  We give a large class of supersymmetric Janus solutions in $\omega$-deformed
(dyonic) $SO(8)$ maximal gauged supergravity with $\omega=\frac{\pi}{8}$.
Unlike the purely electric counterpart, the dyonic $SO(8)$ gauged supergravity
exhibits a richer structure of $AdS_4$ vacua with $N=8,2,1,1$ supersymmetries
and $SO(8)$, $U(3)$, $G_2$ and $SU(3)$ symmetries, respectively. Similarly,
domain walls interpolating among these critical points show a very rich
structure as well. In this paper, we show that this gauged supergravity also
accommodates a number of interesting supersymmetric Janus solutions in the form
of $AdS_3$-sliced domain walls asymptotically interpolating between the
aforementioned $AdS_4$ geometries. These solutions could be holographically
interpreted as two-dimensional conformal defects within the superconformal
field theories (SCFTs) of ABJM type dual to the $AdS_4$ vacua. We also give a
class of solutions interpolating among the $SO(8)$, $G_2$ and $U(3)$ $AdS_4$
vacua in the case of $\omega=0$ which have not previously appeared in the
presently known Janus solutions of electric $SO(8)$ gauged supergravity.


  We employ first-principles calculations to study the mechanical, geometrical,
electronic and magnetic properties of Fe atom embedded s-triazine
($\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$) system under the influence of
external environment. Our results show that the binding energy of
$\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ can be modulated by an applied
tensile deformation and perpendicular electric field. The non-magnetic
semiconducting property of pure s-triazine sheet (${\mathrm C_6}{\mathrm N_6}$)
is found to change upon embedding of Fe atom in the porous site of the sheet.
It is revealed that the $\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ system
exhibits half-metallic electronic character with a magnetic moment in the the
order similar to that of an isolated Fe atom. Furthermore, electronic and
magnetic properties of the $\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ systems
are preserved up to a maximum value of 10 V/nm in electric field strength and
6\% tensile strain. Interestingly, we find that the half-metallic electronic
character of $\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ system can be tuned
into a semiconductor via adsorption of atoms and molecules into the
$\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ system. The magnetic moment of
$\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ with adsorbed atoms/molecules is
also modified. Our findings may serve as a guide for future applications of
$\mathrm{Fe}$@${\mathrm C_6}{\mathrm N_6}$ structures in spintronics devices.


  This paper studies inter-trade durations in the NASDAQ limit order market and
finds that inter-trade durations in ultra-high frequency have two modes. One
mode is to the order of approximately 10^{-4} seconds, and the other is to the
order of 1 second. This phenomenon and other empirical evidence suggest that
there are two regimes associated with the dynamics of inter-trade durations,
and the regime switchings are driven by the changes of high-frequency traders
(HFTs) between providing and taking liquidity. To find how the two modes depend
on information in the limit order book (LOB), we propose a two-state
multifactor regime-switching (MF-RSD) model for inter-trade durations, in which
the probabilities transition matrices are time-varying and depend on some
lagged LOB factors. The MF-RSD model has good in-sample fitness and the
superior out-of-sample performance, compared with some benchmark duration
models. Our findings of the effects of LOB factors on the inter-trade durations
help to understand more about the high-frequency market microstructure.


  Literature is the primary expression of scientific knowledge and an important
source of research data. However, scientific knowledge expressed in narrative
text documents is not inherently machine reusable. To facilitate knowledge
reuse, e.g. for synthesis research, scientific knowledge must be extracted from
articles and organized into databases post-publication. The high time costs and
inaccuracies associated with completing these activities manually has driven
the development of techniques that automate knowledge extraction. Tackling the
problem with a different mindset, we propose a pre-publication approach, known
as reborn, that ensures scientific knowledge is born reusable, i.e. produced in
a machine-reusable format during knowledge production. We implement the
approach using the Open Research Knowledge Graph infrastructure for FAIR
scientific knowledge organization. We test the approach with three use cases,
and discuss the role of publishers and editors in scaling the approach. Our
results suggest that the proposed approach is superior compared to classical
manual and semi-automated post-publication extraction techniques in terms of
knowledge richness and accuracy as well as technological simplicity.


  We present a neural network architecture, Bispectral Neural Networks (BNNs)
for learning representations that are invariant to the actions of compact
commutative groups on the space over which a signal is defined. The model
incorporates the ansatz of the bispectrum, an analytically defined group
invariant that is complete -- that is, it preserves all signal structure while
removing only the variation due to group actions. Here, we demonstrate that
BNNs are able to simultaneously learn groups, their irreducible
representations, and corresponding equivariant and complete-invariant maps
purely from the symmetries implicit in data. Further, we demonstrate that the
completeness property endows these networks with strong invariance-based
adversarial robustness. This work establishes Bispectral Neural Networks as a
powerful computational primitive for robust invariant representation learning


  Active learning is a promising alternative to alleviate the issue of high
annotation cost in the computer vision tasks by consciously selecting more
informative samples to label. Active learning for object detection is more
challenging and existing efforts on it are relatively rare. In this paper, we
propose a novel hybrid approach to address this problem, where the
instance-level uncertainty and diversity are jointly considered in a bottom-up
manner. To balance the computational complexity, the proposed approach is
designed as a two-stage procedure. At the first stage, an Entropy-based
Non-Maximum Suppression (ENMS) is presented to estimate the uncertainty of
every image, which performs NMS according to the entropy in the feature space
to remove predictions with redundant information gains. At the second stage, a
diverse prototype (DivProto) strategy is explored to ensure the diversity
across images by progressively converting it into the intra-class and
inter-class diversities of the entropy-based class-specific prototypes.
Extensive experiments are conducted on MS COCO and Pascal VOC, and the proposed
approach achieves state of the art results and significantly outperforms the
other counterparts, highlighting its superiority.


  I review recent results by Fadin,Lipatov and collaborators and by our
group,leading to the almost complete calculation of the next-to-leading BFKL
kernel,of its eigenvalues,and of the resummed gluon anomalous dimension.
Qualitative implications for small-x structure functions include consistent
running coupling effects and a sizeable decrease of the Pomeron intercept,so as
to slow down the small-x rise at low values of Q.


  Infrared and visible image fusion has gradually proved to be a vital fork in
the field of multi-modality imaging technologies. In recent developments,
researchers not only focus on the quality of fused images but also evaluate
their performance in downstream tasks. Nevertheless, the majority of methods
seldom put their eyes on the mutual learning from different modalities,
resulting in fused images lacking significant details and textures. To overcome
this issue, we propose an interactive graph neural network (GNN)-based
architecture between cross modality for fusion, called IGNet. Specifically, we
first apply a multi-scale extractor to achieve shallow features, which are
employed as the necessary input to build graph structures. Then, the graph
interaction module can construct the extracted intermediate features of the
infrared/visible branch into graph structures. Meanwhile, the graph structures
of two branches interact for cross-modality and semantic learning, so that
fused images can maintain the important feature expressions and enhance the
performance of downstream tasks. Besides, the proposed leader nodes can improve
information propagation in the same modality. Finally, we merge all graph
features to get the fusion result. Extensive experiments on different datasets
(TNO, MFNet and M3FD) demonstrate that our IGNet can generate visually
appealing fused images while scoring averagely 2.59%<EMAIL_ADDRESS>and 7.77% mIoU
higher in detection and segmentation than the compared state-of-the-art
methods. The source code of the proposed IGNet can be available at
https://github.com/lok-18/IGNet.


  In this paper, the evolutions of longitudinal proton structure function have
been obtained at small-$x$ up to next-to-next-to-leading order using a hard
Pomeron behavior. In our paper, evolutions of gluonic as well as heavy
longitudinal structure functions have been obtained separately and the total
contributions have been calculated. The total longitudinal structure functions
have been compared with results of Donnachie-Landshoff (DL) model, Color Dipole
(CD) model, $k_{T}$ factorization and H1 data.


  We introduce a family of layer to layer transfer matrices in a
three-dimensional (3D) lattice model which can be viewed as partition functions
of the $q$-oscillator valued six-vertex model on $m \times n$ square lattice.
By invoking the tetrahedron equation we establish their commutativity and
bilinear relations mixing various boundary conditions. At $q=0$ and $m=n$, they
ultimately yield a new proof of the steady state formula for the $n$-species
totally asymmetric simple exclusion process (TASEP) obtained recently by the
authors, revealing the 3D integrability in the matrix product construction.


  Universities have been expanding the data science programs for undergraduate
students, with the simultaneous goal of reaching and retaining students from
underrepresented groups in the data science workforce. The set of new programs
also offer opportunities to involve graduate students, fostering their growth
as future leaders in data science education. We describe two programs that use
the near peer mentoring structure to provide pathways for graduate students to
develop teaching and mentoring skills, while providing research and learning
opportunities for undergraduate students from diverse backgrounds. In the Data
Science for Social Good Summer program, graduate students mentor a group of
undergraduate fellows as they tackle a data science project with positive
social impact. In the Inclusive Mentoring in Data Science course, graduate
students participate in workshops on effective and inclusive mentorship
strategies. In an experiential learning framework, they are paired with
undergraduate students from non-R1 schools, who they mentor through weekly
one-on-one on-line meetings. These initiatives offer a prototype of future
programs that serve the dual goal of providing both hands-on mentoring
experience for graduate students and research opportunities for undergraduate
students, in a high-touch inclusive and encouraging environment.


  For a graph $G$ and an abelian group $A$, a labeling of the vertices of $G$
induces a labeling of the edges via the sum of adjacent vertex labels. Hovey
introduced the notion of an $A$-cordial vertex labeling when both the vertex
and edge labels are as evenly distributed as possible. Much work has since been
done with trees, hypertrees, paths, cycles, ladders, prisms, hypercubes, and
bipartite graphs. In this paper we show that all trees are
$\mathbb{Z}_2^2$-cordial except for $P_4$ and $P_5$. In addition, we give
numerous results relating to $\mathbb{Z}_m$-cordiality of the friendship graph
$F_n$. The most general result shows that when $m$ is an odd multiple of $3$,
then $F_n$ is $\mathbb{Z}_m$-cordial for all $n$. We also give a general
conjecture to determine when $F_n$ is $\mathbb{Z}_m$-cordial.


