  Ni et al. report minimum bulk trap densities of 10^11 cm-3 and 1-4 orders
change in interfacial trap densities derived from drive-level capacitance
profiling of lead halide perovskites. From basic electrostatic arguments, we
show that such bulk trap densities cannot be resolved for a p-i-n perovskite
solar cell for the reported layer thicknesses, while the apparent interfacial
charge densities are a consequence of the geometrical capacitance and of charge
injection into the perovskite layer.


  Finite-dimensional square-free algebras have been completely characterized by
Anderson and D'Ambrosia as certain twisted semigroup algebras over a
square-free semigroup S with coefficients in a field K. D'Ambrosia extended the
definition of square-free to artinian rings with unity and showed every
square-free ring has an associated division ring D and square-free semigroup S.
We show a square-free ring can be characterized as a twisted semigroup ring
over a square-free semigroup S with coefficients in a division ring D. Also, to
each square-free ring there exists a short exact sequence connecting the outer
automorphisms of a square-free ring to certain cohomology groups related to S
and D.


  The fixed-event forecasting setup is common in economic policy. It involves a
sequence of forecasts of the same (`fixed') predictand, so that the difficulty
of the forecasting problem decreases over time. Fixed-event point forecasts are
typically published without a quantitative measure of uncertainty. To construct
such a measure, we consider forecast postprocessing techniques tailored to the
fixed-event case. We develop regression methods that impose constraints
motivated by the problem at hand, and use these methods to construct prediction
intervals for gross domestic product (GDP) growth in Germany and the US.


  The resonant scalar particle generation for a swinging cavity resonator in
the Casimir vacuum is examined. It is shown that the number of particles grows
exponentially when the cavity rotates at some specific external frequency.


  This paper studies the security of a secure communication scheme based on two
discrete-time intermittently-chaotic systems synchronized via a common random
driving signal. Some security defects of the scheme are revealed: 1) the key
space can be remarkably reduced; 2) the decryption is insensitive to the
mismatch of the secret key; 3) the key-generation process is insecure against
known/chosen-plaintext attacks. The first two defects mean that the scheme is
not secure enough against brute-force attacks, and the third one means that an
attacker can easily break the cryptosystem by approximately estimating the
secret key once he has a chance to access a fragment of the generated
keystream. Yet it remains to be clarified if intermittent chaos could be used
for designing secure chaotic cryptosystems.


  We argue that the ground state of a field theory, in the presence of charged
particles, becomes an entangled state involving an infinity of soft photons.
The quantum field vacuum is altered by the passage of a uniformly moving
charge, leaving in its wake a different dressed ground state. In this sense a
charged particle leaves its electromagnetic scent even after passing by. Unlike
in classical electrodynamics the effect of the charge remains even at infinite
time. The calculation is done in detail for the ground state of a spacetime
wedge, although the results are more general. This agrees in spirit with recent
results over the infrared aspects of field theory, although the technical
details are different. These considerations open the possibility that the
information carried by quantum fields, being nonlocal, does not disappear
beyond the horizon of black holes.


  Towards the end of LHC Run1, gas leaks were observed in some parts of the
Transition Radiation Tracker (TRT) of ATLAS. Due to these leaks, primary Xenon
based gas mixture was replaced with Argon based mixture in various parts.
Test-beam studies with a dedicated Transition Radiation Detector (TRD)
prototype were carried out in 2015 in order to understand transition radiation
performance with mixtures based on Argon and Krypton. We present and discuss
the results of these test-beam studies with different active gas compositions.


  There is growing evidence that when magnetic reconnection occurs in high
Lundquist number plasmas such as in the Solar Corona or the Earth's
Magnetosphere it does so within a fragmented, rather than a smooth current
layer. Within the extent of these fragmented current regions the associated
magnetic flux transfer and energy release occurs simultaneously in many
different places. This investigation focusses on how best to quantify the rate
at which reconnection occurs in such layers. An analytical theory is developed
which describes the manner in which new connections form within fragmented
current layers in the absence of magnetic nulls. It is shown that the
collective rate at which new connections form can be characterized by two
measures; a total rate which measures the true rate at which new connections
are formed and a net rate which measures the net change of connection
associated with the largest value of the integral of $E_{\|}$ through all of
the non-ideal regions. Two simple analytical models are presented which
demonstrate how each should be applied and what they quantify.


  The quark condensate which enters the Gell-Mann-Oakes-Renner (GMOR) relation,
is investigated in the framework of one-gluon-exchange models. The usual
definition of the quark condensate via the trace of the quark propagator
produces a logarithmic divergent condensate. In the product of current mass and
condensate, this divergence is precisely compensated by the bare current mass.
The finite value of the product in fact does not contradict the relation
recently obtained by Cahill and Gunner. Therefore the GMOR relation is still
satisfied.


  In this work, a linear Kirchhoff-Love shell formulation in the framework of
scaled boundary isogeometric analysis is presented that aims to provide a
simple approach to trimming for NURBS-based shell analysis. To obtain a global
C1-regular test function space for the shell discretization, an inter-patch
coupling is applied with adjusted basis functions in the vicinity of the
scaling center to ensure the approximation ability. Doing so, the scaled
boundary geometries are related to the concept of analysis-suitable G1
parametrizations. This yields a coupling of patch boundaries in a strong sense
that is restricted to G1-smooth surfaces. The proposed approach is advantageous
to trimmed geometries due to the incorporation of the trimming curve in the
boundary representation that provides an exact representation in the planar
domain. The potential of the approach is demonstrated by several problems of
untrimmed and trimmed geometries of Kirchhoff-Love shell analysis evaluated
against error norms and displacements. Lastly, the applicability is highlighted
in the analysis of a violin structure including arbitrarily shaped patches.


  Qualitative research delves deeply into individual complex perspectives on
technology and various phenomena. However, a meticulous analysis of qualitative
data often requires a significant amount of time, especially during the crucial
coding stage. Although there is software specifically designed for qualitative
evaluation, many of these platforms fall short in terms of automatic coding,
intuitive usability, and cost-effectiveness. With the rise of Large Language
Models (LLMs) such as GPT-3 and its successors, we are at the forefront of a
transformative era for enhancing qualitative analysis. In this paper, we
introduce QualiGPT, a specialized tool designed after considering challenges
associated with ChatGPT and qualitative analysis. It harnesses the capabilities
of the Generative Pretrained Transformer (GPT) and its API for thematic
analysis of qualitative data. By comparing traditional manual coding with
QualiGPT's analysis on both simulated and actual datasets, we verify that
QualiGPT not only refines the qualitative analysis process but also elevates
its transparency, credibility, and accessibility. Notably, compared to existing
analytical platforms, QualiGPT stands out with its intuitive design,
significantly reducing the learning curve and operational barriers for users.


  Earlier, the linear magnetoelectric effect of chromium sesquioxide Cr_2 O_3
has been determined experimentally as a function of temperature. One measures
the electric field-induced magnetization on Cr_2 O_3 crystals or the magnetic
field-induced polarization. From the magnetoelectric moduli of Cr_2 O_3 we
extract a 4-dimensional relativistic invariant pseudoscalar \tilde\alpha. It is
temperature dependent and of the order of ~ 10^{-4} Y_0, with Y_0 as vacuum
admittance. We show that the new pseudoscalar \tilde\alpha is odd under parity
transformation and odd under time inversion. Moreover, \tilde\alpha is for Cr_2
O_3 what Tellegen's gyrator is for two port theory, the axion field for axion
electrodynamics, and the PEMC (perfect electromagnetic conductor) for
electrical engineering.


  In this talk I describe MAGIC, an efficient approach to covariance estimation
and signal reconstruction for Gaussian random fields (MAGIC Allows Global
Inference of Covariance). It solves a long-standing problem in the field of
cosmic microwave background (CMB) data analysis but is in fact a general
technique that can be applied to noisy, contaminated and incomplete or censored
measurements of either spatial or temporal Gaussian random fields. In this talk
I will phrase the method in a way that emphasizes its general structure and
applicability but I comment on applications in the CMB context. The method
allows the exploration of the full non-Gaussian joint posterior density of the
signal and parameters in the covariance matrix (such as the power spectrum)
given the data. It generalizes the familiar Wiener filter in that it
automatically discovers signal correlations in the data as long as a noise
model is specified and priors encode what is known about potential
contaminants. The key methodological difference is that instead of attempting
to evaluate the likelihood (or posterior density) or its derivatives, this
method generates an asymptotically exact Monte Carlo sample from it. I present
example applications to power spectrum estimation and signal reconstruction
from measurements of the CMB. For these applications the method achieves
speed-ups of many orders of magnitude compared to likelihood maximization
techniques, while offering greater flexibility in modeling and a full
characterization of the uncertainty in the estimates.


  Observations of TRES-1b transits made during the late summer and fall 2004
observing season reveal a statistically significant but low amplitude
brightening event during egress.


  We introduce the notion of lattice path matroidal subdivisions, or LPM
subdivisions for short, and show that these subdivisions are regular and hence
the weight vectors for them lie in the Dressian. This leads us to explore the
structure of the set of these weights inside the Dressian and owing to the fact
that Lattice path matroids are positroids, we move to the positive Dressian
which in turn is equal to the positive tropical Grassmannian, an object of
immense interest currently in Physics. This is related to the amplituhedron and
positive configuration space, which we describe here and wish to explore these
connections further.


  Log anomaly detection is a key component in the field of artificial
intelligence for IT operations (AIOps). Considering log data of variant
domains, retraining the whole network for unknown domains is inefficient in
real industrial scenarios. However, previous deep models merely focused on
extracting the semantics of log sequences in the same domain, leading to poor
generalization on multi-domain logs. To alleviate this issue, we propose a
unified Transformer-based framework for Log anomaly detection (LogFormer) to
improve the generalization ability across different domains, where we establish
a two-stage process including the pre-training and adapter-based tuning stage.
Specifically, our model is first pre-trained on the source domain to obtain
shared semantic knowledge of log data. Then, we transfer such knowledge to the
target domain via shared parameters. Besides, the Log-Attention module is
proposed to supplement the information ignored by the log-paring. The proposed
method is evaluated on three public and one real-world datasets. Experimental
results on multiple benchmarks demonstrate the effectiveness of our LogFormer
with fewer trainable parameters and lower training costs.


