property of
that numerical method "global convergence". Results of numerical experiments
for the case of the backscattering data are presented.


  We develop the concept of integral Menger curvature for a large class of
nonsmooth surfaces. We prove uniform Ahlfors regularity and a
$C^{1,\lambda}$-a-priori bound for surfaces for which this functional is
finite. In fact, it turns out that there is an explicit length scale $R>0$
which depends only on an upper bound $E$ for the integral Menger curvature
$M_p(\Sigma)$ and the integrability exponent $p$, and \emph{not} on the surface
$\Sigma$ itself; below that scale, each surface with energy smaller than $E$
looks like a nearly flat disc with the amount of bending controlled by the
(local) $M_p$-energy. Moreover, integral Menger curvature can be defined a
priori for surfaces with self-intersections or branch points; we prove that a
posteriori all such singularities are excluded for surfaces with finite
integral Menger curvature. By means of slicing and iterative arguments we
bootstrap the H\"{o}lder exponent $\lambda$ up to the optimal one,
$\lambda=1-(8/p)$, thus establishing a new geometric `Morrey-Sobolev' imbedding
theorem.
  As two of the various possible variational applications we prove the
existence of surfaces in given isotopy classes minimizing integral Menger
curvature with a uniform bound on area, and of area minimizing surfaces
subjected to a uniform bound on integral Menger curvature.


  Recently, solutions of the Ishibashi, Kawai, Kitazawa and Tsuchiya matrix
theory have been found, which can be interpreted as 3+1-dimensional quantum
geometries describing an effective Friedmann-Lema\^{i}tre-Robertson-Walker
cosmology with a big bounce. In this paper, we examine the propagation of a
scalar field in an open Friedmann-Lema\^{i}tre-Robertson-Walker spacetime
arising within this framework. The paper is divided into two parts. In the
first one, we perform a classical investigation by resorting to
general-relativity tools where we show that both massless and massive
non-interacting particles can travel across the big bounce. In the second part,
we evaluate the scalar field propagator by means of quantum-field-theory
techniques. This analysis reveals that in the late-time regime the scalar
propagator resembles the standard Feynman propagator of flat Minkowski space,
whereas for early times it gives rise to a well-defined correlation between two
points on opposite sheets of the spacetime.


  This paper considers optimization of smooth nonconvex functionals in smooth
infinite dimensional spaces. A H\"older gradient descent algorithm is first
proposed for finding approximate first-order points of regularized polynomial
functionals. This method is then applied to analyze the evaluation complexity
of an adaptive regularization method which searches for approximate first-order
points of functionals with $\beta$-H\"older continuous derivatives. It is shown
that finding an $\epsilon$-approximate first-order point requires at most
$O(\epsilon^{-\frac{p+\beta}{p+\beta-1}})$ evaluations of the functional and
its first $p$ derivatives.


  In the present work, we adopt the scalar, pseudoscalar, vector, axialvector
and tensor (anti)diquark operators as the elementary building blocks to
construct vector and tensor local four-quark currents without introducing
explicit P-waves, and explore the mass spectrum of the vector hidden-charm
tetraquark states via the QCD sum rules comprehensively, and revisit the
interpretations of the existing $Y$ states in the scenario of vector tetraquark
states. We resort to the energy scale formula to enhance the pole contributions
and improve the convergent behaviors of the operator product expansion, and we
should bear in mind that the predictions are rather sensitive to the particular
energy scales which obey the uniform/same constraint. The predicted vector
hidden-charm tetraquark states can be confronted to the experimental data in
the future.


  As the packing density of resistive random access memory (RRAM) devices
increases, the effect of thermal cross-talk across the devices in a crossbar
array arrangement influences their overall operation significantly. The
electro-thermal effects in a densely packed RRAM crossbar can accelerate the
retention and endurance degradation; hence poses a serious reliability threat.
This paper systematically investigates the electro-thermal effects in passive
RRAM crossbar arrays using COMSOL multi-physics simulations. Furthermore, we
propose a methodology to model the thermal cross-talk effect and incorporate it
in a SPICE-compatible physics-based RRAM compact model. Finally, we demonstrate
the impact of thermal coupling on RRAM crossbar array operation in terms of
vector-matrix multiplication using calibrated SPICE simulations.


  Facial expression recognition (FER) plays a significant role in our daily
life. However, annotation ambiguity in the datasets could greatly hinder the
performance. In this paper, we address FER task via label distribution learning
paradigm, and develop a dual-branch Adaptive Distribution Fusion (Ada-DF)
framework. One auxiliary branch is constructed to obtain the label
distributions of samples. The class distributions of emotions are then computed
through the label distributions of each emotion. Finally, those two
distributions are adaptively fused according to the attention weights to train
the target branch. Extensive experiments are conducted on three real-world
datasets, RAF-DB, AffectNet and SFEW, where our Ada-DF shows advantages over
the state-of-the-art works.


  In the last decade, graphene has become an exciting platform for electron
optical experiments, in many aspects superior to conventional two-dimensional
electron gases (2DEGs). A major advantage, besides the ultra-large mobilities,
is the fine control over the electrostatics, which gives the possibility of
realising gap-less and compact p-n interfaces with high precision. The latter
host non-trivial states, \eg, snake states in moderate magnetic fields, and
serve as building blocks of complex electron interferometers. Thanks to the
Dirac spectrum and its non-trivial Berry phase, the internal (valley and
sublattice) degrees of freedom, and the possibility to tailor the band
structure using proximity effects, such interferometers open up a completely
new playground based on novel device architectures. In this review, we
introduce the theoretical background of graphene electron optics, fabrication
methods used to realise electron-optical devices, and techniques for
corresponding numerical simulations. Based on this, we give a comprehensive
review of ballistic transport experiments and simple building blocks of
electron optical devices both in single and bilayer graphene, highlighting the
novel physics that is brought in compared to conventional 2DEGs. After
describing the different magnetic field regimes in graphene p-n junctions and
nanostructures, we conclude by discussing the state of the art in
graphene-based Mach-Zender and Fabry-Perot interferometers.


  Topological photonics has been developed for more than ten years. It has been
proved that the combination of topology and photons is very beneficial to the
design of robust optical devices against some disturbances. However, most of
the work for robust optical logic devices stays at the theoretical level. There
are very few topologically-protected logic devices fabricated in experiments.
Here, we report the experimental fabrication of a series of
topologically-protected all-optical logic gates. Seven topologically-protected
all-optical logic gates (OR, XOR, NOT, XNOR, NAND, NOR, and AND) are fabricated
on silicon photonic platforms, which show strong robustness even if some
disorders exist. These robust logic devices are potentially applicable in
future optical signal processing and computing.


  This paper introduces the first theoretical framework for quantifying the
efficiency and performance gain opportunity size of adaptive inference
algorithms. We provide new approximate and exact bounds for the achievable
efficiency and performance gains, supported by empirical evidence demonstrating
the potential for 10-100x efficiency improvements in both Computer Vision and
Natural Language Processing tasks without incurring any performance penalties.
Additionally, we offer insights on improving achievable efficiency gains
through the optimal selection and design of adaptive inference state spaces.


  We investigate how the entanglement properties of a two-mode state can be
improved by performing a coherent superposition operation of photon subtraction
and addition, proposed by Lee and Nha [Phys. Rev. A 82, 053812 (2010)], on each
mode. We show that the degree of entanglement, the EPR-type correlation, and
the performance of quantum teleportation can be all enhanced for the output
state when the coherent operation is applied to a two-mode squeezed state. The
effects of the coherent operation are more prominent than those of the mere
photon subtraction and the addition particularly in the small squeezing regime,
whereas the optimal operation becomes the photon subtraction in the
large-squeezing regime.


  LiteX is a GitHub-hosted SoC builder / IP library and utilities that can be
used to create SoCs and full FPGA designs. Besides being open-source and BSD
licensed, its originality lies in the fact that its IP components are entirely
described using Migen Python internal DSL, which simplifies its design in
depth. LiteX already supports various softcores CPUs and essential peripherals,
with no dependencies on proprietary IP blocks or generators. This paper
provides an overview of LiteX: two real SoC designs on FPGA are presented. They
both leverage the LiteX approach in terms of design entry, libraries and
integration capabilities. The first one is based on RISC-V core, while the
second is based on a LM32 core. In the second use case, we further demonstrate
the use of a fully open-source toolchain coupled with LiteX.


  Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.


  The earliest evidence on spatial distributions of solar energetic particles
(SEPs) compared events from many different source longitudes on the Sun, but
the early Pioneers provided the first evidence of the large areas of equal SEP
intensities across the magnetically-confined "reservoirs" late in the events.
More-detailed measurements of the importance of self-generated waves and
trapping structures around the shock waves that accelerate SEPs were obtained
from the Helios mission plus IMP 8, especially during the year when the two
Voyager spacecraft also happened by. The extent of the dozen widest SEP events
in a solar cycle, that effectively wrap around the Sun, was revealed by the
widely separated STEREO spacecraft with three-point intensities fit to
Gaussians. Element abundances of the broadest SEP events favor average coronal
element abundances with little evidence of heavy-element-enhanced "impulsive
suprathermal" ions that often dominate the seed population of the shocks, even
in extremely energetic local events. However, it is hard to define a
distribution with two or three points. Advancing the physics of SEPs may
require a return to the closer spacing of the Helios era with coverage mapped
by a half-dozen spacecraft to help disentangle the distribution of the SEPs
from the underlying structure of the magnetic field and the accelerating shock.