  In this article we deduce some algebraic properties for the group
$\mathrm{Sp}_{2n} (\mathcal{O}(X))$ of holomorphic symplectic matrices on a
Stein space $X$: holomorphic factorization, exponential factorization, and
Kazhdan's property (T). In holomorphic factorization we combine a recent result
of the third author and K-theory tools to give explicit bounds for the case
when $X$ is one-dimensional or two-dimensional. Next we use them to find bounds
for exponential factorization. As a further application, we show that the
elementary symplectic group $\mathrm{Ep}_{2n}(\mathcal{O}(X))$ admits Kazhdan's
property (T).


  Extensions of nuclear physics to the strange sector are reviewed, covering
data and models of Lambda and other hypernuclei, multi-strange matter, and
anti-kaon bound states and condensation. Past achievements are highlighted,
present unresolved problems discussed, and future directions outlined.


  In this note, we obtain a formula which leads to a practical and efficient
method to calculate the number of partitions of n into parts not divisible by m
for given natural numbers n and m.


  Erosion, entrainment and deposition are complex and dominant, but yet poorly
understood, mechanical processes in geophysical mass flows. Here, we propose a
novel, process-based, two-phase, erosion-deposition model capable of adequately
describing these complex phenomena commonly observed in landslides, avalanches,
debris flows and bedload transport. The model is based on the jump in the
momentum flux including changes of material and flow properties along the
flow-bed interface and enhances an existing general two-phase mass flow model
(Pudasaini, 2012). A two-phase variably saturated erodible basal morphology is
introduced and allows for the evolution of erosion-deposition-depths,
incorporating the inherent physical process including momentum and rheological
changes of the flowing mixture. By rigorous derivation, we show that
appropriate incorporation of the mass and momentum productions or losses in
conservative model formulation is essential for the physically correct and
mathematically consistent description of erosion-entrainment-deposition
processes. We show that mechanically deposition is the reversed process of
erosion. We derive mechanically consistent closures for coefficients emerging
in the erosion rate models. We prove that effectively reduced friction in
erosion is equivalent to the momentum production. With this, we solve the long
standing dilemma of mass mobility, and show that erosion enhances the mass flow
mobility. The novel enhanced real two-phase model reveals some major aspects of
the mechanics associated with erosion, entrainment and deposition. The model
appropriately captures the emergence and propagation of complex frontal surge
dynamics associated with the frontal ambient-drag with erosion.


  Nuclear physics facilities, like the Facility for Rare Isotope Beams (FRIB),
can potentially perform many nuclear mass measurements of exotic isotopes. Each
measurement comes with a particular cost, both in time and money, and thus it
is important to establish which mass measurements are the most informative. In
this article, we show that one can use the Kullback-Leibler divergence to
determine the information gained by a mass measurement. We model the
information gain obtained by nuclear mass measurements from two perspectives:
first from the perspective of theoretical nuclear mass models, and the second
from the perspective of r-process nucleosynthesis. While this work specifically
analyzes the abilities of FRIB, other facilities worldwide could benefit from a
similar use of information gain in order to decide which experiments are
optimal.


  Using synthetic absorption lines generated from 3D hydro-dynamical
simulations we explore how the velocity of a starburst-driven galactic wind
correlates with the star formation rate (SFR) and SFR density. We find strong
correlations until the scaling relations flatten abruptly at a point set by the
mass loading of the starburst. Below this point the scaling relation depends on
the temperature regime being probed by the absorption line, not on the mass
loading. The exact scaling relation depends on whether the maximum or mean
velocity of the absorption line is used. We find that the outflow velocity of
neutral gas is four to five times lower than the average velocity of the
hottest gas, with the difference in velocity between the neutral and ionized
gas increasing with gas ionization. Thus, absorption lines of neutral or low
ionized gas will underestimate the outflow velocity of hot gas, severely
underestimating outflow energetics.


  Quantum microwave photonics (QMWP) is an innovative approach that combines
energy-time entangled biphoton sources as the optical carrier with
time-correlated single-photon detection for high-speed RF signal recovery. This
groundbreaking method offers unique advantages such as nonlocal RF signal
encoding and robust resistance to dispersion-induced frequency fading. This
paper explores the versatility of processing the quantum microwave photonic
signal by utilizing coincidence window selection on the biphoton coincidence
distribution. The demonstration includes finely-tunable RF phase shifting,
flexible multi-tap transversal filtering (with up to 15 taps), and photonically
implemented RF mixing, leveraging the nonlocal RF mapping characteristic of
QMWP. These accomplishments significantly enhance the capability of microwave
photonic systems in processing ultra-weak signals, opening up new possibilities
for various applications.


  Machine learning has been applied on a wide variety of models, from classical
statistical mechanics to quantum strongly correlated systems for the
identification of phase transitions. The recently proposed quantum
convolutional neural network (QCNN) provides a new framework for using quantum
circuits instead of classical neural networks as the backbone of classification
methods. We present here the results from training the QCNN by the
wavefunctions of the variational quantum eigensolver for the one-dimensional
transverse field Ising model (TFIM). We demonstrate that the QCNN identifies
wavefunctions which correspond to the paramagnetic phase and the ferromagnetic
phase of the TFIM with good accuracy. The QCNN can be trained to predict the
corresponding phase of wavefunctions around the putative quantum critical
point, even though it is trained by wavefunctions far away from it. This
provides a basis for exploiting the QCNN to identify the quantum critical
point.


  A two-dimensional crystal on the surface of a sphere experiences elastic
stress due to the incompatibility of the crystal axes and the curvature. A
common mechanism to relax elastic stress is the Asaro-Tiller-Grinfeld (ATG)
instability. With a combined numerical and analytical approach we demonstrate,
that also curvature induced stress in surface crystals can be relaxed by the
long wave length ATG instability. The numerical results are obtained using a
surface phase-field crystal (PFC) model, from which we determine the
characteristic wave numbers of the ATG instability for various surface
coverages corresponding to different curvature induced compressions. The
results are compared with an analytic expression for the characteristic wave
number, obtained from a continuum approach which accounts for hexagonal
crystals and intrinsic PFC symmetries. We find our numerical results in
accordance with the analytical predictions.


  Diffusive molecular dynamics is a novel model for materials with atomistic
resolution that can reach diffusive time scales. The main ideas of diffusive
molecular dynamics are to first minimize an approximate variational Gaussian
free energy of the system with respect to the mean atomic coordinates
(averaging over many vibrational periods), and to then to perform a diffusive
step where atoms and vacancies (or two species in a binary alloy) flow on a
diffusive time scale via a master equation. We present a mathematical framework
for studying this algorithm based upon relative entropy, or Kullback-Leibler
divergence. This adds flexibility in how the algorithm is implemented and
interpreted. We then compare our formulation, relying on relative entropy and
absolute continuity of measures, to existing formulations. The main difference
amongst the equations appears in a model for vacancy diffusion, where
additional entropic terms appear in our development.


  In this work we examine how fine-tuning impacts the fairness of contrastive
Self-Supervised Learning (SSL) models. Our findings indicate that Batch
Normalization (BN) statistics play a crucial role, and that updating only the
BN statistics of a pre-trained SSL backbone improves its downstream fairness
(36% worst subgroup, 25% mean subgroup gap). This procedure is competitive with
supervised learning, while taking 4.4x less time to train and requiring only
0.35% as many parameters to be updated. Finally, inspired by recent work in
supervised learning, we find that updating BN statistics and training residual
skip connections (12.3% of the parameters) achieves parity with a fully
fine-tuned model, while taking 1.33x less time to train.


  In this note, we present some results on the classification of quantum
cellular automata (QCA) in 1D under strong equivalence rather than stable
equivalence. Under strong equivalence, we only allow adding ancillas carrying
the original on-site representation of the symmetry, while under stable
equivalence, we allow adding ancillas carrying any representation of the
symmetry. The former may be more realistic, because in physical systems
especially in AMO/quantum computing contexts, we would not expect additional
spins carrying arbitrary representations of the symmetry to be present.
Ref.~\onlinecite{mpu} proposed two kinds of symmetry-protected indices (SPIs)
for QCA with discrete symmetries under strong equivalence. In this note, we
show that the more refined of these SPIs still only has a one-to-one
correspondence to equivalence classes of $\mathbb{Z}_N$ symmetric QCA when $N$
is prime. We show a counter-example for $N=4$. We show that QCA with
$\mathbb{Z}_2$ symmetry under strong equivalence, for a given on-site
representation, are classified by $\mathbb{Z}^{pq}$ where $p$ is the number of
prime factors of the on-site Hilbert space dimension and $q$ is the number of
prime factors of the trace of the nontrivial on-site $\mathbb{Z}_2$ element.
Finally, we show that the GNVW index has a formulation in terms of a
$\mathbb{Z}_2$ SPI in a doubled system, and we provide a direct connection
between the SPI formulation of the GNVW index and a second Renyi version of the
mutual information formula for the GNVW index.


  We seek to obtain a picture of the interstellar magnetic field in the
Galactic center region that is as clear and complete as possible.
  To that end, we review the observational knowledge that has built up over the
past 25 years on interstellar magnetic fields within ~ 200 pc of the Galactic
center. We then critically discuss the various theoretical interpretations and
scenarios proposed to explain the existing observations. We also study the
possible connections with the general Galactic magnetic field and describe the
observational situation in external galaxies.
  We propose a coherent picture of the magnetic field near the Galactic center,
which reconciles some of the seemingly divergent views and which best accounts
for the vast body of observations. Our main conclusions are the following. In
the diffuse intercloud medium, the large-scale magnetic field is approximately
poloidal and its value is generally close to equipartition with cosmic rays (~
10 microG), except in localized filaments where the field strength can reach ~
1 mG. In dense interstellar clouds, the magnetic field is approximately
horizontal and its value is typically ~ 1 mG.


  This article is a survey concerning the state-of-the-art mathematical theory
of the Euler equations of incompressible homogenous ideal fluid. Emphasis is
put on the different types of emerging instability, and how they may be related
to the description of turbulence.