  We present a critique of the many-world interpretation of quantum mechanics,
based on different ``pictures'' that describe the time evolution of an isolated
quantum system. Without an externally imposed frame to restrict these possible
pictures, the theory cannot yield non-trivial interpretational statements. This
is analogous to Goodman's famous ``grue-bleen'' problem of language and
induction. Using a general framework applicable to many kinds of dynamical
theories, we try to identify the kind of additional structure (if any) required
for the meaningful interpretation of a theory. We find that the ``grue-bleen''
problem is not restricted to quantum mechanics, but also affects other theories
including classical Hamiltonian mechanics. For all such theories, absent
external frame information, an isolated system has no interpretation.


  We present an analysis of Hubble Space Telescope observations of globular
clusters (GCs) in six ultra-diffuse galaxies (UDGs) in the Coma cluster, a
sample that represents UDGs with large effective radii ($R_{\rm e}$), and use
the results to evaluate competing formation models. We eliminate two
significant sources of systematic uncertainty in the determination of the
number of GCs, $N_{\rm GC}$ by using sufficiently deep observations that (i)
reach the turnover of the GC luminosity function and (ii) provide a sufficient
number of GCs with which to measure the GC number radial distribution. We find
that $N_{\rm GC}$ for these galaxies is on average $\sim$20, which implies an
average total mass, $M_{\rm total}$, $\sim$ $10^{11}$ $M_{\odot}$ when applying
the relation between $N_{\rm GC}$ and $M_{\rm total}$. This value of $N_{\rm
GC}$ lies at the upper end of the range observed for dwarf galaxies of the same
stellar mass and is roughly a factor of two larger than the mean. The GC
luminosity function, radial profile and average colour are more consistent with
those observed for dwarf galaxies than with those observed for the more massive
($L^*$) galaxies, while both the radial and azimuthal GC distributions closely
follow those of the stars in the host galaxy. Finally, we discuss why our
observations, specifically the GC number and GC distribution around these six
UDGs, pose challenges for several of the currently favoured UDG formation
models.


  We obtain an asymptotic formula for the smoothly weighted first moment of
primitive quadratic Dirichlet L-functions at the central point, with an error
term that is "square-root" of the main term. Our approach uses a recursive
technique that feeds the result back into itself, successively improving the
error term.


  Using numerical hydrodynamics simulations we studied the gravitational
collapse of pre-stellar cores of sub-solar mass embedded into a low-density
external environment. Four models with different magnitude and direction of
rotation of the external environment with respect to the central core were
studied and compared with an isolated model. We found that the infall of matter
from the external environment can significantly alter the disk properties as
compared to those seen in the isolated model. Depending on the magnitude and
direction of rotation of the external environment, a variety of disks can form
including compact (<= 200 AU) ones shrinking in size due to infall of external
matter with low angular momentum, as well as extended disks forming due to
infall of external matter with high angular momentum. The former are usually
stable against gravitational fragmentation, while the latter are prone to
fragmentation and formation of stellar systems with sub-stellar/very-low-mass
companions. In the case of counterrotating external environment, very compact
(< 5 AU) and short-lived (<= a few * 10^5 yr) disks can form when infalling
material has low angular momentum. The most interesting case is found for the
infall of counterrotating external material with high angular momentum, leading
to the formation of counterrotating inner and outer disks separated by a deep
gap at a few tens AU. The gap migrates inward due to accretion of the inner
disk onto the protostar, turns into a central hole, and finally disappears
giving way to the outer strongly gravitationally unstable disk. This model may
lead to the emergence of a transient stellar system with
sub-stellar/very-low-mass components counterrotating with respect to that of
the star.


  For the maximal violation of all Bell inequalities by an arbitrary pure
two-qudit state of any dimension, we derive a new lower bound expressed via the
concurrence of this pure state. This new lower bound and the upper bound on the
maximal Bell violation, found in [J. Phys. A: Math. Theor. 55, 285301 (2022)]
and also expressed via the concurrence, analytically quantify Bell nonlocality
of a pure two-qudit state via its entanglement, in particular, prove explicitly
that entanglement of a pure two-qudit state is necessary and sufficient for its
Bell nonlocality. By re-visiting the pure two-qubit case, we also find and
rigorously prove the new results on the correlation properties of an arbitrary
pure two-qubit state.


  We present new results on the relation between purely symbolic context-free
parsing strategies and their probabilistic counter-parts. Such parsing
strategies are seen as constructions of push-down devices from grammars. We
show that preservation of probability distribution is possible under two
conditions, viz. the correct-prefix property and the property of strong
predictiveness. These results generalize existing results in the literature
that were obtained by considering parsing strategies in isolation. From our
general results we also derive negative results on so-called generalized LR
parsing.


  We investigate two schemes of the quantum teleportation with a $W$ state,
which belongs to a different class from a Greenberger-Horne-Zeilinger class. In
the first scheme, the $W$ state is shared by three parties one of whom, called
a sender, performs a Bell measurement. It is shown that quantum information of
an unknown state is split between two parties and recovered with a certain
probability. In the second scheme, a sender takes two particles of the $W$
state and performs positive operator valued measurements in two ways. For two
schemes, we calculate the success probability and the average fidelity. We show
that the average fidelity of the second scheme cannot exceed that of the first
one.


  In many classification tasks there is a requirement of monotonicity.
Concretely, if all else remains constant, increasing (resp. decreasing) the
value of one or more features must not decrease (resp. increase) the value of
the prediction. Despite comprehensive efforts on learning monotonic
classifiers, dedicated approaches for explaining monotonic classifiers are
scarce and classifier-specific. This paper describes novel algorithms for the
computation of one formal explanation of a (black-box) monotonic classifier.
These novel algorithms are polynomial in the run time complexity of the
classifier and the number of features. Furthermore, the paper presents a
practically efficient model-agnostic algorithm for enumerating formal
explanations.


  Spinning black holes tend to expel magnetic fields. In this way they are
similar to superconductors. It has been a persistent concern that this black
hole "Meissner effect" could quench jet power at high spins. This would make it
impossible for the rapidly rotating black holes in Cyg X-1 and GRS 1915+105 to
drive Blandford-Znajek jets. We give a simple geometrical argument why fields
which become entirely radial near the horizon are not expelled by the Meissner
effect and may continue to power jets up to the extremal limit. A simple and
natural example is a split-monopole field. We stress that ordinary
Blandford-Znajek jets are impossible if the Meissner effect operates and expels
the field. Finally, we note that in our general relativistic
magnetohydrodynamic simulations of black hole jets, there is no evidence that
jets are quenched by the Meissner effect. The simulated jets develop a large
split monopole component spontaneously which supports our proposal for how the
Meissner effect is evaded and jets from rapidly rotating black holes are
powered in nature.


  We study the relation between $\mathcal{W}_{1+\infty}$ algebra and
Arbesfeld-Schiffmann-Tsymbaliuk Yangian using the Maulik-Okounkov R-matrix. The
central object linking these two pictures is the Miura transformation. Using
the results of Nazarov and Sklyanin we find an explicit formula for the mixed
R-matrix acting on two Fock spaces associated to two different asymptotic
directions of the affine Yangian. Using the free field representation we
propose an explicit identification of Arbesfeld-Schiffmann-Tsymbaliuk
generators with the generators of Maulik-Okounkov Yangian. In the last part we
use the Miura transformation to give a conformal field theoretic construction
of conserved quantities and ladder operators in the quantum mechanical rational
and trigonometric Calogero-Sutherland models on which a vector representation
of the Yangian acts.


  Research shows that gene duplication followed by either repurposing or
removal of duplicated genes is an important contributor to evolution of gene
and protein interaction networks. We aim to identify which characteristics of a
network can arise through this process, and which must have been produced in a
different way. To model the network evolution, we postulate vertex duplication
and edge deletion as evolutionary operations on graphs. Using the novel concept
of an ancestrally distinguished subgraph, we show how features of present-day
networks require certain features of their ancestors. In particular,
ancestrally distinguished subgraphs cannot be introduced by vertex duplication.
Additionally, if vertex duplication and edge deletion are the only evolutionary
mechanisms, then a graph's ancestrally distinguished subgraphs must be
contained in all of the graph's ancestors. We analyze two experimentally
derived genetic networks and show that our results accurately predict lack of
large ancestrally distinguished subgraphs, despite this feature being
statistically improbable in associated random networks. This observation is
consistent with the hypothesis that these networks evolved primarily via vertex
duplication. The tools we provide open the door for analysing ancestral
networks using current networks. Our results apply to edge-labeled (e.g.
signed) graphs which are either undirected or directed.


  Designing effective prompts can empower LLMs to understand user preferences
and provide recommendations by leveraging LLMs' intent comprehension and
knowledge utilization capabilities. However, existing research predominantly
concentrates on task-wise prompting, developing fixed prompt templates composed
of four patterns (i.e., role-playing, history records, reasoning guidance, and
output format) and applying them to all users for a given task. Although
convenient, task-wise prompting overlooks individual user differences, leading
to potential mismatches in capturing user preferences. To address it, we
introduce the concept of instance-wise prompting to personalize discrete
prompts for individual users and propose Reinforced Prompt Personalization
(RPP) to optimize the four patterns in prompts using multi-agent reinforcement
learning (MARL). To boost efficiency, RPP formulates prompt personalization as
selecting optimal sentences holistically across the four patterns, rather than
optimizing word-by-word. To ensure the quality of prompts, RPP meticulously
crafts diverse expressions for each of the four patterns, considering multiple
analytical perspectives for specific recommendation tasks. In addition to RPP,
our proposal of RPP+ aims to enhance the scalability of action space by
dynamically refining actions with LLMs throughout the iterative process. We
evaluate the effectiveness of RPP/RPP+ in ranking tasks over various datasets.
Experimental results demonstrate the superiority of RPP/RPP+ over traditional
recommender models, few-shot methods, and other prompt-based methods,
underscoring the significance of instance-wise prompting for LLMs in
recommendation tasks and validating the effectiveness of RPP/RPP+. Our code is
available at https://github.com/maowenyu-11/RPP.