  Talking Face Generation (TFG) aims to reconstruct facial movements to achieve
high natural lip movements from audio and facial features that are under
potential connections. Existing TFG methods have made significant advancements
to produce natural and realistic images. However, most work rarely takes visual
quality into consideration. It is challenging to ensure lip synchronization
while avoiding visual quality degradation in cross-modal generation methods. To
address this issue, we propose a universal High-Definition Teeth Restoration
Network, dubbed HDTR-Net, for arbitrary TFG methods. HDTR-Net can enhance teeth
regions at an extremely fast speed while maintaining synchronization, and
temporal consistency. In particular, we propose a Fine-Grained Feature Fusion
(FGFF) module to effectively capture fine texture feature information around
teeth and surrounding regions, and use these features to fine-grain the feature
map to enhance the clarity of teeth. Extensive experiments show that our method
can be adapted to arbitrary TFG methods without suffering from lip
synchronization and frame coherence. Another advantage of HDTR-Net is its
real-time generation ability. Also under the condition of high-definition
restoration of talking face video synthesis, its inference speed is $300\%$
faster than the current state-of-the-art face restoration based on
super-resolution.


  An antiferromagnetic domain wall in a thermal gradient is found to experience
a force towards colder regions upon the application of a uniform magnetic field
along the easy axis. This force increases with the strength of the applied
field and, for sufficiently high values, it overcomes the entropic force the
that pushes wall towards the hotter regions. The force is proportional to the
thermal gradient and it shows a linear dependence with the net magnetic moment
of the domain wall induced by the field. The origin of this force lies on the
increase of the domain wall reflectivity due the field-induced sizable break of
antiferromagnetic order inside it, which turns it into an efficient barrier for
magnons, which transfer linear momentum to the domain wall when they are
reflected on it


  We report measurements of the de Haas - van Alphen effect on a series of
compounds, LaMIn$_{5}$ (M = Co, Rh, Ir). The results show that each of the Co
and Ir Fermi surfaces (FSs) exhibit some portions that are two dimensional and
some portions that are three dimensional. The most two dimensional character is
exhibited in LaCoIn$_{5}$, less two dimensional behavior is seen in
LaIrIn$_{5}$, no part of Fermi surface of LaRhIn$_{5}$ is found to have a two
dimensional character. Thus the two dimensionality of portions of the FSs is
largely determined by the d character of the energy bands while all of the
effective masses remain $\leq$ 1.2. This fact has implications for the causes
of the heavy fermion nature of superconductivity and magnetism in the Ce-based
compounds having the similar composition and structure. All of the measurements
were performed at the National High Magnetic Field Laboratory using either
cantilever magnetometry or field modulation methods.


  Similar to other industries, the software engineering domain is plagued by
psychological diseases such as burnout, which lead developers to lose interest,
exhibit lower activity and/or feel powerless. Prevention is essential for such
diseases, which in turn requires early identification of symptoms. The
emotional dimensions of Valence, Arousal and Dominance (VAD) are able to derive
a person's interest (attraction), level of activation and perceived level of
control for a particular situation from textual communication, such as emails.
As an initial step towards identifying symptoms of productivity loss in
software engineering, this paper explores the VAD metrics and their properties
on 700,000 Jira issue reports containing over 2,000,000 comments, since issue
reports keep track of a developer's progress on addressing bugs or new
features. Using a general-purpose lexicon of 14,000 English words with known
VAD scores, our results show that issue reports of different type (e.g.,
Feature Request vs. Bug) have a fair variation of Valence, while increase in
issue priority (e.g., from Minor to Critical) typically increases Arousal.
Furthermore, we show that as an issue's resolution time increases, so does the
arousal of the individual the issue is assigned to. Finally, the resolution of
an issue increases valence, especially for the issue Reporter and for quickly
addressed issues. The existence of such relations between VAD and issue report
activities shows promise that text mining in the future could offer an
alternative way for work health assessment surveys.


  Though vision transformers (ViTs) have exhibited impressive ability for
representation learning, we empirically find that they cannot generalize well
to unseen domains with previous domain generalization algorithms. In this
paper, we propose a novel approach DoPrompt based on prompt learning to embed
the knowledge of source domains in domain prompts for target domain prediction.
Specifically, domain prompts are prepended before ViT input tokens from the
corresponding source domain. Each domain prompt learns domain-specific
knowledge efficiently since it is optimized only for one domain. Meanwhile, we
train a prompt adapter to produce a suitable prompt for each input image based
on the learned source domain prompts. At test time, the adapted prompt
generated by the prompt adapter can exploit the similarity between the feature
of the out-of-domain image and source domains to properly integrate the source
domain knowledge. Extensive experiments are conducted on four benchmark
datasets. Our approach achieves 1.4% improvements in the averaged accuracy,
which is 3.5 times the improvement of the state-of-the-art algorithm with a ViT
backbone.


  Due to their excellent optical properties, glasses are used for various
applications ranging from smartphone screens to telescopes. Developing
compositions with tailored Abbe number (Vd) and refractive index (nd), two
crucial optical properties, is a major challenge. To this extent, machine
learning (ML) approaches have been successfully used to develop
composition-property models. However, these models are essentially black-box in
nature and suffer from the lack of interpretability. In this paper, we
demonstrate the use of ML models to predict the composition-dependent
variations of Vd and n at 587.6 nm (nd). Further, using Shapely Additive
exPlanations (SHAP), we interpret the ML models to identify the contribution of
each of the input components toward a target prediction. We observe that the
glass formers such as SiO2, B2O3, and P2O5, and intermediates like TiO2, PbO,
and Bi2O3 play a significant role in controlling the optical properties.
Interestingly, components that contribute toward increasing the nd are found to
decrease the Vd and vice-versa. Finally, we develop the Abbe diagram, also
known as the "glass veil", using the ML models, allowing accelerated discovery
of new glasses for optical properties beyond the experimental pareto front.
Overall, employing explainable ML, we discover the hidden compositional control
on the optical properties of oxide glasses.


  Training a multi-speaker Text-to-Speech (TTS) model from scratch is
computationally expensive and adding new speakers to the dataset requires the
model to be re-trained. The naive solution of sequential fine-tuning of a model
for new speakers can lead to poor performance of older speakers. This
phenomenon is known as catastrophic forgetting. In this paper, we look at TTS
modeling from a continual learning perspective, where the goal is to add new
speakers without forgetting previous speakers. Therefore, we first propose an
experimental setup and show that serial fine-tuning for new speakers can cause
the forgetting of the earlier speakers. Then we exploit two well-known
techniques for continual learning, namely experience replay and weight
regularization. We reveal how one can mitigate the effect of degradation in
speech synthesis diversity in sequential training of new speakers using these
methods. Finally, we present a simple extension to experience replay to improve
the results in extreme setups where we have access to very small buffers.


  We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold lambda_1 in (0,1]; and (ii) qualitative constraint which is a special
case of quantitative constraint with lambda_1=1. We consider the computation of
the almost-sure winning set, where the controller needs to ensure that the path
constraint is satisfied with probability 1. Our main results for qualitative
path constraint are as follows: (i) the problem of deciding the existence of a
finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding
the existence of an infinite-memory controller is undecidable. For quantitative
path constraint we show that the problem of deciding the existence of a
finite-memory controller is undecidable.


  The antibracket in BRST theory is known to define a map $\rm{H^p \times H^q
\longrightarrow H^{p+q+1}}$ associating with two equivalence classes of BRST
invariant observables of respective ghost number p and q an equivalence class
of BRST invariant observables of ghost number p+q+1. It is shown that this map
is trivial in the space of all functionals, i.e., that its image contains only
the zeroth class. However it is generically non trivial in the space of local
functionals. Implications of this result for the problem of consistent
interactions among fields with a gauge freedom are then drawn. It is shown that
the obstructions to constructing such interactions lie precisely in the image
of the antibracket map and are accordingly inexistent if one does not insist on
locality. However consistent local interactions are severely constrained. The
example of the Chern-Simons theory is considered. It is proved that the only
consistent, local, Lorentz covariant interactions for the abelian models are
exhausted by the non-abelian Chern-Simons extensions.


  We prove functional, distributional limit theorems for the occupation times
of pointwise dual ergodic transformations at "tied-down" times immediately
after "excursions". The limiting processes are tied down Mittag-Leffler
processes and the transformations involved exhibit functional tied-down renewal
mixing properties strengthening those of [AS19].


  In this work we present a new method to compute the delays of delay
differential equations (DDEs), such that the DDE has a purely imaginary
eigenvalue. For delay differential equations with multiple delays, the critical
curves or critical surfaces in delay space (that is, the set of delays where
the DDE has a purely imaginary eigenvalue) are parameterized. We show how the
method is related to other works in the field by treating the case where the
delays are integer multiples of some delay value, i.e., commensurate delays.
  The parametrization is done by solving a {\em quadratic eigenvalue problem}
which is constructed from the vectorization of a matrix equation and hence
typically of large size. For commensurate delay differential equations, the
corresponding equation is a polynomial eigenvalue problem. As a special case of
the proposed method, we find a closed form for a parameterization of the
critical surface for the scalar case.
  We provide several examples with visualizations where the computation is done
with some exploitation of the structure of eigenvalue problems.