  While Koopman-based techniques like extended Dynamic Mode Decomposition are
nowadays ubiquitous in the data-driven approximation of dynamical systems,
quantitative error estimates were only recently established. To this end, both
sources of error resulting from a finite dictionary and only finitely-many data
points in the generation of the surrogate model have to be taken into account.
We generalize the rigorous analysis of the approximation error to the control
setting while simultaneously reducing the impact of the curse of dimensionality
by using a recently proposed bilinear approach. In particular, we establish
uniform bounds on the approximation error of state-dependent quantities like
constraints or a performance index enabling data-based optimal and predictive
control with guarantees.


  Prompt-tuning methods have shown comparable performance as
parameter-efficient fine-tuning (PEFT) methods in various natural language
understanding tasks. However, existing prompt tuning methods still utilize the
entire model architecture; thus, they fail to accelerate inference speed in the
application. In this paper, we propose a novel approach called SKIll-localized
Prompt tuning (SKIP), which is extremely efficient in inference time. Our
method significantly enhances inference efficiency by investigating and
utilizing a skill-localized subnetwork in a language model. Surprisingly, our
method improves the inference speed up to 160% while pruning 52% of the
parameters. Furthermore, we demonstrate that our method is applicable across
various transformer-based architectures, thereby confirming its practicality
and scalability.


  We present a Kleene realizability semantics for the intensional level of the
Minimalist Foundation, for short mtt, extended with inductively generated
formal topologies, Church's thesis and axiom of choice. This semantics is an
extension of the one used to show consistency of the intensional level of the
Minimalist Foundation with the axiom of choice and formal Church's thesis in
previous work. A main novelty here is that such a semantics is formalized in a
constructive theory represented by Aczel's constructive set theory CZF extended
with the regular extension axiom.


  A sample of 86 contact binary systems in 14 globular clusters with available
color index data in (B-V) or in (V-I) has been analyzed. At least one third of
all systems are numerous foreground Galactic Disk projections over long lines
of sight to the clusters. Since the selection of the cluster members has been
based on the MV(logP, color) calibrations, the matter of a
metallicity-correction required particular attention with the result that such
a correction is apparently not needed at the present level of accuracy.
Analysis of the color-magnitude and period-color relations shows that globular
cluster members are under-luminous relative to the Galactic Disk contact
systems mainly because of the smaller sizes and, consequently, shorter orbital
periods; the color-index effect of the diminished blanketing is less important,
especially for (V-I). The variability amplitudes for the Blue Straggler systems
show a significantly different distribution from that for systems below the
Turn Off Point (TOP): The BS systems show only small amplitudes while the
distribution for the systems below the TOP is peculiar in containing only large
amplitude systems. This difference is linked to an observational selection
effect so that efforts at determining the frequency of occurrence of the
contact systems below the TOP have been judged to be premature; the frequency
among the BS stars could be at about 45+-10 BS stars per one contact BS binary.


  Heterogeneity in dynamics in the form of non-Gaussian molecular displacement
distributions appears ubiquitously in soft matter. We address the
quantification of such heterogeneity using an information-theoretic measure of
the distance between the actual displacement distribution and its nearest
Gaussian estimation. We explore the usefulness of this measure in two generic
scenarios of random walkers in heterogeneous media. We show that our proposed
measure leads to a better quantification of non-Gaussianity than the
conventional ones based on moment ratios.


  The Rizal Technological University offers a formal degree program in
Astronomy for both undergraduate and graduate levels. Also, the university
hosts the most sophisticated optical telescopes in the country, and the
astronomical research hub Center for Astronomy Research and Development.
Through the efforts of the Department of Science and Technology, it offers
support for scholarships and research funding. The astronomy curriculum of the
university aimed to craft students to become researchers and scientists in the
future, and notably associates the tracer study that the graduates are equipped
as researchers and possesses the computational and mathematical ability for the
job. The increased in enrollees shows the cogency of the aforecited statements
as it enticed students to study in the field of astronomy. Other efforts are
discussed in detail in this paper.


  Arp 104 is a pair of luminous interacting galaxies consisting of NGC 5216, an
elliptical, and NGC 5218, a disturbed disk galaxy and joined by a stellar
bridge. We obtained optical imaging to support photometric and color studies of
the system. NGC 5216 lies on the red sequence, while the unusual distribution
of stellar population properties in combination with intense central star
formation in a dusty region result in NGC 5218 being a nearby example of an
intermediate color (green valley) system. The stellar bridge has remarkably
uniform optical surface brightness, with colors consistent with its stars
coming from the outskirts of NGC 5218, but is relatively gas-poor while the
northern tidal tail is rich in HI. While both galaxies contain shells, the
shell structures in NGC 5218 are pronounced, and some appear to be associated
with counter-rotating gas. This combination of features suggests that Arp 104
could be the product of distinct multiple interactions in a small galaxy group,
possibly resulting from a hierarchical merging process, and likely leading to
the birth of a relatively massive and isolated early-type galaxy.


  As a first step towards the computation of the NLO corrections to the photon
impact factor in the $\gamma^*\gamma^* \to \gamma^* \gamma^*$ scattering
process, we calculate the one loop corrections to the coupling of the reggeized
gluon to the $\gamma^*\to q\bar{q}$ vertex. We list the results for the Feynman
diagrams which contribute: all loop integrations are carried out, and the
results are presented in the helicity basis of photon, quark, and antiquark.


  We present preliminary studies of bottomonia spectral functions at zero and
finite temperature using quenched anisotropic lattices. The heavy quark is
treated within Fermilab approach. We find no modification of the $\eta_b$ and
$\Upsilon$ states up to temperatures $2.3T_c$ while our study suggest
dissolution of $\chi_b$ state at $1.15T_c$.


  We show that for every $r \ge 2$ there exists $\epsilon_r > 0$ such that any
$r$-uniform hypergraph with $m$ edges and maximum vertex degree $o(\sqrt{m})$
contains a set of at most $(\frac{1}{2} - \epsilon_r)m$ edges the removal of
which breaks the hypergraph into connected components with at most $m/2$ edges.
We use this to give an algorithm running in time $d^{(1 - \epsilon_r)m}$ that
decides satisfiability of $m$-variable $(d, k)$-CSPs in which every variable
appears in at most $r$ constraints, where $\epsilon_r$ depends only on $r$ and
$k\in o(\sqrt{m})$. Furthermore our algorithm solves the corresponding #CSP-SAT
and Max-CSP-SAT of these CSPs. We also show that CNF representations of
unsatisfiable $(2, k)$-CSPs with variable frequency $r$ can be refuted in
tree-like resolution in size $2^{(1 - \epsilon_r)m}$. Furthermore for Tseitin
formulas on graphs with degree at most $k$ (which are $(2, k)$-CSPs) we give a
deterministic algorithm finding such a refutation.


  The motion of a black hole about the centre of gravity of its host galaxy
induces a strong response from the surrounding stellar population. We treat the
case of a harmonic potential analytically and show that half of the stars on
circular orbits in that potential shift to an orbit of lower energy, while the
other half receive a positive boost and recede to a larger radius. The black
hole itself remains on an orbit of fixed amplitude and merely acts as a
catalyst for the evolution of the stellar energy distribution function f(E). We
show that this effect is operative out to a radius of approx 3 to 4 times the
hole's influence radius, R_bh. We use numerical integration to explore more
fully the response of a stellar distribution to black hole motion. We consider
orbits in a logarithmic potential and compare the response of stars on circular
orbits, to the situation of a `warm' and `hot' (isotropic) stellar velocity
field. While features seen in density maps are now wiped out, the kinematic
signature of black hole motion still imprints the stellar line-of-sight mean
velocity to a magnitude ~18% the local root mean-square velocity dispersion
sigma.


  We present a new analysis of a 9-day long XMM-Newton monitoring of the Narrow
Line Seyfert 1 galaxy Mrk 766. We show that the strong changes in spectral
shape which occurred during this observation can be interpreted as due to Broad
Line Region clouds crossing the line of sight to the X-ray source. Within the
occultation scenario, the spectral and temporal analysis of the eclipses
provides precise estimates of the geometrical structure, location and physical
properties of the absorbing clouds. In particular, we show that these clouds
have cores with column densities of at least a few 10^23 cm^-2 and velocities
in the plane of the sky of the order of thousands km/s. The three different
eclipses monitored by XMM-Newton suggest a broad range in cloud velocities (by
a factor ~4-5). Moreover, two iron absorption lines clearly associated with
each eclipse suggest the presence of highly ionized gas around the obscuring
clouds, and an outflow component of the velocity spanning from 3,000 to 15,000
km/s


  We have loaded an ultracold gas of fermionic atoms into a far off resonance
optical dipole trap and precisely controlled the spin composition of the
trapped gas. We have measured a magnetic-field Feshbach resonance between atoms
in the two lowest energy spin-states, |9/2, -9/2> and |9/2, -7/2>. The
resonance peaks at a magnetic field of 201.5 plus or minus 1.4 G and has a
width of 8.0 plus or minus 1.1 G. Using this resonance we have changed the
elastic collision cross section in the gas by nearly 3 orders of magnitude.


  By means of 3D hydrodynamic simulations, we explore the effects of rotation
in the formation of second-generation (SG) stars in globular clusters (GC). Our
simulations follow the SG formation in a first-generation (FG) internally
rotating GC; SG stars form out of FG asymptotic giant branch (AGB) ejecta and
external pristine gas accreted by the system. We have explored two different
initial rotational velocity profiles for the FG cluster and two different
inclinations of the rotational axis with respect to the direction of motion of
the external infalling gas, whose density has also been varied. For a low
(10^-24 g cm^-3) external gas density, a disk of SG helium-enhanced stars is
formed. The SG is characterized by distinct chemo-dynamical phase space
patterns: it shows a more rapid rotation than the FG with the helium-enhanced
SG subsystem rotating more rapidly than the moderate helium-enhanced one. In
models with high external gas density (10^-23 g cm^-3), the inner SG disc is
disrupted by the early arrival of external gas and only a small fraction of
highly enhanced helium stars preserves the rotation acquired at birth.
Variations in the inclination angle between the rotation axis and the direction
of the infalling gas and the velocity profile can slightly alter the extent of
the stellar disc and the rotational amplitude. No significant variation has
been found in the timespan of our simulations when changing the inclination
angle between the rotation axis and the direction of the infalling gas, while
different velocity profiles can slightly alter the extent of the stellar disc
and the rotational amplitude. The results of our simulations illustrate the
complex link between dynamical and chemical properties of multiple populations
and provide new elements for the interpretation of observational studies and
future investigations of the dynamics of multiple-population GCs.