  Preconditioned iterative methods for numerical solution of large matrix
eigenvalue problems are increasingly gaining importance in various application
areas, ranging from material sciences to data mining. Some of them, e.g., those
using multilevel preconditioning for elliptic differential operators or graph
Laplacian eigenvalue problems, exhibit almost optimal complexity in practice,
i.e., their computational costs to calculate a fixed number of eigenvalues and
eigenvectors grow linearly with the matrix problem size. Theoretical
justification of their optimality requires convergence rate bounds that do not
deteriorate with the increase of the problem size. Such bounds were pioneered
by E. D'yakonov over three decades ago, but to date only a handful have been
derived, mostly for symmetric eigenvalue problems. Just a few of known bounds
are sharp. One of them is proved in [doi:10.1016/S0024-3795(01)00461-X] for the
simplest preconditioned eigensolver with a fixed step size. The original proof
has been greatly simplified and shortened in [doi:10.1137/080727567] by using a
gradient flow integration approach. In the present work, we give an even more
succinct proof, using novel ideas based on Karush-Kuhn-Tucker theory and
nonlinear programming.


  We present a study of the low-frequency radio properties of broad absorption
line quasars (BALQSOs) from the LOFAR Two-metre Sky-Survey Data Release 1
(LDR1). The value-added LDR1 catalogue contains Pan-STARRS counterparts, which
we match with the Sloan Digital Sky Survey (SDSS) DR7 and DR12 quasar
catalogues. We find that BALQSOs are twice as likely to be detected at
144$\,$MHz than their non-BAL counterparts, and BALQSOs with low-ionisation
species present in their spectra are three times more likely to be detected
than those with only high-ionisation species. The BALQSO fraction at 144$\,$MHz
is constant with increasing radio luminosity, which is inconsistent with
previous results at 1.4$\,$GHz, indicating that observations at the different
frequencies may be tracing different sources of radio emission. We cross-match
radio sources between the Faint Images of the Radio Sky at Twenty Centimeters
(FIRST) survey and LDR1, which provides a bridge via the LDR1 Pan-STARRS
counterparts to identify BALQSOs in SDSS. Consequently we expand the sample of
BALQSOs detected in FIRST by a factor of three. The LDR1-detected BALQSOs in
our sample are almost exclusively radio-quiet (\logr $\,<2$), with radio sizes
at 144$\,$MHz typically less than $200\,$kpc; these radio sizes tend to be
larger than those at 1.4$\,$GHz, suggesting more extended radio emission at low
frequencies. We find that although the radio detection fraction increases with
increasing balnicity index (BI), there is no correlation between BI and either
low-frequency radio power or radio-loudness. This suggests that both radio
emission and BI may be linked to the same underlying process, but are spatially
distinct phenomena.


  We study the stability of synchronized fixed-point state for linear
fractional-order coupled map lattice(CML). We observe that the eigenvalues of
the connectivity matrix determine the stability as for integer-order CML. These
eigenvalues can be determined exactly in certain cases. We find exact bounds in
one-dimensional lattice with translationally invariant coupling using the
theory of circulant matrices. This can be extended to any finite dimension.
Similar analysis can be carried out for the synchronized fixed point of
nonlinear coupled fractional maps where eigenvalues of the Jacobian matrix play
the same role. The analysis is generic and demonstrates that the eigenvalues of
connectivity matrix play a pivotal role in stability analysis of synchronized
fixed point even in coupled fractional maps.


  Motivated by the recent discovery of the near-ubiquity of Ly$\alpha$ emission
around $z \gtrsim 3$ QSOs, we performed a systematic study of QSO
circumgalactic Ly$\alpha$ emission at $z\approx2$, utilizing the unique
capability of the Keck Cosmic Web Imager (KCWI) -- a new wide-field, blue
sensitive integral-field spectrograph (IFU). In this paper, we present KCWI
observations on a sample of 16 ultraluminous Type-I QSOs at $z=2.1-2.3$ with
ionizing luminosities of $L_{\rm{\nu_{\rm{LL}}}}=10^{31.1-32.3}$ erg s$^{-1}$
Hz$^{-1}$. We found that 14 out of 16 QSOs are associated with Ly$\alpha$
nebulae with projected linear-sizes larger than 50 physical kpc (pkpc). Among
them, four nebulae have enormous Ly$\alpha$ emission with the Ly$\alpha$
surface brightness $SB_{\rm{Ly\alpha}} >10^{-17}$ erg s$^{-1}$ cm$^{-2}$
arcsec$^{-2}$ on the $>100$ kpc scale, extending beyond the field of view of
KCWI. Our KCWI observations reveal that most $z\approx2$ QSO nebulae have a
more irregular morphology compared to those at $z\gtrsim3$. In turn, we measure
that the circularly-averaged surface brightness (SB) at $z\approx2$ is 0.4 dex
fainter than the redshift-corrected, median SB at $z\gtrsim 3$. The Ly$\alpha$
SB profile (SB$_{\rm{Ly\alpha}}$) of QSOs at $z\approx 2$ can be described by a
power law of SB$_{\rm{Ly\alpha},z\approx2.3}=
3.7\times10^{-17}\times(r/40)^{-1.8}$ erg s$^{-1}$ cm$^{-2}$ arcsec$^{-2}$,
with the slope similar to that at $z\gtrsim3$. The observed lower
redshift-corrected, circularly-averaged SB may be mainly due to the lower
covering factor of cool gas clouds in massive halos at $z\approx2$.


  Wi-Fi sensing has become an attractive option for non-invasive monitoring of
human activities and vital signs. This paper explores the feasibility of using
state-of-the-art commercial off-the-shelf (COTS) devices for Wi-Fi sensing
applications, particularly respiration monitoring and motion detection. We
utilize the Intel AX210 network interface card (NIC) to transmit Wi-Fi signals
in both 2.4 GHz and 6 GHz frequency bands. Our experiments rely on channel
frequency response (CFR) and received signal strength indicator (RSSI) data,
which are processed using a moving average algorithm to extract human behavior
patterns. The experimental results demonstrate the effectiveness of our
approach in capturing and representing human respiration and motion patterns.
Furthermore, we compare the performance of Wi-Fi sensing across different
frequency bands, highlighting the advantages of using higher frequencies for
improved sensitivity and clarity. Our findings showcase the practicality of
using COTS devices for Wi-Fi sensing and lay the groundwork for the development
of non-invasive, contactless sensing systems. These systems have potential
applications in various fields, including healthcare, smart homes, and
Metaverse.


  Recently, there has been an increased interest in NeRF methods which
reconstruct differentiable representation of three-dimensional scenes. One of
the main limitations of such methods is their inability to assess the
confidence of the model in its predictions. In this paper, we propose a new
neural network model for the formation of extended vector representations,
called uSF, which allows the model to predict not only color and semantic label
of each point, but also estimate the corresponding values of uncertainty. We
show that with a small number of images available for training, a model
quantifying uncertainty performs better than a model without such
functionality. Code of the uSF approach is publicly available at
https://github.com/sevashasla/usf/.


  We compute the the Balmer spectra of compact objects of tensor triangulated
categories whose objects are filtered or graded objects of (or sheaves valued
in) another tensor triangulated category. Notable examples include the filtered
derived category of a scheme as well as the homotopy category of filtered
spectra. We use an $\infty$-categorical method to properly formulate and deal
with the problem. Our computations are based on a point-free approach, so that
distributive lattices and semilattices are used as key tools.
  In the appendix, we prove that the $\infty$-topos of hypercomplete sheaves on
an $\infty$-site is recovered from a basis, which may be of independent
interest.


  Domain-specific knowledge graphs constructed from natural language text are
ubiquitous in today's world. In many such scenarios the base text, from which
the knowledge graph is constructed, concerns itself with practical, on-hand,
actual or ground-reality information about the domain. Product documentation in
software engineering domain are one example of such base texts. Other examples
include blogs and texts related to digital artifacts, reports on emerging
markets and business models, patient medical records, etc. Though the above
sources contain a wealth of knowledge about their respective domains, the
conceptual knowledge on which they are based is often missing or unclear.
Access to this conceptual knowledge can enormously increase the utility of
available data and assist in several tasks such as knowledge graph completion,
grounding, querying, etc.
  Our contributions in this paper are twofold. First, we propose a novel
Markovian stochastic model for document generation from conceptual knowledge.
The uniqueness of our approach lies in the fact that the conceptual knowledge
in the writer's mind forms a component of the parameter set of our stochastic
model. Secondly, we solve the inverse problem of learning the best conceptual
knowledge from a given document, by finding model parameters which maximize the
likelihood of generating the specific document over all possible parameter
values. This likelihood maximization is done using an application of Baum-Welch
algorithm, which is a known special case of Expectation-Maximization (EM)
algorithm. We run our conceptualization algorithm on several well-known natural
language sources and obtain very encouraging results. The results of our
extensive experiments concur with the hypothesis that the information contained
in these sources has a well-defined and rigorous underlying conceptual
structure, which can be discovered using our method.


  Recent observations of changes in radioactive decay rates associated with the
annual variation of the Earth-Sun distance, with solar rotation, and
particularly with solar storms, suggest that radioactive decay rates may be
responding to small changes in ambient neutrino/antineutrino flux. We propose
to build a network of detectors (NU-SETI), based on monitoring radioactive
decays, to search for pulsed signals from an extra-terrestrial source carried
by neutrinos or antineutrinos.


  One of the promising methods to search for life on extra-solar planets
(exoplanets) is to detect life's signatures in their atmospheres. Spectra of
exoplanet atmospheres at the modest resolution needed to search for oxygen,
carbon dioxide, water, and methane will demand large collecting areas and large
diameters to capture and isolate the light from planets in the habitable zones
around the stars. For telescopes using coronagraphs to isolate the light from
the planet, each doubling of telescope diameter will increase the available
sample of stars by an order of magnitude, indicating a high scientific return
if the technical difficulties of constructing very large space telescopes can
be overcome. For telescopes detecting atmospheric signatures of transiting
planets, the sample size increases only linearly with diameter, and the
available samples are probably too small to guarantee detection of life-bearing
planets. Using samples of nearby stars suitable for exoplanet searches, this
paper shows that the demands of searching for life with either technique will
require large telescopes, with diameters of order 10m or larger in space.