  We show that the variance of a probability measure $\mu$ on a compact subset
$X$ of a complete metric space $M$ is bounded by the square of the circumradius
$R$ of the canonical embedding of $X$ into the space $P(M)$ of probability
measures on $M$, equipped with the Wasserstein metric. When barycenters of
measures on $X$ are unique (such as on CAT($0$) spaces), our approach shows
that $R$ in fact coincides with the circumradius of $X$ and so this result
extends a recent result of Lim-McCann from Euclidean space. Our approach
involves bi-linear minimax theory on $P(X) \times P(M)$ and extends easily to
the case when the variance is replaced by very general moments. As an
application, we provide a simple proof of Jung's theorem on CAT($k$) spaces, a
result originally due to Dekster and Lang-Schroeder.


  We have conducted a revised analysis of the first-order phase transition that
is associated with symmetry breaking in a classically scale-invariant model
that has been extended with a new $SU(2)$ gauge group. By incorporating recent
developments in the understanding of supercooled phase transitions, we were
able to calculate all of its features and significantly limit the parameter
space. We were also able to predict the gravitational wave spectra generated
during this phase transition and found that this model is well-testable with
LISA. Additionally, we have made predictions regarding the relic dark matter
abundance. Our predictions are consistent with observations but only within a
narrow part of the parameter space. We have placed significant constraints on
the supercool dark matter scenario by improving the description of percolation
and reheating after the phase transition, as well as including the running of
couplings. Finally, we have also analyzed the renormalization-scale dependence
of our results.


  We study the behavior of energy levels in two dimensions for exotic atoms,
i.e., when a long-range attractive potential is supplemented by a short-range
interaction, and compare the results with these of the one- and
three-dimensional cases. The energy shifts are well reproduced by a scattering
length formula $ \delta{E}= A_0^2/\ln (a/R)$, where $a$ is the scattering
length in the short-range potential, $A_0^2/(2\,\pi)$ the square of the wave
function at the origin in the external potential, and $R$ is related to the
derivative with respect to the energy of the solution that is regular at large
distances.


  In this paper we introduce and experimentally compare alternative algorithms
to join uncertain relations. Different algorithms are based on specific
principles, e.g., sorting, indexing, or building intermediate relational tables
to apply traditional approaches. As a consequence their performance is affected
by different features of the input data, and each algorithm is shown to be more
efficient than the others in specific cases. In this way statistics explicitly
representing the amount and kind of uncertainty in the input uncertain
relations can be used to choose the most efficient algorithm.


  We consider group actions of topological groups on C*-algebras of the types
which occur in many physics models. These are singular actions in the sense
that they need not be strongly continuous, or the group need not be locally
compact. We develop a "crossed product host" in analogy to the usual crossed
product for strongly continuous actions of locally compact groups, in the sense
that its representation theory is in a natural bijection with the covariant
representation theory of the action. We prove a uniqueness theorem for crossed
product hosts, and analyze existence conditions. We also present a number of
examples where a crossed product host exists, but the usual crossed product
does not. For actions where a crossed product host does not exist, we obtain a
"maximal" invariant subalgebra for which a crossed product host exists. We
further study the case of a discontinuous action of a locally compact group in
detail.


  We present a new type of clogging DoS attacks, with the highest amplification
factors achieved by off-path attackers, using only puppets, i.e., sandboxed
malware on victim machines. Specifically, we present off-path variants of the
Opt-ack, Ack-storm and Coremelt DoS attacks, achieving results comparable to
these achieved previously achieved by eavesdropping/MitM attackers and
(unrestricted) malware. In contrast to previous off-path attacks, which
attacked the client (machine) running the malware, our attacks address a very
different goal: large-scale clogging DoS of a third party, or even of backbone
connections.
  Our clogging attacks are based on off-path TCP injections. Indeed, as an
additional contribution, we present improved off-path TCP injection attacks.
Our new attacks significantly relax the requirements cf. to the known attacks;
specifically, our injection attack requires only a Java script in browser
sandbox (not 'restricted malware'), does not depend on specific operating
system properties, and is efficient even when client's port is determined using
recommended algorithm. Our attacks are constructed modularly, allowing reuse of
modules for other scenarios and replacing modules as necessary. We present
specific defenses, however, this work is further proof to the need to base
security on sound foundations, using cryptography to provide security even
against MitM attackers.


  The properties of UO$_{2}$ result from rich $f$-electron physics, including
electronic Coulomb interactions, spin-orbit and crystal field effects, as well
as inter-ionic multipolar coupling. We present a comprehensive theoretical
study of the electronic structure of UO$_{2}$ using a combined application of
self-consistent DFT+$U$ calculations and a model Hamiltonian. The $\Gamma_{5}$
ground state of U$^{4+}$ and the energies of crystal field excitations
$\Gamma_{5} \rightarrow \Gamma_{3,4,1}$ are reproduced in very good agreement
with experiment. We also investigate competing non-collinear magnetic
structures and confirm 3-k as the T=0 K ground state magnetic structure of
UO$_2$.


  In this paper, we develop a resource allocation algorithm for uplink of
in-band full-duplex (FD) cellular networks. The FD cellular network is assumed
to be based on orthogonal frequency division multiple access (OFDMA) and
consists of a base station communicating with multiple users. Some of the users
in the network act as relay for other users and help them to transmit their
data to the base station. These relays are FD and work based on amplify and
forward (AF) protocol. By appropriate selection of the relays and optimized
allocation of subcarriers and powers to all users, we try to maximize the total
sum rate of the network. During this optimization, we also impose some
constraints on the users' quality of service (QoS) and power. We propose a new
algorithm to select the best relays based on the users' maximum data rate and
also use Linear Assignment Problem Jonker-Volgenant (LAPJV) algorithm for
subcarrier assignment. It is proved that the resulting optimization problem can
be converted to a convex problem, and hence it can be solved by standard
numerical methods. The simulation results demonstrate the effect of the
proposed scheme on the sum rate and coverage of the network.


  The use of mathematical models to make predictions about tumor growth and
response to treatment has become increasingly more prevalent in the clinical
setting. The level of complexity within these models ranges broadly, and the
calibration of more complex models correspondingly requires more detailed
clinical data. This raises questions about how much data should be collected
and when, in order to minimize the total amount of data used and the time until
a model can be calibrated accurately. To address these questions, we propose a
Bayesian information-theoretic procedure, using a gradient-based score function
to determine the optimal data collection times for model calibration. The novel
score function introduced in this work eliminates the need for a weight
parameter used in a previous study's score function, while still yielding
accurate and efficient model calibration using even fewer scans on a sample set
of synthetic data, simulating tumors of varying levels of radiosensitivity. We
also conduct a robust analysis of the calibration accuracy and certainty, using
both error and uncertainty metrics. Unlike the error analysis of the previous
study, the inclusion of uncertainty analysis in this work|as a means for
deciding when the algorithm can be terminated|provides a more realistic option
for clinical decision-making, since it does not rely on data that will be
collected later in time.


  We develop the analytic theory describing the formation and evolution of
entangled quantum states for a fermionic quantum emitter coupled to a quantized
electromagnetic field in a nanocavity and quantized phonon or mechanical
vibrational modes. The theory is applicable to a broad range of cavity quantum
optomechanics problems and emerging research on plasmonic nanocavities coupled
to single molecules and other quantum emitters. The optimal conditions for a
tri-state entanglement are realized near the parametric resonances in a coupled
system. The model includes decoherence effects due to coupling of the fermion,
photon, and phonon subsystems to their dissipative reservoirs within the
stochastic evolution approach, which is derived from the Heisenberg-Langevin
formalism. Our theory provides analytic expressions for the time evolution of
the quantum state and observables, and the emission spectra. The limit of a
classical acoustic pumping and the interplay between parametric and standard
one-photon resonances are analyzed.


  We present electronic structure calculations, electrical resistivity data and
the first specific heat measurements in the normal and superconducting states
of quasi-one-dimensional M2Mo6Se6 (M = Tl, In, Rb). Rb2Mo6Se6 undergoes a
metal-insulator transition at ~170K: electronic structure calculations indicate
that this is likely to be driven by the formation of a dynamical charge density
wave. However, Tl2Mo6Se6 and In2Mo6Se6 remain metallic down to low temperature,
with superconducting transitions at Tc = 4.2K and 2.85K respectively. The
absence of any metal-insulator transition in these materials is due to a larger
in-plane bandwidth, leading to increased inter-chain hopping which suppresses
the density wave instability. Electronic heat capacity data for the
superconducting compounds reveal an exceptionally low density of states DEF =
0.055 states eV^-1 atom^-1, with BCS fits showing 2Delta/kBTc >= 5 for
Tl2Mo6Se6 and 3.5 for In2Mo6Se6. Modelling the lattice specific heat with a set
of Einstein modes, we obtain the approximate phonon density of states F(w).
Deconvolving the resistivity for the two superconductors then yields their
electron-phonon transport coupling function a^2F(w). In Tl2Mo6Se6 and
In2Mo6Se6, F(w) is dominated by an optical "guest ion" mode at ~5meV and a set
of acoustic modes from ~10-30meV. Rb2Mo6Se6 exhibits a similar spectrum;
however, the optical phonon has a lower intensity and is shifted to ~8meV.
Electrons in Tl2Mo6Se6 couple strongly to both sets of modes, whereas In2Mo6Se6
only displays significant coupling in the 10-18meV range. Although pairing is
clearly not mediated by the guest ion phonon, we believe it has a beneficial
effect on superconductivity in Tl2Mo6Se6, given its extraordinarily large
coupling strength and higher Tc compared to In2Mo6Se6.