  We discuss how mathematical semantics has evolved, and suggest some new
directions for future work. As an example, we discuss some recent work on
encapsulating model comparison games as comonads, in the context of finite
model theory.


  We study a device for entangling electrons as cotunneling occurs through a
quantum dot where on-site electron-electron interactions $U$ are in place. The
main advantage of this device is that single particle processes are forbidden
by energy conservation as proposed by Oliver et al\cite{oli02}. Within this
model we calculated two electron transition amplitude, in terms of the
T-matrix, to all orders in the coupling to the dot, and consider a finite lead
bandwidth. The model filters singlet entangled pairs with the sole requirement
of Pauli principle. Feynman paths involving consecutive and doubly occupied dot
interfere destructively and produce a transition amplitude minimum at a
critical value of the onsite repulsion $U$. Singlet filtering is demonstrated
as a function of a gate voltage applied to the dot with a special resonance
condition when the dot levels are symmetrically placed about the input lead
energy.


  We present results underlining the conjecture that affine extensions for
non-crystallographic Coxeter groups are suitable mathematical objects for the
description of the symmetries of Carbon onions and Carbon nanotubes. It is the
hope that these considerations will shed new light on open questions concerning
structure, stability and formation of these fullerenes.


  The microcanonical effective partition function, constructed from a
Feynman-Hibbs potential, is derived using generalized ensemble theory. The form
of the effective Hamiltonian is amenable to Monte Carlo simulation techniques
and the relevant Metropolis function is presented. Using the derived expression
for the microcanonical effective partition function, the low-temperature
entropy of a proton in an anharmonic potential is numerically evaluated and
compared with the exact quantum mechanical canonical result.


  Let $f_i$ be polynomials in $n$ variables without a common zero. Hilbert's
Nullstellensatz says that there are polynomials $g_i$ such that $\sum
g_if_i=1$. The effective versions of this result bound the degrees of the $g_i$
in terms of the degrees of the $f_j$. The aim of this paper is to generalize
this to the case when the $f_i$ are replaced by arbitrary ideals. Applications
to the B\'ezout theorem, to \L ojasiewicz--type inequialities and to
deformation theory are also discussed.


  We propose an algorithmic test to check whether a two-input system is
linearizable by an endogenous dynamic feedback with a dimension of at most two.
This test furthermore provides a procedure for systematically deriving flat
outputs for this class of systems.


  This paper critically analyzes conventional and biomimetic robotic arms,
underscoring the trade-offs between size, motion range, and load capacity in
current biomimetic models. By delving into the human shoulder's mechanical
intelligence, particularly the glenohumeral joint's intricate features such as
its unique ball-and-socket structure and self-locking mechanism, we pinpoint
innovations that bolster both stability and mobility while maintaining
compactness. To substantiate these insights, we present a groundbreaking
biomimetic robotic glenohumeral joint that authentically mirrors human
musculoskeletal elements, from ligaments to tendons, integrating the biological
joint's mechanical intelligence. Our exhaustive simulations and tests reveal
enhanced flexibility and load capacity for the robotic joint. The advanced
robotic arm demonstrates notable capabilities, including a significant range of
motions and a 4 kg payload capacity, even exerting over 1.5 Nm torque. This
study not only confirms the human shoulder joint's mechanical innovations but
also introduces a pioneering design for a next-generation biomimetic robotic
arm, setting a new benchmark in robotic technology.


  We investigate the monogamy relations related to the concurrence and the
entanglement of formation. General monogamy inequalities given by the
{\alpha}th power of concurrence and entanglement of formation are presented for
N-qubit states. The monogamy relation for entanglement of assistance is also
established. Based on these general monogamy relations, the residual
entanglement of concurrence and entanglement of formation are studied. Some
relations among the residual entanglement, entanglement of assistance, and
three tangle are also presented.


  The dynamics of epidemics depend on how people's behavior changes during an
outbreak. At the beginning of the epidemic, people do not know about the virus,
then, after the outbreak of epidemics and alarm, they begin to comply with the
restrictions and the spreading of epidemics may decline. Over time, some people
get tired/frustrated by the restrictions and stop following them (exhaustion),
especially if the number of new cases drops down. After resting for a while,
they can follow the restrictions again. But during this pause the second wave
can come and become even stronger then the first one. Studies based on SIR
models do not predict the observed quick exit from the first wave of epidemics.
Social dynamics should be considered. The appearance of the second wave also
depends on social factors. Many generalizations of the SIR model have been
developed that take into account the weakening of immunity over time, the
evolution of the virus, vaccination and other medical and biological details.
However, these more sophisticated models do not explain the apparent
differences in outbreak profiles between countries with different intrinsic
socio-cultural features. In our work, a system of models of the COVID-19
pandemic is proposed, combining the dynamics of social stress with classical
epidemic models. Social stress is described by the tools of sociophysics. The
combination of a dynamic SIR-type model with the classical triad of stages of
the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible
to describe with high accuracy the available statistical data for 13 countries.
The sets of kinetic constants corresponding to optimal fit of model to data
were found. They characterize the ability of society to mobilize efforts
against epidemics and maintain this concentration over time, and can further
help in the development of strategies specific to a particular society.


  In Evolutionary Robotics, evolutionary algorithms are used to co-optimize
morphology and control. However, co-optimizing leads to different challenges:
How do you optimize a controller for a body that often changes its number of
inputs and outputs? Researchers must then make some choice between centralized
or decentralized control. In this article, we study the effects of centralized
and decentralized controllers on modular robot performance and morphologies.
This is done by implementing one centralized and two decentralized continuous
time recurrent neural network controllers, as well as a sine wave controller
for a baseline. We found that a decentralized approach that was more
independent of morphology size performed significantly better than the other
approaches. It also worked well in a larger variety of morphology sizes. In
addition, we highlighted the difficulties of implementing centralized control
for a changing morphology, and saw that our centralized controller struggled
more with early convergence than the other approaches. Our findings indicate
that duplicated decentralized networks are beneficial when evolving both the
morphology and control of modular robots. Overall, if these findings translate
to other robot systems, our results and issues encountered can help future
researchers make a choice of control method when co-optimizing morphology and
control.


  We propose and study a mathematical model that qualitatively reproduces
several ancient ornamental designs than one can see in historical museums of
Crete and Athens. The designs contain several rings that circumscribe a fixed
number of centers or spirals, specific to each design. The model is based on a
complex differential equation of weak resonance (Arnold 1977). We analyze the
role of the model parameters in giving rise to different peculiarities of the
repeated designs, in particular, the dynamical indeterminacy. The model allows
tracing design changes under parameter variation, as well as to construct some
new ornamental designs. We discuss how observed ornamental design may reflect
some philosophical ideas of ancient inhabitants of Greece.


  There is an impressive body of work on developing heuristics and other
reasoning algorithms to guide search in optimal and anytime planning algorithms
for classical planning. However, very little effort has been directed towards
developing analogous techniques to guide search towards high-quality solutions
in hierarchical planning formalisms like HTN planning, which allows using
additional domain-specific procedural control knowledge. In lieu of such
techniques, this control knowledge often needs to provide the necessary search
guidance to the planning algorithm, which imposes a substantial burden on the
domain author and can yield brittle or error-prone domain models. We address
this gap by extending recent work on a new hierarchical goal-based planning
formalism called Hierarchical Goal Network (HGN) Planning to develop the
Hierarchically-Optimal Goal Decomposition Planner (HOpGDP), an HGN planning
algorithm that computes hierarchically-optimal plans. HOpGDP is guided by
$h_{HL}$, a new HGN planning heuristic that extends existing admissible
landmark-based heuristics from classical planning to compute admissible cost
estimates for HGN planning problems. Our experimental evaluation across three
benchmark planning domains shows that HOpGDP compares favorably to both optimal
classical planners due to its ability to use domain-specific procedural
knowledge, and a blind-search version of HOpGDP due to the search guidance
provided by $h_{HL}$.


  In this paper, we formulate the statistical mechanics in Snyder space that
supports the existence of a minimal length scale. We obtain the corresponding
invariant Liouville volume which properly determines the number of microstates
in the semiclassical regime. The results show that the number of accessible
microstates drastically reduces at the high energy regime such that there is
only one degree of freedom for a particle. Using the Liouville volume, we
obtain the deformed partition function and we then study the thermodynamical
properties of the ideal gas in this setup. Invoking the equipartition theorem,
we show that $2/3$ of the degrees of freedom freeze at the high temperature
regime when the thermal de Broglie wavelength becomes of the order of the
Planck length. This reduction of the number of degrees of freedom suggests an
effective dimensional reduction of the space from $3$ to $1$ at the Planck
scale.


  The total number of globular clusters (GCs) in a galaxy rises continuously
with the galaxy luminosity L, while the relative number of galaxies decreases
with L following the Schechter function. The product of these two very
nonlinear functions gives the relative number of GCs contained by all galaxies
at a given L. It is shown that GCs, in this universal sense, are most commonly
found in galaxies within a narrow range around $L_{\star}$. In addition, blue
(metal-poor) GCs outnumber the red (metal-richer) ones globally by 4 to 1 when
all galaxies are added, pointing to the conclusion that the earliest stages of
galaxy formation were especially favorable to forming massive, dense star
clusters.


  We give a detailed exposition of the Jet Isomorphism Theorem of
pseudo-Riemannian geometry. In its weak form this theorem states that the
Taylor expansion up to order k+2 of a pseudo-Riemannian metric in normal
coordinates can be reconstructed in a universal way from suitable
symmetrizations of the covariant derivatives of the curvature tensor up to
order k. In its full generality it states the equivalence of Taylor expansions
of metrics in normal coordinates up to order k+2, abstract curvature k-jets and
their symmetrizations as affine vector bundles. This theorem seems to be a
cornerstone of local pseudo-Riemannian geometry which is hardly mentioned in
the present literature anymore.