  In sequence to sequence learning, the self-attention mechanism proves to be
highly effective, and achieves significant improvements in many tasks. However,
the self-attention mechanism is not without its own flaws. Although
self-attention can model extremely long dependencies, the attention in deep
layers tends to overconcentrate on a single token, leading to insufficient use
of local information and difficultly in representing long sequences. In this
work, we explore parallel multi-scale representation learning on sequence data,
striving to capture both long-range and short-range language structures. To
this end, we propose the Parallel MUlti-Scale attEntion (MUSE) and MUSE-simple.
MUSE-simple contains the basic idea of parallel multi-scale sequence
representation learning, and it encodes the sequence in parallel, in terms of
different scales with the help from self-attention, and pointwise
transformation. MUSE builds on MUSE-simple and explores combining convolution
and self-attention for learning sequence representations from more different
scales. We focus on machine translation and the proposed approach achieves
substantial performance improvements over Transformer, especially on long
sequences. More importantly, we find that although conceptually simple, its
success in practice requires intricate considerations, and the multi-scale
attention must build on unified semantic space. Under common setting, the
proposed model achieves substantial performance and outperforms all previous
models on three main machine translation tasks. In addition, MUSE has potential
for accelerating inference due to its parallelism. Code will be available at
https://github.com/lancopku/MUSE


  We propose a new search for light scalar singlets in rare meson decays. For
tiny interactions, the scalar is long-lived at detector scales and decays into
displaced pairs of leptons or light mesons. We show that Belle II has a
remarkable potential to probe scalars in the GeV range with couplings as small
as $10^{-5}$. The predicted sensitivity is higher than at the long-baseline
experiments FASER and NA62. We also investigate signatures of invisibly
decaying scalars in rare meson decays with missing energy.


  A measurement of the differential cross section for the inclusive production
of isolated prompt photons in proton-proton collisions at a centre-of-mass
energy of 7 TeV is presented. The data sample corresponds to an integrated
luminosity of 36 inverse picobarns recorded by the CMS detector at the LHC. The
measurement covers the pseudorapidity range |eta|<2.5 and the transverse energy
range 25 < ET < 400 GeV, corresponding to the kinematic region 0.007 < xT <
0.114. Photon candidates are identified with two complementary methods, one
based on photon conversions in the silicon tracker and the other on isolated
energy deposits in the electromagnetic calorimeter. The measured cross section
is presented as a function of ET in four pseudorapidity regions. The
next-to-leading-order perturbative QCD calculations are consistent with the
measured cross section.


  An agent who interacts with a wide population of other agents needs to be
aware that there may be variations in their understanding of the world.
Furthermore, the machinery which they use to perceive may be inherently
different, as is the case between humans and machines. In this work, we present
both an image reference game between a speaker and a population of listeners
where reasoning about the concepts other agents can comprehend is necessary and
a model formulation with this capability. We focus on reasoning about the
conceptual understanding of others, as well as adapting to novel gameplay
partners and dealing with differences in perceptual machinery. Our experiments
on three benchmark image/attribute datasets suggest that our learner indeed
encodes information directly pertaining to the understanding of other agents,
and that leveraging this information is crucial for maximizing gameplay
performance.


  Low-rank matrix completion consists of computing a matrix of minimal
complexity that recovers a given set of observations as accurately as possible.
Unfortunately, existing methods for matrix completion are heuristics that,
while highly scalable and often identifying high-quality solutions, do not
possess any optimality guarantees. We reexamine matrix completion with an
optimality-oriented eye. We reformulate these low-rank problems as convex
problems over the non-convex set of projection matrices and implement a
disjunctive branch-and-bound scheme that solves them to certifiable optimality.
Further, we derive a novel and often tight class of convex relaxations by
decomposing a low-rank matrix as a sum of rank-one matrices and incentivizing
that two-by-two minors in each rank-one matrix have determinant zero. In
numerical experiments, our new convex relaxations decrease the optimality gap
by two orders of magnitude compared to existing attempts, and our disjunctive
branch-and-bound scheme solves nxn rank-r matrix completion problems to
certifiable optimality in hours for n<=150 and r<=5.


  Let $p$ be a prime number and $N$ an integer prime to $p$. We show that the
operator $U_p$ on the space of cuspidal modular forms of level $pN$ and weight
two is semi-simple. It follows from this that the Hecke algebra acting on the
space of weight two forms of level $M$ is reduced if $M$ is cube free. Assuming
Tate's conjecture for cycles on smooth projective varieties over finite fields,
we generalize these results to higher weights. The main point in the proof is
that the crystalline Frobenius of the reduction mod $p$ of the motive
associated to a newform of level prime to $p$ and weight at least two cannot be
a scalar. Assuming Tate's conjecture, it follows that Ramanujan's inequality is
strict. For $N$ prime, we relate the discriminant of the weight two Hecke
algebra to the height of the modular curve $X_0(N)$, for which we get an upper
bound.


  Our brains extract durable, generalizable knowledge from transient
experiences of the world. Artificial neural networks come nowhere close to this
ability. When tasked with learning to classify objects by training on
non-repeating video frames in temporal order (online stream learning), models
that learn well from shuffled datasets catastrophically forget old knowledge
upon learning new stimuli. We propose a new continual learning algorithm,
Compositional Replay Using Memory Blocks (CRUMB), which mitigates forgetting by
replaying feature maps reconstructed by combining generic parts. CRUMB
concatenates trainable and re-usable "memory block" vectors to compositionally
reconstruct feature map tensors in convolutional neural networks. Storing the
indices of memory blocks used to reconstruct new stimuli enables memories of
the stimuli to be replayed during later tasks. This reconstruction mechanism
also primes the neural network to minimize catastrophic forgetting by biasing
it towards attending to information about object shapes more than information
about image textures, and stabilizes the network during stream learning by
providing a shared feature-level basis for all training examples. These
properties allow CRUMB to outperform an otherwise identical algorithm that
stores and replays raw images, while occupying only 3.6% as much memory. We
stress-tested CRUMB alongside 13 competing methods on 7 challenging datasets.
To address the limited number of existing online stream learning datasets, we
introduce 2 new benchmarks by adapting existing datasets for stream learning.
With only 3.7-4.1% as much memory and 15-43% as much runtime, CRUMB mitigates
catastrophic forgetting more effectively than the state-of-the-art. Our code is
available at https://github.com/MorganBDT/crumb.git.


  We compare two distinct approaches for querying data in the context of the
life sciences. The first approach utilizes conventional databases to store the
data and intuitive form-based interfaces to facilitate easy querying of the
data. These interfaces could be seen as implementing a set of "pre-canned"
queries commonly used by the life science researchers that we study. The second
approach is based on semantic Web technologies and is knowledge (model) driven.
It utilizes a large OWL ontology and same datasets as before but associated as
RDF instances of the ontology concepts. An intuitive interface is provided that
allows the formulation of RDF triples-based queries. Both these approaches are
being used in parallel by a team of cell biologists in their daily research
activities, with the objective of gradually replacing the conventional approach
with the knowledge-driven one. This provides us with a valuable opportunity to
compare and qualitatively evaluate the two approaches. We describe several
benefits of the knowledge-driven approach in comparison to the traditional way
of accessing data, and highlight a few limitations as well. We believe that our
analysis not only explicitly highlights the specific benefits and limitations
of semantic Web technologies in our context but also contributes toward
effective ways of translating a question in a researcher's mind into precise
computational queries with the intent of obtaining effective answers from the
data. While researchers often assume the benefits of semantic Web technologies,
we explicitly illustrate these in practice.


  It is shown that step moving to meet solution flow can be unstable against
lateral perturbations. The instability of long-wavelength perturbations occurs
at values of the solution flow intensity less than some critical value
depending on the step velocity. At given intensity of the solution flow, the
instability comes at the step velocity exceeding a critical velocity. Decay of
short-wavelength fluctuations is conditioned by the line tension of the step.
The step moving along the solution flow is laterally stable at all values of
the step velocity and the intensity of the solution flow. The overlapping
diffusion field of the neighbour steps suppresses the lateral instability but
it gives an instability of the step train against doubling of the period, i.e.
neighbouring steps are attracted. The equidistant train moving to meet the
solution flow is stable against the period variations.


  This paper studies disjunctive cutting planes in Mixed-Integer Conic
Programming. Building on conic duality, we formulate a cut-generating conic
program for separating disjunctive cuts, and investigate the impact of the
normalization condition on its resolution. In particular, we show that a
careful selection of normalization guarantees its solvability and conic strong
duality. Then, we highlight the shortcomings of separating conic-infeasible
points in an outer-approximation context, and propose conic extensions to the
classical lifting and monoidal strengthening procedures. Finally, we assess the
computational behavior of various normalization conditions in terms of gap
closed, computing time and cut sparsity. In the process, we show that our
approach is competitive with the internal lift-and-project cuts of a
state-of-the-art solver.


  In this paper, we show that Cremona groups are sofic. We actually introduce a
quantitative notion of soficity, called sofic profile, and show that the group
of birational transformations of a d-dimensional variety has sofic profile at
most polynomial of degree d. We also observe that finitely generated subgroups
of the Cremona group have a solvable word problem. This provides examples of
finitely generated groups with no embeddings into any Cremona group, answering
a question of S. Cantat.


  We investigate the particle content of parity-preserving Weyl gauge theories
of gravity (WGT$^+$) about a Minkowski background. Within a subset of the full
theory, we use a systematic method previously presented in [arXiv:1812.02675]
to determine 862 critical cases for which the parameter values in the action
lead to additional gauge invariances. We find that 168 of these cases are free
of ghosts and tachyons, provided the parameters satisfy certain conditions that
we also determine. We further identify 40 of these cases that are also
power-counting renormalizable and determine the corresponding conditions on the
parameters. Of these theories, 11 have only massless tordion propagating
particles, 23 have only a massive tordion propagating mode, and 6 have both. We
also repeat our analysis for WGT$^+$ with vanishing torsion or curvature,
respectively. We compare our findings with the very few previous results in the
literature.