  Under mild conditions we show that the affinity dimension of a planar
self-affine set is equal to the supremum of the Lyapunov dimensions of
self-affine measures supported on self-affine proper subsets of the original
set. These self-affine subsets may be chosen so as to have stronger separation
properties and in such a way that the linear parts of their affinities are
positive matrices. Combining this result with some recent breakthroughs in the
study of self-affine measures and their associated Furstenberg measures, we
obtain new criteria under which the Hausdorff dimension of a self-affine set
equals its affinity dimension. For example, applying recent results of
B\'{a}r\'{a}ny, Hochman-Solomyak and Rapaport, we provide new explicit examples
of self-affine sets whose Hausdorff dimension equals its affinity dimension,
and for which the linear parts do not satisfy any domination assumptions.


  We introduce a new geometric flow called the chord shortening flow which is
the negative gradient flow for the length functional on the space of chords
with end points lying on a fixed submanifold in Euclidean space. As an
application, we give a simplified proof of a classical theorem of Lusternik and
Schnirelmann (and a generalization by Riede and Hayashi) on the existence of
multiple orthogonal geodesic chords. For a compact convex planar domain, we
show that any convex chord which is not orthogonal to the boundary would shrink
to a point in finite time under the flow.


  To date, endowing robots with an ability to assess social appropriateness of
their actions has not been possible. This has been mainly due to (i) the lack
of relevant and labelled data, and (ii) the lack of formulations of this as a
lifelong learning problem. In this paper, we address these two issues. We first
introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB),
which contains appropriateness labels of robot actions annotated by humans. To
be able to control but vary the configurations of the scenes and the social
settings, MANNERS-DB has been created utilising a simulation environment by
uniformly sampling relevant contextual attributes. Secondly, we train and
evaluate a baseline Bayesian Neural Network (BNN) that estimates social
appropriateness of actions in the MANNERS-DB. Finally, we formulate learning
social appropriateness of actions as a continual learning problem using the
uncertainty of the BNN parameters. The experimental results show that the
social appropriateness of robot actions can be predicted with a satisfactory
level of precision. Our work takes robots one step closer to a human-like
understanding of (social) appropriateness of actions, with respect to the
social context they operate in. To facilitate reproducibility and further
progress in this area, the MANNERS-DB, the trained models and the relevant code
will be made publicly available.


  At the heart of the Pyramid evaluation method for text summarization lie
human written summary content units (SCUs). These SCUs are concise sentences
that decompose a summary into small facts. Such SCUs can be used to judge the
quality of a candidate summary, possibly partially automated via natural
language inference (NLI) systems. Interestingly, with the aim to fully automate
the Pyramid evaluation, Zhang and Bansal (2021) show that SCUs can be
approximated by automatically generated semantic role triplets (STUs). However,
several questions currently lack answers, in particular: i) Are there other
ways of approximating SCUs that can offer advantages? ii) Under which
conditions are SCUs (or their approximations) offering the most value? In this
work, we examine two novel strategies to approximate SCUs: generating SCU
approximations from AMR meaning representations (SMUs) and from large language
models (SGUs), respectively. We find that while STUs and SMUs are competitive,
the best approximation quality is achieved by SGUs. We also show through a
simple sentence-decomposition baseline (SSUs) that SCUs (and their
approximations) offer the most value when ranking short summaries, but may not
help as much when ranking systems or longer summaries.


  In the framework of the Lindblad theory for open quantum systems, a master
equation for the quantum harmonic oscillator interacting with a dissipative
environment, in particular with a thermal bath, is derived for the case when
the interaction is based on deformed algebra. The equations of motion for
observables strongly depend on the deformation function. The expectation values
of the number operator and squared number operator are calculated in the limit
of a small deformation parameter for the case of zero temperature of the
thermal bath. The steady state solution of the equation for the density matrix
in the number representation is obtained and its independence of the
deformation is shown.


  This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence, d-separation, a graphical criterion for
identifying independencies in a DAG, is shown to uncover more valid
independencies then any other criterion. In addition, we employ the Armstrong
property of conditional independence to show that the dependence relationships
displayed by a DAG are inherently consistent, i.e. for every DAG D there exists
some probability distribution P that embodies all the conditional
independencies displayed in D and none other.


  We introduce a Kojima-Megiddo-Mizuno type continuation method for solving
tensor complementarity problems. We show that there exists a bounded
continuation trajectory when the tensor is strictly semi-positive and any limit
point tracing the trajectory gives a solution of the tensor complementarity
problem. Moreover, when the tensor is strong strictly semi-positive, tracing
the trajectory will converge to the unique solution. Some numerical results are
given to illustrate the effectiveness of the method.


  The general formalism for the nonextensive statistics based on the
Landsberg-Vedral entropy was derived. The formula for the first law of
thermodynamics and the exact relations of the thermodynamic quantities to their
ensemble averages were obtained. It was found that under the transformation
$q\to 2-q$ the probabilities of microstates of the nonextensive statistics
based on the Landsberg-Vedral entropy formally resemble the corresponding
probabilities of the Tsallis statistics with escort probabilities. However, the
nonextensive statistics with the Landsberg-Vedral entropy does not require
introduction of the escort probabilities and generalized expectation values
which are used in this version of the Tsallis statistics.


  We propose a hybrid tree algorithm for reducing calculation and communication
cost of collision-less N-body simulations. The concept of our algorithm is that
we split interaction force into two parts: hard-force from neighbor particles
and soft-force from distant particles, and applying different time integration
for the forces. For hard-force calculation, we can efficiently reduce the
calculation and communication cost of the parallel tree code because we only
need data of neighbor particles for this part. We implement the algorithm on
GPU clusters to accelerate force calculation for both hard and soft force. As
the result of implementing the algorithm on GPU clusters, we were able to
reduce the communication cost and the total execution time to 40% and 80% of
that of a normal tree algorithm, respectively. In addition, the reduction
factor relative the normal tree algorithm is smaller for large number of
processes, and we expect that the execution time can be ultimately reduced down
to about 70% of the normal tree algorithm.


  Android Malware has emerged as a consequence of the increasing popularity of
smartphones and tablets. While most previous work focuses on inherent
characteristics of Android apps to detect malware, this study analyses indirect
features and meta-data to identify patterns in malware applications. Our
experiments show that: (1) the permissions used by an application offer only
moderate performance results; (2) other features publicly available at Android
Markets are more relevant in detecting malware, such as the application
developer and certificate issuer, and (3) compact and efficient classifiers can
be constructed for the early detection of malware applications prior to code
inspection or sandboxing.


  The Fomin-Kirillov algebra $\mathcal E_n$ is a noncommutative algebra with a
generator for each edge in the complete graph on $n$ vertices. For any graph
$G$ on $n$ vertices, let $\mathcal E_G$ be the subalgebra of $\mathcal E_n$
generated by the edges in $G$. We show that the commutative quotient of
$\mathcal E_G$ is isomorphic to the Orlik-Terao algebra of $G$. As a
consequence, the Hilbert series of this quotient is given by $(-t)^n
\chi_G(-t^{-1})$, where $\chi_G$ is the chromatic polynomial of $G$. We also
give a reduction algorithm for the graded components of $\mathcal E_G$ that do
not vanish in the commutative quotient and show that their structure is
described by the combinatorics of noncrossing forests.


  We propose a novel reinforcement learning based framework PoBRL for solving
multi-document summarization. PoBRL jointly optimizes over the following three
objectives necessary for a high-quality summary: importance, relevance, and
length. Our strategy decouples this multi-objective optimization into different
subproblems that can be solved individually by reinforcement learning.
Utilizing PoBRL, we then blend each learned policies together to produce a
summary that is a concise and complete representation of the original input.
Our empirical analysis shows state-of-the-art performance on several
multi-document datasets. Human evaluation also shows that our method produces
high-quality output.


  We analytically evaluate charge and spin density response functions of the
clean two-dimensional electron gas with Rashba spin-orbit coupling at finite
momenta and frequencies. On the basis of our exact expressions we discuss the
accuracy of the long-wavelength and the quasiclassical approximations. We also
derive the static limit of spin susceptibilities and demonstrate, in
particular, how the Kohn-like anomalies in their derivatives are related to the
spin-orbit modification of the Ruderman-Kittel-Kasuya-Yosida interaction.
Taking into account screening and exchange effects of the Coulomb interaction,
we describe the collective charge and spin density excitation modes which
appear to be coupled due to nonvanishing spin-charge response function.


  Musical performance requires prediction to operate instruments, to perform in
groups and to improvise. In this paper, we investigate how a number of digital
musical instruments (DMIs), including two of our own, have applied predictive
machine learning models that assist users by predicting unknown states of
musical processes. We characterise these predictions as focussed within a
musical instrument, at the level of individual performers, and between members
of an ensemble. These models can connect to existing frameworks for DMI design
and have parallels in the cognitive predictions of human musicians.
  We discuss how recent advances in deep learning highlight the role of
prediction in DMIs, by allowing data-driven predictive models with a long
memory of past states. The systems we review are used to motivate musical
use-cases where prediction is a necessary component, and to highlight a number
of challenges for DMI designers seeking to apply deep predictive models in
interactive music systems of the future.