  I review some standard theory of convex bodies in $\mathbb{R}^3$ and rephrase
it in a formalism of Ted Newman to show the relation between the
St\"utzfunktion of the former theory and the cut function introduced by Ted.
This leads to a conjectured inequality for space-like two-spheres in Minkowski
space that generalises Minkowski's inequality and is implied by Penrose's
cosmic censorship hypothesis.


  A graph is pseudo-outerplanar if each of its blocks has an embedding in the
plane so that the vertices lie on a fixed circle and the edges lie inside the
disk of this circle with each of them crossing at most one another. It is
proved that every pseudo-outerplanar graph with maximum degree \Delta\geq 5 is
totally (\Delta+1)-choosable.


  Causal representation learning has been proposed to encode relationships
between factors presented in the high dimensional data. However, existing
methods suffer from merely using a large amount of labeled data and ignore the
fact that samples generated by the same causal mechanism follow the same causal
relationships. In this paper, we seek to explore such information by leveraging
do-operation to reduce supervision strength. We propose a framework that
implements do-operation by swapping latent cause and effect factors encoded
from a pair of inputs. Moreover, we also identify the inadequacy of existing
causal representation metrics empirically and theoretically and introduce new
metrics for better evaluation. Experiments conducted on both synthetic and real
datasets demonstrate the superiorities of our method compared with
state-of-the-art methods.


  Millimeter wave (mmWave) communications has been regarded as a key enabling
technology for 5G networks. In contrast to conventional
multiple-input-multiple-output (MIMO) systems, precoding in mmWave MIMO cannot
be performed entirely at baseband using digital precoders, as only a limited
number of signal mixers and analog-to-digital converters (ADCs) can be
supported considering their cost and power consumption. As a cost-effective
alternative, a hybrid precoding transceiver architecture, combining a digital
precoder and an analog precoder, has recently received considerable attention.
However, the optimal design of such hybrid precoders has not been fully
understood. In this paper, treating the hybrid precoder design as a matrix
factorization problem, effective alternating minimization (AltMin) algorithms
will be proposed for two different hybrid precoding structures, i.e., the
fully-connected and partially-connected structures. In particular, for the
fully-connected structure, an AltMin algorithm based on manifold optimization
is proposed to approach the performance of the fully digital precoder, which,
however, has a high complexity. Thus, a low-complexity AltMin algorithm is then
proposed, by enforcing an orthogonal constraint on the digital precoder.
Furthermore, for the partially-connected structure, an AltMin algorithm is also
developed with the help of semidefinite relaxation. For practical
implementation, the proposed AltMin algorithms are further extended to the
broadband setting with orthogonal frequency division multiplexing (OFDM)
modulation. Simulation results will demonstrate significant performance gains
of the proposed AltMin algorithms over existing hybrid precoding algorithms.
Moreover, based on the proposed algorithms, simulation comparisons between the
two hybrid precoding structures will provide valuable design insights.


  One fundamental obstacle to efficient ferromagnetic spintronics is magnetic
precession, which intrinsically limits the dynamics of magnetic textures, We
demonstrate that the domain wall precession fully vanishes with a record
mobility when the net angular momentum is compensated (TAC) in DWs driven by
spin-orbit torque in a ferrimagnetic GdFeCo/Pt track. We use transverse
in-plane fields to reveal the internal structure of DWs and provide a robust
and parameter-free measurement of TAC. Our results highlight the mechanism of
faster and more efficient dynamics in materials with multiple spin lattices and
reduced net angular momentum, promising for high-speed, low-power spintronics
applications.


  As an emerging mode of urban transportation, Autonomous Mobility-on-Demand
(AMoD) systems show the potential in improving mobility in cities through
timely and door-to-door services. However, the spatiotemporal imbalances
between mobility demand and supply may lead to inefficiencies and a low quality
of service. Vehicle rebalancing (i.e., dispatching idle vehicles to high-demand
areas), is a potential solution for efficient AMoD fleet management. In this
paper, we formulate the vehicle rebalancing problem as a coverage control
problem for the deployment of a fleet of mobile agents for AMoD operation in
urban areas. Performance is demonstrated via microscopic simulations
representing a large urban road network of Shenzhen, China. Results reveal the
potential of the proposed method in improving service rates and decreasing
passenger waiting times.


  This report presents a novel Mamba-based encoder-decoder architecture,
QueryMamba, featuring an integrated verb-noun interaction module that utilizes
a statistical verb-noun co-occurrence matrix to enhance video action
forecasting. This architecture not only predicts verbs and nouns likely to
occur based on historical data but also considers their joint occurrence to
improve forecast accuracy. The efficacy of this approach is substantiated by
experimental results, with the method achieving second place in the Ego4D LTA
challenge and ranking first in noun prediction accuracy.


  Semileptonic decays of polarized $\tau$ leptons are investigated. The most
general angular distribution of three meson final states ($\tau\rightarrow
\pi\pi\nu,\,K\pi\nu,\, \pi\pi\pi\nu,\\ \,K\pi\pi\nu,\, KK\pi\nu,\, KKK\nu,\,
\eta\pi\pi\nu,\,\ldots{}$) is discussed. It is shown, that the most general
distribution can be characterized by 16 structure functions, most of which can
be determined in currently ongoing high statistics experiments. Emphasis is put
on $\tau$ decays in $e^{+}e^{-}$ experiments where the neutrino escapes
detection and the $\tau$ rest frame cannot be reconstructed. The structure of
the hadronic matrix elements, based on CVC and chiral lagrangians, is
discussed.


  We study the topological crystalline insulator phase protected by the glide
symmetry, which is characterized by the Z2 topological number. In the present
paper, we derive a formula for the Z2 topological invariant protected by glide
symmetry in a nonprimitive lattice, from that in a primitive lattice. We
establish a formula for the glide-Z2 invariant for the space group No. 9 with
glide symmetry in the base-centered lattice, by folding the Brillouin zone into
that of the primitive lattice where the formula for the glide-Z2 invariant is
known. The formula is written in terms of integrals of the Berry curvatures and
Berry phases in the k-space. We also derive a formula of the glide-Z2
invariantwhen the inversion symmetry is added, and the space group becomes No.
15. This reduces the formula into the Fu-Kane-like formula, expressed in terms
of the irreducible representations at high-symmetry points in $k$ space. We
also construct these topological invariants by the layer-construction approach,
and the results completely agree with those from the k-space approach.


  We consider asymptotically anti-de Sitter black holes in $d$-spacetime
dimensions in the thermodynamically stable regime. We show that the
Bekenstein-Hawking entropy and its leading order corrections due to thermal
fluctuations can be reproduced by a weakly interacting fluid of bosons and
fermions (`dual gas') in $\Delta=\alpha(d-2)+1$ spacetime dimensions, where the
energy-momentum dispersion relation for the constituents of the fluid is
assumed to be $\epsilon = \kappa p^\alpha$. We examine implications of this
result for entropy bounds and the holographic hypothesis.


  Pre-trained point cloud models have found extensive applications in 3D
understanding tasks like object classification and part segmentation. However,
the prevailing strategy of full fine-tuning in downstream tasks leads to large
per-task storage overhead for model parameters, which limits the efficiency
when applying large-scale pre-trained models. Inspired by the recent success of
visual prompt tuning (VPT), this paper attempts to explore prompt tuning on
pre-trained point cloud models, to pursue an elegant balance between
performance and parameter efficiency. We find while instance-agnostic static
prompting, e.g. VPT, shows some efficacy in downstream transfer, it is
vulnerable to the distribution diversity caused by various types of noises in
real-world point cloud data. To conquer this limitation, we propose a novel
Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point
cloud models. The essence of IDPT is to develop a dynamic prompt generation
module to perceive semantic prior features of each point cloud instance and
generate adaptive prompt tokens to enhance the model's robustness. Notably,
extensive experiments demonstrate that IDPT outperforms full fine-tuning in
most tasks with a mere 7% of the trainable parameters, providing a promising
solution to parameter-efficient learning for pre-trained point cloud models.
Code is available at \url{https://github.com/zyh16143998882/ICCV23-IDPT}.


  We study the problem of robust reinforcement learning under adversarial
corruption on both rewards and transitions. Our attack model assumes an
\textit{adaptive} adversary who can arbitrarily corrupt the reward and
transition at every step within an episode, for at most $\epsilon$-fraction of
the learning episodes. Our attack model is strictly stronger than those
considered in prior works. Our first result shows that no algorithm can find a
better than $O(\epsilon)$-optimal policy under our attack model. Next, we show
that surprisingly the natural policy gradient (NPG) method retains a natural
robustness property if the reward corruption is bounded, and can find an
$O(\sqrt{\epsilon})$-optimal policy. Consequently, we develop a Filtered Policy
Gradient (FPG) algorithm that can tolerate even unbounded reward corruption and
can find an $O(\epsilon^{1/4})$-optimal policy. We emphasize that FPG is the
first that can achieve a meaningful learning guarantee when a constant fraction
of episodes are corrupted. Complimentary to the theoretical results, we show
that a neural implementation of FPG achieves strong robust learning performance
on the MuJoCo continuous control benchmarks.


  The ability to dynamically extend a model to new data and classes is critical
for multiple organ and tumor segmentation. However, due to privacy regulations,
accessing previous data and annotations can be problematic in the medical
domain. This poses a significant barrier to preserving the high segmentation
accuracy of the old classes when learning from new classes because of the
catastrophic forgetting problem. In this paper, we first empirically
demonstrate that simply using high-quality pseudo labels can fairly mitigate
this problem in the setting of organ segmentation. Furthermore, we put forward
an innovative architecture designed specifically for continuous organ and tumor
segmentation, which incurs minimal computational overhead. Our proposed design
involves replacing the conventional output layer with a suite of lightweight,
class-specific heads, thereby offering the flexibility to accommodate newly
emerging classes. These heads enable independent predictions for newly
introduced and previously learned classes, effectively minimizing the impact of
new classes on old ones during the course of continual learning. We further
propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings
into the organ-specific heads. These embeddings encapsulate the semantic
information of each class, informed by extensive image-text co-training. The
proposed method is evaluated on both in-house and public abdominal CT datasets
under organ and tumor segmentation tasks. Empirical results suggest that the
proposed design improves the segmentation performance of a baseline neural
network on newly-introduced and previously-learned classes along the learning
trajectory.