  In the minimum planarization problem, given some $n$-vertex graph, the goal
is to find a set of vertices of minimum cardinality whose removal leaves a
planar graph. This is a fundamental problem in topological graph theory. We
present a $\log^{O(1)} n$-approximation algorithm for this problem on general
graphs with running time $n^{O(\log n/\log\log n)}$. We also obtain a
$O(n^\varepsilon)$-approximation with running time $n^{O(1/\varepsilon)}$ for
any arbitrarily small constant $\varepsilon > 0$. Prior to our work, no
non-trivial algorithm was known for this problem on general graphs, and the
best known result even on graphs of bounded degree was a
$n^{\Omega(1)}$-approximation [Chekuri and Sidiropoulos 2013].
  As an immediate corollary, we also obtain improved approximation algorithms
for the crossing number problem on graphs of bounded degree. Specifically, we
obtain $O(n^{1/2+\varepsilon})$-approximation and $n^{1/2} \log^{O(1)}
n$-approximation algorithms in time $n^{O(1/\varepsilon)}$ and $n^{O(\log
n/\log\log n)}$ respectively. The previously best-known result was a
polynomial-time $n^{9/10}\log^{O(1)} n$-approximation algorithm [Chuzhoy 2011].
  Our algorithm introduces several new tools including an efficient grid-minor
construction for apex graphs, and a new method for computing irrelevant
vertices. Analogues of these tools were previously available only for exact
algorithms. Our work gives efficient implementations of these ideas in the
setting of approximation algorithms, which could be of independent interest.


  We elaborate on the connection between Gel'fand-Kapranov-Zelevinsky systems,
de Rham theory for twisted cohomology groups, and Pfaffian equations for
Feynman integrals. We propose a novel, more efficient algorithm to compute
Macaulay matrices, which are used to derive Pfaffian systems of differential
equations. The Pfaffian matrices are then employed to obtain linear relations
for ${\cal A}$-hypergeometric (Euler) integrals and Feynman integrals, through
recurrence relations and through projections by intersection numbers.


  The shear viscosity of liquid cobalt at the pressure $p=1.5$~bar and at the
temperatures corresponding to equilibrium liquid and supercooled liquid states
is measured experimentally and evaluated by means of molecular dynamics
simulations. Further, the shear viscosity is also calculated within the
microscopic theoretical model. Comparison of our experimental, simulation and
theoretical results with other available data allows one to examine the issue
about the correct temperature dependence of the shear viscosity of liquid
cobalt. It is found a strong correlation between the viscosity and the
configuration entropy of liquid cobalt over the considered temperature range,
which can be taken into account by the Rosenfeld's model.


  We propose a teleportation scheme that relies only on single-photon
measurements and Faraday rotation, for teleportation of many-qubit entangled
states stored in the electron spins of a quantum dot system. The interaction
between a photon and the two electron spins, via Faraday rotation in
microcavities, establishes Greenberger-Horne-Zeilinger entanglement in the
spin-photon-spin system. The appropriate single-qubit measurements, and the
communication of two classical bits, produce teleportation. This scheme
provides the essential link between spintronic and photonic quantum information
devices by permitting quantum information to be exchanged between them.


  Quantum walks are known to have nontrivial interactions with absorbing
boundaries. In particular it has been shown that an absorbing boundary in the
one dimensional quantum walk partially reflects information, as observed by
absorption probability computations. In this paper, we shift our sights from
the local phenomena of absorption probabilities to the global behavior of
finite absorbing quantum walks in one dimension. We conduct our analysis by
approximating the eigenbasis of the associated absorbing quantum walk operator
matrix Q_n where n is the lattice size. The conditional probability
distributions of these finite absorbing quantum walks exhibit distinct behavior
at various timescales, namely wavelike reflections for t = O(n), fractional
quantum revivals for t = O(n^2), and stability for t = O(n^3). At the end of
this paper, we demonstrate the existence of these quantum revivals in other
sufficiently regular quantum walk systems.


  A simple derivation of the low-energy effective action for brane worlds is
given, highlighting the role of conformal invariance. We show how to improve
the effective action for a positive- and negative-tension brane pair using the
AdS/CFT correspondence.


  Aims. Two-dimensional MHD simulations are used to model the emission
properties of TeV-bright shell-type supernova remnants (SNRs) and to explore
their nature. Methods. In the leptonic scenario for the TeV emission, the
$\gamma$-ray emission is produced via Inverse Compton scattering of background
soft photons by high-energy electrons accelerated by the shocks of the SNRs.
The TeV emissivity is proportional to the magnetic field energy density and MHD
simulations can be used to model the TeV structure of such remnants directly.
2D MHD simulations for SNRs are then performed under the assumption that the
ambient interstellar medium is turbulent with the magnetic field and density
fluctuations following a Kolmogorov-like power-law spectrum. Results. (1) As
expected, these simulations confirm early 1D and 2D modelings of these sources,
namely the hydrodynamical evolution of the shock waves and amplification of
magnetic field by Rayleigh-Taylor convective flows and by shocks propagating in
a turbulent medium; (2) We reproduce rather complex morphological structure for
$\gamma$-rays, suggesting intrinsic variations of the source morphology not
related to the structure of the progenitor and environment; (3)The observed
radial profile of several remnants are well reproduced with an ambient medium
density of $0.1-1$ cm$^{-3}$. An even lower ambient density leads to a sharper
drop of the TeV brightness with radius than what is observed near the outer
edge of these remnants. Conclusions. In a turbulent background medium, we can
reproduce the observed characteristics of several shell-type TeV SNRs with
reasonable parameters except for a higher ambient density than that inferred
from X-ray observations.


  4D mmWave radar sensors are suitable for roadside perception in city-scale
Intelligent Transportation Systems (ITS) due to their long sensing range,
weatherproof functionality, simple mechanical design, and low manufacturing
cost. In this work, we investigate radar-based ITS for scalable traffic
analysis. Localization of these radar sensors at city scale is a fundamental
task in ITS. For flexible sensor setups, it requires even more effort. To
address this task, we propose a self-localization approach that matches two
descriptions of the "road": the one from the geometry of the motion
trajectories of cumulatively observed vehicles, and the other one from the
aerial laser scan. An Iterative Closest Point (ICP) algorithm is used to
register the motion trajectory in the road section of the laser scan. The
resulting estimate of the transformation matrix represents the sensor pose in a
global reference frame. We evaluate the results and show that it outperforms
other map-based radar localization methods, especially for the orientation
estimation. Beyond the localization result, we project radar sensor data onto a
city-scale laser scan and generate a scalable occupancy heat map as a traffic
analysis tool. This is demonstrated using two radar sensors monitoring an urban
area in the real world.


  Empirical constructions having maximal contact numbers of unit balls as
putative best ones are presented for 20-27 balls.


  The aim of this article is to determine the dynamic response of the railway
system composed of train, track and bridge, suggesting a method for assembling
the elementary matrices to obtain differential equations of the overall system.
The model studied consists of a moving part which is the vehicle modeled by a
mass-spring-damper system, and a fixed part made up of the rail and bridge
deck, modeled by two Bernoulli beams. The ballast layer of the rail bed is
represented by springs and continuous dampers. The motion equations are
established using the principle of total stationary potential energy in
combination with the finite element method. The Newmark method was used to
solve the motion equations, whose coefficients depend on time. It is an
explicit numerical integration method with unconditional stability and high
accuracy, which does not involve any iterative procedure. The concept of the
method is explained herein and applied to the railway system studied. A
computer program was developed on MATLAB software to analyze the system's
dynamic responses. The assembly procedure presented, and the numerical method
used to solve the equations in the time domain have been validated there an
illustrated example in literature.


  Social goods, such as healthcare, smart city, and information networks, often
produce ordered event data in continuous time. The generative processes of
these event data can be very complex, requiring flexible models to capture
their dynamics. Temporal point processes offer an elegant framework for
modeling event data without discretizing the time. However, the existing
maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting
the intensity function beforehand and cannot directly monitor the
goodness-of-fit of the estimated model in the process of training. To alleviate
the risk of model-misspecification in MLE, we propose to generate samples from
the generative model and monitor the quality of the samples in the process of
training until the samples and the real data are indistinguishable. We take
inspiration from reinforcement learning (RL) and treat the generation of each
event as the action taken by a stochastic policy. We parameterize the policy as
a flexible recurrent neural network and gradually improve the policy to mimic
the observed event distribution. Since the reward function is unknown in this
setting, we uncover an analytic and nonparametric form of the reward function
using an inverse reinforcement learning formulation. This new RL framework
allows us to derive an efficient policy gradient algorithm for learning
flexible point process models, and we show that it performs well in both
synthetic and real data.


  We use the "Dark Energy and Massive Neutrino Universe" (DEMNUni) simulations
to compare the constraining power of "sufficient statistics" with the standard
matter power spectrum on the sum of neutrino masses, $M_\nu \equiv \sum m_\nu$.
In general, the power spectrum, even supplemented with higher moments of the
distribution, captures only a fraction of the available cosmological
information due to correlations between the Fourier modes. In contrast, the
non-linear transform of sufficient statistics, approximated by a logarithmic
mapping A=ln(1+\delta), was designed to capture all the available cosmological
information contained in the matter clustering; in this sense it is an optimal
observable. Our analysis takes advantage of the recent analytical model
developed by Carron et al. 2014 to estimate both the matter power spectrum and
the A-power spectrum covariance matrices. Using a Fisher information approach,
we find that using sufficient statistics increases up to 8 times the available
information on the total neutrino mass at z=0, thus tightening the constraints
by almost a factor of 3 compared to the matter power spectrum.