  In this paper, we prove an optimal inequality between the potential function
of a complete Schouten soliton and the norm of its gradient. We also prove that
these metrics have bounded scalar curvature of defined sign. As an application,
we prove that the potential function of a Shrinking Schouten soliton grows
linearly and provide optimal estimates for the growth of the volume of geodesic
balls, without any further assumption.


  In quantitative Magnetic Resonance Imaging, traditional methods suffer from
the so-called Partial Volume Effect (PVE) due to spatial resolution
limitations. As a consequence of PVE, the parameters of the voxels containing
more than one tissue are not correctly estimated. Magnetic Resonance
Fingerprinting (MRF) is not an exception. The existing methods addressing PVE
are neither scalable nor accurate. We propose to formulate the recovery of
multiple tissues per voxel as a nonconvex constrained least-squares
minimisation problem. To solve this problem, we develop a memory efficient,
greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our
method adaptively finds the regions of interest on the manifold of fingerprints
defined by the MRF sequence. We generalise our method to compensate for phase
errors appearing in the model, using an alternating minimisation approach. We
show, through simulations on synthetic data with PVE, that our algorithm
outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN
phantom and on in vivo datasets.


  We study a refrigerator model which consists of two $n$-level systems
interacting via a pulsed external field. Each system couples to its own thermal
bath at temperatures $T_h$ and $T_c$, respectively ($\theta\equiv T_c/T_h<1$).
The refrigerator functions in two steps: thermally isolated interaction between
the systems driven by the external field and isothermal relaxation back to
equilibrium. There is a complementarity between the power of heat transfer from
the cold bath and the efficiency: the latter nullifies when the former is
maximized and {\it vice versa}. A reasonable compromise is achieved by
optimizing the product of the heat-power and efficiency over the Hamiltonian of
the two system. The efficiency is then found to be bounded from below by
$\zeta_{\rm CA}=\frac{1}{\sqrt{1-\theta}}-1$ (an analogue of the Curzon-Ahlborn
efficiency), besides being bound from above by the Carnot efficiency
$\zeta_{\rm C} = \frac{1}{1-\theta}-1$. The lower bound is reached in the
equilibrium limit $\theta\to 1$. The Carnot bound is reached (for a finite
power and a finite amount of heat transferred per cycle) for $\ln n\gg 1$. If
the above maximization is constrained by assuming homogeneous energy spectra
for both systems, the efficiency is bounded from above by $\zeta_{\rm CA}$ and
converges to it for $n\gg 1$.


  A Grasstope is the image of the totally nonnegative Grassmannian
$\text{Gr}_{\geq 0}(k,n)$ under a linear map $\text{Gr}(k,n)\dashrightarrow
\text{Gr}(k,k+m)$. This is a generalization of the amplituhedron, a geometric
object of great importance to calculating scattering amplitudes in physics. The
amplituhedron is a Grasstope arising from a totally positive linear map. While
amplituhedra are relatively well-studied, much less is known about general
Grasstopes. We study Grasstopes in the $m=1$ case and show that they can be
characterized as unions of cells of a hyperplane arrangement satisfying a
certain sign variation condition, extending work of Karp and Williams. Inspired
by this characterization, we also suggest a notion of a Grasstope arising from
an arbitrary oriented matroid.


  Management of vestibular schwannoma (VS) is based on tumour size as observed
on T1 MRI scans with contrast agent injection. Current clinical practice is to
measure the diameter of the tumour in its largest dimension. It has been shown
that volumetric measurement is more accurate and more reliable as a measure of
VS size. The reference approach to achieve such volumetry is to manually
segment the tumour, which is a time intensive task. We suggest that
semi-automated segmentation may be a clinically applicable solution to this
problem and that it could replace linear measurements as the clinical standard.
Using high-quality software available for academic purposes, we ran a
comparative study of manual versus semi-automated segmentation of VS on MRI
with 5 clinicians and scientists. We gathered both quantitative and qualitative
data to compare the two approaches; including segmentation time, segmentation
effort and segmentation accuracy. We found that the selected semi-automated
segmentation approach is significantly faster (167s versus 479s, p<0.001), less
temporally and physically demanding and has approximately equal performance
when compared with manual segmentation, with some improvements in accuracy.
There were some limitations, including algorithmic unpredictability and error,
which produced more frustration and increased mental effort in comparison to
manual segmentation. We suggest that semi-automated segmentation could be
applied clinically for volumetric measurement of VS on MRI. In future, the
generic software could be refined for use specifically for VS segmentation,
thereby improving accuracy.


  Deep inelastic scattering data on $F_2$ structure function obtained by BCDMS,
SLAC and NMC collaborations in fixed-target experiments were analyzed in the
non-singlet approximation with next-to-next-to-leading-order accuracy. The
strong coupling constant is found to be $\alpha_s(M_Z^2) = 0.1157 \pm 0.0022
(total\,\,\,exp.error) + \biggl\{\begin{array}{l} +0.0028 \\ -0.0016
\end{array} (theor)$, which is seen to be well compatible with the average
world value. Results obtained in the present paper by carrying out fits similar
to those done in our earlier study, with the exception for systematic errors in
BCDMS data taken into account in a different way, confirm those which were
derived in that same paper.


  Jacobian conjectures (that nonsingular implies a global inverse) for rational
everywhere defined maps of real n-space to itself are considered, with no
requirement for a constant Jacobian determinant or a rational inverse. The
birational case is proved and the Galois case clarified. Two known special
cases of the Strong Real Jacobian Conjecture (SRJC) are generalized to the
rational map context. For an invertible map, the associated extension of
rational function fields must be of odd degree and must have no nontrivial
automorphisms. That disqualifies the Pinchuk counter examples to the SRJC as
candidates for invertibility.


  We derive the Green's functions (concentrated force and couple in an infinite
space) for the isotropic planar relaxed micromorphic model. Since the relaxed
micromorphic model particularises into the microstretch, Cosserat (micropolar),
couple-stress, and linear elasticity model for certain choices of material
parameters, we recover the fundamental solutions in all these cases.


  It is well known that the allowed wavefunctions for an $N$-electron system
should be antisymmetric with respect to the permutation of any pair of electron
labels. On the other hand, the Hamiltonian for such system is invariant under
any permutation of electron labels and, consequently, its eigenfunctions are
basis for the irreducible representations of the symmetric group $S_N$.Here, we
investigate which symmetry species of the $S_N$ group are compatible with the
antisymmetry principle. We illustrate the conclusions by means of simple
$N$-particle one-dimensional models with harmonic interactions.


  We establish new product identities involving the $q$-analogue of the
Fibonacci numbers. We show that the identities lead to alternate expressions of
generating functions for close-packed dimers on non-orientable surfaces.


  Recently, implicit neural representations (INR) have made significant strides
in various vision-related domains, providing a novel solution for Multispectral
and Hyperspectral Image Fusion (MHIF) tasks. However, INR is prone to losing
high-frequency information and is confined to the lack of global perceptual
capabilities. To address these issues, this paper introduces a Fourier-enhanced
Implicit Neural Fusion Network (FeINFN) specifically designed for MHIF task,
targeting the following phenomena: The Fourier amplitudes of the HR-HSI latent
code and LR-HSI are remarkably similar; however, their phases exhibit different
patterns. In FeINFN, we innovatively propose a spatial and frequency implicit
fusion function (Spa-Fre IFF), helping INR capture high-frequency information
and expanding the receptive field. Besides, a new decoder employing a complex
Gabor wavelet activation function, called Spatial-Frequency Interactive Decoder
(SFID), is invented to enhance the interaction of INR features. Especially, we
further theoretically prove that the Gabor wavelet activation possesses a
time-frequency tightness property that favors learning the optimal bandwidths
in the decoder. Experiments on two benchmark MHIF datasets verify the
state-of-the-art (SOTA) performance of the proposed method, both visually and
quantitatively. Also, ablation studies demonstrate the mentioned contributions.
The code will be available on Anonymous GitHub
(https://anonymous.4open.science/r/FeINFN-15C9/) after possible acceptance.


  The Schr\"odinger equation on a circle with an initially localized profile of
the wave function is known to give rise to revivals or replications, where the
probability density of the particle is partially reproduced at rational times.
As a consequence of the convolutional form of the general solution it is
deduced that a piecewise constant initial wave function remains piecewise
constant at rational times as well. For a sphere instead, it is known that this
piecewise revival does not necessarily occur, indeed the wave function becomes
singular at some specific locations at rational times. It may be desirable to
study the same problem, but with an initial condition being a localized Dirac
delta instead of a piecewise constant function, and this is the purpose of the
present work. By use of certain summation formulas for the Legendre polynomials
together with properties of Gaussian sums, it is found that revivals on the
sphere occur at rational times for some specific locations, and the structure
of singularities of the resulting wave function is characterized in detail. In
addition, a partial study of the regions where the density vanishes, named
before valley of shadows in the context of the circle, is initiated here. It is
suggested that, differently from the circle case, these regions are not lines
but instead some specific set of points along the sphere. A conjecture about
the precise form of this set is stated and the intuition behind it is
clarified.