  Within dynamical mean-field theory, we study the metal-insulator transition
of a twofold orbitally degenerate Hubbard model as a function of a splitting
\Delta of the degeneracy. The phase diagram in the U-\Delta plane exhibits
two-band and one-band metals, as well as the Mott insulator. The correlated
two-band metal is easily driven to the insulator state by a strikingly weak
splitting \Delta << W of the order of the Kondo-peak width zW, where z << 1 is
the metal quasiparticle weight. The possible relevance of this result to the
insulator-metal transition in the orthorhombic expanded fulleride NH3 K3C60 is
discussed.


  Zero-shot learning is the problem of predicting instances over classes not
seen during training. One approach to zero-shot learning is providing auxiliary
class information to the model. Prior work along this vein have largely used
expensive per-instance annotation or singular class-level descriptions, but
per-instance descriptions are hard to scale and single class descriptions may
not be rich enough. Furthermore, these works have used natural-language
descriptions exclusively, simple bi-encoders models, and modality or
task-specific methods. These approaches have several limitations: text
supervision may not always be available or optimal and bi-encoders may only
learn coarse relations between inputs and class descriptions. In this work, we
present SemSup, a novel approach that uses (1) a scalable multiple description
sampling method which improves performance over single descriptions, (2)
alternative description formats such as JSON that are easy to generate and
outperform text on certain settings, and (3) hybrid lexical-semantic similarity
to leverage fine-grained information in class descriptions. We demonstrate the
effectiveness of SemSup across four datasets, two modalities, and three
generalization settings. For example, across text and image datasets, SemSup
increases unseen class generalization accuracy by 15 points on average compared
to the closest baseline.


  A quantum state can be characterized from the violation of a Bell inequality.
The well-known CHSH inequality for example can be used to quantify the fidelity
(up to local isometries) of the measured state with respect to the singlet
state. In this work, we look for the minimum CHSH violation leading to a
non-trivial fidelity. In particular, we provide a new analytical approach to
explore this problem in a device-independent framework, where the fidelity
bound holds without assumption about the internal working of devices used in
the CHSH test. We give an example which pushes the minimum CHSH threshold from
$\approx2.0014$ to $\approx2.05,$ far from the local bound. This is in sharp
contrast with the device-dependent (two-qubit) case, where entanglement is
one-to-one related to a non-trivial singlet fidelity. We discuss this result in
a broad context including device-dependent/independent state characterizations
with various classical resources.


  Classic contextual bandit algorithms for linear models, such as LinUCB,
assume that the reward distribution for an arm is modeled by a stationary
linear regression. When the linear regression model is non-stationary over
time, the regret of LinUCB can scale linearly with time. In this paper, we
propose a novel multiscale changepoint detection method for the non-stationary
linear bandit problems, called Multiscale-LinUCB, which actively adapts to the
changing environment. We also provide theoretical analysis of regret bound for
Multiscale-LinUCB algorithm. Experimental results show that our proposed
Multiscale-LinUCB algorithm outperforms other state-of-the-art algorithms in
non-stationary contextual environments.


  This paper proposes a novel approach by integrating sensor fusion with deep
reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to
develop an optimal control policy for self-driving cars. Our system employs a
two-branch fusion method for vehicle image and tracking sensor data, leveraging
the strengths of residual structures and identity mapping to enhance agent
training. Through comprehensive comparisons, we demonstrate the efficacy of
information fusion and establish the superiority of our selected algorithm over
alternative approaches. Our work advances the field of autonomous driving and
demonstrates the potential of reinforcement learning in enabling intelligent
vehicle decision-making.


  In this work I present a full analysis of proton decay in an $SO(10)$ model
previously proposed by Dutta, Mimura, and Mohapatra. The $\bf{10}$,
$\overline{\bf{126}}$, and $\bf{120}$ Yukawa couplings contributing to fermion
masses in this model have well-motivated restrictions on their textures
intended to give favorable results for proton lifetime as well as a realistic
fermion sector without the need for fine-tuning and for either type-I or
type-II dominance in the neutrino mass matrix. I obtain a valid fit for the
entire fermion sector for both types of seesaw dominance, including
$\theta_{13}$ in good agreement with the most recent data. For the case with
type-II seesaw, I find that using the Yukawa couplings fixed by the successful
fermion sector fit, proton partial lifetime limits are satisfied for nearly
every pertinent decay mode, even for nearly arbitrary values of the triplet
Higgs mixing parameters, with only the $K^+ \bar\nu$ mode requiring a minor
${\cal O}(10^{-1})$ cancellation in order to satisfy the experimental limit. I
also find a maximum lifetime for that mode of $\tau(K^+ \bar\nu) \sim 10^{36}$
years, which should be tested by eventual experiments. For the type-I seesaw
case, I find that all six pertinent decay modes of interest are satisfied for
values of the triplet mixing parameters giving no major enhancement, with modes
other than $K^+ \bar\nu$ easily satisfied for arbitrary mixing values, and with
a maximum lifetime for $K^+ \bar\nu$ of nearly $10^{38}$ years.


  This paper addresses a novel five-transistor (5T) CMOS SRAM design with high
performance and reliability in 65nm CMOS, and illustrates how it reduces the
dynamic power consumption in comparison with the conventional and low-power 6T
SRAM counterparts. This design can be used as cache memory in processors and
low-power portable devices. The proposed SRAM cell features ~13% area reduction
compared to a conventional 6T cell, and features a unique bit-line and negative
supply voltage biasing methodology and ground control architecture to enhance
performance, and suppress standby leakage power.


  Conventional deep models predict a test sample with a single forward
propagation, which, however, may not be sufficient for predicting
hard-classified samples. On the contrary, we human beings may need to carefully
check the sample many times before making a final decision. During the recheck
process, one may refine/adjust the prediction by referring to related samples.
Motivated by this, we propose to predict those hard-classified test samples in
a looped manner to boost the model performance. However, this idea may pose a
critical challenge: how to construct looped inference, so that the original
erroneous predictions on these hard test samples can be corrected with little
additional effort. To address this, we propose a general Closed-Loop Inference
(CLI) method. Specifically, we first devise a filtering criterion to identify
those hard-classified test samples that need additional inference loops. For
each hard sample, we construct an additional auxiliary learning task based on
its original top-$K$ predictions to calibrate the model, and then use the
calibrated model to obtain the final prediction. Promising results on ImageNet
(in-distribution test samples) and ImageNet-C (out-of-distribution test
samples) demonstrate the effectiveness of CLI in improving the performance of
any pre-trained model.


  The diffusion of information and behaviors over social networks is of
considerable interest in research fields ranging from sociology to computer
science and application domains such as marketing, finance, human health, and
national security. Of particular interest is the possibility to develop
predictive capabilities for social diffusion, for instance enabling early
identification of diffusion processes which are likely to become "viral" and
propagate to a significant fraction of the population. Recently we have shown,
using theoretical analysis, that the dynamics of social diffusion may depend
crucially upon the interactions of social network communities, that is, densely
connected groupings of individuals which have only relatively few links between
groups. This paper presents an empirical investigation of two related
hypotheses which follow from this finding: 1.) inter-community interaction is
predictive of the reach of social diffusion and 2.) dispersion of the diffusion
phenomenon across network communities is a useful early indicator that the
propagation will be "successful". We explore these hypotheses with case studies
involving the emergence of the Swedish Social Democratic Party at the turn of
the 20th century, the spread of the SARS virus in 2002-2003, and blogging
dynamics associated with real world protest activity. These empirical studies
demonstrate that network community-based diffusion metrics do indeed possess
predictive power, and in fact can be more useful than standard measures.


  No-hair like relations between the multipole moments of the exterior
gravitational field of neutron stars have recently been found to be
approximately independent of the star's internal structure. This approximate,
equation-of-state universality arises after one adimensionalizes the multipole
moments appropriately, which then begs the question of whether there are better
ways to adimensionalize the moments to obtain stronger universality. We here
investigate this question in detail by considering slowly-rotating neutron
stars both in the non-relativistic limit and in full General Relativity. We
find that there exist normalizations that lead to stronger equation-of-state
universality in the relations among the moment of inertia and the quadrupole,
octopole and hexadecapole moments of neutron stars. We determine the optimal
normalization that minimizes the equation-of-state dependence in these
relations. The results found here may have applications in the modeling of
X-ray pulses and atomic line profiles from millisecond pulsars with NICER and
LOFT.


  To launch black-box attacks against a Deep Neural Network (DNN) based Face
Recognition (FR) system, one needs to build \textit{substitute} models to
simulate the target model, so the adversarial examples discovered from
substitute models could also mislead the target model. Such
\textit{transferability} is achieved in recent studies through querying the
target model to obtain data for training the substitute models. A real-world
target, likes the FR system of law enforcement, however, is less accessible to
the adversary. To attack such a system, a substitute model with similar quality
as the target model is needed to identify their common defects. This is hard
since the adversary often does not have the enough resources to train such a
powerful model (hundreds of millions of images and rooms of GPUs are needed to
train a commercial FR system).
  We found in our research, however, that a resource-constrained adversary
could still effectively approximate the target model's capability to recognize
\textit{specific} individuals, by training \textit{biased} substitute models on
additional images of those victims whose identities the attacker want to cover
or impersonate. This is made possible by a new property we discovered, called
\textit{Nearly Local Linearity} (NLL), which models the observation that an
ideal DNN model produces the image representations (embeddings) whose distances
among themselves truthfully describe the human perception of the differences
among the input images. By simulating this property around the victim's images,
we significantly improve the transferability of black-box impersonation attacks
by nearly 50\%. Particularly, we successfully attacked a commercial system
trained over 20 million images, using 4 million images and 1/5 of the training
time but achieving 62\% transferability in an impersonation attack and 89\% in
a dodging attack.


  The aim of this paper is to provide an overview of recent development related
to Bregman distances outside its native areas of optimization and statistics.
We discuss approaches in inverse problems and image processing based on Bregman
distances, which have evolved to a standard tool in these fields in the last
decade. Moreover, we discuss related issues in the analysis and numerical
analysis of nonlinear partial differential equations with a variational
structure. For such problems Bregman distances appear to be of similar
importance, but are currently used only in a quite hidden fashion. We try to
work out explicitely the aspects related to Bregman distances, which also lead
to novel mathematical questions and may also stimulate further research in
these areas.