  In recent years, Transformers have garnered significant attention for
Hyperspectral Image Classification (HSIC) due to their self-attention
mechanism, which provides strong classification performance. However, these
models face major challenges in computational efficiency, as their complexity
increases quadratically with the sequence length. The Mamba architecture,
leveraging a State Space Model, offers a more efficient alternative to
Transformers. This paper introduces the Spatial-Spectral Morphological Mamba
(MorpMamba) model. In the MorpMamba model, a token generation module first
converts the Hyperspectral Image (HSI) patch into spatial-spectral tokens.
These tokens are then processed by a morphology block, which computes
structural and shape information using depthwise separable convolutional
operations. The extracted information is enhanced in a feature enhancement
module that adjusts the spatial and spectral tokens based on the center region
of the HSI sample, allowing for effective information fusion within each block.
Subsequently, the tokens are refined in a multi-head self-attention block to
further improve the feature space. Finally, the combined information is fed
into the state space block for classification and the creation of the ground
truth map. Experiments on widely used Hyperspectral (HS) datasets demonstrate
that the MorpMamba model outperforms (parametric efficiency) both CNN and
Transformer models.


  Orienteering problems (OPs) are a variant of the well-known prize-collecting
traveling salesman problem, where the salesman needs to choose a subset of
cities to visit within a given deadline. OPs and their extensions with
stochastic travel times (SOPs) have been used to model vehicle routing problems
and tourist trip design problems. However, they suffer from two limitations
travel times between cities are assumed to be time independent and the route
provided is independent of the risk preference (with respect to violating the
deadline) of the user. To address these issues, we make the following
contributions: We introduce (1) a dynamic SOP (DSOP) model, which is an
extension of SOPs with dynamic (time-dependent) travel times; (2) a
risk-sensitive criterion to allow for different risk preferences; and (3) a
local search algorithm to solve DSOPs with this risk-sensitive criterion. We
evaluated our algorithms on a real-world dataset for a theme park navigation
problem as well as synthetic datasets employed in the literature.


  Optical broad-band polarimetry is presented for near-infrared color-selected
AGN classified QSOs based on their K-band luminosity. More than 10% of a sample
of 70 QSOs discovered in the Two Micron All Sky Survey (2MASS) with J-K > 2 and
M_K < -23 show high broad-band linear polarization (P > 3%), and values range
to a maximum of P = 11%. High polarization tends to be associated with the most
luminous objects at K, and with QSOs having the highest near-IR-to-optical flux
ratios. The 2MASS QSO sample includes objects possessing a wide range of
optical spectral types. High polarization is seen in two of 22 broad
emission-line (Type 1) objects, but 1/4 of the QSOs of intermediate spectral
type (Type 1.5-1.9) are highly polarized. None of the nine QSOs classified as
Type 2 exhibit P > 3%. It is likely that the unavoidable inclusion of
unpolarized starlight from the host galaxy within the observation aperture
results in reduced polarization for the narrow emission-line objects. The high
polarization of 2MASS-discovered QSOs supports the conclusion inferred from
their near-IR and optical colors, that the nuclei of many of these objects are
obscured to some degree by dust. The 2MASS QSO sample is compared to other,
predominantly radio-quiet, QSOs and is found to be consistent with the idea
that the orientation of AGN to the line of sight plays a major role in
determining their observed properties.


  We describe the concept of a dry electrode sensor for biopotential
measurement applications (ENOBIO) designed to eliminate the noise and
inconvenience associated to the use of electrolytic gel. ENOBIO uses
nanotechnology to remove gel-related noise, as well as maintaining a good
contact impedance to minimise interference noise. The contact surface of the
electrode will be covered with an array/forest of carbon nanotubes and will
also be tested with an Ag/AgCl coating to provide ionic-electronic
transduction. The nanotubes are to penetrate the outer layers of the skin, the
Stratum Corneum, improving electrical contact. We discuss requirements, skin
properties, nanotube penetration and transduction, noise sources, prototype
design logic and biocompatibility. A future paper will report test results.


  When a fermion interacts with a global vortex or cosmic string a solenoidal
"gauge" field is induced. This results in a non-trivial scattering
cross-section. For scalars and non-relativistic fermions the cross-section is
similar to that of Aharonov and Bohm, but with corrections. A cosmological
example is compared to one in liquid He$^{3}$-A and important differences are
discovered.


  For discrete data, the likelihood $P(x)$ can be rewritten exactly and
parametrized into $P(X = x) = P(X = x | H = f(x)) P(H = f(x))$ if $P(X | H)$
has enough capacity to put no probability mass on any $x'$ for which $f(x')\neq
f(x)$, where $f(\cdot)$ is a deterministic discrete function. The log of the
first factor gives rise to the log-likelihood reconstruction error of an
autoencoder with $f(\cdot)$ as the encoder and $P(X|H)$ as the (probabilistic)
decoder. The log of the second term can be seen as a regularizer on the encoded
activations $h=f(x)$, e.g., as in sparse autoencoders. Both encoder and decoder
can be represented by a deep neural network and trained to maximize the average
of the optimal log-likelihood $\log p(x)$. The objective is to learn an encoder
$f(\cdot)$ that maps $X$ to $f(X)$ that has a much simpler distribution than
$X$ itself, estimated by $P(H)$. This "flattens the manifold" or concentrates
probability mass in a smaller number of (relevant) dimensions over which the
distribution factorizes. Generating samples from the model is straightforward
using ancestral sampling. One challenge is that regular back-propagation cannot
be used to obtain the gradient on the parameters of the encoder, but we find
that using the straight-through estimator works well here. We also find that
although optimizing a single level of such architecture may be difficult, much
better results can be obtained by pre-training and stacking them, gradually
transforming the data distribution into one that is more easily captured by a
simple parametric model.


  A Neumann series of Bessel functions (NSBF) representation for solutions of
Sturm-Liouville equations and for their derivatives is obtained. The
representation possesses an attractive feature for applications: for all real
values of the spectral parameter $\omega$ the difference between the exact
solution and the approximate one (the truncated NSBF) depends on $N$ (the
truncation parameter) and the coefficients of the equation and does not depend
on $\omega$. A similar result is valid when $\omega\in\mathbb{C}$ belongs to a
strip $|Im\omega|<C$. This feature makes the NSBF representation especially
useful for applications requiring computation of solutions for large intervals
of $\omega$. Error and decay rate estimates are obtained. An algorithm for
solving initial value, boundary value or spectral problems for the
Sturm-Liouville equation is developed and illustrated on a test problem.


  In the SU(3) Einstein-Skyrme system static spherically symmetric
particle-like solutions and black holes exist for both the SU(2) and the SO(3)
embedding. The SO(3) embedding leads to new particle-like solutions and black
holes, sharing many features with the SU(2) solutions. In particular, there are
always two branches of solutions, forming a cusp at a critical coupling
constant. The regular SO(3) solutions have even topological charge $B$. The
mass of the $B=2$ SO(3) solutions is less than twice the mass of the $B=1$
SU(2) solutions. We conjecture, that the lowest SO(3) branches correspond to
stable particle-like solutions and stable black holes.


  Inspired by recent theoretical arguments, physics-informed echo state network
(ESN) is discussed on the attempt to train a reservoir model absolutely in
physics-informed manner. As the plainest work on such a purpose, an ODE
(ordinary differential equation) approximator is designed to replicate the
solution in sequence with respect to the recurrent evaluations. On the
principal invariance of differential equations, the constraint in recurrence
just takes shape to secure a proper regression method for the ESN-based ODE
approximator. After then, the actual training process is established on the
idea of two-pass strategy for regression. Aiming at the fully physics-informed
reservoir model, a couple of nonlinear dynamical problems are demonstrated as
the computations obtained from the proposed method in this study.


  We propose very general vortex nucleation mechanisms analogous to a
hydrodynamic instability and calculate associated critical velocity in
agreement with experiments.
  The creation of vortices via extrinsic mechanism is driven by a formation of
the surface vorticity sheet created by the flow, which reaches a critical size.
Such a sheet screens an attraction of a half-vortex ring to the wall, the
barrier for the vortex nucleation disappears and the vortex nucleation is
started. In the intrinsic mechanism the creation of a big vortex ring, which
transforms into the vortex, is driven by a fluctuative generation of small
vortex rings


  Unsupervised cross-domain action recognition aims at adapting the model
trained on an existing labeled source domain to a new unlabeled target domain.
Most existing methods solve the task by directly aligning the feature
distributions of source and target domains. However, this would cause negative
transfer during domain adaptation due to some negative training samples in both
domains. In the source domain, some training samples are of low-relevance to
target domain due to the difference in viewpoints, action styles, etc. In the
target domain, there are some ambiguous training samples that can be easily
classified as another type of action under the case of source domain. The
problem of negative transfer has been explored in cross-domain object
detection, while it remains under-explored in cross-domain action recognition.
Therefore, we propose a Multi-modal Instance Refinement (MMIR) method to
alleviate the negative transfer based on reinforcement learning. Specifically,
a reinforcement learning agent is trained in both domains for every modality to
refine the training data by selecting out negative samples from each domain.
Our method finally outperforms several other state-of-the-art baselines in
cross-domain action recognition on the benchmark EPIC-Kitchens dataset, which
demonstrates the advantage of MMIR in reducing negative transfer.


  Recent years have witnessed a surge of interest in performing measurements
within topological phases of matter, e.g., symmetry-protected topological (SPT)
phases and topological orders. Notably, measurements of certain SPT states have
been known to be related to Kramers-Wannier duality and Jordan-Wigner
transformations, giving rise to long-range entangled states and invertible
phases, such as the Kitaev chain. Moreover, measurements of topologically
ordered states correspond to charge condensations. In this work, we present a
field-theoretic framework for describing measurements within topological field
theories. We employ various lattice models as examples to illustrate the
outcomes of measuring local symmetry operators within topological phases,
demonstrating their agreement with the predictions from field-theoretic
descriptions. We demonstrate that these measurements can lead to SPT,
spontaneous symmetry-breaking, and topologically ordered phases. Specifically,
when there is emergent symmetry after measurement, the remaining symmetry and
emergent symmetry will have a mixed anomaly, which leads to long-ranged
entanglement.