  We study the adjoint of the double layer potential associated with the
Laplacian (the adjoint of the Neumann-Poincar\'e operator), as a map on the
boundary surface $\Gamma$ of a domain in $\mathbb{R}^3$ with conical points.
The spectrum of this operator directly reflects the well-posedness of related
transmission problems across $\Gamma$. In particular, if the domain is
understood as an inclusion with complex permittivity $\epsilon$, embedded in a
background medium with unit permittivity, then the polarizability tensor of the
domain is well-defined when $(\epsilon+1)/(\epsilon-1)$ belongs to the
resolvent set in energy norm. We study surfaces $\Gamma$ that have a finite
number of conical points featuring rotational symmetry. On the energy space, we
show that the essential spectrum consists of an interval. On $L^2(\Gamma)$,
i.e. for square-integrable boundary data, we show that the essential spectrum
consists of a countable union of curves, outside of which the Fredholm index
can be computed as a winding number with respect to the essential spectrum. We
provide explicit formulas, depending on the opening angles of the conical
points. We reinforce our study with very precise numerical experiments,
computing the energy space spectrum and the spectral measures of the
polarizability tensor in two different examples. Our results indicate that the
densities of the spectral measures may approach zero extremely rapidly in the
continuous part of the energy space spectrum.


  We analyzed the daily sunspot-group data reported by the Greenwich
Photoheliographic Results (GPR) during the period 1874-1976 and Debrecen
Photoheliographic Data (DPD) during the period 1977-2017 and studied
North-South asymmetry in the maxima and minima of the Solar Cycles 12-24. We
derived the time series of the 13-month smoothed monthly mean corrected
whole-spot areas of the sunspot groups in the Sun's whole sphere (WSGA),
northern hemisphere (NSGA), and southern hemisphere (SSGA). From these smoothed
time series we obtained the values of the maxima and minima, and the
corresponding epochs, of the WSGA, NSGA, and SSGA Cycles 12-24. We find that
there exists a 44-66 years periodicity in the North-South asymmetry of the
minimum. A long periodicity (130-140 years) may exist in the asymmetry of the
maximum. A statistically significant correlation exists between the maximum of
SSGA Cycle n and the rise time of WSGA Cycle n + 2. A reasonably significant
correlation also exists between the maximum of WSGA Cycle n and the decline
time of WSGA Cycle n + 2. These relations suggest that the solar dynamo carries
memory over at least three solar cycles. Using these relations we obtained the
values 11.7 + or - 0.15 years, 11.2 + or - 0.2 years, and 11.45 + or - 0.3
years for the lengths of WSGA Cycles 24, 25, and 26, respectively, and hence,
July 2020, October 2031, and March 2043 for the minimum epochs (start dates) of
WSGA Cycles 25, 26, and 27, respectively. We also obtained May 2025 and March
2036 for the maximum epochs of WSGA Cycles 25 and 26, respectively. It seems
during the late Maunder minimum sunspot activity was absent around the epochs
of the maxima of the NSGA-cycles and the minima of the SSGA-cycles, and some
activity was present at the epochs of the maxima of some SSGA-cycles and the
minima of some NSGA-cycles.


  The majority of sources of coherent optical radiation rely on laser
oscillators driven by population inversion. Despite their technological
importance in communications, medicine, industry, and other fields, it remains
a challenge to access the spectral range of 0.1-10 THz (the "terahertz gap"), a
frequency band for applications ranging from spectroscopy to security and
high-speed wireless communications. Here, we propose a way to produce coherent
radiation spanning the THz gap by efficient second-harmonic generation (SHG) in
low-loss dielectric structures, starting from technologically mature electronic
oscillators (EOs) in the ~100 GHz range. To achieve this goal, we introduce
hybrid THz-band dielectric cavity designs that combine (1) extreme field
concentration in high-quality-factor resonators with (2) nonlinear materials
enhanced by phonon resonances. We theoretically predict conversion efficiencies
of >$10^3$ %/W and the potential to bridge the THz gap with 1 W of input power.
This approach enables efficient, cascaded parametric frequency converters,
representing a new generation of light sources extensible into the mid-IR
spectrum and beyond.


  We report $^{75}$As NMR measurements in CaFe$_2$As$_2$, made under applied
pressures up to 0.83 GPa produced by a standard clamp pressure cell. Our data
reveal phase segregation of paramagnetic (PM) and antiferromagnetic (AFM)
phases over a range of pressures, with the AFM phase more than 90 % dominant at
low temperatures. In situ RF susceptibility measurements indicate the presence
of superconductivity. $^{75}$As spin-lattice relaxation experiments indicate
that the $^{75}$As nuclei sample the superconductivity while in the
magnetically-ordered environment.


  Axions are considered as a candidate of dark matter. The axions form coherent
clouds which delay and amplify gravitational waves (GWs) at a resonant
frequency. That is, the coherent axion clouds produce secondary GWs following a
primary wave. All GWs from compact binary mergers detected so far propagate in
the Milky Way halo composed of dark matter so that such GWs are followed by the
secondary GWs. Since the properties of the secondary GWs depend on the axion
mass and coupling with the parity violating sector of gravity, we can search
for a characteristic signal produced by axions. In our previous study, we have
developed a search method optimized for the axion signals, and obtained about
ten times stronger constraint on the coupling than the previous best one for
the axion mass range, $[1.7 \times 10^{-13},\,8.5 \times
10^{-12}]\,\mathrm{eV}$. However, in our previous search, we assume that dark
matter is homogeneously distributed in the Milky Way halo, which is
unrealistic. In this paper, we extend the dark matter profile to a more
realistic one called the core NFW profile, and find that the constraint in our
previous study is robust to the dark matter profile.


  We review the paradox of low energy excitations about an AdS black hole. An
appropriately chosen unitary operator in the boundary theory can create a
locally strong excitation near the black hole horizon, whose global energy is
small as a result of the gravitational redshift. The paradox is that this seems
to violate a general rule of statistical mechanics, which states that an
operator with energy parametrically smaller than $k T$ cannot create a
significant excitation in a thermal system. When we carefully examine the
position dependence of the boundary unitary operator that produces the
excitation and the bulk observable necessary to detect the anomalously large
effect, we find that they do not both fit in a single causal patch. This
follows from a remarkable property of position space AdS correlators that we
establish explicitly, and resolves the paradox in a generic state of the
system, since no combination of observers can both create the excitation and
observe its effect. As a special case of our analysis, we show how this
resolves the "Born rule" paradox of arXiv:1506.01337 and we verify our solution
using an independent calculation. We then consider boundary states that are
finely tuned to display a spontaneous excitation outside the causal patch of
the infalling observer, and we propose a version of causal patch
complementarity in AdS/CFT that resolves the paradox for such states as well.


  We calculate the one-loop quantum corrections in the cubic Galileon theory,
using cutoff regularization. We confirm the expected form of the one-loop
effective action and that the couplings of the Galileon theory do not get
renormalized. However, new terms, not included in the tree-level action, are
induced by quantum corrections. We also consider the one-loop corrections in an
effective brane theory, which belongs to the Horndeski or generalized Galileon
class. We find that new terms are generated by quantum corrections, while the
tree-level couplings are also renormalized. We conclude that the structure of
the generalized Galileon theories is altered by quantum corrections more
radically than that of the Galileon theory.


  This paper contains applications of the quantum theory for gravity developed
in the paper " A Sketch for a Quantum Theory of Gravity". Firstly, it is shown
that the theory gives a direct derivation of the implications of Dirac's large
number hypothesis. The three dimensionless large numbers are obtained as three
closed formulae with definite coefficients constructed from known physical
constants. Secondly, the theory is used to derive two quantum Friedman
cosmologies. The first of these Cosmologies is a very simple first
approximation involving a key angular parameter being zero and the second is a
more accurate one involving the same key parameter not being zero. The
cosmological constant plays a basic and fundamental part in the formalism with
very accurate agreement with measurement pertaining.


  We explore quantum corrections of electrically charged black holes subject to
vacuum polarization effects of fermion fields in QED. Solving this problem
exactly is challenging so we restrict to perturbative corrections that one can
obtain using the heat kernel expansion in the one-loop effective action for
electrons. Starting from the corrections originally computed by Drummond and
Hathrell, we solve the full semiclassical Einstein-Maxwell system of coupled
equations to leading order in Planck's constant, and find a new electrically
charged, static black hole solution. To probe these quantum corrections, we
study electromagnetic and gravitational (axial) perturbations on this
background, and derive the coupled system of Regge-Wheeler master equations
that govern the propagation of these waves. In the classical limit our results
agree with previous findings in the literature. We finally compare these
results with those that one can obtain by working out the Euler-Heisenberg
effective action. We find again a new electrically charged static black hole
spacetime, and derive the coupled system of Regge-Wheeler equations governing
the propagation of axial electromagnetic and gravitational perturbations.
Results are qualitatively similar in both cases. We briefly discuss some
challenges found in the numerical computation of the quasinormal mode frequency
spectra when quantum corrections are included.