  Let $X$ be a compact K\"ahler manifold and $\{\theta\}$ be a big cohomology
class. We prove several results about the singularity type of full mass
currents, answering a number of open questions in the field. First, we show
that the Lelong numbers and multiplier ideal sheaves of
$\theta$-plurisubharmonic functions with full mass are the same as those of the
current with minimal singularities. Second, given another big and nef class
$\{\eta\}$, we show the inclusion $\mathcal{E}(X,\eta) \cap {PSH}(X,\theta)
\subset \mathcal{E}(X,\theta).$ Third, we characterize big classes whose full
mass currents are "additive". Our techniques make use of a characterization of
full mass currents in terms of the envelope of their singularity type. As an
essential ingredient we also develop the theory of weak geodesics in big
cohomology classes. Numerous applications of our results to complex geometry
are also given.


  In this paper we compute the Casimir energy for a coupled
fermion-pseudoscalar field system. In the model considered in this paper the
pseudoscalar field is \textit{static} and \textit{prescribed} with two
adjustable parameters. These parameters determine the values of the field at
infinity ($\pm \theta_0$) and its scale of variation ($\mu$). One can build up
a field configuration with arbitrary topological charge by changing $\theta_0$,
and interpolate between the extreme adiabatic and non-adiabatic regimes by
changing $\mu$. This system is exactly solvable and therefore we compute the
Casimir energy exactly and unambiguously by using an energy density subtraction
scheme. We show that in general the Casimir energy goes to zero in the extreme
adiabatic limit, and in the extreme non-adiabatic limit when the asymptotic
values of the pseudoscalar field properly correspond to a configuration with an
arbitrary topological charge. Moreover, in general the Casimir energy is always
positive and on the average an increasing function of $\theta_0$ and always has
local maxima when there is a zero mode, showing that these configurations are
energetically unfavorable. We also compute and display the energy densities
associated with the spectral deficiencies in both of the continua, and those of
the bound states. We show that the energy densities associated with the
distortion of the spectrum of the states with $E>0$ and $E<0$ are mirror images
of each other. We also compute and display the Casimir energy density. Finally
we compute the energy of a system consisting of a soliton and a valance
electron and show that the Casimir energy of the system is comparable with the
binding energy.


  A model-independent analytical analysis for charmless B decays is presented.
It is demonstrated that the CP-averaging branching ratio difference $\Delta R =
R_c - R_n$ in $B\to \pi K$ decays with $R_c = 2Br(\pi^0K^-)/Br(\pi^-\bar{K}^0)$
and $R_n =Br(\pi^+K^-)/2Br(\pi^0\bar{K}^0)$ defines a sensitive quantity for
probing new physics as $\Delta R$ is dominated by the second order of
electroweak penguin contributions. A large discrepancy between experimental
data and standard model (SM) prediction $\Delta R^{exp}/\Delta R^{SM} > 9.0\pm
5.0$ strongly indicates a signal of new physics in the electroweak penguin
sector. Within the SM, the current $\pi K$ data favor a very large
color-suppressed tree amplitude $|C'/T'|\sim 2$, large CP violations
($A_{CP}(\pi^0\bar{K}^0)\sim 0.69$ and $A_{CP}(\pi^0\bar{K}^-)\sim 0.56$),
which is connected to $\Delta R$ and be solved simultaneously with extra
electroweak penguin contributions. More accurate measurements on the ratio
difference $\Delta R$ and CP violation in $B\to \pi \pi, \pi K$ decays may
provide a window for probing new physics and testing the isospin and SU(3)
symmetries.


  Relational Quantum Mechanics (RQM) claims to be an interpretation of quantum
theory [see arXiv:2109.09170, which appears in the Oxford Handbook of the
History of Interpretation of Quantum Physics]. However, there are significant
departures from quantum theory: (i) in RQM measurement outcomes arise from
interactions which entangle a system $S$ and an observer $A$ without
decoherence, and (ii) such an outcome is a "fact" relative to the observer $A$,
but it is not a fact relative to another observer $B$ who has not interacted
with $S$ or $A$ during the foregoing measurement process. For $B$ the system $S
\otimes A$ remains entangled. We derive a GHZ-like contradiction showing that
relative facts described by these statements are incompatible with quantum
theory. Hence Relational Quantum Mechanics should not be considered an
interpretation of quantum theory, according to a criterion for interpretations
that we have introduced. The criterion states that whenever an interpretation
introduces a notion of outcomes, these outcomes, whatever they are, must follow
the probability distribution specified by the Born rule.


  Fractional differential equations provide a tractable mathematical framework
to describe anomalous behavior in complex physical systems, yet they introduce
new sensitive model parameters, i.e. derivative orders, in addition to model
coefficients. We formulate a sensitivity analysis of fractional models by
developing a fractional sensitivity equation method. We obtain the adjoint
fractional sensitivity equations, in which we present a fractional operator
associated with logarithmic-power law kernel. We further construct a
gradient-based optimization algorithm to compute an accurate parameter
estimation in fractional model construction. We develop a fast, stable, and
convergent Petrov-Galerkin spectral method to numerically solve the coupled
system of original fractional model and its corresponding adjoint fractional
sensitivity equations.


  We numerically explore the many body localization (MBL) transition through
the lens of the {\it entanglement spectrum}. While a direct transition from
localization to thermalization is believed to obtain in the thermodynamic limit
(the exact details of which remain an open problem), in finite system sizes
there exists an intermediate `quantum critical' regime. Previous numerical
investigations have explored the crossover from thermalization to criticality,
and have used this to place a numerical {\it lower} bound on the critical
disorder strength for MBL. A careful analysis of the {\it high energy} part of
the entanglement spectrum (which contains universal information about the
critical point) allows us to make the first ever observation in exact numerics
of the crossover from criticality to MBL and hence to place a numerical {\it
upper bound} on the critical disorder strength for MBL.


  We consider a general model for representing and manipulating parametric
curves, in which a curve is specified by a black box mapping a parameter value
between 0 and 1 to a point in Euclidean d-space. In this model, we consider the
nearest-point-on-curve and farthest-point-on-curve problems: given a curve C
and a point p, find a point on C nearest to p or farthest from p. In the
general black-box model, no algorithm can solve these problems. Assuming a
known bound on the speed of the curve (a Lipschitz condition), the answer can
be estimated up to an additive error of epsilon using O(1/epsilon) samples, and
this bound is tight in the worst case. However, many instances can be solved
with substantially fewer samples, and we give algorithms that adapt to the
inherent difficulty of the particular instance, up to a logarithmic factor.
More precisely, if OPT(C,p,epsilon) is the minimum number of samples of C that
every correct algorithm must perform to achieve tolerance epsilon, then our
algorithm performs O(OPT(C,p,epsilon) log (epsilon^(-1)/OPT(C,p,epsilon)))
samples. Furthermore, any algorithm requires Omega(k log (epsilon^(-1)/k))
samples for some instance C' with OPT(C',p,epsilon) = k; except that, for the
nearest-point-on-curve problem when the distance between C and p is less than
epsilon, OPT is 1 but the upper and lower bounds on the number of samples are
both Theta(1/epsilon). When bounds on relative error are desired, we give
algorithms that perform O(OPT log (2+(1+epsilon^(-1)) m^(-1)/OPT)) samples
(where m is the exact minimum or maximum distance from p to C) and prove that
Omega(OPT log (1/epsilon)) samples are necessary on some problem instances.


  Empirical Mode Decomposition(EMD) is an adaptive data analysis technique for
analyzing nonlinear and nonstationary data[1]. EMD decomposes the original data
into a number of Intrinsic Mode Functions(IMFs)[1] for giving better physical
insight of the data. Permutation Entropy(PE) is a complexity measure[3]
function which is widely used in the field of complexity theory for analyzing
the local complexity of time series. In this paper we are combining the
concepts of PE and EMD to resolve the mode mixing problem observed in
determination of IMFs.


  We theoretically study the quantum speed limit of a single atom trapped in a
Fabry-Perot microresonator. The cavity mode will be squeezed when a driving
laser is applied to the second-order nonlinear medium, and the effective
Hamiltonian can be obtained under the Bogoliubov squeezing transformation. The
analytical expression of evolved atom state can be obtained by using the
non-Hermitian Schr\"{o}dinger equation for the initial excited state, and the
quantum speed limit time coincides very well for both the analytical expression
and the master equation method. From the perspective of quantum speed limit, it
is more conducive to accelerate the evolution of the quantum state for the
large detuning, strong driving and coupling strength. For the initial
superposition state case, the form of initial state has more influence on the
evolution speed. The quantum speed limit time is not only dependent on the
system parameters but also determined by the initial state.


  Three contradictory but state-of-the-art concepts for defining and evaluating
stored electromagnetic energy are treated in this communication, and are
collated with the widely accepted definition of stored energy, which is the
total energy minus the radiated energy. All three concepts are compared, and
the results are discussed on an example of a dominant spherical mode, which is
known to yield dissimilar results for the concepts dealt with here. It is shown
that various definitions of stored energy density immanently imply diverse
meanings of the term "radiation".


  The recent discovery of zero-determinant strategies for the iterated
Prisoner's Dilemma sparked a surge of interest in the surprising fact that a
player can exert unilateral control over iterated interactions. These
remarkable strategies, however, are known to exist only in games in which
players choose between two alternative actions such as "cooperate" and
"defect." Here we introduce a broader class of autocratic strategies by
extending zero-determinant strategies to iterated games with more general
action spaces. We use the continuous Donation Game as an example, which
represents an instance of the Prisoner's Dilemma that intuitively extends to a
continuous range of cooperation levels. Surprisingly, despite the fact that the
opponent has infinitely many donation levels from which to choose, a player can
devise an autocratic strategy to enforce a linear relationship between his or
her payoff and that of the opponent even when restricting his or her actions to
merely two discrete levels of cooperation. In particular, a player can use such
a strategy to extort an unfair share of the payoffs from the opponent.
Therefore, although the action space of the continuous Donation Game dwarfs
that of the classical Prisoner's Dilemma, players can still devise relatively
simple autocratic and, in particular, extortionate strategies.