  This work presents three dimensional, magneto-hydrodynamical simulations of
the formation and early evolution of a subgroup of planetary nebulae that
exhibit a variety of point-symmetric structures. For bipolar nebulae, the
formation of point-symmetric nebular shapes along the inner borders of their
opposing lobes and/or collimated outflows or jets internal or external to their
cavities, is reproduced by considering a steady misalignment of the magnetic
collimation axis with respect to the symmetry axis of the bipolar wind outflow,
defined perpendicular to the equatorial density enhancement. Elliptical
planetary nebulae with ansae displaced from the symmetry axis in
point-symmetric fashion are reproduced through the same process by reducing the
equatorial density enhancement. This mechanism represents an alternative
explanation to some cases where morphological appearances give the impression
of the action of a symmetric, rotating or precessing jet from the central
source. The computational survey reveals that jet formation is detected only
for dense enough winds with mass loss rates $\gtrsim 10^{-7}$ \Moy. For lower
mass loss rates the jets tend to vanish leaving behind only ansae-like
structures at the tips of the lobes, as observed in some cases. The results are
rather independent of the wind terminal velocity, since magnetized bubbles
behave adiabatically for low wind velocities ($\simeq$ $100 \kms$), which in
the absence of a magnetic field would behave as momentum driven.


  The limited scale of current 3D shape datasets hinders the advancements in 3D
shape understanding, and motivates multi-modal learning approaches which
transfer learned knowledge from data-abundant 2D image and language modalities
to 3D shapes. However, even though the image and language representations have
been aligned by cross-modal models like CLIP, we find that the image modality
fails to contribute as much as the language in existing multi-modal 3D
representation learning methods. This is attributed to the domain shift in the
2D images and the distinct focus of each modality. To more effectively leverage
both modalities in the pre-training, we introduce TriAdapter Multi-Modal
Learning (TAMM) -- a novel two-stage learning approach based on three
synergistic adapters. First, our CLIP Image Adapter mitigates the domain gap
between 3D-rendered images and natural images, by adapting the visual
representations of CLIP for synthetic image-text pairs. Subsequently, our Dual
Adapters decouple the 3D shape representation space into two complementary
sub-spaces: one focusing on visual attributes and the other for semantic
understanding, which ensure a more comprehensive and effective multi-modal
pre-training. Extensive experiments demonstrate that TAMM consistently enhances
3D representations for a wide range of 3D encoder architectures, pre-training
datasets, and downstream tasks. Notably, we boost the zero-shot classification
accuracy on Objaverse-LVIS from 46.8\% to 50.7\%, and improve the 5-way 10-shot
linear probing classification accuracy on ModelNet40 from 96.1\% to 99.0\%.
Project page: https://alanzhangcs.github.io/tamm-page.


  We have investigated the electrical Hanle effect with magnetic fields applied
at an oblique angle ({\theta}) to the spin direction (the oblique Hanle effect,
OHE) in CoFe/MgO/semiconductor (SC) contacts by employing a three-terminal
measurement scheme. The electrical oblique Hanle signals obtained in
CoFe/MgO/Si and CoFe/MgO/Ge contacts show clearly different line shapes
depending on the spin lifetime of the host SC. Notably, at moderate magnetic
fields, the asymptotic values of the oblique Hanle signals (in both contacts)
are consistently reduced by a factor of cos^2({\theta}) irrespective of the
bias current and temperature. These results are in good agreement with
predictions of the spin precession and relaxation model for the electrical
oblique Hanle effect. At high magnetic fields where the magnetization of CoFe
is significantly tilted from the film plane to the magnetic field direction, we
find that the observed angular dependence of voltage signals in the CoFe/MgO/Si
and CoFe/MgO/Ge contacts are well explained by the OHE, considering the
misalignment angle between the external magnetic field and the magnetization of
CoFe.


  We present the canonical and quantum cosmological investigation of a
four-dimensional, spatially flat, Friedmann-Robertson-Walker (FRW) model that
is derived from the bosonic Neveu-Schwarz/Neveu-Schwarz sector of the
low-energy M-theory effective action. We discuss in detail the phase space of
the classical theory. We find the quantum solutions of the model and obtain the
positive norm Hilbert space of states. Finally, the correspondence between wave
functions and classical solutions is outlined.


  The purpose of this paper is to model mathematically certain mechanical
aspects of defibrillation. The time deformation of the heart tissue is modeled
with the elastodynamics equations dealing with the displacement field as main
unknown. These equations are coupled with a pressure whose variations
characterize the defibrillation process. The pressure variable corresponds to a
Lagrange multiplier associated with the so-called global injectivity condition.
We develop a hybrid optimal control approach in a general framework that covers
in particular the maximization of the variations of this pressure, and also the
time the maximum is reached. The control operator is distributed, and can be
described in a form that corresponds to geometric aspects of the modeling. For
mathematical convenience a damping term is added, and mathematical analysis
based on the $\L^p$-parabolic maximal regularity is provided for the state
equations and the rigorous derivation of optimality conditions. Numerical
simulations for a toy-model exploit these optimality conditions and illustrate
the capacity of the approach.


  We consider Internet of Things (IoT) organized on the principles of cell-free
massive MIMO. Since the number of things is very large, orthogonal pilots
cannot be assigned to all of them even if the things are stationary. This
results in an unavoidable pilot contamination problem, worsened by the fact
that, for IoT, since the things are operating at very low transmit power. To
mitigate this problem and achieve a high throughput, we use cell-free systems
with optimal linear minimum mean squared error (LMMSE) channel estimation,
while traditionally simple suboptimal estimators have been used in such
systems. We further derive the analytical uplink and downlink
signal-to-interference-plus-noise ratio (SINR) expressions for this scenario,
which depends only on large scale fading coefficients. This allows us to design
new power control algorithms that require only infrequent transmit power
adaptation. Simulation results show a 40% improvement in uplink and downlink
throughputs and 95% in energy efficiency over existing cell-free wireless
systems and at least a three-fold uplink improvement over known IoT systems
based on small-cell systems.


  In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense
tracking and mapping system that seamlessly fuses neural implicit
representations with traditional volumetric fusion techniques. Building upon
the concept of implicit mapping and positioning systems, our approach extends
its applicability to real-world scenarios. Our system employs a voxel-based
neural implicit surface representation, enabling efficient encoding and
optimization of the scene within each voxel. To handle diverse environments
without prior knowledge, we incorporate an octree-based structure for scene
division and dynamic expansion. To achieve real-time performance, we propose a
high-performance multi-process framework. This ensures the system's suitability
for applications with stringent time constraints. Additionally, we adopt the
idea of multi-maps to handle large-scale scenes, and leverage loop detection
and hierarchical pose optimization strategies to reduce long-term pose drift
and remove duplicate geometry. Through comprehensive evaluations, we
demonstrate that our method outperforms previous methods in terms of
reconstruction quality and accuracy across various scenarios. We also show that
our Vox-Fusion++ can be used in augmented reality and collaborative mapping
applications. Our source code will be publicly available at
\url{https://github.com/zju3dv/Vox-Fusion_Plus_Plus}


  We describe a scalable distributed imaging algorithm framework for
next-generation radio telescopes, managing the Fourier transform from apertures
to sky (or vice versa) with a focus on minimising memory load, data transfers,
and computation. Our algorithm uses smooth window functions to isolate the
influence between specific regions of spatial-frequency and image space. This
allows the distribution of image data between nodes and the construction of
segments of frequency space exactly when and where needed.
  The developed prototype distributes terabytes of image data across many
nodes, while generating visibilities at throughput and accuracy competitive
with existing software. Scaling is demonstrated to be better than cubic in
problem complexity (for baseline length and field of view), reducing the risk
involved in growing radio astronomy processing to large telescopes like the
Square Kilometre Array.


  Two interaction mechanisms of particles in a fluid are proposed on base of
forces, mediated by hydrodynamic thermal fluctuations. The first one is similar
to the conventional van der Waals interaction, but instead of been mediated by
electromagnetic fluctuations, it is mediated by fluctuations of hydrodynamic
sound waves. The second one is due to a thermal drift of particles to the
region with a bigger effective mass, which is formed by the involved
surrounding fluid and depends on an inter-particle distance. The both
mechanisms likely can be relevant in interpretation of the observed long-range
attraction of colloidal particles, since a set different experiments shows the
attraction energy of the order of kT and, perhaps, only a fluctuation mechanism
of attarction provides this universality.


  Comparing high-resolution specific heat and thermal expansion measurements to
exact finite-size diagonalization, we demonstrate that Cs$_2$CoCl$_4$ for a
magnetic field along the crystallographic b axis realizes the
spin-$\frac{1}{2}$ XXZ chain in a transverse field. Exploiting both thermal as
well as virtual excitations of higher crystal field states, we find that the
spin chain is in the XY-limit with an anisotropy $J_z/J_\perp \approx 0.12$
substantially smaller than previously believed. A spin-flop Ising quantum phase
transition occurs at a critical field of $\mu_0 H_b^{\rm cr} \approx 2$ T
before around 3.5 T the description in terms of an effective spin-$\frac{1}{2}$
chain becomes inapplicable.


  Quantum tunneling is a fundamental quantum mechanical effect involved in
plenty of physical phenomena. Its control would impact these phenomena and the
technologies based on them. We show that the quantum tunneling probability
through a potential barrier can be increased to approach unity in a quantum
Zeno dynamics undergone by the tunneling particle in which the direction of the
momentum is frequently monitored. We first model the measurements of the
momentum direction as selective von Neumann projections, and then as
nonselective, direction-sensitive interactions of the particle with probe
particles. Nonselective measurements are more efficient than selective
measurements in enhancing the quantum tunneling probability.


  The Mathisson-Papapetrou-Dixon (MPD) equations describe the motion of an
extended test body in general relativity. This system of equations, though, is
underdetermined and has to be accompanied by constraining supplementary
conditions, even in its simplest version, which is the pole-dipole
approximation corresponding to a spinning test body. In particular, imposing a
spin supplementary condition (SSC) fixes the center of the mass of the spinning
body, i.e. the centroid of the body. In the present study, we examine whether
characteristic features of the centroid of a spinning test body, moving in a
circular equatorial orbit around a massive black hole, are preserved under the
transition to another centroid of the same physical body, governed by a
different SSC. For this purpose, we establish an analytical algorithm for
deriving the orbital frequency of a spinning body, moving in the background of
an arbitrary, stationary, axisymmetric spacetime with reflection symmetry, for
the Tulzcyjew-Dixon, the Mathisson-Pirani and the Ohashi-Kyrian-Semer\'{a}k
SSCs. Then, we focus on the Schwarzschild black hole background and a power
series expansion method is developed, in order to investigate the discrepancies
in the orbital frequencies expanded in power series of the spin among the
different SSCs. Lastly, by employing the fact that the position of the centroid
and the measure of the spin alters under the centroid's transition, we impose
proper corrections to the power expansion of the orbital frequencies, which
allows to improve the convergence between the SSCs. Our concluding argument is
that when we shift from one circular equatorial orbit to another in the
Schwarzschild background, under the change of a SSC, the convergence between
the SSCs holds only up to certain powers in the spin expansion, and it cannot
be achieved for the whole power series.