  Direct speech-to-speech translation (S2ST) with discrete self-supervised
representations has achieved remarkable accuracy, but is unable to preserve the
speaker timbre of the source speech. Meanwhile, the scarcity of high-quality
speaker-parallel data poses a challenge for learning style transfer during
translation. We design an S2ST pipeline with style-transfer capability on the
basis of discrete self-supervised speech representations and codec units. The
acoustic language model we introduce for style transfer leverages
self-supervised in-context learning, acquiring style transfer ability without
relying on any speaker-parallel data, thereby overcoming data scarcity. By
using extensive training data, our model achieves zero-shot cross-lingual style
transfer on previously unseen source languages. Experiments show that our model
generates translated speeches with high fidelity and speaker similarity. Audio
samples are available at http://stylelm.github.io/ .


  Computational modeling of the manufacturing process of Lithium-Ion Battery
(LIB) composite electrodes based on mechanistic approaches, allows predicting
the influence of manufacturing parameters on electrode properties. However,
ensuring that the calculated properties match well with experimental data, is
typically time and resources consuming In this work, we tackled this issue by
proposing a functional data-driven framework combining Functional Principal
Component Analysis and K-Nearest Neighbors algorithms. This aims first to
recover the early numerical values of a mechanistic electrode manufacturing
simulation to predict if the observable being calculated is prone to match or
not, \textit{i.e} screening step. In a second step it recovers additional
numerical values of the ongoing mechanistic simulation iterations to predict
the mechanistic simulation result, \textit{i.e} forecasting step. We
demonstrated this approach in context of LIB manufacturing through
non-equilibrium molecular dynamics (NEMD) simulations, aiming to capture the
rheological behavior of electrode slurries. We discuss in full details our
novel methodology and we report that the expected mechanistic simulation
results can be obtained 11 times faster with respect to running the complete
mechanistic simulation, while being accurate enough from an experimental point
of view, with a $F1_{score}$ equals to 0.90, and a $R^2_{score}$ equals to 0.96
for the learnings validation. This paves the way towards a powerful tool to
drastically reduce the utilization of computational resources while running
mechanistic simulations of battery manufacturing electrodes.


  This is a book on elementary mathematics suitable to use in courses for
first-year students of science and engineering. Students often find the formal
approach to math courses rather discouraging, and demand 'rel-life
applications' in order to grasp the need for these courses. The text has plenty
of such applications, treated with the free software Maxima (a CAS) and
Geogebra (a DGS). Its level is more or less that of a 'Pre-Calculus' course,
but the contents and style of presentation are different in that they are
determined by the applications in each chapter.


  The azimuthal anisotropic flow of identified and unidentified charged
particles has been systematically studied in Cu+Au collisions at
$\sqrt{s_{_{NN}}}$ = 200 GeV for harmonics $n=$ 1-4 in the pseudorapidity range
$|\eta|<1$. The directed flow in Cu+Au collisions is compared with the
rapidity-odd and, for the first time, the rapidity-even components of charged
particle directed flow in Au+Au collisions at $\sqrt{s_{_{NN}}}$ = 200~GeV. The
slope of the directed flow pseudorapidity dependence in Cu+Au collisions is
found to be similar to that in Au+Au collisions, with the intercept shifted
toward positive $\eta$ values, i.e., the Cu-going direction. The mean
transverse momentum projected onto the spectator plane, $\langle p_x\rangle$,
in Cu+Au collision also exhibits approximately linear dependence on $\eta$ with
the intercept at about $\eta\approx-0.4$, closer to the rapidity of the Cu+Au
system center-of-mass. The observed dependencies find natural explanation in a
picture of the directed flow originating partly due the "tilted source" and
partly due to the rapidity dependent asymmetry in the initial density
distribution. Charge-dependence of the $\langle p_x\rangle$ was also observed
in Cu+Au collisions, indicating an effect of the initial electric field created
by charge difference of the spectator protons in two colliding nuclei. The
rapidity-even component of directed flow in Au+Au collisions is close to that
in Pb+Pb collisions at $\sqrt{s_{_{NN}}}$ = 2.76 TeV, indicating a similar
magnitude of dipole-like fluctuations in the initial-state density
distribution. Higher harmonic flow in Cu+Au collisions exhibits similar trends
to those observed in Au+Au and Pb+Pb collisions and is qualitatively reproduced
by a viscous hydrodynamic model and a multi-phase transport model. For all
harmonics with $n\ge2$ we observe an approximate scaling of $v_n$ with the
number of constituent quarks.


  The metal-poor sub-population of globular cluster (GC) systems exhibits a
correlation between the GC average colour and luminosity, especially in those
systems associated with massive elliptical galaxies. More luminous (more
massive) GCs are typically redder and hence more metal-rich. This 'blue tilt'
is often interpreted as a mass-metallicity relation stemming from GC
self-enrichment, whereby more massive GCs retain a greater fraction of the
enriched gas ejected by their evolving stars, fostering the formation of more
metal-rich secondary generations. We examine the E-MOSAICS simulations of the
formation and evolution of galaxies and their GC populations, and find that
their GCs exhibit a colour-luminosity relation similar to that observed in
local galaxies, without the need to invoke mass-dependent self-enrichment. We
find that the blue tilt is most appropriately interpreted as a dearth of
massive, metal-poor GCs: the formation of massive GCs requires high
interstellar gas surface densities, conditions that are most commonly fostered
by the most massive, and hence most metal rich, galaxies, at the peak epoch of
GC formation. The blue tilt is therefore a consequence of the intimate coupling
between the small-scale physics of GC formation and the evolving properties of
interstellar gas hosted by hierarchically-assembling galaxies.


  Previous research about sensor based attacks on Android platform focused
mainly on accessing or controlling over sensitive device components, such as
camera, microphone and GPS. These approaches get data from sensors directly and
need corresponding sensor invoking permissions.
  This paper presents a novel approach (GVS-Attack) to launch permission
bypassing attacks from a zero permission Android application (VoicEmployer)
through the speaker. The idea of GVS-Attack utilizes an Android system built-in
voice assistant module -- Google Voice Search. Through Android Intent
mechanism, VoicEmployer triggers Google Voice Search to the foreground, and
then plays prepared audio files (like "call number 1234 5678") in the
background. Google Voice Search can recognize this voice command and execute
corresponding operations. With ingenious designs, our GVS-Attack can forge
SMS/Email, access privacy information, transmit sensitive data and achieve
remote control without any permission.
  Also we found a vulnerability of status checking in Google Search app, which
can be utilized by GVS-Attack to dial arbitrary numbers even when the phone is
securely locked with password. A prototype of VoicEmployer has been implemented
to demonstrate the feasibility of GVS-Attack in real world. In theory, nearly
all Android devices equipped with Google Services Framework can be affected by
GVS-Attack. This study may inspire application developers and researchers
rethink that zero permission doesn't mean safety and the speaker can be treated
as a new attack surface.


  We study the finite-size scaling behaviour at the critical point, resulting
from the addition of a homogeneous size-dependent perturbation, decaying as an
inverse power of the system size. The scaling theory is first formulated in a
general framework and then illustrated using three concrete problems for which
exact results are obtained.


  We consider the theoretical description of intense laser pulses propagating
through gases. Starting from a first-principles description of both the
electromagnetic field and the electron motion within the gas atoms, we derive a
hierarchy of reduced models. We obtain a parallel set of models, where the
atomic electrons are treated classically on the one hand, and
quantum-mechanically on the other. By working consistently in either a
Lagrangian formulation or a Hamiltonian formulation, we ensure that our reduced
models preserve the variational structure of the parent models. Taking
advantage of the Hamiltonian formulation, we deduce a number of conserved
quantities of the reduced models.


  The relativistic mean-field plus RPA calculations, based on effective
Lagrangians with density-dependent meson-nucleon vertex functions, are employed
in a microscopic analysis of the nuclear matter compressibility and symmetry
energy. We compute the isoscalar monopole and the isovector dipole response of
$^{208}$Pb, as well as the differences between the neutron and proton radii for
$^{208}$Pb and several Sn isotopes. The comparison of the calculated excitation
energies with the experimental data on the giant monopole resonance in
$^{208}$Pb, restricts the nuclear matter compression modulus of structure
models based on the relativistic mean-field approximation to $K_{\rm nm}\approx
250 - 270$ MeV. The isovector giant dipole resonance in $^{208}$Pb, and the
available data on differences between neutron and proton radii, limit the range
of the nuclear matter symmetry energy at saturation (volume asymmetry) to 32
MeV $\leq a_4 \leq$ 36 MeV.


  It is well a known and fundamental result that the Jones polynomial can be
expressed as Potts and vertex partition functions of signed plane graphs. Here
we consider constructions of the Jones polynomial as state models of unsigned
graphs and show that the Jones polynomial of any link can be expressed as a
vertex model of an unsigned embedded graph.
  In the process of deriving this result, we show that for every diagram of a
link in the 3-sphere there exists a diagram of an alternating link in a
thickened surface (and an alternating virtual link) with the same Kauffman
bracket. We also recover two recent results in the literature relating the
Jones and Bollobas-Riordan polynomials and show they arise from two different
interpretations of the same embedded graph.