  We show explicitly how uncertainty can arise in a trajectory representation.
Then we show that the formal utilization of the WKB like hierarchy structure of
dKdV in the description of (X,psi) duality does not encounter norm constraints.


  In the present paper, perturbations against a Q-ball solution are considered.
It is shown that if we calculate the U(1) charge and the energy of the modes,
which are solutions to linearized equations of motion, up to the second order
in perturbations, we will get incorrect results. In particular, for the
time-dependent modes we will obtain nonzero terms, which explicitly depend on
time, indicating the nonconservation over time of the charge and the energy. It
is shown that, as expected, this problem can be resolved by considering
nonlinear equations of motion for the perturbations, providing second-order
corrections to the solutions of linearized equations of motion. It turns out
that contributions of these corrections to the charge and the energy can be
taken into account without solving explicitly the nonlinear equations of motion
for the perturbations. It is also shown that the use of such nonlinear
equations not only recovers the conservation over time of the charge and the
energy but also results in the additivity of the charge and the energy of
different modes forming the perturbation.


  What do different contrastive learning (CL) losses actually optimize for?
Although multiple CL methods have demonstrated remarkable representation
learning capabilities, the differences in their inner workings remain largely
opaque. In this work, we analyse several CL families and prove that, under
certain conditions, they admit the same minimisers when optimizing either their
batch-level objectives or their expectations asymptotically. In both cases, an
intimate connection with the hyperspherical energy minimisation (HEM) problem
resurfaces. Drawing inspiration from this, we introduce a novel CL objective,
coined Decoupled Hyperspherical Energy Loss (DHEL). DHEL simplifies the problem
by decoupling the target hyperspherical energy from the alignment of positive
examples while preserving the same theoretical guarantees. Going one step
further, we show the same results hold for another relevant CL family, namely
kernel contrastive learning (KCL), with the additional advantage of the
expected loss being independent of batch size, thus identifying the minimisers
in the non-asymptotic regime. Empirical results demonstrate improved downstream
performance and robustness across combinations of different batch sizes and
hyperparameters and reduced dimensionality collapse, on several computer vision
datasets.


  There is a natural probability measure on the set of isomorphism classes of
principally polarized Abelian varieties of dimension $g$ over $\mathbb{F}_q$,
weighted by the number of automorphisms. The distributions of the number of
$\mathbb{F}_q$-rational points are related to the cohomology of fiber powers of
the universal family of principally polarized Abelian varieties. To that end we
compute the cohomology $H^i(\mathcal{X}^{\times n}_g,\mathbb{Q}_\ell)$ for
$g=1$ using results of Eichler-Shimura and for $g=2$ using results of
Lee-Weintraub and Petersen, and we compute the compactly supported Euler
characteristics $e_\mathrm{c}(\mathcal{X}^{\times n}_g,\mathbb{Q}_\ell)$ for
$g=3$ using results of Hain and conjectures of Bergstr\"om-Faber-van der Geer.
In each of these cases we identify the range in which the point counts
$\#\mathcal{X}^{\times n}_g(\mathbb{F}_q)$ are polynomial in $q$. Using results
of Borel and Grushevsky-Hulek-Tommasi on cohomological stability, we adapt
arguments of Achter-Erman-Kedlaya-Wood-Zureick-Brown to pose a conjecture about
the asymptotics of the point counts $\#\mathcal{X}^{\times n}_g(\mathbb{F}_q)$
in the limit $g\rightarrow\infty$.


  With the development of multimedia data types and available bandwidth there
is huge demand of video retrieval systems, as users shift from text based
retrieval systems to content based retrieval systems. Selection of extracted
features play an important role in content based video retrieval regardless of
video attributes being under consideration. These features are intended for
selecting, indexing and ranking according to their potential interest to the
user. Good features selection also allows the time and space costs of the
retrieval process to be reduced. This survey reviews the interesting features
that can be extracted from video data for indexing and retrieval along with
similarity measurement methods. We also identify present research issues in
area of content based video retrieval systems.


  We investigated the onset of the many-body coherence in the f-orbital single
crystalline alloys Ce(1-x)Yb(x)CoIn5 through thermodynamic and
magneto-transport measurements. Our study shows the evolution of the many-body
electronic state as the Kondo lattice of Ce moments is transformed into an
array of Ce impurities. Specifically, we observe a smooth crossover from the
predominantly localized Ce moment regime to the predominantly itinerant Yb
f-electronic states regime for about 50% of Yb doping. Our analysis of the
residual resistivity data unveils the presence of correlations between Yb ions,
while from our analysis of specific heat data we conclude that for
0.65<x<0.775, ytterbium f-electrons strongly interact with the conduction
electrons while the Ce moments remain completely decoupled. The sub-linear
temperature dependence of resistivity across the whole range of Yb
concentrations suggest the presence of a nontrivial scattering mechanism for
the conduction electrons.


  Many multi-loop calculations make use of integration by parts relations to
reduce the large number of complicated Feynman integrals that arise in such
calculations to a simpler basis of master integrals. Recently, Gluza, Kajda,
and Kosower argued that the reduction to master integrals is complicated by the
presence of integrals with doubled propagator denominators in the integration
by parts relations and they introduced a novel reduction procedure which
eliminates all such integrals from the start. Their approach has the advantage
that it automatically produces integral bases which mesh well with generalized
unitarity. The heart of their procedure is an algorithm which utilizes the
weighty machinery of computational commutative algebra to produce complete sets
of unitarity-compatible integration by parts relations. In this paper, we
propose a conceptually simpler algorithm for the generation of complete sets of
unitarity-compatible integration by parts relations based on recent results in
the mathematical literature. A striking feature of our algorithm is that it can
be described entirely in terms of straightforward linear algebra.


  The task of crowd counting in varying density scenes is an extremely
difficult challenge due to large scale variations. In this paper, we propose a
novel dual path multi-scale fusion network architecture with attention
mechanism named SFANet that can perform accurate count estimation as well as
present high-resolution density maps for highly congested crowd scenes. The
proposed SFANet contains two main components: a VGG backbone convolutional
neural network (CNN) as the front-end feature map extractor and a dual path
multi-scale fusion networks as the back-end to generate density map. These dual
path multi-scale fusion networks have the same structure, one path is
responsible for generating attention map by highlighting crowd regions in
images, the other path is responsible for fusing multi-scale features as well
as attention map to generate the final high-quality high-resolution density
maps. SFANet can be easily trained in an end-to-end way by dual path joint
training. We have evaluated our method on four crowd counting datasets
(ShanghaiTech, UCF CC 50, UCSD and UCF-QRNF). The results demonstrate that with
attention mechanism and multi-scale feature fusion, the proposed SFANet
achieves the best performance on all these datasets and generates better
quality density maps compared with other state-of-the-art approaches.


  The burgeoning field of brain health research increasingly leverages
artificial intelligence (AI) to interpret and analyze neurological data. This
study introduces a novel approach towards the creation of medical foundation
models by integrating a large-scale multi-modal magnetic resonance imaging
(MRI) dataset derived from 41,400 participants in its own. Our method involves
a novel two-stage pretraining approach using vision transformers. The first
stage is dedicated to encoding anatomical structures in generally healthy
brains, identifying key features such as shapes and sizes of different brain
regions. The second stage concentrates on spatial information, encompassing
aspects like location and the relative positioning of brain structures. We
rigorously evaluate our model, BrainFounder, using the Brain Tumor Segmentation
(BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS
v2.0) datasets. BrainFounder demonstrates a significant performance gain,
surpassing the achievements of the previous winning solutions using fully
supervised learning. Our findings underscore the impact of scaling up both the
complexity of the model and the volume of unlabeled training data derived from
generally healthy brains, which enhances the accuracy and predictive
capabilities of the model in complex neuroimaging tasks with MRI. The
implications of this research provide transformative insights and practical
applications in healthcare and make substantial steps towards the creation of
foundation models for Medical AI. Our pretrained models and training code can
be found at https://github.com/lab-smile/GatorBrain.


  Although deep learning-based image restoration methods have made significant
progress, they still struggle with limited generalization to real-world
scenarios due to the substantial domain gap caused by training on synthetic
data. Existing methods address this issue by improving data synthesis
pipelines, estimating degradation kernels, employing deep internal learning,
and performing domain adaptation and regularization. Previous domain adaptation
methods have sought to bridge the domain gap by learning domain-invariant
knowledge in either feature or pixel space. However, these techniques often
struggle to extend to low-level vision tasks within a stable and compact
framework. In this paper, we show that it is possible to perform domain
adaptation via the noise-space using diffusion models. In particular, by
leveraging the unique property of how the multi-step denoising process is
influenced by auxiliary conditional inputs, we obtain meaningful gradients from
noise prediction to gradually align the restored results of both synthetic and
real-world data to a common clean distribution. We refer to this method as
denoising as adaptation. To prevent shortcuts during training, we present
useful techniques such as channel shuffling and residual-swapping contrastive
learning. Experimental results on three classical image restoration tasks,
namely denoising, deblurring, and deraining, demonstrate the effectiveness of
the proposed method. Code will be released at:
https://github.com/KangLiao929/Noise-DA/.


  We study the spontaneous scalarization of spherically symmetric,
asymptotically flat boson stars in the $(\alpha {\cal R} + \gamma {\cal G})
\phi^2$ scalar-tensor gravity model. These compact objects are made of a
complex valued scalar field that has harmonic time dependence, while their
space-time is static and they can reach densities and masses similar to that of
supermassive black holes. We find that boson stars can be scalarized for both
signs of the scalar-tensor coupling $\alpha$ and $\gamma$, respectively. This
is, in particular, true for boson stars that are {\it a priori} stable with
respect to decay into individual bosonic particles. A fundamental difference
between the $\alpha$- and $\gamma$-scalarization exists, though: while we find
an interval in $\alpha > 0$ for which boson stars can {\it never} be scalarized
when $\gamma=0$, there is no restriction on $\gamma\neq 0$ when $\alpha=0$.
Typically, two branches of solutions exist that differ in the way the boson
star gets scalarized: either the scalar field is maximal at the center of the
star, or on a shell with finite radius which roughly corresponds to the outer
radius of the boson star. We also demonstrate that the former solutions can be
radially excited.