  Quantum coherence is an essential feature of quantum mechanics and is an
important physical resource in quantum information. Recently, the resource
theory of quantum coherence has been established parallel with that of
entanglement. In the resource theory, a resource can be well defined if given
three ingredients: the free states, the resource, the (restricted) free
operations. In this paper, we study the resource theory of coherence in a
different light, that is, we consider the total coherence defined by the
basis-free coherence maximized among all potential basis. We define the
distillable total coherence and the total coherence cost and in both the
asymptotic regime and the single-copy regime show the reversible transformation
between a state with certain total coherence and the state with the unit
reference total coherence. Extensively, we demonstrate that the total coherence
can also be completely converted to the total correlation with the equal amount
by the free operations. We also provide the alternative operational meanings of
the total coherence, respectively, based on the entanglement and the total
correlation in a different way.


  We reconstruct $F(\mathcal{G})$ gravity theory with an exponential scale
factor to realize the bouncing behavior in the early universe and examine the
asymptotic behaviour of late-time solutions in this model. We propose an
approach to the construction of asymptotic expansions of solutions of the
Friedmann equations on the basis of Puiseux series.


  Spam domains are sources of unsolicited mails and one of the primary vehicles
for fraud and malicious activities such as phishing campaigns or malware
distribution. Spam domain detection is a race: as soon as the spam mails are
sent, taking down the domain or blacklisting it is of relative use, as spammers
have to register a new domain for their next campaign. To prevent malicious
actors from sending mails, we need to detect them as fast as possible and,
ideally, even before the campaign is launched. In this paper, using
near-real-time passive DNS data from Farsight Security, we monitor the DNS
traffic of newly registered domains and the contents of their TXT records, in
particular, the configuration of the Sender Policy Framework, an anti-spoofing
protocol for domain names and the first line of defense against devastating
Business Email Compromise scams. Because spammers and benign domains have
different SPF rules and different traffic profiles, we build a new method to
detect spam domains using features collected from passive DNS traffic. Using
the SPF configuration and the traffic to the TXT records of a domain, we
accurately detect a significant proportion of spam domains with a low false
positives rate demonstrating its potential in real-world deployments. Our
classification scheme can detect spam domains before they send any mail, using
only a single DNS query and later on, it can refine its classification by
monitoring more traffic to the domain name.


  We prove that boundary value problems for fully nonlinear second-order
parabolic equations admit $L_{p}$-viscosity solutions, which are in
$C^{1+\alpha}$ for an $\alpha\in(0,1)$. The equations have a special structure
that the "main" part containing only second-order derivatives is given by a
positive homogeneous function of second-order derivatives and as a function of
independent variables it is measurable in the time variable and, so to speak,
VMO in spatial variables.


  Stable periodic structures containing optimal ratchet transport, recently
found in the parameter space dissipation versus ratchet parameter [PRL 106,
234101 (2011)], are shown to be resistant to reasonable temperatures,
reinforcing the expectation that they are essential to explain the optimal
ratchet transport in nature. Critical temperatures for their destruction, valid
from the overdamping to close to the conservative limits, are obtained
numerically and shown to be connected to the current efficiency, given here
analytically. Results are demonstrated for a discrete ratchet model and
generalized to the Langevin equation with an additional external oscillating
force.


  We propose a superconducting circuit platform for simulating spin-1 models.
To this purpose we consider a chain of N ultrastrongly coupled qubit-resonator
systems interacting through a grounded SQUID. The anharmonic spectrum of the
qubit-resonator system and the selection rules imposed by the global parity
symmetry allow us to activate well controlled two-body quantum gates via
AC-pulses applied to the SQUID. We show that our proposal has the same
simulation time for any number of spin-1 interacting particles. This scheme may
be implemented within the state-of-the-art circuit QED in the ultrastrong
coupling regime.


  Suppose there are $N$ distributed databases each storing a full set of $M$
independent files. A user wants to retrieve $r$ out of the $M$ files without
revealing the identity of the $r$ files. When $r=1$ it is the classic problem
of private information retrieval (PIR). In this paper we study the problem of
private multi-file retrieval (PMFR) which covers the case of general $r$. We
first prove an upper bound on the capacity of PMFR schemes which indicates the
minimum possible download size per unit of retrieved files. Then we design a
general PMFR scheme which happens to attain the upper bound when
$r\geq\frac{M}{2}$, thus achieving the optimal communication cost. As $r$ goes
down we show the trivial approach of executing $r$ independent PIR instances
achieves the near optimal communication cost. Comparing with the
capacity-achieving PIR schemes, our PMFR scheme reduces the number of
subpackages needed for each file from $N^M$ to $N^2$, which implies a great
reduction of implementation complexity.


  In this note, we characterize the products of simplicial generators for the
Chow ring of a loopless matroid, extending a result of Backman, Eur, and
Simpson. We prove that the stable intersection of a collection of tropical
hyperplanes centered at the origin with the Bergman fan of a matroid is the
Bergman fan of the dual of a certain Rado matroid.


  Bjorken scaling violation in deep inelastic electron-proton scattering (DIS)
is related to the rise of hadronic cross sections by using the additive quark
model. Of special interest is the connection between saturation in the low-$x$
behavior of the DIS structure functions (SF) and possible slow-down of the $pp$
cross section rise due to saturation effects. We also identify saturation
effects in the DIS SF with phase transition that can be described by the Van
der Waals equation of state.


  For each pair $(e,\sigma)$ of integers satisfying $2e+3\sigma\ge 0$,
$\sigma\leq -2$, and $e+\sigma\equiv 0\pmod{4}$, with four exceptions, we
construct a minimal, simply connected symplectic 4-manifold with Euler
characteristic $e$ and signature $\sigma$. We also produce simply connected,
minimal symplectic 4-manifolds with signature zero (resp. signature -1) with
Euler characteristic $4k$ (resp. $4k+1$) for all $k\ge 46$ (resp. $k\ge 49$).


  I study a Lindblad dynamics modeling a quantum test particle in a Dirac comb
that collides with particles from a background gas. The main result is a
homogenization theorem in an adiabatic limiting regime involving large initial
momentum for the test particle. Over the time interval considered, the particle
would exhibit essentially ballistic motion if either the singular periodic
potential or the kicks from the gas were removed. However, the particle behaves
diffusively when both sources of forcing are present. The conversion of the
motion from ballistic to diffusive is generated by occasional quantum
reflections that result when the test particle's momentum is driven through a
collision near to an element of the half-spaced reciprocal lattice of the Dirac
comb.


  For a successful point-contact spectroscopy (PCS) measurement, metallic tips
of proper shape and smoothness are essential to ensure the ballistic nature of
a point-contact junction. Until recently, the fabrication of Au tips suitable
for use in point-contact spectroscopy has remained more of an art involving a
trial and error method rather than an automated scientific process. To address
these issues, we have developed a technique with which one can prepare high
quality Au tips reproducibly and systematically. It involves an electronic
control of the driving voltages used for an electrochemical etching of a gold
wire in an HCl-glycerol mixture or an HCl solution. We find that a stopping
current, below which the circuit is set to shut off, is a single very important
parameter to produce an Au tip of desired shape. We present detailed
descriptions for a two-step etching process for Au tips and also test results
from PCS measurements using them.


  Uncoupling algorithms transform a linear differential system of first order
into one or several scalar differential equations. We examine two approaches to
uncoupling: the cyclic-vector method (CVM) and the
Danilevski-Barkatou-Z\"urcher algorithm (DBZ). We give tight size bounds on the
scalar equations produced by CVM, and design a fast variant of CVM whose
complexity is quasi-optimal with respect to the output size. We exhibit a
strong structural link between CVM and DBZ enabling to show that, in the
generic case, DBZ has polynomial complexity and that it produces a single
equation, strongly related to the output of CVM. We prove that algorithm CVM is
faster than DBZ by almost two orders of magnitude, and provide experimental
results that validate the theoretical complexity analyses.


  Low frequency shot noise and dc current profiles for a double-barrier
resonant-tunneling structure (DBRTS) under a strong magnetic field applied
perpendicular to the interfaces have been studied. Both the structures with 3D
and 2D emitter have been considered. The calculations, carried out with the
Keldysh Green's function technique, show strong dependencies of both the
current and noise profiles on the bias voltage and magnetic field. The noise
spectrum appears sensitive to charge accumulation due to barriere capacitances
and both noise and dc-current are extremely sensitive to the Landau levels'
broadening in the emitter electrode and can be used as a powerful tool to
investigate the latter. As an example, two specific shapes of the levels'
broadening have been considered - a semi-elliptic profile resulting from
self-consistent Born approximation, and a Gaussian one resulting from the
lowest order cumulant expansion.


  Large Language Models (LLMs) have demonstrated remarkable versatility across
various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution
with Language Feedback), a novel approach that enables LLMs to self-improve
through self-reflection, akin to human learning processes. SELF initiates with
a meta-skill learning process that equips the LLMs with capabilities for
self-feedback and self-refinement. Subsequently, the model undergoes an
iterative process of self-evolution. In each iteration, it utilizes an
unlabeled dataset of instructions to generate initial responses. These
responses are enhanced through self-feedback and self-refinement. The model is
then fine-tuned using this enhanced data. The model undergoes progressive
improvement through this iterative self-evolution process. Moreover, the SELF
framework enables the model to apply self-refinement during inference, which
further improves response quality. Our experiments in mathematics and general
tasks demonstrate that SELF can enhance the capabilities of LLMs without human
intervention. The SELF framework indicates a promising direction for the
autonomous evolution of LLMs, transitioning them from passive information
receivers to active participants in their development.