  Purpose: to assess the efficacy of THEIA, an artificial intelligence for
screening diabetic retinopathy in a multi-center prospective study. To validate
the potential application of THEIA as clinical decision making assistant in a
national screening program. Methods: 902 patients were recruited from either an
urban large eye hospital, or a semi-rural optometrist led screening provider,
as they were attending their appointment as part of New Zealand Diabetic
Screening programme. These clinics used a variety of retinal cameras and a
range of operators. The de-identified images were then graded independently by
three senior retinal specialists, and final results were aggregated using New
Zealand grading scheme, which is then converted to referable\non-referable and
Healthy\mild\more than mild\vision threatening categories. Results: compared to
ground truth, THEIA achieved 100% sensitivity and [95.35%-97.44%] specificity,
and negative predictive value of 100%. THEIA also did not miss any patients
with more than mild or vision threatening disease. The level of agreement
between the clinicians and the aggregated results was (k value: 0.9881, 0.9557,
and 0.9175), and the level of agreement between THEIA and the aggregated labels
was (k value: 0.9515). Conclusion: Our multi-centre prospective trial showed
that THEIA does not miss referable disease when screening for diabetic
retinopathy and maculopathy. It also has a very high level of granularity in
reporting the disease level. Since THEIA is being tested on a variety of
cameras, operating in a range of clinics (rural\urban,
ophthalmologist-led\optometrist-led), we believe that it will be a suitable
addition to a public diabetic screening program.


  Characterizing the dust thermal structure in protoplanetary disks is a
fundamental task as the dust surface temperature can affect both the planetary
formation and the chemical evolution. Since the temperature is dependent on
many parameters, including the grain size, properly modeling the grain
temperature structure can be challenging. Many chemistry disk models usually
employ a sophisticated single dust structure designed to reproduce the effect
of a realistic population presumably composed of a large diversity of sizes.
This generally represents a good approximation in most cases. Nonetheless, this
dilutes the effects of the complex radiative interactions between the different
grain populations on the resulting dust temperature, and thus the chemistry. We
seek to show that the radiative interactions between dust grains of different
sizes can induce a non-trivial dust temperature structure that cannot be
reproduced by a single dust population and that can significantly affect the
chemical outcome. The disk thermal structures are computed using the
Monte-Carlo radiative transfer code RADMC-3D. The thermal structures are
post-processed using the gas-grain code NAUTILUS to calculate the evolution of
the chemical abundance. We find that simultaneously using at least two
independent dust grain populations in disk models produces a complex
temperature structure due to the starlight intercepted by the upper layers of
the disk. In particular, we find that micron-sized dust grains are warmer than
larger grains and can even show a radial temperature bump in some conditions.
This dust temperature spread between the grains populations results in the
segregation of the CO snowline and the presence of an unexpected CO gas hole
along the midplane. We compare the results with observed close to edge-on class
I/II disks.


  An exact solution based on a unique procedure is presented for free vibration
of stepped circular and annular functionally graded (FG) plates via first-order
shear deformation plate theory of Mindlin. A power-law distribution of the
volume fraction of the components is considered for the Young's Modulus and
Poisson's ratio of the studied FG plate. Free vibration of the plate is solved
by introducing some new potential functions and the use of separation of
variables method. Finally, several comparisons of the developed model were
presented with the FEA analysis, to demonstrate the accuracy of the proposed
exact procedure. The effect of the geometrical parameters such as step
thickness ratios and step locations on the natural frequencies of FG plates is
also investigated.


  We consider the transmission of short packets over a bidirectional
communication link where multiple devices, e.g., sensors and actuators,
exchange small-data payloads with a base station equipped with a large antenna
array. Using results from finite-blocklength information theory, we
characterize the minimum SNR required to achieve a target error probability for
a fixed packet length and a fixed payload size. Our nonasymptotic analysis,
which applies to the scenario in which the bidirectional communication is
device-initiated, and also to the more challenging case when it is base-station
initiated, provides guidelines on the design of massive multiple-input
multiple-output links that need to support sporadic ultra-reliable low-latency
transmissions. Specifically, it allows us to determine the optimal amount of
resources that need to be dedicated to the acquisition of channel state
information.


  An intuitive way for a human to write paraphrase sentences is to replace
words or phrases in the original sentence with their corresponding synonyms and
make necessary changes to ensure the new sentences are fluent and grammatically
correct. We propose a novel approach to modeling the process with
dictionary-guided editing networks which effectively conduct rewriting on the
source sentence to generate paraphrase sentences. It jointly learns the
selection of the appropriate word level and phrase level paraphrase pairs in
the context of the original sentence from an off-the-shelf dictionary as well
as the generation of fluent natural language sentences. Specifically, the
system retrieves a set of word level and phrase level araphrased pairs derived
from the Paraphrase Database (PPDB) for the original sentence, which is used to
guide the decision of which the words might be deleted or inserted with the
soft attention mechanism under the sequence-to-sequence framework. We conduct
experiments on two benchmark datasets for paraphrase generation, namely the
MSCOCO and Quora dataset. The evaluation results demonstrate that our
dictionary-guided editing networks outperforms the baseline methods.


  The iron-based high temperature superconductors share a number of
similarities with their copper-based counterparts, such as reduced
dimensionality, proximity to states of competing order, and a critical role for
3d electron orbitals. Their respective temperature-doping phase diagrams also
contain certain commonalities that have led to claims that the metallic and
superconducting properties of both families are governed by their proximity to
a quantum critical point (QCP) located inside the superconducting dome. In this
review, we critically examine these claims and highlight significant
differences in the bulk physical properties of both systems. While there is now
a large body of evidence supporting the presence of a (magnetic) QCP in the
iron pnictides, the situation in the cuprates is much less apparent, at least
for the end point of the pseudogap phase. We argue that the opening of the
normal state pseudogap in cuprates, so often tied to a putative QCP, arises
from a momentum-dependent breakdown of quasiparticle coherence that sets in at
much higher doping levels but which is driven by the proximity to the Mott
insulating state at half filling. Finally, we present a new scenario for the
cuprates in which this loss of quasiparticle integrity and its evolution with
momentum, temperature and doping plays a key role in shaping the resultant
phase diagram.


  Biased attributes, spuriously correlated with target labels in a dataset, can
problematically lead to neural networks that learn improper shortcuts for
classifications and limit their capabilities for out-of-distribution (OOD)
generalization. Although many debiasing approaches have been proposed to ensure
correct predictions from biased datasets, few studies have considered learning
latent embedding consisting of intrinsic and biased attributes that contribute
to improved performance and explain how the model pays attention to attributes.
In this paper, we propose a novel debiasing framework, Debiasing Global
Workspace, introducing attention-based information bottlenecks for learning
compositional representations of attributes without defining specific bias
types. Based on our observation that learning shape-centric representation
helps robust performance on OOD datasets, we adopt those abilities to learn
robust and generalizable representations of decomposable latent embeddings
corresponding to intrinsic and biasing attributes. We conduct comprehensive
evaluations on biased datasets, along with both quantitative and qualitative
analyses, to showcase our approach's efficacy in attribute-centric
representation learning and its ability to differentiate between intrinsic and
bias-related features.


  Heat capacity, magnetic susceptibility, NMR, and resistivity of SrNi2P2
single crystals are presented, illustrating a purely structural transition at
325 K with no magnetism. Bulk superconductivity is found at 1.4 K. The
magnitude of the transition temperature T_c, fits to the heat capacity data,
the small upper critical field $H_{c2}$ = 390 Oe, and Ginzburg-Landau parameter
$\kappa$ = 2.1 suggests a conventional fully gapped superconductor. With
applied pressure a second structural phase transition occurs which results in
an 8% reduction in the c/a ratio of lattice parameters. We find that
superconductivity persists into this high pressure phase, although the
transition temperature is monotonically suppressed with increasing pressure.
Comparison of these Ni-P data as well as layered Fe-As and Ni-As superconductor
indicates that reduced dimensionality can be a mechanism for increasing the
transition temperature.


  In this paper, we obtain the three-dimensional regular extremal black string
solution incorporating $\alpha'$ corrections and a non-trivial Kalb-Ramond
field. The difficulty in considering the Kalb-Ramond field lies in the fact
that it transforms the original equations of motion into an infinite summation
form involving matrices, making it difficult to calculate the matrix
differential equations. To solve this problem, we introduce a new method that
transforms the infinite summation of matrix differential equations into a
simple trace of the matrix. As a result, we are able to obtain a
non-perturbative and non-singular extremal black string solution. Indeed, this
work serves as a good example for studying more complicated non-perturbative
solutions that incorporate the Kalb-Ramond field via complete $\alpha'$
corrections.


  We provide a new exponential concentration inequality for
  First Passage Percolation valid for a wide class of edge times distributions.
This improves and extends a result by Benjamini, Kalai and Schramm which gave a
variance bound for Bernoulli edge times. Our approach is based on some
functional inequalities extending the work of Rossignol and Falik and
Samorodnitsky.


  We consider D-dimensional Einstein gravity coupled to (n-1) U(1) vector
fields and (n-2) dilatonic scalars. We find that for some appropriate
exponential dilaton couplings of the field strengths, the equations of motion
for the static charged ansatz can be reduced to a set of one-dimensional
SL(n,R) Toda equations. This allows us to obtain a general class of explicit
black holes with mass and (n-1) independent charges. The near-horizon geometry
in the extremal limit is AdS_2 x S^{D-2}. The n=2 case gives the
Reissner-Nordstrom solution, and the n=3 example includes the Kaluza-Klein
dyon. We study the global structure and the black hole thermodynamics and
obtain the universal entropy product formula. We also discuss the
characteristics of extremal multi-charge black holes that have positive, zero
or negative binding energies.